Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered.
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data-Ed: Show Me the Money: The Business Value of Data and ROIData Blueprint
This webinar originally aired on Tuesday, December 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management. Join us and learn how you can apply similar tactics at your organization to justify funding and gain management approval.
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
The document discusses data governance and outlines several key points:
1) Data governance is about bringing business and IT together to govern data as a key enterprise asset and ensure there is a common understanding of what data means.
2) Existing tools and approaches are insufficient for handling today's data complexity, and semantic technology can help by clarifying the meaning of data elements.
3) Effective data governance requires a combination of technology, organizational structure, methodology, and culture to define roles and processes for validating and reconciling data across stakeholders.
Real-World Data Governance: Setting Appropriate Business ExpectationsDATAVERSITY
This document announces a webinar on setting appropriate business expectations for data governance. The webinar will discuss level-setting expectations with business stakeholders and sponsors to define what success means for governing data at their organization. It will also cover considerations for setting expectations, such as existing governance capabilities and maintaining a non-invasive approach. Common mistakes to avoid include lack of executive support and proper planning.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
For over four decades, IT strategy has been about the alignment of technology with the needs of the “customer,” be it an organization, business, end user, or device. The most important part of system acquisition is deciding what to build or buy, as it is better to deliver no solution at all than it is to deliver the wrong solution. But there are two distinct dimensions to getting requirements and ensuring that they, and the IT solution that results, not only aligns with the business as it is, but is built in such a way that it can sustain that alignment in a cost-effective and time-efficient manner. Specifically, (1) narrow requirements, which focus on the short-term needs for specific parts, functions, or processes of the business; and, (2) broad requirements, which focus on a comprehensive, enterprise-wide approach with holistic and longer-range objectives like simplicity, suppleness, and total cost of ownership. We typically call these “Systems Analysis and Design” and “Enterprise Architecture” respectively. Ideally, organizations should be able to do both well, and effectively balance the inevitable tradeoffs between them. Sadly, in the vast majority of organizations, that is not yet the case.
Professor Kappelman will present the results of a ground-breaking study from the Society for Information Management (SIM) Enterprise Architecture Working Group that developed and validated measures for these two distinct types of requirements capabilities. Findings include:
• Empirical validation that there is, in fact, a difference between requirement capabilities in a narrow or individual system context (i.e., Systems Analysis and Design within the bounds of a specific development project), and requirements capabilities in a broad or enterprise context (i.e., Enterprise Architecture regarding how those individual systems fit together in an enterprise-wide strategic design).
• Strong evidence that requirements capabilities overall are immature, with narrow activities more mature than the corresponding broad enterprise capabilities.
• Solid evidence, based on fifteen years of studies, that software development capabilities are generally maturing, but are still fairly immature.
This research provides requirements engineers, software designers, software developers, and other IT practitioners with tools to assess their own requirements engineering and software development capabilities. and compare them with those of their peers. Suggestions for improvements are made.
DataEd Online: Data Architecture and Data Modeling Differences — Achieving a ...DATAVERSITY
<!-- wp:paragraph -->
<p>Many can be confused when it comes to data topics. Architecture, models, data — it can seem a bit overwhelming. This program offers a clear explanation of Data Modeling and Data Architecture with a focus on the power of their interdependence. Both Data Architecture and data models are made more useful by each other. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, Data Architecture is the sum of the organizational data models. However, coverage is rarely complete. Anytime you are talking about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take steps to better understand what they have and what they need to accomplish by employing Data Modeling and Data Architecture.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>This program's learning objectives include:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>Understanding the role played by models</li><li>Incorporating the interrelated concepts of architecture/engineering</li><li>What is taught: forward engineering with a goal of building</li><li>What is also needed: reverse engineering with a goal of understanding</li><li>How increasing coordination requirements increase design simplicity</li></ul>
<!-- /wp:list -->
Convincing Stakeholders Data Governance Is EssentialDATAVERSITY
Organizations are investing heavily in becoming data-centric. Data Governance practitioners must begin to deploy effective Data Governance techniques to support these investments. One of these techniques is to tackle the problem of convincing stakeholders that Data Governance is necessary. This webinar will help you address that challenge.
Join Bob Seiner for this RWDG webinar, where he will provide three questions that must be answered thoroughly and honestly from a business and technical perspective. The answers to these questions will provide practitioners with the artillery needed to break down barriers preventing the organization from being convinced that the time is right to formalize Data Governance.
This webinar will focus on:
- Identifying the stakeholders that must be convinced
- The three questions that must be asked of the stakeholders
- What answers you should expect to receive
- The answers that may surprise you
- Using the answers to convince stakeholders that Data Governance is necessary
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data-Ed: Show Me the Money: The Business Value of Data and ROIData Blueprint
This webinar originally aired on Tuesday, December 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management. Join us and learn how you can apply similar tactics at your organization to justify funding and gain management approval.
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
The document discusses data governance and outlines several key points:
1) Data governance is about bringing business and IT together to govern data as a key enterprise asset and ensure there is a common understanding of what data means.
2) Existing tools and approaches are insufficient for handling today's data complexity, and semantic technology can help by clarifying the meaning of data elements.
3) Effective data governance requires a combination of technology, organizational structure, methodology, and culture to define roles and processes for validating and reconciling data across stakeholders.
Real-World Data Governance: Setting Appropriate Business ExpectationsDATAVERSITY
This document announces a webinar on setting appropriate business expectations for data governance. The webinar will discuss level-setting expectations with business stakeholders and sponsors to define what success means for governing data at their organization. It will also cover considerations for setting expectations, such as existing governance capabilities and maintaining a non-invasive approach. Common mistakes to avoid include lack of executive support and proper planning.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
For over four decades, IT strategy has been about the alignment of technology with the needs of the “customer,” be it an organization, business, end user, or device. The most important part of system acquisition is deciding what to build or buy, as it is better to deliver no solution at all than it is to deliver the wrong solution. But there are two distinct dimensions to getting requirements and ensuring that they, and the IT solution that results, not only aligns with the business as it is, but is built in such a way that it can sustain that alignment in a cost-effective and time-efficient manner. Specifically, (1) narrow requirements, which focus on the short-term needs for specific parts, functions, or processes of the business; and, (2) broad requirements, which focus on a comprehensive, enterprise-wide approach with holistic and longer-range objectives like simplicity, suppleness, and total cost of ownership. We typically call these “Systems Analysis and Design” and “Enterprise Architecture” respectively. Ideally, organizations should be able to do both well, and effectively balance the inevitable tradeoffs between them. Sadly, in the vast majority of organizations, that is not yet the case.
