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.
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.
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.
Data Systems Integration & Business Value PT. 3: Warehousing Data 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.
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.
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/
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
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.
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: 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.
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.
Data Systems Integration & Business Value PT. 3: Warehousing Data 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.
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.
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/
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
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.
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
DataEd Slides: Data Management Best PracticesDATAVERSITY
It is clear that Data Management best practices exist, and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes — permitting organizations the opportunity to benefit from the best of both. It also permits organizations to understand:
• Their current Data Management practices
• Strengths that should be leveraged
• Remediation opportunities
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
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 -->
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
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.
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
DataEd Slides: Expressing Data Improvements as Business OutcomesDATAVERSITY
Join us and learn how you can better align your Data Management projects with business objectives to justify funding and gain management approval. 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 that data practices are the root causes of many business problems, they become more willing to make the required investments. However, we need to also approach them. The No. 1 reason that data programs fail to deliver is that they do not set or measure specific objectives that are meaningful to management. While there are opportunities to assist at the project level, data improvements are better able to be leveraged at the organization level. An improvable, dedicated data program can only be achieved by repeated application of data practices in service of specific business objectives. Data improvements typically do not maintain an ROI calculation. ROIs expressed in terms that board/executive management cares about deeply ensure data program viability. Improving organizational execution of specific data practice improvements must lead directly to specific improvements in organizational KPIs. While organizations may not be currently practiced in this ability, it is quite easy to learn. This presentation uses a number of specific examples calculating the business impact of data improvements. Program learning objectives include:
• Coming to grips with the state of practice
• Understanding the need for a comparable baseline measure
• Seeing application in a number of contexts
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.
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.
Real-World Data Governance: Comparing World Class Solutions in Data Governanc...DATAVERSITY
This document outlines the agenda for a webinar on comparing world class data governance solutions. The webinar will feature a panel of practitioners discussing their approaches to data stewardship, metadata, and master data governance. The panelists include professionals from PNC Bank, the Church of Latter Day Saints, and the Data Governance Institute. The webinar will be moderated by Robert Seiner and cover identifying data stewards, handling metadata, governing master data, and taking questions from the audience.
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.
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
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.
Data stewards are the implementation arm of Data Governance. They are also the first line of defense against bad data practices. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s shared data is reliably interconnected. Whether starting or restarting your Data Stewardship program, success comes from:
- Understanding the cadence/role of foundational data practices supporting organizational operations
- Proving value with tangible ROI
- Improving effectiveness/efficiencies using organization-wide insight
- Comprehending how stewards need to be multifunctional and dexterous, especially at first
- Integrating the role of data debt fighting
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
Slides: How AI Makes Analytics More HumanDATAVERSITY
Augmented analytics uses AI to enhance human decision making with analytics rather than replace it. Qlik's augmented analytics solution includes the Qlik Cognitive Engine that provides insight generation, automates tasks, and supports natural language interaction through NLP and NLG. It also uses machine learning to improve relevance of insights over time while leveraging the Qlik Associative Engine for context awareness. Qlik delivers a full range of augmented analytics capabilities across the analytics lifecycle through Insight Advisor to accelerate insight generation, enable conversational and search-based analytics, and boost data literacy.
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
Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering 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.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
DataEd Slides: Data Management vs. Data StrategyDATAVERSITY
This document appears to be a slide presentation on data management given by Peter Aiken. The presentation covers the following key points:
1. It provides Peter Aiken's background and experience in data management.
2. It discusses the current state of data literacy and the confusion that exists between IT, data, and business roles and responsibilities regarding data.
3. It defines data management and explains why effective data management is important for organizations. Poor data management can lead to poor quality data and bad organizational outcomes.
4. It highlights some of the current challenges in data management, including a general lack of data literacy, "second world data challenges" of fixing existing poor data, and the need for interoper
Lucy Nelson provides an overview of her experience, of adopting Enterprise Architect at UCLan.
Presented at the second JISC Emerging Practices workshop (2012/07/03).
http://emergingpractices.jiscinvolve.org/wp/doing-ea-workshop-2/
DataEd Slides: Data Management Best PracticesDATAVERSITY
It is clear that Data Management best practices exist, and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes — permitting organizations the opportunity to benefit from the best of both. It also permits organizations to understand:
• Their current Data Management practices
• Strengths that should be leveraged
• Remediation opportunities
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
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 -->
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
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.
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
DataEd Slides: Expressing Data Improvements as Business OutcomesDATAVERSITY
Join us and learn how you can better align your Data Management projects with business objectives to justify funding and gain management approval. 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 that data practices are the root causes of many business problems, they become more willing to make the required investments. However, we need to also approach them. The No. 1 reason that data programs fail to deliver is that they do not set or measure specific objectives that are meaningful to management. While there are opportunities to assist at the project level, data improvements are better able to be leveraged at the organization level. An improvable, dedicated data program can only be achieved by repeated application of data practices in service of specific business objectives. Data improvements typically do not maintain an ROI calculation. ROIs expressed in terms that board/executive management cares about deeply ensure data program viability. Improving organizational execution of specific data practice improvements must lead directly to specific improvements in organizational KPIs. While organizations may not be currently practiced in this ability, it is quite easy to learn. This presentation uses a number of specific examples calculating the business impact of data improvements. Program learning objectives include:
• Coming to grips with the state of practice
• Understanding the need for a comparable baseline measure
• Seeing application in a number of contexts
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.
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.
Real-World Data Governance: Comparing World Class Solutions in Data Governanc...DATAVERSITY
This document outlines the agenda for a webinar on comparing world class data governance solutions. The webinar will feature a panel of practitioners discussing their approaches to data stewardship, metadata, and master data governance. The panelists include professionals from PNC Bank, the Church of Latter Day Saints, and the Data Governance Institute. The webinar will be moderated by Robert Seiner and cover identifying data stewards, handling metadata, governing master data, and taking questions from the audience.
