This document outlines a presentation on developing a data-centric strategy and roadmap. It discusses the importance of aligning data management goals to business needs through frameworks like Porter's competitive strategies and operating models. Metrics and success criteria must be defined by collaborating with business partners to measure improvements in specific opportunities. An example shows how a chemical company defined objects of measurement and metrics to quantify increased efficiency from a data integration solution. Developing a holistic solution requires understanding a business's competitive advantage, goals and needs.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Business Process Modelling PowerPoint Presentation SlidesSlideTeam
This PPT deck displays thirty four slides with in depth research. Our Business Process Modelling Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographics for an inclusive and comprehensive Business Process Modelling Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
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
Microsoft Data Platform - What's includedJames Serra
The pace of Microsoft product innovation is so fast that even though I spend half my days learning, I struggle to keep up. And as I work with customers I find they are often in the dark about many of the products that we have since they are focused on just keeping what they have running and putting out fires. So, let me cover what products you might have missed in the Microsoft data platform world. Be prepared to discover all the various Microsoft technologies and products for collecting data, transforming it, storing it, and visualizing it. My goal is to help you not only understand each product but understand how they all fit together and there proper use case, allowing you to build the appropriate solution that can incorporate any data in the future no matter the size, frequency, or type. Along the way we will touch on technologies covering NoSQL, Hadoop, and open source.
Identity and Access Management Reference Architecture for Cloud ComputingJohn Bauer
This presentation will outline a comprehensive reference architecture for meeting the secure access and provisioning demands of outsourcing business and technology processes to “the cloud”. The attendee will walk away with a more solid understanding of what identity and access management challenges face organizations looking to move application and business process support to cloud computing providers as well as offer a reference architecture that outlines how to build standards based solutions for each challenge.
John F. Bauer III has over 20 years of Information Technology and Security delivery experience. John is currently the Enterprise Security Architect for Key Bank and has previous held leadership positions at British Petroleum, Cliffs Natural Resources, MTD Products, and National City/PNC Bank. John has spoken previously on the topic of Information Security at CA World, Oracle Open World, Digital ID World and NACHA conferences. John has both a Computer Science degree and MBA from Case Western Reserve University’s Weatherhead School of Management and is a frequent Adjunct Professor on Network Security at Cuyahoga Community College. John also maintains an active blog: MidwestITSurvival.com.
Data Center 101: What to Look for in a Colocation ProviderHostway|HOSTING
When you’re evaluating possible colocation providers, the sheer amount of information can be overwhelming. However, there are distinct characteristics of the physical building, security, network and more that you must consider.
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
Forget Big Data. It's All About Smart DataAlan McSweeney
This proposes an initial smart data framework and structure to allow the nuggets of value contained in the deluge of largely irrelevant and useless data to be isolated and extracted. It enables your organisation to ask the questions to understand where it should be in terms of its data state and profile and what it should do to achieve the desired skills level across the competency areas of the framework.
Every organisation operates within a data landscape with multiple sources of data relating to its activities that is acquired, transported, stored, processed, retained, analysed and managed. Interactions across the data landscape generate primary data. When you extend the range of possible interactions business processes outside the organisation you generate a lot more data.
Smart data means being:
• Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including organisation structures, roles, devolution and delegation of decision-making, processes and automation
• Smart in being realistic, pragmatic and even skeptical about what can be achieved and knowing what value can be derived and how to maximise value obtained
• Smart in defining an achievable, benefits-lead strategy integrated with the needs business and in its implementation
• Smart in selecting the channels and interactions to include – smart data use cases
Smart data competency areas comprise a complete set of required skills and abilities to design, implement and operate an appropriate smart data programme.
IT Infrastructure Management Powerpoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of seventy slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed IT Infrastructure Management Powerpoint Presentation Slides complete deck. https://bit.ly/3sGXmkZ
Solution architects must be aware of the need for solution security and of the need to have enterprise-level controls that solutions can adopt.
The sets of components that comprise the extended solution landscape, including those components that provide common or shared functionality, are located in different zones, each with different security characteristics.
The functional and operational design of any solution and therefore its security will include many of these components, including those inherited by the solution or common components used by the solution.
The complete solution security view should refer explicitly to the components and their controls.
While each individual solution should be able to inherit the security controls provided by these components, the solution design should include explicit reference to them for completeness and to avoid unvalidated assumptions.
There is a common and generalised set of components, many of which are shared, within the wider solution topology that should be considered when assessing overall solution architecture and solution security.
Individual solutions must be able to inherit security controls, facilities and standards from common enterprise-level controls, standards, toolsets and frameworks.