Professor Kappelman will present the results of a ground-breaking study from the Society for Information Management (SIM) Enterprise Architecture Working Group that developed and validated measures for these two distinct types of requirements capabilities. Findings include:
• Empirical validation that there is, in fact, a difference between requirement capabilities in a narrow or individual system context (i.e., Systems Analysis and Design within the bounds of a specific development project), and requirements capabilities in a broad or enterprise context (i.e., Enterprise Architecture regarding how those individual systems fit together in an enterprise-wide strategic design).
• Strong evidence that requirements capabilities overall are immature, with narrow activities more mature than the corresponding broad enterprise capabilities.
• Solid evidence, based on fifteen years of studies, that software development capabilities are generally maturing, but are still fairly immature.
This research provides requirements engineers, software designers, software developers, and other IT practitioners with tools to assess their own requirements engineering and software development capabilities. and compare them with those of their peers. Suggestions for improvements are made.
DataEd Online: Data Architecture and Data Modeling Differences — Achieving a ...DATAVERSITY
<!-- wp:paragraph -->
<p>Many can be confused when it comes to data topics. Architecture, models, data — it can seem a bit overwhelming. This program offers a clear explanation of Data Modeling and Data Architecture with a focus on the power of their interdependence. Both Data Architecture and data models are made more useful by each other. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, Data Architecture is the sum of the organizational data models. However, coverage is rarely complete. Anytime you are talking about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take steps to better understand what they have and what they need to accomplish by employing Data Modeling and Data Architecture.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>This program's learning objectives include:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>Understanding the role played by models</li><li>Incorporating the interrelated concepts of architecture/engineering</li><li>What is taught: forward engineering with a goal of building</li><li>What is also needed: reverse engineering with a goal of understanding</li><li>How increasing coordination requirements increase design simplicity</li></ul>
<!-- /wp:list -->
Convincing Stakeholders Data Governance Is EssentialDATAVERSITY
Organizations are investing heavily in becoming data-centric. Data Governance practitioners must begin to deploy effective Data Governance techniques to support these investments. One of these techniques is to tackle the problem of convincing stakeholders that Data Governance is necessary. This webinar will help you address that challenge.
Join Bob Seiner for this RWDG webinar, where he will provide three questions that must be answered thoroughly and honestly from a business and technical perspective. The answers to these questions will provide practitioners with the artillery needed to break down barriers preventing the organization from being convinced that the time is right to formalize Data Governance.
This webinar will focus on:
- Identifying the stakeholders that must be convinced
- The three questions that must be asked of the stakeholders
- What answers you should expect to receive
- The answers that may surprise you
- Using the answers to convince stakeholders that Data Governance is necessary
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords such as “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities o
Necessary Prerequisites to Data SuccessDATAVERSITY
Far more organizations attempt to do more with data than succeed. Understanding common prerequisites to unrestricted data practices will help you determine the extent of these challenges in your organization and increase your chances of success. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that, there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers — aka the “Seven Deadly Data Sins” — and in the process will also
- Elaborate upon the three critical factors that lead to strategy failure
- Demonstrate a two-stage Data Strategy implementation process
- Explore the sources and rationales behind the “Seven Deadly Data Sins” and recommend solutions
Early Warning Signs of IT Project Failure -- The Deadly Dozen and the Four Ho...Leon Kappelman
The document discusses early warning signs of IT project failure. It identifies the top 12 early warning signs, called the "Deadly Dozen", which are grouped into people and process factors. The people factors center around five groups: top management, project management, project team members, subject matter experts, and stakeholders. The process factors center around five key project management processes. Addressing these early warning signs is important as the cost of fixing issues rises over time and human nature can exacerbate problems. Process, tools, and best practices can help mitigate risks.
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
Real-World Data Governance: Metadata to Empower Data Stewards - Introducing t...DATAVERSITY
Metadata is the most valuable tool of the Data Steward. Where the stewards get their metadata and how they participate in the process of delivering core metadata is an issue organizations have been struggling with for years. The Operational Metadata Store or OMS may be the answer.
The traditional Operational Data Store or ODS is a database designed to integrate data from numerous sources that supports business operations and then feeds that data back into the operational systems. This Real-World Data Governance webinar with Bob Seiner and a panel of industry pundits will hold a lively discussion on the practicality of creating the ODS using metadata as the data, utilizing the metadata from a variety of existing sources to operationalize your data stewards.
The session will focus on:
Identifying the most significant metadata for your organization
Identifying existing sources of metadata – known and hidden
Identifying when that metadata will be most useful to your data stewards
Defining a lifecycle that encourages data steward participation
Delivering a model that incorporates all of the above
Kappelman it strategy, governance, & value hoLeon Kappelman
This document discusses IT strategy, governance, and value. It emphasizes the importance of enterprise architecture in modeling the enterprise holistically rather than through a reductionist lens. Effective governance requires describing the enterprise with shared representations over time. The document also discusses typical IS department structures and the importance of business owners governing technology for the good of the enterprise.
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers – the titular “Seven Deadly Data Sins” – and in the process will also:
• Elaborate upon the three critical factors that lead to strategy failure
• Demonstrate a two-stage Data Strategy implementation process
• Explore the sources and rationales behind the “Seven Deadly Data Sins,” and recommend solutions
DataEd Slides: Growing Practical Data Governance ProgramsDATAVERSITY
At its core, Data Governance (DG) is managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance, and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a necessary prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/stewardship programs that manage data in support of the organizational strategy. Program learning objectives include:
• Understanding why Data Governance can be tricky for organizations due to data’s confounding characteristics
• Strategy #1: Keeping DG practically focused
• Strategy #2: DG must exist at the same level as HR
• Strategy #3: Gradually add ingredients
• Data Governance in action: storytelling
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Many are confused when it comes to data. Architecture, models, data - it can seem a bit overwhelming. This webinar offers a clear explanation of Data Modeling as the primary means of achieving better understanding of Data Architecture. Using a storytelling format, this webinar presents an organization approaching the daunting process of attempting to better leverage its data. The organization is currently not knowledgeable of these concepts and begins the process of understating its current state as well as a desired future state. We join as the organization takes steps to better understand what is has and what it needs to accomplish to employ Data Modeling and Architecture to achieve its mission.
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
The Five Pillars of Data Governance 2.0 SuccessDATAVERSITY
What’s the state of data governance readiness within your organization?
Do you have an executive sponsor?
Is a standard definition understood across the enterprise?
How does your IT team view it?
How does your organization approach analytics, business intelligence and decision-making?
Have you implemented any technology to provide the necessary capabilities?
These are just a few of the questions you should be asking to determine whether your organization is a data governance leader, laggard or novice. With the General Data Protection Regulation (GDPR) about to take effect, there’s no time to waste in determining whether your’re really ready.
erwin and DATAVERSITY want to help you shore up your data governance initiative so you can use your data to produce the desired results, including but not limited to meeting information security and compliance requirements.