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.
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
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.
Data stewards are the implementation arm of Data Governance. They are also the first line of defense against bad data practices. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s shared data is reliably interconnected. Whether starting or restarting your Data Stewardship program, success comes from:
- Understanding the cadence/role of foundational data practices supporting organizational operations
- Proving value with tangible ROI
- Improving effectiveness/efficiencies using organization-wide insight
- Comprehending how stewards need to be multifunctional and dexterous, especially at first
- Integrating the role of data debt fighting
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
Slides: How AI Makes Analytics More HumanDATAVERSITY
Augmented analytics uses AI to enhance human decision making with analytics rather than replace it. Qlik's augmented analytics solution includes the Qlik Cognitive Engine that provides insight generation, automates tasks, and supports natural language interaction through NLP and NLG. It also uses machine learning to improve relevance of insights over time while leveraging the Qlik Associative Engine for context awareness. Qlik delivers a full range of augmented analytics capabilities across the analytics lifecycle through Insight Advisor to accelerate insight generation, enable conversational and search-based analytics, and boost data literacy.
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
Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering 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.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
DataEd Slides: Data Management vs. Data StrategyDATAVERSITY
This document appears to be a slide presentation on data management given by Peter Aiken. The presentation covers the following key points:
1. It provides Peter Aiken's background and experience in data management.
2. It discusses the current state of data literacy and the confusion that exists between IT, data, and business roles and responsibilities regarding data.
3. It defines data management and explains why effective data management is important for organizations. Poor data management can lead to poor quality data and bad organizational outcomes.
4. It highlights some of the current challenges in data management, including a general lack of data literacy, "second world data challenges" of fixing existing poor data, and the need for interoper
Lucy Nelson provides an overview of her experience, of adopting Enterprise Architect at UCLan.
Presented at the second JISC Emerging Practices workshop (2012/07/03).
http://emergingpractices.jiscinvolve.org/wp/doing-ea-workshop-2/
The document discusses the evolving role of the Chief Information Officer (CIO) over time from an IT operations focus to a strategic business leader. It outlines four main CIO archetypes - business leader, operational expert, innovation agent, and turnaround artist - and how CIOs allocate their time across functions. The document also discusses the potential role of a Strategy Execution Officer (SEO) to ensure effective implementation of business strategies and enterprise platforms.
Your Leadership Brand - The CIO as Business Strategist driving innovation. CI...Livingstone Advisory
Your Leadership Brand - The CIO as Business Strategist driving innovation
When CIOs think like business strategists, they shift from primarily managing technology projects to become highly business relevant. The ability of CIOs to successfully make this shift is key to underpinning the transformation of IT within the organisation. This transformation is critical to organisations that are looking for ways of driving innovation and entrepreneurship within the IT group, which in turn drives sustainable business value. The role of the CIO is at the core of this transformation.
In this engaging and practical session, Rob Livingstone provides valuable insights on how organisations and CIOs alike can ensure this transition is made successfully, and rapidly.
Agenda included
Surveying the broader landscape
Is enterprise innovation the Job of IT?
The Future-State CIO Model
Getting past ‘Business – IT Alignment’
Creating Influence
Your leadership brand.
Your personal Brand – as you!.
Cinco consejos de los expertos Cutter (Cuitláhuac Osorio)Software Guru
Cuitláhuac Osorio forma parte del consorcio Cutter donde nos habla de cómo hacer que las TI importen y que funcionen.
Además, nos comparte 5 consejos de los expertos.
The document discusses key challenges in IT transformation including financial constraints, legacy infrastructure issues, lack of processes, and need for technical skills updates. It identifies quick wins like implementing change control and architectural blueprints. New opportunities include business-IT collaboration and proliferation of technologies. The way forward involves reducing distractions, implementing quick wins, and developing strategic and tactical plans covering people, processes, and technology. This would help build an agile IT environment leveraging approaches like cloud, outsourcing, and maturity models.
The document discusses the role of the Chief Information Officer (CIO) and argues that their role will change in the next 10 years. Currently, the CIO role is often poorly defined and misunderstood. However, CIOs play an important role in developing business and information strategies, managing information as a key asset, and driving innovation and change within organizations. In the future, the CIO may take on more of a leadership role bridging the gap between business and IT to facilitate better information management.
Tammy Taylor is an IT professional seeking a leadership position to utilize her expertise in process methodologies and strong organizational skills. She has extensive experience in project management, IT governance, and improving IT operations. Her background includes roles managing IT disaster recovery programs, data center operations, and an IT consulting firm.
¿te has preguntado cual es la diferencia que hace exitoso la implementación de un programa de transformación digital y otro que no?. Seguramente encontrarás varias diferencias y una de ellas sera las metodologías, practicas y herramientas que se utilizaron durante la definición, planeación, ejecución, monitoreo y control de la estrategia. Arquitectura Empresarial es un marco de referencia que aunado a otras disciplinas permite el éxito de cualquier transformación.
My presentation in IT secure Forum 2012 conducted in Dubai 17-September-2012.
I have discussed the Business-IT alignment, how to measure it and how to measure the maturity out of it to make sure that IT is doing what it suppose to do,
I have tried to not to talk about theories only and to merge with real life examples.
more over I have talk about IT strategy , IT Governance, Knowledge management; IT metrics; Enterprise architecture and partnership
I hope you guys will find it enjoyable and benefit out of it.