Individual solutions must not be forced to implement individual infrastructural security facilities and controls. This is wasteful of solution implementation resources, results in multiple non-standard approaches to security and represents a security risk to the organisation.
The extended solution landscape potentially consists of a large number of interacting components and entities located in different zones, each with different security profiles, requirements and concerns. Different security concerns and therefore controls apply to each of these components.
Solution security is not covered by a single control. It involves multiple overlapping sets of controls providing layers of security.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Business Process Modelling PowerPoint Presentation SlidesSlideTeam
This PPT deck displays thirty four slides with in depth research. Our Business Process Modelling Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographics for an inclusive and comprehensive Business Process Modelling Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
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
Microsoft Data Platform - What's includedJames Serra
The pace of Microsoft product innovation is so fast that even though I spend half my days learning, I struggle to keep up. And as I work with customers I find they are often in the dark about many of the products that we have since they are focused on just keeping what they have running and putting out fires. So, let me cover what products you might have missed in the Microsoft data platform world. Be prepared to discover all the various Microsoft technologies and products for collecting data, transforming it, storing it, and visualizing it. My goal is to help you not only understand each product but understand how they all fit together and there proper use case, allowing you to build the appropriate solution that can incorporate any data in the future no matter the size, frequency, or type. Along the way we will touch on technologies covering NoSQL, Hadoop, and open source.
Identity and Access Management Reference Architecture for Cloud ComputingJohn Bauer
This presentation will outline a comprehensive reference architecture for meeting the secure access and provisioning demands of outsourcing business and technology processes to “the cloud”. The attendee will walk away with a more solid understanding of what identity and access management challenges face organizations looking to move application and business process support to cloud computing providers as well as offer a reference architecture that outlines how to build standards based solutions for each challenge.
John F. Bauer III has over 20 years of Information Technology and Security delivery experience. John is currently the Enterprise Security Architect for Key Bank and has previous held leadership positions at British Petroleum, Cliffs Natural Resources, MTD Products, and National City/PNC Bank. John has spoken previously on the topic of Information Security at CA World, Oracle Open World, Digital ID World and NACHA conferences. John has both a Computer Science degree and MBA from Case Western Reserve University’s Weatherhead School of Management and is a frequent Adjunct Professor on Network Security at Cuyahoga Community College. John also maintains an active blog: MidwestITSurvival.com.
Data Center 101: What to Look for in a Colocation ProviderHostway|HOSTING
When you’re evaluating possible colocation providers, the sheer amount of information can be overwhelming. However, there are distinct characteristics of the physical building, security, network and more that you must consider.
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
Forget Big Data. It's All About Smart DataAlan McSweeney
This proposes an initial smart data framework and structure to allow the nuggets of value contained in the deluge of largely irrelevant and useless data to be isolated and extracted. It enables your organisation to ask the questions to understand where it should be in terms of its data state and profile and what it should do to achieve the desired skills level across the competency areas of the framework.
Every organisation operates within a data landscape with multiple sources of data relating to its activities that is acquired, transported, stored, processed, retained, analysed and managed. Interactions across the data landscape generate primary data. When you extend the range of possible interactions business processes outside the organisation you generate a lot more data.
Smart data means being:
• Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including organisation structures, roles, devolution and delegation of decision-making, processes and automation
• Smart in being realistic, pragmatic and even skeptical about what can be achieved and knowing what value can be derived and how to maximise value obtained
• Smart in defining an achievable, benefits-lead strategy integrated with the needs business and in its implementation
• Smart in selecting the channels and interactions to include – smart data use cases
Smart data competency areas comprise a complete set of required skills and abilities to design, implement and operate an appropriate smart data programme.
IT Infrastructure Management Powerpoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of seventy slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed IT Infrastructure Management Powerpoint Presentation Slides complete deck. https://bit.ly/3sGXmkZ
Solution architects must be aware of the need for solution security and of the need to have enterprise-level controls that solutions can adopt.
The sets of components that comprise the extended solution landscape, including those components that provide common or shared functionality, are located in different zones, each with different security characteristics.
The functional and operational design of any solution and therefore its security will include many of these components, including those inherited by the solution or common components used by the solution.
The complete solution security view should refer explicitly to the components and their controls.
While each individual solution should be able to inherit the security controls provided by these components, the solution design should include explicit reference to them for completeness and to avoid unvalidated assumptions.
There is a common and generalised set of components, many of which are shared, within the wider solution topology that should be considered when assessing overall solution architecture and solution security.
Individual solutions must be able to inherit security controls, facilities and standards from common enterprise-level controls, standards, toolsets and frameworks.
Individual solutions must not be forced to implement individual infrastructural security facilities and controls. This is wasteful of solution implementation resources, results in multiple non-standard approaches to security and represents a security risk to the organisation.