You’ll learn what it takes to build and sustain an enterprise data governance experience – not just an isolated program – for greater visibility, control and value to achieve regulatory compliance and so much more.
Data Management vs. Data Governance ProgramDATAVERSITY
This document contains a presentation by Peter Aiken on data programs, specifically distinguishing between data management and data governance. Some key points:
- Data management focuses on understanding current and future data needs and making data effective and efficient for business activities. Data governance establishes authority and control over data management.
- Both data management and governance are needed for success. Data management executes practices while data governance provides oversight and guidance.
- Messaging should emphasize the critical importance of data and having a singular focus on improving data's role in achieving organizational strategy.
- A data strategy should define each practice area's relationship and focus on continuous improvement over multiple iterations.
RGA Master Data Management at TDWI St. LouisTDWI St. Louis
RGA is a global life and health reinsurer and the second largest in North America. It implemented a Master Data Management (MDM) system to support a $20 million enterprise resource planning project. Lessons from the project highlighted the need for consistent data dictionaries and a versioning process. RGA's MDM strategy now includes financial, organizational, and investment master data and aims to expand into claims and valuation data. Governance processes involve data stewards, business rules, and request forms to maintain authoritative master data across systems.
The Analytical HR Professional: A Look at Data-Driven Talent ManagementHuman Capital Media
Faced with unprecedented generational shifts and evolving business imperatives for smart growth, the need for a complete and comprehensive view of the workforce - from skills, competencies and performance to hiring and retiring - has never been higher. However, many organizations lack the critical insight needed to identify high performers, develop high potentials, ensure that the right employees receive the right training and effectively deploy one of their most valuable resources - their people - to execute the organization's strategy. Join Lisa Rowan of IDC Research as she discusses the role of workforce for HR leaders seeking a more proactive role in driving business strategy. She will discuss how organizations can use a data-driven approach to HR to advance the workforce and seize market opportunities, and share tips for getting started.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
DataEd Slides: Data Management + Data Strategy = InteroperabilityDATAVERSITY
Few organizations operate without having to exchange data. (Many do it professionally and well!) The larger the data exchange burden (DEB), the greater the organizational overhead incurred. This death by 1,000 cuts must be factored into each organization’s calculations. Unfortunately, most organizations do not know if their organization’s DEB is great or small. A somewhat greater number of organizations have organized Data Management practices. Focusing Data Management efforts on increasing interoperability by decreasing the DEB friction is a good area to “practice.”
Learning Objectives:
• Gaining a good understanding of both important topics
• Understanding that data only operates at a very intricate, specifically dependent intent and what this means
• Understand state-of-the-practice
• Coordination is key, requiring necessary but insufficient interdependencies and sequencing
• Practice makes perfect
Data-Ed Online Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter Dr. Peter Aiken will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/
Data-Ed Online: Let's Talk Metadata: Strategies and Successes Data Blueprint
This webinar originally aired on Tuesday, September 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
Commonly described as metadata management, properly implemented metadata practices incorporate data structures into more abstract processing. By using data about the data to enhance its value, its understandability, ease of use and many other options, organizations have developed sophisticated ways to enhance their data management and especially their data quality engineering efforts. Join us to learn more about specific metadata benefits and how to leverage it to achieve success within your organization.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords such as “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities o
Necessary Prerequisites to Data SuccessDATAVERSITY
Far more organizations attempt to do more with data than succeed. Understanding common prerequisites to unrestricted data practices will help you determine the extent of these challenges in your organization and increase your chances of success. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that, there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers — aka the “Seven Deadly Data Sins” — and in the process will also
- Elaborate upon the three critical factors that lead to strategy failure
- Demonstrate a two-stage Data Strategy implementation process
- Explore the sources and rationales behind the “Seven Deadly Data Sins” and recommend solutions
Early Warning Signs of IT Project Failure -- The Deadly Dozen and the Four Ho...Leon Kappelman
The document discusses early warning signs of IT project failure. It identifies the top 12 early warning signs, called the "Deadly Dozen", which are grouped into people and process factors. The people factors center around five groups: top management, project management, project team members, subject matter experts, and stakeholders. The process factors center around five key project management processes. Addressing these early warning signs is important as the cost of fixing issues rises over time and human nature can exacerbate problems. Process, tools, and best practices can help mitigate risks.
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
Real-World Data Governance: Metadata to Empower Data Stewards - Introducing t...DATAVERSITY
Metadata is the most valuable tool of the Data Steward. Where the stewards get their metadata and how they participate in the process of delivering core metadata is an issue organizations have been struggling with for years. The Operational Metadata Store or OMS may be the answer.
The traditional Operational Data Store or ODS is a database designed to integrate data from numerous sources that supports business operations and then feeds that data back into the operational systems. This Real-World Data Governance webinar with Bob Seiner and a panel of industry pundits will hold a lively discussion on the practicality of creating the ODS using metadata as the data, utilizing the metadata from a variety of existing sources to operationalize your data stewards.
The session will focus on:
Identifying the most significant metadata for your organization
Identifying existing sources of metadata – known and hidden
Identifying when that metadata will be most useful to your data stewards
Defining a lifecycle that encourages data steward participation
Delivering a model that incorporates all of the above
Kappelman it strategy, governance, & value hoLeon Kappelman
This document discusses IT strategy, governance, and value. It emphasizes the importance of enterprise architecture in modeling the enterprise holistically rather than through a reductionist lens. Effective governance requires describing the enterprise with shared representations over time. The document also discusses typical IS department structures and the importance of business owners governing technology for the good of the enterprise.
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers – the titular “Seven Deadly Data Sins” – and in the process will also:
• Elaborate upon the three critical factors that lead to strategy failure
• Demonstrate a two-stage Data Strategy implementation process
• Explore the sources and rationales behind the “Seven Deadly Data Sins,” and recommend solutions
DataEd Slides: Growing Practical Data Governance ProgramsDATAVERSITY
At its core, Data Governance (DG) is managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance, and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a necessary prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/stewardship programs that manage data in support of the organizational strategy. Program learning objectives include:
• Understanding why Data Governance can be tricky for organizations due to data’s confounding characteristics
• Strategy #1: Keeping DG practically focused
• Strategy #2: DG must exist at the same level as HR
• Strategy #3: Gradually add ingredients
• Data Governance in action: storytelling
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Many are confused when it comes to data. Architecture, models, data - it can seem a bit overwhelming. This webinar offers a clear explanation of Data Modeling as the primary means of achieving better understanding of Data Architecture. Using a storytelling format, this webinar presents an organization approaching the daunting process of attempting to better leverage its data. The organization is currently not knowledgeable of these concepts and begins the process of understating its current state as well as a desired future state. We join as the organization takes steps to better understand what is has and what it needs to accomplish to employ Data Modeling and Architecture to achieve its mission.