Loudoun SBDC Information Technology (IT) Investment CIO and Due Diligence Str...Ted McLaughlan
Information Technology (IT) concerns and strategies a Chief Information Officer (CIO) deals with are equally relevant for small businesses and startups, as they are for larger businesses. A presentation to the Loudoun Small Business Development Center (SBDC).
Larry Quinlan, Global CIO at Deloitte - 2013 Tech Trends – Elements of postdi...Global Business Events
The document summarizes key technology trends from Deloitte's 2013 Tech Trends report, including the CIO taking a leadership role in driving business value from emerging technologies ("CIO as the Postdigital Catalyst"), the need to design systems focused exclusively on mobile use rather than just being mobile-friendly ("Mobile Only"), and the reality that complete cybersecurity is impossible so organizations must focus on rapid detection and response to inevitable breaches ("No Such Thing as Hacker-proof"). It also briefly outlines some other trends covered in the report like using gamification and big data analytics ("Finding the Face of Your Data") and reinventing legacy systems like ERP for faster innovation.
These slides--based on the webinar featuring Dennis Drogseth, VP of research at leading IT analyst firm Enterprise Management Associates (EMA)--leverage EMA’s consulting expertise and extensive research in digital and IT transformation, IT analytics and requirements to optimize IT for cost and value.
It Governance Slides for MISA Ontario June 2009Ben Perry
The document discusses the importance of IT governance for organizations. It notes that firms with superior IT governance have 20% higher profits than those with poor governance. IT governance involves defining decision rights, policies, standards, and methods for prioritizing and measuring IT initiatives. The document outlines some common challenges organizations face with IT projects and governance. It emphasizes that effective IT governance requires engagement between IT and business stakeholders to establish principles, architectures, investment processes and accountability frameworks.
Kappelman - Becoming a 21st Century EnterpriseLeon Kappelman
Slide deck from a talk given at the Society for Information Management's annual SIMposium conference on 14 November 2011 in Orlando, Florida.
Enterprises that are more agile and adaptable are more able to succeed in an Information Age world that demands they do more with less, faster, while traditional boundaries blur, and the rules of engagement change. Succeeding in such a world requires that organizations skillfully manage information about their products, customers, suppliers, markets, assets, and liabilities. Fortunately, most enterprises are skilled in such matters. But succeeding in the world of today, and to a even greater extent in the world of tomorrow, also demands that enterprises master the management all of the knowledge about itself, including details about all of its people and processes, intelligence and knowledge, things and places, timings and motivations, plans and measures, rules and jobs, structures and more. We are in the early stages of developing such skills and capabilities. Enterprise Architecture (EA) is the name of this emerging discipline.
EA represents a new way of thinking about and managing the enterprise, including its information technologies. EA is all about achieving the vision of bridging the chasm between strategy and implementation, of capturing all the knowledge about the enterprise and making it available in real time for every imaginable management need, and of having a shared “language” of words, graphics, and other depictions to discuss, document, manage, and make decisions about every important aspect of the enterprise. EA is key to being agile, adaptable, interoperable, integrated, lean, secure, responsive, efficient, effective, and thereby more able to succeed in the Information Age.
IT-Enabled Business Capabilities For turbulent EnvironmentsSangmin Cha
This document discusses IT-enabled business capabilities for turbulent environments. It outlines three key capabilities - operational, dynamic, and improvisational. Dynamic and improvisational capabilities are more important for long-term strategic advantage in turbulent environments. The relationship between IT infrastructure capabilities and these three business capabilities is also explored. Emerging IT infrastructures like event-driven, service-oriented, and self-learning systems can help strengthen an organization's dynamic and improvisational capabilities.
7 Steps to Transform Your Enterprise Architecture Practicepenni333
Enterprise architecture has a critical role in driving business success. But enterprise architects often find that they must create a better understanding for IT and business leaders of the function’s place in strategic planning, application rationalization, and business/IT alignment.
In this slidecast, author Beth Bacheldor explains what steps enterprise architects can take to transform their practice and give colleagues a greater appreciation of its value. The result? The business will have a greater opportunity to profit from enterprise architecture as an essential component of its operations.
Originally posted on: http://smartenterpriseexchange.com/groups/smart-architect
With a plethora of inventory and discovery tools, IT organizations often sink beneath an unreconciled data universe.
These slides, based on the webinar from leading IT analyst firm Enterprise Management Associates (EMA), draw from extensive EMA research and consulting experience to explain how IT organizations can improve their effectiveness in ‘discovering the truth’ about their environments.
Similar to Kappelman it strategy, governance, & value ho (20)
A Peek @ Trends'15 - SIMposium'14 FINAL 2postLeon Kappelman
The document summarizes findings from the 2015 SIM IT Trends Study, which surveyed over 1000 senior IT executives. Key findings include:
1) Organizations are undergoing profound changes in how they focus technology spending, deliver IT, and structure IT departments and leadership roles.
2) IT budgets are changing slightly, with a projected 1.9% increase in 2014 and 0.9% increase in 2015 on average. Spending is shifting from hardware to cloud and business services.
3) IT organization structures continue trending away from centralized models, with 71% now having decentralized, federated or hybrid structures.
SIM IT Trends Study 2013 - SIMposium SessionLeon Kappelman
Since 1980 the Society for Information Management (SIM) has conducted a survey of its senior IT executive members to gauge trends within the IT industry. SIM's members are among the most accomplished and innovative leaders in IT, so their responses help to benchmark various areas such as major management issues, largest and most worrisome IT investments, sourcing, CIO roles, staffing, spending, and salaries. SIM's IT Trends Study is widely recognized as one of the most representative barometers of the information technology industry. More information at http://www.simnet.org/?ITTrendsStudy.