The extended solution landscape potentially consists of a large number of interacting components and entities located in different zones, each with different security profiles, requirements and concerns. Different security concerns and therefore controls apply to each of these components.
Solution security is not covered by a single control. It involves multiple overlapping sets of controls providing layers of security.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Find more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
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.
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Check out more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Find more data management webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Find more Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Every great product needs a clear, well-defined product roadmap. This Slideshare explains the whats, whys and hows of Product Roadmaps in plain English.
Data-Ed: Best Practices with the Data Management Maturity ModelData Blueprint
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Takeaways:
- Organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business strategy.
- Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.
- There are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed Online Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to cope with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic expectations, and help demonstrate the value of this process to both internal and external decision makers.
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
Data-Ed Online: Trends in Data ModelingDATAVERSITY
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Business Intelligence (BI) and Data Management Basics amorshed
A one-day training course on the Concepts of Data Management and Business Intelligence (BI) in the DX age
A Basic Review of BI and DM
How to Implement BI
A review of BI Tools and 2022 Gartner Quadrant Magic
Basics of Data warehouse (DWH)
An introductions to Power BI
Components of Power BI
Steps for BI Implementation
Data Culture
Intro to ETL and ELT
OLAP files and Architecture
Digital transformation or DX review
A glance at DMBOK2.0 framework
BI Challenges
Data Governance
Data Integration
Data Security and Privacy in DMBOK2.0
Data-Driven Organization
Data and BI Maturity Model
Traditional BI
Self-service BI
who is DMP
who is BI developer
what is Metadata
what is Master data
Data Quality
Data Literacy
Benefits of BI
BI features
How does BI Works?
Modern BI
Data Analytics
BI Architecture
Data Types
Data Lake
Data Mart
Data Silo
Data Visualization
Power BI Architecture and components
With the expertise of our CEO, we've put together a webinar about MVP readiness. If you're low on time, budget, and resources, build a lean solution. A minimum viable product has enough design and development to launch within a shorter time frame. Not only do you save time and money, you'll be able to make iterations and versions post-launch.
See how to prepare for an MVP with Ali Allage, the CEO of Boost Labs.
For more about MVPs, contact us!
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
5 Steps to Transform into a Data-Driven Organization - Ganes Kesari - Gramen...Ganes Kesari
This session was presented on May 27th, 2021, in a Webinar organized by Gramener.
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
Session Details:
Today, organizations struggle to get value from data despite significant investments. Did you know that there's one factor that influences the outcomes of all your data initiatives?
This webinar will highlight how an organization's data maturity influences its performance. It will show how you can assess your data maturity and plan the five steps for data-driven business transformation.
Pain points we would be discussing:
Most organizations stagnate midway in their data journey.
Gartner says that over 87% of organizations in the industry are at lower levels of data maturity (levels 1 and 2 on a scale of 5).
Just doing more data science projects will not improve your capabilities or outcomes. The fact is that the top challenges reported by CDOs fall into five common areas.
This webinar will show what they are and how you can tackle them.
Who should attend
- Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Managers
What Will You Learn?
- What is data science maturity, and why does it matter?
- How do you assess data science maturity and limitations of the assessment?
- How can data science maturity help your organization level up (explained with an example)?
5 Steps To Become A Data-Driven Organization : WebinarGramener
Gramener's Chief Data Scientist and Co-founder Ganes Kesari conducted an interesting webinar that will give you an idea of how to analyze your data maturity and plan the five steps to transforming your business using data.
Who should watch this webinar?
Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Directors, and Managers.
Important points discussed on the webinar:
-The majority of businesses reach a halt in the middle of their data journey.
-According to Gartner, approximately 87% of companies in the business have a poor degree of data maturity (levels 1 and 2 on a scale of 5).
-Adding more data science projects to your portfolio will not boost your talents or results. The truth is that CDOs' primary issues are divided into five categories.
Learnings from this webinar:
-Data Science Maturity. What is it and why is it important?
-How can you determine the maturity of data science and its limitations?
-How does data science maturity (described with an example) assist your business in progressing?
Watch the full webinar on:
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
To know more about Data Maturity visit:
https://gramener.com/data-maturity/#
Data-Ed Webinar: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
•Data system integration challenge analysis
•Understanding of a range of data system-integration technologies including Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
•Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
•How to utilize data warehousing & BI in support of business strategy
The benefits of Hadoop for analytics make it a popular option for many companies looking to expand their analytics suite. However, adding Hadoop as an analytics platform to an existing environment based on more traditional data structures and methods poses several key challenges. Review these slides to understand key challenges and strategies to expanding the analytics suite to use Hadoop, such as: architectural integration with existing platforms, skills and organizational readiness, and the importance of a vision and a clear path forward.