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
The Five Pillars of Data Governance 2.0 SuccessDATAVERSITY
What’s the state of data governance readiness within your organization?
Do you have an executive sponsor?
Is a standard definition understood across the enterprise?
How does your IT team view it?
How does your organization approach analytics, business intelligence and decision-making?
Have you implemented any technology to provide the necessary capabilities?
These are just a few of the questions you should be asking to determine whether your organization is a data governance leader, laggard or novice. With the General Data Protection Regulation (GDPR) about to take effect, there’s no time to waste in determining whether your’re really ready.
erwin and DATAVERSITY want to help you shore up your data governance initiative so you can use your data to produce the desired results, including but not limited to meeting information security and compliance requirements.
You’ll learn what it takes to build and sustain an enterprise data governance experience – not just an isolated program – for greater visibility, control and value to achieve regulatory compliance and so much more.
Data Management vs. Data Governance ProgramDATAVERSITY
This document contains a presentation by Peter Aiken on data programs, specifically distinguishing between data management and data governance. Some key points:
- Data management focuses on understanding current and future data needs and making data effective and efficient for business activities. Data governance establishes authority and control over data management.
- Both data management and governance are needed for success. Data management executes practices while data governance provides oversight and guidance.
- Messaging should emphasize the critical importance of data and having a singular focus on improving data's role in achieving organizational strategy.
- A data strategy should define each practice area's relationship and focus on continuous improvement over multiple iterations.
RGA Master Data Management at TDWI St. LouisTDWI St. Louis
RGA is a global life and health reinsurer and the second largest in North America. It implemented a Master Data Management (MDM) system to support a $20 million enterprise resource planning project. Lessons from the project highlighted the need for consistent data dictionaries and a versioning process. RGA's MDM strategy now includes financial, organizational, and investment master data and aims to expand into claims and valuation data. Governance processes involve data stewards, business rules, and request forms to maintain authoritative master data across systems.
The Analytical HR Professional: A Look at Data-Driven Talent ManagementHuman Capital Media
Faced with unprecedented generational shifts and evolving business imperatives for smart growth, the need for a complete and comprehensive view of the workforce - from skills, competencies and performance to hiring and retiring - has never been higher. However, many organizations lack the critical insight needed to identify high performers, develop high potentials, ensure that the right employees receive the right training and effectively deploy one of their most valuable resources - their people - to execute the organization's strategy. Join Lisa Rowan of IDC Research as she discusses the role of workforce for HR leaders seeking a more proactive role in driving business strategy. She will discuss how organizations can use a data-driven approach to HR to advance the workforce and seize market opportunities, and share tips for getting started.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
DataEd Slides: Data Management + Data Strategy = InteroperabilityDATAVERSITY
Few organizations operate without having to exchange data. (Many do it professionally and well!) The larger the data exchange burden (DEB), the greater the organizational overhead incurred. This death by 1,000 cuts must be factored into each organization’s calculations. Unfortunately, most organizations do not know if their organization’s DEB is great or small. A somewhat greater number of organizations have organized Data Management practices. Focusing Data Management efforts on increasing interoperability by decreasing the DEB friction is a good area to “practice.”
Learning Objectives:
• Gaining a good understanding of both important topics
• Understanding that data only operates at a very intricate, specifically dependent intent and what this means
• Understand state-of-the-practice
• Coordination is key, requiring necessary but insufficient interdependencies and sequencing
• Practice makes perfect
Data-Ed Online Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter Dr. Peter Aiken will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/
Data-Ed Online: Let's Talk Metadata: Strategies and Successes Data Blueprint
This webinar originally aired on Tuesday, September 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
Commonly described as metadata management, properly implemented metadata practices incorporate data structures into more abstract processing. By using data about the data to enhance its value, its understandability, ease of use and many other options, organizations have developed sophisticated ways to enhance their data management and especially their data quality engineering efforts. Join us to learn more about specific metadata benefits and how to leverage it to achieve success within your organization.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
The document discusses cloud-based integration and its prerequisites. It states that for organizations to benefit from cloud integration, data must be (1) of higher quality, (2) lower volume, and (3) more shareable than data residing outside the cloud. Investments in data engineering are needed to cleanse, reduce the size of, and increase the shareability of datasets so that organizations can realize increased capacity, flexibility, and cost savings from cloud-based computing. The webinar will show how to identify opportunities for cloud integration and properly oversee efforts to capitalize on those opportunities.
Data-Ed: Unlock Business Value through Document & Content ManagementData Blueprint
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning Objectives:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
http://www.datablueprint.com/webinar-schedule
Data-Ed: Unlock Business Value through Data GovernanceData Blueprint
If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.
Learning Objectives:
Understanding contextually why data governance can be tricky for most organizations
Demonstrate a variety of “storytelling” techniques
How to use “worst practices” to your advantage
Understanding foundational data governance concepts based on the Data Management Body of Knowledge (DMBOK)
Taking away several novel but tangible examples of generating business value through data governance
Data-Ed Online: Your Documents and Other Content: Managing Unstructured Data Data Blueprint
This webinar originally aired on Tuesday, August 14, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
Non-tabular data plays an increasing role in organizations. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our data management capabilities into more critical and more regulated areas. This presentation provides you with an understanding of the dimensions of this vast new area, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Data-Ed: Unlock Business Value through Data Quality Engineering Data Blueprint
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar focuses on obtaining business value from data quality initiatives. I will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
This webinar aired originally on Tuesday, March 13, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Peter Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
This presentation provides you with an understanding of the data modeling and data development components of data management. Participants will understand how the analysis, design, implementation, deployment, and maintenance of data solutions should be approached in order to maximize the full value of the enterprise data resources and activities. Architecting in quality is imperative at this level and complements a subset of project activities within the system development lifecycle (SDLC) focused on defining data requirements, designing data solution components, and implementing these components. Participants will understand the difficulties organizations experience when interacting with data development efforts and how to best incorporate these efforts into specific data projects.
View the video recording here: http://www.slideshare.net/aberkowitz/dataed-online-practical-data-modeling-12019990
Data-Ed: Unlock Business Value Through Reference & MDM Data Blueprint
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an Understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Check out more of our webinars here: http://www.datablueprint.com/webinar-schedule
The document discusses a webinar on using data architecture as a basic analysis method to understand and resolve business problems. The presenter, Dr. Peter Aiken, will demonstrate various uses of data architecture and how it can inform, clarify, and help solve business issues. The goal is for attendees to recognize how data architecture can raise the utility of this technique for addressing business needs.