Business-IT Alignment:Getting IT AND Keeping IT - Kappelman & PettitLeon Kappelman
Aligning IT with the business is about knowing your customer, the business. In IT we call this "knowing their requirements." Based on research sponsored by the Society for Information Management's Enterprise Architecture Working Group, this presentation provides performance measures to determine and improve your capabilities to do Requirements Analysis: specifically to assess your capabilities to effectively do Systems Analysis and Design and Enterprise Architecture. A software development capabilities measure is also provided.
The four horsemen of IT project doom -- kappelmanLeon Kappelman
Based on a in-depth study, this short paper explains how to spot and what to do about the early warning signs of IT project failure and the four horseman of IT project doom. IT project failure is not a technology problem, it's a management problem rooted in people and process weaknesses. Anyone with eyes can see these early warning signs.
Kappelman tribalnet - trends in IT infrastructure - 16nov2011 hLeon Kappelman
Slide deck from a talk on "Trends in IT Infrastructure - What you don't know CAN hurt you" given at 'the TribalNet Conference on 16 November 2011 in Phoenix.
Enterprises that are more agile and adaptable are more able to succeed in an Information Age world that demands they do more with less, faster, while traditional boundaries blur, and the rules of engagement change. Succeeding in such a world requires that organizations skillfully manage information about their products, customers, suppliers, markets, assets, and liabilities. Fortunately, most enterprises are skilled in such matters. But succeeding in the world of today, and to a even greater extent in the world of tomorrow, also demands that enterprises master the management all of the knowledge about itself, including details about all of its people and processes, intelligence and knowledge, things and places, timings and motivations, plans and measures, rules and jobs, structures and more. We are in the early stages of developing such skills and capabilities. Enterprise Architecture (EA) is the name of this emerging discipline.
EA represents a new way of thinking about and managing the enterprise, including its information technologies. EA is all about achieving the vision of bridging the chasm between strategy and implementation, of capturing all the knowledge about the enterprise and making it available in real time for every imaginable management need, and of having a shared “language” of words, graphics, and other depictions to discuss, document, manage, and make decisions about every important aspect of the enterprise. EA is key to being agile, adaptable, interoperable, integrated, lean, secure, responsive, efficient, effective, and thereby more able to succeed in the Information Age.
The Learning Objectives of Dr. Kappelman’s EA 202 webinar include matters like:
• What is EA and why should you care about it?
• Why and how our mental models and language about enterprises and IT must evolve.
• How to build an EA practice by building on your current capabilities in analysis, design, architecture, governance, planning, and more.
• How EA helps us better manage key trade-offs such as:
• Short-term value versus long-term value.
• Optimizing of parts (e.g., business unit or process) versus optimizing the whole.
• What to expect and assume on your EA journey.
The document provides an introduction to enterprise architecture (EA) through the Society for Information Management Enterprise Architecture Working Group (SIMEAWG). It describes SIMEAWG as a volunteer group of over 80 EA practitioners and thought leaders from various organizations dedicated to understanding and improving EA practices. It also mentions projects of SIMEAWG including an annual EA study, publishing reports and case studies, and an upcoming book on EA. The document aims to define key EA terms and concepts and explain the importance of EA for effectively managing complex organizations and systems.
Kappelman itmpi - ea 102 - modeling organizationsLeon Kappelman
Leon Kappelman is a professor of information systems and founding chair of the Society for Information Management's Enterprise Architecture Working Group. He has extensive experience helping organizations manage their IT assets through strategic planning, governance, and other practices. The document discusses the importance of enterprise architecture for understanding and communicating about an organization's design, structure, and requirements over time, especially as organizations and technologies continue to change. It emphasizes that getting requirements right is essential to avoid project failure.
"Enterprise Architecture and the Information Age Enterprise" @ CSDM2010 Leon Kappelman
Talk I gave in Paris on 28-Oct-10 @ the Complex System Design and Management Conference on "Enterprise Architecture and the Information Age Enterprise." Excellent event, wonderful people, beautiful city.
Enterprise Architecture 201: Creating the Information Age Enterprise (handouts)Leon Kappelman
Graduate-level introduction to enterprise architecture made to the DFW DAMA chapter in October 2009. Overview of EA with a focus on building your EA practice on what you are already doing in areas like data architecture, systems analysis and design, strategic planning, network architecture, rules management, software architecture, and so on. Basically what EA is and how to get started.
The Genesis of BriansClub.cm Famous Dark WEb PlatformSabaaSudozai
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
Understanding User Needs and Satisfying ThemAggregage
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
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At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Easily Verify Compliance and Security with Binance KYCAny kyc Account
Use our simple KYC verification guide to make sure your Binance account is safe and compliant. Discover the fundamentals, appreciate the significance of KYC, and trade on one of the biggest cryptocurrency exchanges with confidence.
Digital Marketing with a Focus on Sustainabilitysssourabhsharma
Digital Marketing best practices including influencer marketing, content creators, and omnichannel marketing for Sustainable Brands at the Sustainable Cosmetics Summit 2024 in New York
Anny Serafina Love - Letter of Recommendation by Kellen Harkins, MS.AnnySerafinaLove
This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
Unveiling the Dynamic Personalities, Key Dates, and Horoscope Insights: Gemin...my Pandit
Explore the fascinating world of the Gemini Zodiac Sign. Discover the unique personality traits, key dates, and horoscope insights of Gemini individuals. Learn how their sociable, communicative nature and boundless curiosity make them the dynamic explorers of the zodiac. Dive into the duality of the Gemini sign and understand their intellectual and adventurous spirit.