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter Dr. Peter Aiken will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find more Data-Ed webinars here: www.datablueprint.com
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Tools alone are not the answer: Career roles and growth tracks for data professionals. In today’s (Big) data-driven information economy, it is even more critical to focus on data as an asset that directly supports business imperatives. But tools alone are not the answer. Organizations that want to rise above their competition can only do so with the help of skilled professionals who know how to manage, mine, and draw actionable insights from the multitudes of (Big) data sources. Numerous new roles and job titles have emerged to address the high demand for specialized data professionals. This webinar brings together three individuals well qualified to contribute to this important industry-wide discussion of data jobs. We will take a closer look at these newer data management roles and present recommendations on how to enhance career paths.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-archive/
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Check out more of our Data-Ed webinars here: www.datablueprint.com/webinar-schedule
Data-Ed: Unlock Business Value through Document & Content ManagementData Blueprint
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning Objectives:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
http://www.datablueprint.com/webinar-schedule
Data-Ed: Unlock Business Value Through Reference & MDM Data Blueprint
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an Understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Check out more of our webinars here: http://www.datablueprint.com/webinar-schedule
Data-Ed: Show Me the Money: Monetizing Data ManagementData Blueprint
Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their business problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management and how it impacts business objectives. Join us and learn how you can better align your data management projects with business objectives to justify funding and gain management approval.
Check out more of our webinars: http://www.datablueprint.com/resource-center/webinar-schedule/
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.
Data Systems Integration & Business Value Pt. 2: CloudData 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.
Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:
Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties and decision-making. This webinar will show you how to recognize the opportunities, ‘size up’ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Unlock Business Value through Data Quality Engineering Data Blueprint
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar focuses on obtaining business value from data quality initiatives. I will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Yes, we face a data deluge and big data seems to be largely about how to deal with it. But 99% of what has been written about big data is focused on selling hardware and services. The truth is that until the concept of big data can be objectively defined, any measurements, claims of success, quantifications, etc. must be viewed skeptically and with suspicion. While both the need for and approaches to these new requirements are faced by virtually every organization, jumping into the fray ill-prepared has (to date) reproduced the same dismal IT project results.
The very real, very rapid, very great increases in data of all forms (charts showing data types and volume increases)
Challenges faced by virtually all data management programs
Means by which big data techniques can compliment existing data management practices
Necessary but insufficient pre-requisites to exploiting big data techniques
Prototyping nature of practicing big data techniques
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Unlock Business Value through Data GovernanceData Blueprint
If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.
Learning Objectives:
Understanding contextually why data governance can be tricky for most organizations
Demonstrate a variety of “storytelling” techniques
How to use “worst practices” to your advantage
Understanding foundational data governance concepts based on the Data Management Body of Knowledge (DMBOK)
Taking away several novel but tangible examples of generating business value through data governance
Leading the Data Asset Management Team: CDO or Top Data Job?Data Blueprint
Join Peter Aiken, Ph.D. and Micheline Casey for this interactive discussion on the role of Chief Data Officer (CDO) or Top Data Job (TDJ). While most agree that data challenges are getting – dare we say it, bigger? – the range of approaches reveals no emerging consensus as to the best way to address these challenges. This webinar features a wide-ranging discussion of a number of aspects of this exciting new career path. For each of these aspects, new data leaders can be congratulated but sometimes they also ought to be consoled. Ms. Casey (as the very first state CDO) and Dr. Aiken will bring certain considerations to the table. They hope to sample the pulse of the community and move towards consensus on a number of issues, including:
What is in a name/title?
Who are this individual’s peers?
Where does one obtain the requisite background to qualify?
How does RACI (a responsibility assignment matrix) apply?
When does data influence IT development efforts?
Why are these issues not better understood?
Data-Ed: Building the Case for the Top Data JobData Blueprint
Reflections on the past 25 years of organizational IT accomplishments, combined with performance measurement data, indicate that current IT management has been called upon to do a job that it cannot do well. Data are assets that deserve to be managed as professionally and aggressively as other company assets. Objective measurements show that approximately 1% of all organizations achieve data management success. In the face of the ongoing “data explosion,” this leaves most organizations wholly unprepared to leverage their sole, non-degrading, strategic asset. The requirements and organizational performance dictate a full time position that does not report to IT and manages the data function from a function that is external to and precedes the SDLC. While transformation may require some organizational discomfort, this move will achieve improved organizational IT performance faster and cheaper than ERPs or any other silver bullet.
Learning Objectives:
Why there typically isn’t and ultimately must be an authority (a chief) on organizational informational asset management
Why CIOS have not been able to devote the required time and attention
The seriousness of the skill gap – requisite expertise is rare
Understanding the ideal relationship between Data and IT.