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data Systems Integration & Business Value Pt. 1: MetadataDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
The content of the document, "Implementing Data Mesh: Six Ways That Can Improve the Odds of Your Success," is a whitepaper authored by Ranganath Ramakrishna from LTIMindtree. The whitepaper introduces the concept of Data Mesh, a socio-technical paradigm that aims to help organizations fully leverage the value of their analytical data.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data-Ed: Design and Manage Data Structures Data Blueprint
This document discusses different data structures and their appropriate usage. It begins with an overview of data structures and how they enable efficient data storage and organization. The webinar will cover various available data structures and when each should be used, with the goal of helping attendees apply the correct structures to fit their business needs and maximize business value. Learning objectives include understanding how different structures create different business value and applying the right structures to business requirements. The webinar will be presented on July 8, 2014 by Dave Marsh and Peter Aiken.
Data-Ed Webinar: Design & Manage Data Structures DATAVERSITY
This document discusses different data structures and their appropriate usage. It begins with an overview of data structures and how they enable efficient data storage and organization. The webinar will cover various available data structures and when each should be used, with the goal of helping attendees apply the correct structures to fit their business needs and maximize business value. Learning objectives include understanding how different structures create different business value and applying the right structures to business requirements. The webinar will be presented on July 8, 2014 by Dave Marsh and Peter Aiken.
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Check out more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Find more Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
Get ahead of the cloud or get left behindMatt Mandich
An enterprise cloud computing strategy results in:
Broad consensus on goals and expected results of moving select processes to the cloud
Standardized, consistent approach to evaluating the benefits and challenges of cloud projects
Clear requirements for the negotiation and monitoring of partnerships with cloud service providers
Understanding and consensus on the enabling and managing role IT will play in future cloud initiatives
Goals and a roadmap for transforming internal IT from asset managers to service broker
Encrypted Data Management With Deduplication In Cloud...Angie Jorgensen
The document discusses some disadvantages of Minitrex's current data management system and proposes solutions based on customer relationship management (CRM) theories. It finds that Minitrex's data is siloed across different departments, leading to issues like duplicate customer records and a lack of a holistic view of customers. It suggests integrating CRM across departments to get a unified view of customers. It also recommends utilizing CRM software to consolidate data to improve data quality, gain insights, and better manage customer relationships. Leadership support and an integrated, holistic approach are identified as important for effective use of CRM.
Data-Ed Webinar: Implementing the Data Management Maturity Model (DMM) - With...DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
- Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational data management and data management maturity
- Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the SameDATAVERSITY
Data warehousing, after decades of widespread adoption, still holds a strong place in today’s organization. Cloud-based technologies have revolutionized the traditional world of data warehousing, offering transformational ways to support analytics and reporting. Join this webinar to understand what has changed in the world of data warehousing with the introduction of cloud-based technologies, and what has remained the same.
SG Data Mgt - Findings and Recommendations.pptxssuser57f752
The document provides an assessment of smart grid data management at an electric utility. Some key highlights:
- There is a lack of a coordinated smart grid data management strategy to handle exponential data growth from new sensors and enable business objectives.
- The assessment evaluated the current state of data governance, processes, technology and information use across different business units and projects.
- The maturity levels were found to range from level 1 to 4, with most areas being at level 2-3, indicating some basic level of data management but a lack of formal processes and enterprise-wide coordination.
- Recommendations focus on developing a data governance strategy, addressing master data management and a business intelligence strategy to improve information sharing and
Data-Ed Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
The document describes how a company implemented a modern data management approach to support a multi-billion dollar merger between two large food service companies. They consolidated master data from both companies' systems in 5 months to support business goals. After the merger was blocked, Company A incorporated the customer segmentation and category management applications into their strategies, and realized rationalization benefits from the advanced MDM platform. Company B is re-evaluating their MDM strategy without the merger.
Effectively Leveraging Graph Technology - Ann Grubbs, Lockheed MartinNeo4j
The document discusses how Lockheed Martin is leveraging graph technology in their data management strategy. It provides an overview of Lockheed Martin as a company with over 110,000 employees and 375+ facilities. It then discusses how graph databases can be used to create a master data graph that connects different types of data like product data, manufacturing data, supply chain data, and more. This allows for insights into relationships across domains and enables data-driven decisions. The benefits of a graph approach are also summarized as gaining new insights, enabling complex relationship questions, and supporting master data management and data science.
Similar to Data Systems Integration & Business Value PT. 3: Warehousing (20)
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Find more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
This document outlines a presentation on developing a data-centric strategy and roadmap. It discusses the importance of aligning data management goals to business needs through frameworks like Porter's competitive strategies and operating models. Metrics and success criteria must be defined by collaborating with business partners to measure improvements in specific opportunities. An example shows how a chemical company defined objects of measurement and metrics to quantify increased efficiency from a data integration solution. Developing a holistic solution requires understanding a business's competitive advantage, goals and needs.
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Find more data management webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The document discusses emerging trends in data modeling. It provides an overview of different types of data models including conceptual, logical and physical models. It also discusses different modeling approaches such as third normal form, star schema, and data vault. Additionally, it covers new technologies like NoSQL and key-value stores. The webinar aims to address trends in data model application technologies and the practice of data modeling itself.
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Best Practices with the Data Management Maturity ModelData Blueprint
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Tools alone are not the answer: Career roles and growth tracks for data professionals. In today’s (Big) data-driven information economy, it is even more critical to focus on data as an asset that directly supports business imperatives. But tools alone are not the answer. Organizations that want to rise above their competition can only do so with the help of skilled professionals who know how to manage, mine, and draw actionable insights from the multitudes of (Big) data sources. Numerous new roles and job titles have emerged to address the high demand for specialized data professionals. This webinar brings together three individuals well qualified to contribute to this important industry-wide discussion of data jobs. We will take a closer look at these newer data management roles and present recommendations on how to enhance career paths.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-archive/
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Check out more of our Data-Ed webinars here: www.datablueprint.com/webinar-schedule
Data-Ed: Show Me the Money: Monetizing Data ManagementData Blueprint
Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their business problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management and how it impacts business objectives. Join us and learn how you can better align your data management projects with business objectives to justify funding and gain management approval.
Check out more of our webinars: http://www.datablueprint.com/resource-center/webinar-schedule/
Yes, we face a data deluge and big data seems to be largely about how to deal with it. But 99% of what has been written about big data is focused on selling hardware and services. The truth is that until the concept of big data can be objectively defined, any measurements, claims of success, quantifications, etc. must be viewed skeptically and with suspicion. While both the need for and approaches to these new requirements are faced by virtually every organization, jumping into the fray ill-prepared has (to date) reproduced the same dismal IT project results.