Structural Design Process: Step-by-Step Guide for BuildingsChandresh Chudasama
The structural design process is explained: Follow our step-by-step guide to understand building design intricacies and ensure structural integrity. Learn how to build wonderful buildings with the help of our detailed information. Learn how to create structures with durability and reliability and also gain insights on ways of managing structures.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
Industrial Tech SW: Category Renewal and CreationChristian Dahlen
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
Discover timeless style with the 2022 Vintage Roman Numerals Men's Ring. Crafted from premium stainless steel, this 6mm wide ring embodies elegance and durability. Perfect as a gift, it seamlessly blends classic Roman numeral detailing with modern sophistication, making it an ideal accessory for any occasion.
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Navigating the world of forex trading can be challenging, especially for beginners. To help you make an informed decision, we have comprehensively compared the best forex brokers in India for 2024. This article, reviewed by Top Forex Brokers Review, will cover featured award winners, the best forex brokers, featured offers, the best copy trading platforms, the best forex brokers for beginners, the best MetaTrader brokers, and recently updated reviews. We will focus on FP Markets, Black Bull, EightCap, IC Markets, and Octa.
How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....Lacey Max
“After being the most listed dog breed in the United States for 31
years in a row, the Labrador Retriever has dropped to second place
in the American Kennel Club's annual survey of the country's most
popular canines. The French Bulldog is the new top dog in the
United States as of 2022. The stylish puppy has ascended the
rankings in rapid time despite having health concerns and limited
color choices.”
How MJ Global Leads the Packaging Industry.pdfMJ Global
MJ Global's success in staying ahead of the curve in the packaging industry is a testament to its dedication to innovation, sustainability, and customer-centricity. By embracing technological advancements, leading in eco-friendly solutions, collaborating with industry leaders, and adapting to evolving consumer preferences, MJ Global continues to set new standards in the packaging sector.
2. Enterprise Strategy, Alignment, & Architecture
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Enterprise Architecture
Strategy
St t
Goals/Objectives (e.g., Alignment)
IT Architecture
IT Projects
3. Assessment
&
Continuous Improvement
p
Basic Feedback Loop
•Measurement provides feedback about execution & performance.
•It tells us how the organization is doing against planned goals,
objectives, targets, milestones, outcomes, and values.
5. Architecture? What’s that?
What s
Architecture “the set of descriptive
the
representations about an object”.
[John Zachman]
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Enterprise Architecture is “the
p
holistic set of descriptions about the
enterprise over time“. [SIMEAWG]
Enterprise Architecture is
modeling the enterprise.
g p
6. Why Enterprise A hi
Wh E i Architecture?
?
•If you can’t “see” it, then you can’t
effectively change it or manage it.
it
•If you can t “describe” it, then you
If can’t describe
can’t communicate about it.
•Especially if it’s complicated or big, or
will grow, evolve, or change at some
grow evolve
p
point in time.
7. EA is about the creation of a shared
language (of words, images, and so on) to
communicate about, think about, and
manage the enterprise.
If th people i the enterprise cannot communicate
the l in th t i t i t
well enough to align their ideas and thoughts
about th enterprise (
b t the t i (e.g., strategy, goals,
t t l
objectives, purpose, …),
then they cannot align the things they manage (e.g.,
applications, data, projects, goods and services,
jobs, vehicles, people, …). Nor can they
optimally govern, devise strategy, create value, …
8. Top IT Management Concerns 1980-2010
1980-
IT Management 2010 2009 2008 2007 2006 2005 2004 2003 1994 1990 1986 1985 1983 1980
Concerns
C
Business productivity & cost
1 1 7 4
reduction
Business agility and speed to market 2 3 13 17 7 5 7
IT and business alignment 3 2 1 2 1 1 1 1 9 7 5 2 7 9
IT reliability and efficiency 3 6
Business Process Reengineering 3 4 18 15 11 5 10 10 2
IT Strategic planning 6 7 3 8 4 4 4 2 10 3 1 1 1 1
Revenue generating IT innovations 6 8
THIS IS SYMPTOMATIC OF NOT SUFFICIENTLY
IT cost reduction 8 5 7 4
UNDERSTANDING THE “REQUIREMENTS”):
Security
Sec rit and privacy
pri ac
Globalization
“REQUIREMENTS”) 9
10
9
15
8 6 3 2 3 3 19 18 6 14 12
11 14 6 7 3 2 3 3 19 18 6 14 12
• SPECIFIC DETAILS OF A PARTICULAR
Change management
Outsou c g/ve do
Outsourcing/vendor management
a age e t 12 11
OBJECTIVE, ACTIVITY, AND/OR PROCESS.
Enterprise architecture
IT human resource considerations
13
13
11
17
11 33 15 15 9 8 4 1 8
13 17
• OVERALL CONTEXT – THE BIG PICTURE OF
Knowledge management
Project management 13 11 10 23 5 10
HOW IT ALL FITS TOGETHER.
Sourcing decisions
CIO leadership role
13 17
10 16 10
IT organization design
g g 15
• OR BOTH
Societal implications of IT 20
9. It’s not that we don’t govern, devise strategy, create
It s don t
value, build & run great ISs, and succeed.
• It s that we do so in a reductionist manner. Rather
It’s
than a holistic manner.
•An attempt or tendency to explain a complex set of
e p o e de cy o e p co p e se o
facts, entities, phenomena, or structures by another,
simpler set
p
•"For the last 400 years science has advanced by
reductionism ... The idea is that you could understand
y
the world, all of nature, by examining smaller and
smaller pieces of it. When assembled, the small pieces
p , p
would explain the whole" (John Holland).
• This leads to stovepipes, excessive complexity, dis-
pp , p y, dis-
integration, redundancy, high cost, and slow change.