Data-Ed: Unlocking business value through data modeling and data architecture...Data Blueprint
When asked why they are architecting data, many in the practice answer: "Because that is what must be done." However, a better approach to this question is to speak in terms that are understood in the executive suite – business results! All of our organizations are faced with various organizational challenges that require analysis. Building new systems is just one example. This webinar describes the use of data architecting as a basic analysis method (one of many that good analysts should keep in their “toolbox"). I will demonstrate various uses of data architecting to inform, clarify, understand, and resolve aspects of a variety of business problems. As opposed to showing how to architect data, I will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Learning Objectives:
Understanding how to contribute to organizational challenges beyond traditional data architecting
Realizing the fundamental difference between "definition" and "purpose"
Guiding analyses through data analysis
Using data modeling in conjunction with architecture/engineering techniques
Understanding foundational data architecture concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data architecting in support of business strategy
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
The affect of service quality and online reviews on customer loyalty in the E...
Strategy and roadmap slides
1. Copyright 2014 by Data Blueprint
1
Data-centric Strategy & Roadmap
Date: January 13, 2015
Time: 2:00 PM ET
11:00 AM PT
Presenters: Peter Aiken,
Lewis Broome
2. Copyright 2014 by Data Blueprint
2
Commonly Asked Questions
1) Will I get copies of the slides
after the event?
2) Is this being recorded so I
can view it afterwards?
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to open the image, or the image may have been corrupted. Restart your
computer, and then open the file again. If the red x still appears, you may have
to delete the image and then insert it again.
3. Copyright 2014 by Data Blueprint
3
Get Social with Us!
Live Twitter Feed
#dataed
Like Us
www.facebook.com/datablueprint
Join the Group
Data Management & Business Intelligence
4. Building a Data-centric Strategy &
Roadmap
What needs to be done… avoiding a haphazard
approach
Presented by Peter Aiken, Ph.D. and Lewis Broome
5. Copyright 2014 by Data Blueprint
5
• 30+ years DM
experience
• 9 books/
many articles
• Experienced with
500+ data
management
practices
• Multi-year
immersions: US DoD,
Nokia, Deutsche
Bank, Wells Fargo, &
Commonwealth of VA
Lewis Broome Peter Aiken
• CEO Data Blueprint
• 20+ years in data
management
• Experienced leader driving
global solutions for
Fortune 100 companies
• Creatively disrupting the
approach to data
management
• Published in multiple
industry periodicals
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displayed. Your computer
may not have enough
memory to open the image,
or the image may have been
corrupted. Restart your
computer, and then open the
file again. If the red x still
appears, you may have to
delete the image and then
insert it again.
6. Copyright 2014 by Data Blueprint
6
Outline
• Data Strategy Overview
• Determining the Business Needs
• Measurement & Success Criteria
• Current State Analysis
• Developing a Solution to Address Needs
• Developing a Roadmap and Plan
• Q&A
7. Copyright 2014 by Data Blueprint
7
"The significant
problems we
face cannot be
solved at the
same level of
thinking we
were at when
we created
them."
- Albert Einstein
8. Copyright 2014 by Data Blueprint
8
Wayne Gretzky’s
Definition of Strategy
He skates to where he
thinks the puck will be ...
9. Copyright 2014 by Data Blueprint
9
The
Importance
of Strategy
Organizational
Strategy
IT Strategy
Data Strategy
10. Copyright 2014 by Data Blueprint
10
Summary: Enterprise Data Strategy Choices
Q3
Using data to create
strategic opportunities
Q4
Both (Cash Cow)
Q1
Keeping the doors open
(little or no proactive data
management)
Q2
Increasing organizational
efficiencies/effectiveness
Improve Operations
Innovation
Only 1 in 10 organizations has a
board approved data strategy!
11. Copyright 2014 by Data Blueprint
11
Understanding WHY Data is Important to the Business
• Data linked to, and part of, the products & services
being offered
• Information is power (Analytics!)