The very real, very rapid, very great increases in data of all forms (charts showing data types and volume increases)
Challenges faced by virtually all data management programs
Means by which big data techniques can compliment existing data management practices
Necessary but insufficient pre-requisites to exploiting big data techniques
Prototyping nature of practicing big data techniques
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Leading the Data Asset Management Team: CDO or Top Data Job?Data Blueprint
Join Peter Aiken, Ph.D. and Micheline Casey for this interactive discussion on the role of Chief Data Officer (CDO) or Top Data Job (TDJ). While most agree that data challenges are getting – dare we say it, bigger? – the range of approaches reveals no emerging consensus as to the best way to address these challenges. This webinar features a wide-ranging discussion of a number of aspects of this exciting new career path. For each of these aspects, new data leaders can be congratulated but sometimes they also ought to be consoled. Ms. Casey (as the very first state CDO) and Dr. Aiken will bring certain considerations to the table. They hope to sample the pulse of the community and move towards consensus on a number of issues, including:
What is in a name/title?
Who are this individual’s peers?
Where does one obtain the requisite background to qualify?
How does RACI (a responsibility assignment matrix) apply?
When does data influence IT development efforts?
Why are these issues not better understood?
Data-Ed: Building the Case for the Top Data JobData Blueprint
Reflections on the past 25 years of organizational IT accomplishments, combined with performance measurement data, indicate that current IT management has been called upon to do a job that it cannot do well. Data are assets that deserve to be managed as professionally and aggressively as other company assets. Objective measurements show that approximately 1% of all organizations achieve data management success. In the face of the ongoing “data explosion,” this leaves most organizations wholly unprepared to leverage their sole, non-degrading, strategic asset. The requirements and organizational performance dictate a full time position that does not report to IT and manages the data function from a function that is external to and precedes the SDLC. While transformation may require some organizational discomfort, this move will achieve improved organizational IT performance faster and cheaper than ERPs or any other silver bullet.
Learning Objectives:
Why there typically isn’t and ultimately must be an authority (a chief) on organizational informational asset management
Why CIOS have not been able to devote the required time and attention
The seriousness of the skill gap – requisite expertise is rare
Understanding the ideal relationship between Data and IT.
Data-Ed: Unlocking business value through data modeling and data architecture...Data Blueprint
When asked why they are architecting data, many in the practice answer: "Because that is what must be done." However, a better approach to this question is to speak in terms that are understood in the executive suite – business results! All of our organizations are faced with various organizational challenges that require analysis. Building new systems is just one example. This webinar describes the use of data architecting as a basic analysis method (one of many that good analysts should keep in their “toolbox"). I will demonstrate various uses of data architecting to inform, clarify, understand, and resolve aspects of a variety of business problems. As opposed to showing how to architect data, I will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Learning Objectives:
Understanding how to contribute to organizational challenges beyond traditional data architecting
Realizing the fundamental difference between "definition" and "purpose"
Guiding analyses through data analysis
Using data modeling in conjunction with architecture/engineering techniques
Understanding foundational data architecture concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data architecting in support of business strategy
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Data Systems Integration & Business Value PT. 3: Warehousing
1. Copyright 2013 by Data Blueprint
Data Systems Integration & Business Value Part 3: Warehousing
Certain systems are more data focused than others. Usually their
primary focus is on accomplishing integration of disparate data. In
these cases, failure is most often attributable to the adoption of a single
pillar (silver bullet). The three webinars in the Data Systems Integration
and Business Value series are designed to illustrate that good systems
development more often depends on at least three DM disciplines (pie
wedges) in order to provide a solid foundation. Integrating data across
systems has been a perpetual challenge. Unfortunately, the current
technology-focused solutions have not helped IT to improve its dismal
project success statistics. Data warehouses, BI implementations, and
general analytical efforts achieve the same levels of success as other
IT projects – approximately 1/3rd are considered successes when
measured against price, schedule, or functionality objectives. The first
step is determining the appropriate analysis approach to the data
system integration challenge. The second step is understanding the
strengths and weaknesses of various approaches. Turns out that
proper analysis at this stage makes actual technology selection far
more accurate. Only when these are accomplished can proper
matching between problem and capabilities be achieved as the third
step and true business value be delivered.
Date: September 10, 2013
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D.
1
2. Copyright 2013 by Data Blueprint
Commonly Asked Questions
1) Will I get copies of the
slides after the event?
2) Is this being recorded so I
can view it afterwards?
2
3. Copyright 2013 by Data Blueprint
Get Social With Us!
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Join the conversation!
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Join the Group
Data Management & Business
Intelligence
Ask questions, gain insights and
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4. Copyright 2013 by Data Blueprint
4
Peter Aiken, PhD
• 25+ years of experience in data
management
• Multiple international awards &
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• President, DAMA International (dama.org)
• 8 books and dozens of articles
• Experienced w/ 500+ data
management practices in 20 countries
• Multi-year immersions with
organizations as diverse as the
US DoD, Nokia, Deutsche Bank,
Wells Fargo, and the Commonwealth
of Virginia
2
6. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
6
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
7. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
7
8. Data Program
Coordination
Feedback
Data
Development
Copyright 2013 by Data Blueprint
Standard
Data
Five Integrated DM Practice Areas
Organizational Strategies
Goals
Business
Data
Business Value
Application
Models &
Designs
Implementation
Direction
Guidance
8
Organizational
Data Integration
Data
Stewardship
Data Support
Operations
Data
Asset Use
Integrated
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity subject
area data
integration
Provide reliable data
access
Achieve sharing of data within a
business area
9. Copyright 2013 by Data Blueprint
Five Integrated DM Practice Areas
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program
Coordination
Organizational Data
Integration
Data Stewardship Data Development
Data Support
Operations
9
10. Copyright 2013 by Data Blueprint
Hierarchy of Data Management Practices (after Maslow)
• 5 Data management
practices areas /
data management
basics ...
• ... are necessary but
insufficient
prerequisites to
organizational data
leveraging
applications that is
self actualizing data
or advanced data
practices Basic Data Management Practices
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations
http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
W
arehousing
11. • Data Management Body of Knowledge
(DMBOK)
– Published by DAMA International, the professional
association for
Data Managers (40 chapters worldwide)
– Organized around primary data management
functions focused around data delivery to the
organization and several environmental elements
• Certified Data Management Professional
(CDMP)
– Series of 3 exams by DAMA International and
ICCP
– Membership in a distinct group of
fellow professionals
– Recognition for specialized knowledge in a
choice of 17 specialty areas
– For more information, please visit:
• www.dama.org, www.iccp.org
Copyright 2013 by Data Blueprint
DAMA DM BoK & CDMP
11
12. Copyright 2013 by Data Blueprint
Series Context
• Certain systems are more data
focused than others. Usually
their primary focus is on
accomplishing integration of
disparate data. In these cases,
failure is most often attributable
to the adoption of a single
technological pillar (silver bullet).
The three webinars in the Data
Systems Integration and Business Value
series are designed to illustrate that
good systems development more often depends on at least three
DM disciplines (pie wedges) in order to provide a solid foundation.
• Data Systems Integration & Business Value
– Pt. 1: Metadata Practices
– Pt. 2: Cloud-based Integration
– Pt. 3: Warehousing, et al.