14. It starts with “Structure”
Structure
Organizational Structure of IS
Departments/Functions
IT department’s structure
p
reflects IT’s mission:
To manage technology for
the good of the enterprise.
h d f h i
15. Simplified IS Department Structure
CEO
CIO
User IT Communications IS
Support Personnel Development
Data IT Operations
IT Planning Administration
Other critical concerns:
• Project management office
• Chief Enterprise Architect
• IS Audit/Performance Measurement
• Legal
• Finance & accounting
y
• CyberSecurity y
• Organizational development/Change management
• Continuity (COOP), Disaster prevention & recovery
• ++++
16. Governance “Structure” of IS
G “St t ” f
I/T department’s governance
structure reflects I/T’s mission
fl I/T’ i i
too: To manage technology for
the good of the enterprise.
By having the business
“owners”
“owners” govern.
17. “Executive
CEO Steering
Committee”
Marketing/ Legal
COO CFO CIO HR Strategy
Sales Counsel
Technical Architecture/ IS Project
Operations Development Security
Service Standard Management
End user Data System
Support Administration Development
Program Application
Network
Maintenance Development
Data
Communication
18. What is an Enterprise?
Logical
L i l
Physical
Ph i l
23. Strategist s
Strategist’s Vision
Business Model
B i M d l
Logical M d l
L i l Model
Physical Model
Technician/Contractor’s View
Functioning Enterprise
24.
25. W H W W W W
H O H H H H
A W E O E Y
T ? R ? N ?
? E ?
?
26.
27. I P S G
N O C
S F R H
O
E A
O R T D L
D F A A
L
U
L S
S
A T T
S E
S
/ &
T W R R
/
T
A U E I R
A C P M
O I U
R T
R N L
U G
E R
T S
E
S S
E
30. Zachman s
Zachman’s Framework for EA …
… is an ontology, a data model (schema) for all the
knowledge about the enterprise.
i
… is process and method agnostic. It doesn’t care how
you get the knowledge.
h k l d
… posits that if you want to be aligned, agile, optimized, or
whatever your enterprise design objectives, then these are
h t t i d i bj ti th
the data you must have and use in order to efficiently
and effectively:
• achieve those objectives;
• manage change and complexity;
g g p y;
• manage the enterprise & all its resources
including its technologies.
g g
32. By whatever means you get them, these are
the data you must have and use …
“holistic
holistic
reductionism
reductionism” –
decompose in
context
t t
http://zachman.com
33. Although I did say “governance starts with
structure” the most important dimensions of all
this are really leadership ( make it happen)
y p (to pp )
and organizational culture (to sustain it).
It’s all about becoming a Learning Organization,
“where people are continually learning to see the
whole together.” An organization characterized by:
• Holistic/systems thinking (big picture & connections)
• Team learning (collaboration)
EA • Shared mental models (shared language & models)
• B ildi shared vision ( h d goals)
Building h d i i (shared l)
• Personal mastery (working with great people)
(Peter Senge The Fifth Discipline, 1990)
Senge, Discipline
34. Some M d l and
S Models d
Theories about IT
Strategy,
Strategy
Governance,
Governance and
Value
35. Strategy
IT operations &
Resource Allocation service delivery
36. VA’s IT Governance Structures
VA Executive
Board Strategy
Culture Organizational Executive
Change Steering
Management Committee
Strategic Management
Council
Q
Quality
y
Office of
Cyber Security
Capital
Information Technology
I f ti T h l IT Steering
Board
Resource Investment Committee
Allocation Council IT Strategy
gy
Project Technical
Management
Office
EA Architecture Council Steering
g
Committee
IT project delivery Technology Architecture
42. Strategy
“Alignment” &
Business
Architecture
Resource
Allocation
IT project delivery
Technology
Architecture
43. 5 key areas of IT decisions
Design objectives?
Structures, Processes, and Business Rules
Accountability and Assessment
44. IT Strategic Vision
Technology Strategy & Architecture
IT Strategic Alignment & Resource Allocation
g g
From Peter Weill “DON’T JUST LEAD, GOVERN: HOW TOP-PERFORMING FIRMS GOVERN IT” MIS
Quarterly Executive Vol. 3 No. 1 / March 2004, pp. 1-17.
45. From Peter Weill “DON’T JUST LEAD, GOVERN: HOW TOP-PERFORMING FIRMS GOVERN
IT” MIS Quarterly Executive Vol. 3 No. 1 / March 2004, pp. 1-17.