• Data creatively destructs how we work; skills & the
workforce needed are drastically different
• Data volume, velocity & variety exerting
pressure on operating models
& infrastructure
“…it’s not what you do, it’s why you do
it” – Simon Sinek
http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html
Why
Vision
How
Process
What
Outcome
12. Copyright 2014 by Data Blueprint
12
Putting the Data Strategy Together
Comprehend your organization’s competitive advantage,
operating model & business goals
Define specific business opportunities that impact these
Define the metrics that measure improvement in business
performance
Requires people, process, data and technology while
recognizing strengths and limitations of culture & capability
Outline an achievable implementation plan in a roadmap with
timelines, milestones and level of effort estimates
Get on the same
page with
business partners
Measure
Business Value
Develop a holistic
solution and
approach
Note: For many organizations this requires a transformation in how they think and
operate – this is the greatest challenge in becoming a ‘data-driven’ organization
13. Copyright 2014 by Data Blueprint
13
Outline
• Data Strategy Overview
• Determining the Business Needs
– Foundational Business Understanding
– Identify Specific Business Needs
– Example Data Strategy Goals
• Measurement & Success Criteria
• Current State Analysis
• Developing a Solution to Address Needs
• Developing a Roadmap and Plan
• Q&A
14. Copyright 2014 by Data Blueprint
14
Aligning Data Management Goals to the Business
• Competitive Advantage
– Its not about being the best, its about being different
• Operating Models
– The interactions across processes, business units, customers
and products
• Business Strategy and Goals
– Short and Long Term; Leadership’s Dynamic
priorities and investments
• Use Frameworks for Understanding
Start with Analyzing the Business…..
15. Copyright 2014 by Data Blueprint
15
Porter’s Competitive Strategic Framework
Cost: Are you
competing on cost?
How cost-sensitive is
your market?
Market Scope: Are you
focused on a narrow
market (i.e. niche) or a
broad market of
customers?
Overall Low-Cost
Leadership
Strategy
Broad
Differentiation
Strategy
Focused
Low-Cost
Strategy
Focused
Differentiation
Strategy
Blue Ocean
Brands
Lower Cost Differentiation
Broad
Range of
Buyers
Narrow
Buyer
Segment
Product Differentiation: How specifically focused are your
products?
Note: (Typically) Can’t be all things to all consumers –
where are you?
16. Copyright 2014 by Data Blueprint
16
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computer may not have enough memory
to open the image, or the image may have
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and then open the file again. If the red x
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image and then insert it again.
Competitive Strategic Framework - Example
Overall Low-Cost
Leadership
Strategy
Broad
Differentiation
Strategy
Focused
Low-Cost
Strategy
Focused
Differentiation
Strategy
Blue Ocean
Brands
Lower Cost Differentiation
Broad
Range of
Buyers
Narrow
Buyer
Segment
• Its all about how value is created!
• Works for Non-profits as well (Substitute ‘Mission’ for Value)
17. Copyright 2014 by Data Blueprint
17
Porter’s Five Forces Framework
Bargaining Power of Buyers: The degree
of leverage customers have over your
company
Bargaining Power of Suppliers: The
degree of leverage suppliers have over your
company
Threat of New Entrants: How hard is it for
new competition to enter the market?
Threat of Substitute Products: How easy
(or hard) is it for customers to switch to
alternative products?
Competitive Rivalry: How competitive is
the market place?
Once you find your place in the four quadrants…What is your competitive
advantage?
http://www.strategy-keys.com/michael-porter-five-forces-model.html
18. Copyright 2014 by Data Blueprint
18
Five Forces - Example
Whole Foods
• Customers (weak influence) will seemly pay any price for specially sourced commodities
• Fewer suppliers (strong influence) to support Whole Foods’ customer demands
5 Forces Whole Foods Wal-mart
Threat of New Entrants Weak Weak
Bargaining Power of Buyers Weak to Moderate Moderate to Strong
Bargaining Power of Suppliers Moderate to Strong Very Weak
Threat of Substitutes Strong Moderate to Strong
Competitive Rivalry Moderate Weak
Wal-mart
• Price-sensitive customers. Use strength over suppliers to maintain low costs.
• Heavy investment in keeping operational cost low. Highly efficient internal processes
19. Copyright 2014 by Data Blueprint
19
Operating Model Framework
Coordination
• Shared customers, products or suppliers
• Impact on other business unit transaction
• Operationally unique business units or functions
• Autonomous business management
• Business unit control over process design
• Consensus processes for designing IT infrastructure
services
• IT application decisions made in business units
Unification
• Customers and suppliers may be local or global
• Globally integrated business processes often with
support of enterprise systems
• BU’s with similar or overlapping operations
• Centralized management often applying functional/
process/business unit matrices
• Centrally mandated databases
• IT decisions made centrally
Diversification
• Few, if any, shared customers or suppliers
• Independent transactions
• Operationally unique business units
• Autonomous business management
• Business unit control over business process design
• Few data standards across business units
• Most IT decisions made within business units
Replication
• Few, if any, shared customers
• Independent transactions aggregated at high level
• Operationally similar business units
• Autonomous BU leaders with limited discretion over
processes
• Centralized control over business process design
• Standardized data definitions but locally owned
• Centrally mandated IT services
Business Process Standardization
Low High
HighLow
BusinessProcessIntegration
*Source: Gartner
20. Copyright 2014 by Data Blueprint
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Operating Model - Examples
Coordination Unification
Diversification Replication
Business Process Standardization
Low High
HighLow
BusinessProcessIntegration
*Source: Gartner
21. Copyright 2014 by Data Blueprint
21
Business Strategy and Goals
• A cohesive declaration of organizational direction, strategies,
goals, targets, objectives, approaches and plans
• Usually tied to a time frame
• Constrained by competitive advantage and operating models
• Dynamically created as a result of
opportunities and challenges
• Aligns to overall mission and brand
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22
Business Strategy and Goals - Example
Strategy for a large publicly traded logistics company
“We forge long-term partnerships with key customers that include supply-chain
management as an integral part of their strategy. Working in concert, we drive out cost,
add value and function as an extension of their enterprise. Our strategy is based on
utilizing an integrated, multimodal approach to provide capacity-oriented solutions
centered on delivering customer value and industry-leading service. We believe our
unique operating strategy can add value to customers and increase our profits and
returns to stockholders.”