12
13. Uses
Copyright 2013 by Data Blueprint
Part 1: Metadata Take Aways
• Metadata unlocks the value of data, and therefore
requires management attention [Gartner 2011]
• Metadata is the language of data governance
• Metadata defines the essence of integration challenges
Sources
Metadata Governance
Metadata
Engineering
Metadata
Delivery
Metadata Practices
Metadata
Storage
13
Specialized Team Skills
14. Copyright 2013 by Data Blueprint
Part 2: Take Aways
• Data governance, architecture,
quality, development maturity are
necessary but insufficient
prerequisites to successful data
cloud implementation
• A variety of cloud options will
influence cloud and data
architectures in general
– You must understand your architecture
and strategy in order to evaluate the
options
• Data must be reengineered to be
– Less
– Better quality
– More shareable
– for the cloud
• Failure to do these will result in more
business value for the cloud vendors/
service providers and less for your
organization
15. Copyright 2013 by Data Blueprint
Summary: Data Warehousing & Business Intelligence Management
15
16. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
16
17. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
17
18. Copyright 2013 by Data Blueprint
• Bank accounts are of varying
value and risk
• Cube by
– Social status
– Geographical location
– Net value, etc.
• Balance return on the loan with
risk of default
18
• How to evaluate the portfolio as a whole?
– Least risk loan may be to the very wealthy, but there are a very
limited number
– Many poor customers, but greater risk
• Solution may combine types of analyses
– When to lend, interest rate charged
Example: Portfolio Analysis
19. Copyright 2013 by Data Blueprint
Target Isn't Just Predicting Pregnancies
19
http://rmportal.performedia.com/node/1373
20. Copyright 2013 by Data Blueprint
15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 Best Companies to
Work For.” And we are hiring talented individuals who are interested in:
--solving original, wide-ranging, and open-ended business problems
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
--developing the fundamental skills for a rewarding business career
If that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many of whom
are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, political science). These
analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, what should we pay for it, what
should we price it for?
-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?
-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales?
-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand
-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond?
-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?
-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?
Even early in your career at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruit used data
analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 months with the company.
That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop the fundamental and advanced
skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP of Strategy and Analysis started his
career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who have chosen to pursue a business degree have
enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke.
Your opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailer in the
country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, is bigger than the
home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores and achieve $25+ billion in
annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talented associates, a healthy work-life
balance, and excellent compensation and benefits.
An ideal candidate will have
--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition such as
scholarships, awards, honor societies
-- Passion for business and desire to develop into a strong business leader
We encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us at college_recruiting@carmax.com.
http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3
- datablueprint.com
CarMax Example Job Posting
24
own an area of the business and will be expected to improve it
--solving original, wide-ranging, and open-ended business problems
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
--developing the fundamental skills for a rewarding business career
22. Copyright 2013 by Data Blueprint
22
Definitions, cont’d
• Study of data to discover and
understand historical patterns to
improve future performance
• Use of mathematics in business
• Analytics closely resembles
statistical analysis and data mining
– based on modeling involving
extensive computation.
• Some fields within the area of
analytics are
– enterprise decision management,
marketing analytics, predictive
science, strategy science, credit
risk analysis and fraud analytics.
23. Copyright 2013 by Data Blueprint
23
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
Example: Set Analysis
24. Copyright 2013 by Data Blueprint
Polling Question #1
Do you have start data
warehouse, data marts
and/or other warehousing
forms of integration?
a) Last year (2012)
b) This year (2013)
c) Next Year (2014)
d) Nope
24
25. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
25
26. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
26
27. Copyright 2013 by Data Blueprint
• Inmon:
–"A subject oriented, integrated, time variant, and non-
volatile collection of summary and detailed historical
data used to support the strategic decision-making
processes of the organization."
• Kimball:
–"A copy of transaction data specifically structured for
query and analysis."
• Key concepts focus on:
–Subjects
–Transactions
–Non-volatility
–Restructuring
Warehousing Definitions
27
28. Copyright 2013 by Data Blueprint
Top 10 Data Warehouse Failure Causes
1. The project is over budget
2. Slipped schedule
3. Functions and
capabilities not
implemented
4. Unhappy users
5. Unacceptable performance
6. Poor availability
7. Inability to expand
8. Poor quality data/reports
9. Too complicated for users
10.Project not cost justified
28
from The Data Administration Newsletter, www.tdan.com
29. Copyright 2013 by Data Blueprint
29
Basic Data Warehouse Analysis
• Emphasis on the
cube
• Permits different
users to "slice
and dice"
subsets of data
• Viewing from
different
perspectives
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
30. Copyright 2013 by Data Blueprint
30
Warehouse Analysis
• Users can "drill"
anywhere
• Entire collection is
accessible
• Summaries to
transaction-level
detail
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
32. Copyright 2013 by Data Blueprint
R& D Applications
(researcher supported, no documentation)
Finance Application
(3rd GL, batch
system, no source)
Payroll Application
(3rd GL)
Payroll Data
(database)
Finance
Data
(indexed)
Personnel Data
(database)
R & D
Data
(raw)
Mfg. Data
(home grown
database) Mfg. Applications
(contractor supported)
Marketing Application
(4rd GL, query facilities,
no reporting, very large)
Marketing Data
(external database)
Personnel App.
(20 years old,
un-normalized data)
32
Multiple Sources of (for example) Customer Data
37. Copyright 2013 by Data Blueprint
MetaMatrix Integration Example
37
• EII Enterprise Information Integration
– between ETL and EAI -
delivers tailored views of
information to users at the
time that it is required
38. Copyright 2013 by Data Blueprint
Linked Data
38
Linked Data is about using the Web to connect related data that wasn't
previously linked, or using the Web to lower the barriers to linking data
currently linked using other methods. More specifically, Wikipedia defines
Linked Data as "a term used to describe a recommended best practice for
exposing, sharing, and connecting pieces of data, information, and knowledge
on the Semantic Web using URIs and RDF."
linkeddata.org
39. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
39
40. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
40
42. Copyright 2013 by Data Blueprint
3
Courtesy of: http://www.infosys.com/industries/healthcare/industryofferings/Pages/healthcare
-data-warehousing.aspx
Data Warehousing
43. Copyright 2013 by Data Blueprint
3
Descriptive
Ask: What happened? What is happening?
Find: Structured data
Show: Profiles, Bar/Pie charts, Narrative
Predictive
Ask: What will happen? Why will it happen?
Find: Structured/unstructured data
Show: Risk Profiles, Pros/Cons, Care Recs
Prescriptive
Ask: What should I do? Why should I do it?