47. TM
ENTERPRISE ARCHITECTURE - A FRAMEWORK
DATA What
Wh t FUNCTION How
H NETWORK Where
Wh PEOPLE Who
Wh TIME When
Wh MOTIVATION Why
Wh
SCOPE List of Things Important List of Processes the List of Locations in which List of Organizations List of Events Significant List of Business Goals/Strat SCOPE
Planner s
Planner’s View
to the Business Business Performs the Business Operates Important to the Business to the Business
(CONTEXTUAL) (CONTEXTUAL)
Planner ENTITY = Class of Function = Class of Node = Major Business Ends/Means=Major Bus. Goal/ Planner
Business Thing Business Process Location People = Major Organizations Time = Major Business Event Critical Success Factor
e.g. Semantic Model e.g. Business Process Model e.g. Business Logistics e.g. Work Flow Model e.g. Master Schedule e.g. Business Plan ENTERPRISE
Owner s
Owner’s View
ENTERPRISE System
MODEL MODEL
(CONCEPTUAL) (CONCEPTUAL)
Owner Ent = Business Entity Proc. = Business Process Node = Business Location People = Organization Unit Time = Business Event End = Business Objective Owner
Reln = Business Relationship I/O = Business Resources Link = Business Linkage Work = Work Product Cycle = Business Cycle Means = Business Strategy
e.g. Logical Data Model e.g. Application Architecture e.g. Distributed System e.g. Human Interface e.g. Processing Structure e.g., Business Rule Model
SYSTEM
SYSTEM Architecture Architecture
MODEL
MODEL (LOGICAL)
(LOGICAL)
Node = I/S Function
Ent = Data Entity Proc .= Application Function (Processor, Storage, etc) People = Role Time = System Event End = Structural Assertion
Designer Reln = Data Relationship Cycle = Processing Cycle Designer
I/O = User Views Link = Line Characteristics Work = Deliverable Means =Action Assertion
e.g. Physical Data Model e.g. System Design e.g. Technology Architecture e.g. Presentation Architecture e.g. Control Structure e.g. Rule Design TECHNOLOGY
TECHNOLOGY
MODEL MODEL
(PHYSICAL) (PHYSICAL)
Node = Hardware/System Builder
Builder Ent = Segment/Table/etc. Proc.= Computer Function Software People = User Time = Execute End = Condition
Reln = Pointer/Key/etc. I/O = Data Elements/Sets Link = Line Specifications Work = Screen Format Cycle = Component Cycle Means = Action
DETAILED e.g. Data Definition
g e.g. Program
g g e.g. Network Architecture
g e.g. Security Architecture e.g. Timing Definition
g g e.g. Rule Specification
g p DETAILED
REPRESEN- REPRESEN-
TATIONS TATIONS
(OUT-OF- (OUT-OF
CONTEXT) CONTEXT)
Sub-
Contractor Ent = Field Proc.= Language Stmt Node = Addresses People = Identity Time = Interrupt End = Sub-condition Sub-
Reln = Address I/O = Control Block Link = Protocols Work = Job Cycle = Machine Cycle Means = Step Contractor
FUNCTIONING FUNCTIONING
e.g. DATA e.g. FUNCTION e.g. NETWORK e.g. ORGANIZATION e.g. SCHEDULE e.g. STRATEGY
ENTERPRISE ENTERPRISE
John A. Zachman, Zachman International (810) 231-0531
48. Simplified IS Department Structure
CEO
CIO
See P634(D)
Information
End user
End-user System
Personnel Support Communications Development
Data Operations
Admin istration
CQI/TQM Planning
49. Simplified IS Department Structure
+ Executive (“Planners & Owners”) Level
CEO
CFO COO HR CIO
See P634(D)
CLO S&M
Information
End user
End-user System
Personnel Support Communications Development
Data Operations
Admin istration
CQI/TQM Planning
50. Simplified IS Department Structure
+ Executive (“Planners & Owners”) Level
E ti (“Pl O ”) L l
BofD
CEO
CFO COO HR CIO
See P634(D)
CLO S&M
Information
End-user System
Personnel Support Communications Development
Data Operations
Admin istration
Ad i i t ti
CQI/TQM Planning
51. Simplified IS Governance Structure
+ Executive (“Planners & Owners”) Level
E ec ti e O ners”) Le el
CEO
CFO COO HR CIO
See P634(D)
CLO S&M
“Executive Steering Committee”
g
AND
“IT S
Steering C
i Committee”
i ”
BUT Not always combined into one structure:
Mostly a function of size and culture
52. VA’s IT Governance Structures
VA Executive
Board
Executive
Steering
Committee
Strategic Management
Council
Information Technology
I f ti T h l IT Steering
Board
Committee
Technical
EA Architecture Council Steering
g
Committee
53. TM
ENTERPRISE ARCHITECTURE - A FRAMEWORK
DATA What
Wh t FUNCTION How
H NETWORK Where
Wh PEOPLE Who
Wh TIME When
Wh MOTIVATION Why
Wh
SCOPE List of Things Important List of Processes the List of Locations in which List of Organizations List of Events Significant List of Business Goals/Strat
to the Business Business Performs
SCOPE
the Business Operates Important to the Business to the Business
(CONTEXTUAL) (CONTEXTUAL)
Planner ENTITY = Class of Function = Class of Node = Major Business Ends/Means=Major Bus. Goal/ Planner
Business Thing Business Process Location People = Major Organizations Time = Major Business Event Critical Success Factor
e.g. Semantic Model e.g. Business Process Model e.g. Business Logistics e.g. Work Flow Model e.g. Master Schedule e.g. Business Plan ENTERPRISE
ENTERPRISE System
MODEL MODEL
(CONCEPTUAL) (CONCEPTUAL)
Owner Ent = Business Entity Proc. = Business Process Node = Business Location People = Organization Unit Time = Business Event End = Business Objective Owner
Reln = Business Relationship I/O = Business Resources Link = Business Linkage Work = Work Product Cycle = Business Cycle Means = Business Strategy
e.g. Logical Data Model e.g. Application Architecture e.g. Distributed System e.g. Human Interface e.g. Processing Structure e.g., Business Rule Model
SYSTEM
SYSTEM Architecture Architecture
Designer’s Vi
D i ’ View
MODEL
MODEL (LOGICAL)
(LOGICAL)
Node = I/S Function
Ent = Data Entity Proc .= Application Function (Processor, Storage, etc) People = Role Time = System Event End = Structural Assertion