Brand Promises to their Customers
• Undeniable Flexibility
• Unmatched Capacity
• Unrivaled Service
• Undisputed Experts
• Unprecedented Control
23. Copyright 2014 by Data Blueprint
23
Data Strategy Goals – Example-1
Enterprise
Divisional
• An 360° enterprise level understanding of customers, capacity, orders & vendors
• Asset and driver utilization maximized across the enterprise
• IT solutions leveraged across the enterprise to reduce costs and cycle-time
• Customers seamlessly leverage services across divisions
• A 360° divisional level understanding of customers, capacity, orders & vendors
• IT solutions leveraged to support operational uniqueness of each division
• Minimize cost and maximize revenue per load per division
Division A Division B Division C Division D
Enterprise
Rolls Up To
24. Copyright 2014 by Data Blueprint
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Data Strategy Goals – Example-2
Increase
Operational
Efficiencies
As-IsTo-Be
As-Is Efficiency Challenges
• Complex & un-integrated business processes
• Suboptimal data structures & controls creates poor data
quality
• Lack of transparency and controls creates work-around’s
To-Be Efficiency Improvements
• Eliminate non-value added manual work-around’s
• Maximize auto-accepts (i.e. straight-through-processing)
• Simplify & automate workflows
• Create transparency to enable proactive processes
Increasing operational efficiencies will…
• Lower cost per order/load
• Increase capacity utilization within & across divisions
25. Copyright 2014 by Data Blueprint
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Summary: Aligning Data Management Goals to the Business
A Data Strategy must be Business Focused
• Understand the business fundamentals of your organization
• Develop a common language and shared perspective with your
business partners – enabling collaboration
• Identify specific business opportunities or areas of improvement
• Focus the data strategy solution on improving those
specific business needs
Next Step:
• Measuring business value of
making improvements:
• Metrics, Object of Measurement and Methods
26. Copyright 2014 by Data Blueprint
26
Outline
• Data Strategy Overview
• Determining the Business Needs
• Measurement & Success Criteria
– An Overview
– An Example
• Current State Analysis
• Developing a Solution to Address Needs
• Developing a Roadmap and Plan
• Q&A
27. Copyright 2014 by Data Blueprint
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Measuring Business Value
If something is important to the business it can be observed. If it can
be observed, it is measureable!
• Understanding ‘measurement’; reducing uncertainty, not necessarily
an exact value
• Object of Measurement; often too ambiguously defined
• Methods of Measurement; become familiar with multiple methods and
apply in the right context
Define success criteria as specific metrics
• Not always intuitive and at first seems difficult
• Must be done in collaboration with your business partners
28. Copyright 2014 by Data Blueprint
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Great point of initial
inspiration ...
• Formalizing stuff forces
clarity
• Special shout out to
Chapter 7
– Measuring the value of
information
– ISBN: 0470539399
– http://www.amazon.com/
How-Measure-Anything-
Intangibles-Business
29. Copyright 2014 by Data Blueprint
29
Measuring Business Value – An Example
• $1billion (+) chemical company
• Develops/manufactures additives
enhancing the performance of oils
and fuels ...
• ... to enhance engine/machine
performance
– Helps fuels burn cleaner
– Engines run smoother
– Machines last longer
• Tens of thousands of
tests annually ($25K to $250K each)
International Chemical Company Engine Testing
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Objects of Measurement & Metrics
• Test Execution: Number of tests per customer
product formulation. Grouped by product types
and product complexity.