Find: Unstructured/structured data
Show: Strategic Goals, Support Recs
u Organization-wide
u Volume and Noise
u Utility
u Meaningful scoring
u Actionable recs
u Realistic goals
u Support
u Manage & measure
Analytics in Health Care
44. Copyright 2013 by Data Blueprint
3
Descriptive
Ask: What happened? What is happening?
Find: Structured data
Show: Profiles, Bar/pie charts, Narrative
Predictive
Ask: What will happen? Why will it happen?
Find: Structured/unstructured data
Show: Risk Profiles, Pros/Cons, Care Recs
Prescriptive
Ask: What should I do? Why should I do it?
Find: Unstructured/structured data
Show: Strategic Goals, Support Recs
BioMarin Licenses Factor VIII
Gene Therapy Program for
Hemophilia
Novel Gene Therapy Approach to
Hemophilia B
Sangamo BioSciences Receives
$6.4 Million
Strategic Partnership Award From
California Institute for
Regenerative Medicine to
Develop ZFP Therapeutic®
Treating Hemophilia in the 2010s
Hemophilia Management
45. Copyright 2013 by Data Blueprint
45
Styles of Business Intelligence
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
46. Copyright 2013 by Data Blueprint
Health Care Provider Data Warehouse
• 1.8 million members
• 1.4 million providers
• 800,000 providers no key
• 2.2% prov_number = 9 digits (required)
• 29% prov_ssn ≠ 9 digits
• 1 User
46
"I can take a roomful
of MBAs and
accomplish this
analysis faster!"
48. Copyright 2013 by Data Blueprint
Indiana Jones: Raiders Of The Lost Ark
48
49. Copyright 2013 by Data Blueprint
49
Business Intelligence Features
Problematic Data Quality
50. Copyright 2013 by Data Blueprint
5 Key Business Intelligence Trends
1. There's so much data, but too little
insight. More data translates to a
greater need to manage it and make
it actionable.
2. Market consolidation means fewer
choices for business intelligence users.
3. Business Intelligence expands from the Board Room to the front
lines. Increasingly, business intelligence tools will be available at
all levels of the corporation
4. The convergence of structured and unstructured data Will create
better business intelligence.
5. Applications will provide new views of business intelligence data.
The next generation of business intelligence applications is
moving beyond the pie charts and bar charts into more visual
depictions of data and trends.
50
http://www.cio.com/article/150450/Five_Key_Business_Intelligence_Trends_You_Need_to_Know?page=2&taxonomyId=3002
51. Copyright 2013 by Data Blueprint
Polling Question #2
Do you have?
a) A single enterprise
data warehouse
b) Coordinated data
marts
c) Both
d) Uncoordinated
efforts
e) None
51
52. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
52
53. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
53
54. Copyright 2013 by Data Blueprint
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
Meta Data Models
54
55. Copyright 2013 by Data Blueprint
Metadata Data Model
SCREEN
ELEMENT
screen element id #
data item id #
screen element descr.
INTERFACE
ELEMENT
interface element id #
data item id #
interface element descr.
INPUT
ELEMENT
input element id #
data item id #
input element descr.
OUTPUT
ELEMENT
output element id #
data item id #
output element descr.
MODEL
VIEW
model view element id #
data item id #
model view element des.
DEPENDENCY
dependency elem id #
data item id #
process id #
dependency description
CODE
code id #
data item id #
stored data item #
code location
INFORMATION
information id #
data item id #
information descr.
information request
PROCESS
process id #
data item id #
process description
USER TYPE
user type id #
data item id #
information id #
user type description
LOCATION
location id #
information id #
printout element id #
process id #
stored data items id #
user type id #
location description
PRINTOUT
ELEMENT
printout element id #
data item id #
printout element descr.
STORED DATA ITEM
stored data item id #
data item id #
location id #
stored data description
DATA ITEM
data item id #
data item description
55
56. Copyright 2013 by Data Blueprint
Warehouse
Process
Warehouse
Opera-on
Transforma-on
XML
Record-‐
Oriented
Mul-
Dimensional
Rela-onal
Business
Informa-on
So?ware
Deployment
ObjectModel
(Core,
Behavioral,
Rela-onships,
Instance)
Warehouse
Management
Resources
Analysis
Object-‐
Oriented
(ObjectModel)
Foundation
OLAP
Data
Mining
Informa-on
Visualiza-on
Business
Nomenclature
Data
Types
Expressions
Keys
Index
Type
Mapping
Overview of CWM Metamodel
http://www.omg.org/technology/documents/modeling_spec_catalog.htm
56
57. Copyright 2013 by Data Blueprint
Marco & Jennings's Complete Meta Data Model
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
57
58. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
58
59. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
59
66. Copyright 2013 by Data Blueprint
6 Best Practices for Data Warehousing
66
1.Do some initial architecture
envisioning.
2.Model the details just in time (JIT).
3.Prove the architecture early.
4.Focus on usage.
5.Organize your work by requirements.
6.Active stakeholder participation.
http://www.agiledata.org/essays/dataWarehousingBestPractices.html
67. Copyright 2013 by Data Blueprint
Polling Question #3
Do you have a separate
data warehouse
department, sub-
department, or group?
a) Yes
b)No
67
68. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
68
69. Copyright 2013 by Data Blueprint
1. Data management overview
2. Motivation for warehousing integration
technologies (reporting->BI->Analytics)
3. What are warehousing integration
technologies?
4. Warehousing and architecture focus
5. The use of meta models
6. Guiding principles & best practices
7. Take aways, references and Q&A
Tweeting now:
#dataed
Data Systems Integration & BV Part 3: Warehousing
69
71. Copyright 2013 by Data Blueprint
Series Take Aways
71
• Metadata
– Metadata unlocks the value of data, and therefore requires management
attention [Gartner 2011]
– Metadata is the language of data governance
– Metadata defines the essence of integration challenges
• Cloud
– Data governance, architecture, quality, development maturity are necessary but
insufficient prerequisites to successful data cloud implementation
– A variety of cloud options will influence cloud and data architectures in general
– You must understand your architecture and strategy in order to evaluate the
options
– Data must be reengineered to be: less; better quality; more shareable
– Failure to do these will result in more business value for the cloud vendors/
service providers and less for your organization
• Warehousing
– Business value must precede technical design
75. Copyright 2013 by Data Blueprint
Questions?
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
75
+ =
76. Copyright 2013 by Data Blueprint
Upcoming Events
76
October Webinar:
SHOW ME THE MONEY: MONETIZING DATA MANAGEMENT
October 8, 2013 @ 2:00 PM – 3:30 PM ET
(11:00 AM-12:30 PM PT)
November Webinar:
UNLOCK BUSINESS VALUE THROUGH
REFERENCE & MDM
November 12, 2013 @ 2:00 PM – 3:30 PM ET
(11:00 AM-12:30 PM PT)
Sign up here:
• www.datablueprint.com/webinar-schedule
• www.Dataversity.net
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