Designer Reln = Data Relationship Cycle = Processing Cycle Designer
I/O = User Views Link = Line Characteristics Work = Deliverable Means =Action Assertion
e.g. Physical Data Model e.g. System Design e.g. Technology Architecture e.g. Presentation Architecture e.g. Control Structure e.g. Rule Design TECHNOLOGY
TECHNOLOGY
Builder’s View
MODEL MODEL
(PHYSICAL) (PHYSICAL)
Node = Hardware/System Builder
Builder Ent = Segment/Table/etc. Proc.= Computer Function Software People = User Time = Execute End = Condition
Reln = Pointer/Key/etc. I/O = Data Elements/Sets Link = Line Specifications Work = Screen Format Cycle = Component Cycle Means = Action
Subcontractor’s View
DETAILED e.g. Data Definition
g e.g. Program
g g e.g. Network Architecture
g e.g. Security Architecture e.g. Timing Definition
g g e.g. Rule Specification
g p DETAILED
REPRESEN- REPRESEN-
TATIONS TATIONS
(OUT-OF- (OUT-OF
CONTEXT) CONTEXT)
Sub-
Contractor Ent = Field Proc.= Language Stmt Node = Addresses People = Identity Time = Interrupt End = Sub-condition Sub-
Reln = Address I/O = Control Block Link = Protocols Work = Job Cycle = Machine Cycle Means = Step Contractor
FUNCTIONING FUNCTIONING
e.g. DATA e.g. FUNCTION e.g. NETWORK e.g. ORGANIZATION e.g. SCHEDULE e.g. STRATEGY
ENTERPRISE ENTERPRISE
John A. Zachman, Zachman International (810) 231-0531
54. Simplified IS Governance Structure
BofD
CEO
CFO COO HR CIO
See P634(D)
CLO S&M
Information
End-user System
Personnel Support Communications Development
“Technology Steering Committee” Data Operations
Admin istration
Ad i i t ti
CQI/TQM Planning
55. Executive &
CEO IS Steering
Committee
PMO
Marketing/ Legal
COO CFO CIO HR Strategy
Sales Counsel
Technology
Steering
Committee
Technical Architecture/ IS Project
Operations Development Security
Service Standard Management
End user Data System
Support Administration Development
Program Application
Network
Maintenance Development
Data
Communication
56. Strategy
IT operations &
Resource Allocation service delivery
57. ESC
TSC
ITSC
From Peter Weill “DON’T JUST LEAD, GOVERN: HOW TOP-PERFORMING FIRMS GOVERN IT” MIS
Quarterly Executive Vol. 3 No. 1 / March 2004, pp. 1-17.
59. IT GAP Model
The theoretical model proposed in this research posits
direct and indirect effects among the three constructs
Organizational
g
IT Governance
Performance
IT –Business
Alignment
“TESTING A MODEL OF THE RELATIONSHIPS AMONG ORGANIZATIONAL PERFORMANCE, IT-
IT-
BUSINESS ALIGNMENT, AND IT GOVERNANCE” -- Aurora Sanchez Ortiz
66. IT Assessment - Implementation Guidelines
Measure performance toward objectives on a fair & consistent basis
across organization
Use a prototyping approach: In the early stages manage to speed rather
than
th quality (it is more important to get started than to have perfect data),
lit i i t tt t t t d th t h f td t )
and expect it to evolve rapidly.
Learn to work with “dirty” data, because the data will almost never be as
good as you’d like. But also learn how to assess data quality.
Focus on the harder job of integrating measures with the value-
generating actions than on establishing the information-delivery processes.
information delivery processes
Collect facts needed to demonstrate:
Goal and mission achievement
Effective resource utilization
ec ve esou ce u o
Resist the temptation of automation – it will distract you from more
important tasks.
Embrace learning & continuous improvement. Monitor, evaluate, and
improve on a continuous basis
Expand the use of metrics in IT to continuously improve systems
development and operations in order to better serve the needs of the
enterprise – Eat your own cooking!
67. EA Implementation Guidelines
Build on what you’re already doing (including projects).
Use collaborative approaches to doing & governing EA:
Organize an EA working group or EA council
council.
Learn together & work toward agreement about language, models, methods
Get participation & commitment from IT & business at all
levels (as high as possible). Leadership counts!
possible)
Determine the goals, focus, scope, and priorities:
Aim for completeness & comprehensiveness. Deal with day-to-day needs.
Embrace continuous change, learning, & communication:
change learning
Remember, it’s a journey and a process.
Evangelize. Have an “elevator speech”. Get your “converters” one at a time.
Start small and show early success. Then build on it.
Identify EA initiatives of most value to organization.
Help the value creators, it creates champions and wins hearts and minds.
Monitor, evaluate, and improve on a continuous basis:
, , p
Quantify the benefits
Regularly take a hard look at EA cost-value proposition, and make it better.
Use EA in IT for CONTINUOUS IMPROVEMENT and
COMMUNICATION WITH YOUR CUSTOMERS &
STAKEHOLDERS
68. “No one has to change.
g
Survival is optional.”
p
– Dr. W. Edwards Deming
69. SIM Guide to Enterprise Architecture
A project of the Society for Information
Management’s EA Working Group
(
(SIMEAWG). )
•Free shipping & 40% discount: buy at
http://www.crcpress.com with code 542KA.
•All author royalties go to further the work
of the not-for-profit SIMEAWG.
40% discount Edited by: Leon A. Kappelman, Ph.D.
code = 542KA Foreword by: Jeanne W. Ross, Ph.D.
Contributing Authors, Panelists, & Artists (alphabetically):
at
• Bruce V. Ballengee • George S. Paras
CRCPress.com • Larry Burgess • Alex Pettit
• Ed Cannon
C • Jeanne W Ross
J W. R
• Larry R. DeBoever • Brian Salmans
• Russell Douglas • Anna Sidorova
• Randolph C. Hite • Gary F. Simons
• Leon A. Kappelman • Kathie Sowell
• Mark Lane • Tim Westbrock
• Thomas McGinnis • John A. Zachman