• Customer Satisfaction: Amount of time to
develop a certified custom formulated product;
time from initial request to certification
• Researcher Productivity: Tested and certified
formulations per researcher
Note: Baseline measures were taken from historical data and anecdotal
information
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Overview of Existing Process
1. Manual transfer of digital data
2. Manual file movement/duplication
3. Manual data manipulation
4. Disparate synonym reconciliation
5. Tribal knowledge requirements
6. Non-sustainable technology
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Solution and Business Value Results
• Solution:
– Business process improvements
– Data Architecture Development
– Data Quality Improvements
– Integrated System Development
• Results:
– Reduced the number of tests needed to develop products
– Increase the number of tests per researcher
– Reduce the time to market for new product development
• According to our client’s internal business case development,
they expect to realize a $25 million gain each year thanks to
this data integration
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Summary – Measuring Business Value
• If it’s important to the business, it’s measureable
• Learning to measure business value requires:
– Understanding fundamentally what it means to ‘measure’
– Being clear about what is going to be the object of
measurement and the specific metrics
– Methods that will ensure the metrics captured are
meaningful and consistent
• The old adage – “if you don’t measure it, it can’t be
managed” is true
Next Step:
• Develop a holistic solution and approach to address the
business needs identified in the data strategy
34. Copyright 2014 by Data Blueprint
34
Outline
• Data Strategy Overview
• Determining the Business Needs
• Measurement & Success Criteria
• Current State Analysis
– Analysis Framework Overview
– Examples
• Developing a Solution to Address Needs
• Developing a Roadmap and Plan
• Q&A
35. Copyright 2014 by Data Blueprint
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Analyzing the Current State (ACS)
Why we are analyzing the current state…
• Identify existing assets to be
leveraged
• Identify gaps in assets and
capabilities
• Identify constraints &
interdependencies in the operating
environment
• Measure Cultural Readiness –
scope of change management efforts
• Ensures solutions are achievable
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Data Strategy Framework (DSF)
Business
Need
Current
State
Solution
Target Source
Value Capabilities
DATA STRATEGY
Road Map
• Org. Readiness
• Bus. Processes
• Bus. & Data
Practices
• Data Assets
• Tech Assets
• Bus. Strategy &
Objectives
• Competitive
Advantage
• Bus. Structures
• Bus. Measures
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Analyzing the Current State (ACS)-1
What we are analyzing…
• People and Organization
• Business Processes
• Data Management Practices
• Data Assets
• Technology Assets
Note: Scope of the analysis, across all facets of the current state environment, is
constrained by the business needs definition. This mitigates the risk of over
analyzing the current state.
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Analyzing the Current State (ACS)-2
People &
Organization
Data Assets
Technology Assets
Data Mgmt. Practices
Business Processes
Business
Goals and
Objectives
Creates
Enables
Informs
Enables
Enables
Measures
Delivers
Enables
Enables
Provides
Context
39. Copyright 2014 by Data Blueprint
39
Typical Thinking: Application-Centric
• In support of strategy, organizations develop specific
goals/objectives
• The goals/objectives drive the development of specific
systems/applications
• Development of systems/applications leads to network/
infrastructure requirements
• Data/information are typically considered after the
systems/applications and network/infrastructure have
been articulated
• Problems with this approach:
– Ensures data is formed to the applications and not
around the organizational-wide information
requirements
– Process are narrowly formed around applications
– Very little data reuse is possible Data/Information
Network/Infrastructure
Systems/Applications
Goals/Objectives
Strategy
40. Copyright 2014 by Data Blueprint
40
New Thinking: Data-Centric
• In support of strategy, the organization develops specific
goals/objectives
• The goals/objectives drive the development of specific
data/information assets with an eye to organization-wide
usage
• Network/infrastructure components are developed to
support organization-wide use of data
• Development of systems/applications is derived from the
data/network architecture
• Advantages of this approach:
– Data/information assets are developed from an
organization-wide perspective
– Systems support organizational data needs and
compliment organizational process flows
– Maximum data/information reuse
Data/Information
Network/Infrastructure
Systems/Applications
Goals/Objectives
Strategy
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ACS: People
What we are looking for…
• Organizational Structures
• Skills and capabilities
• Culture
Why we look at People…
• Understand current roles, responsibilities & accountability
• Assess skills & capabilities to determine what’s achievable
• Determine how adaptable the organization is to change
• How the cultural nuances drive the operating environment
42. Copyright 2014 by Data Blueprint
42
2005
2006
2007
2008
2009
2010
20110.000
0.200
0.400
0.600
0.800
IT/InformationSecurity/Privacy
Virtualization
Datacenter/IT
efficiencies/Cloud
SocialMedia
Improvingpeople/leadershipBI/analytics
Standardization/consolidation
IT
workforcedevelopment
IT
governance
Riskmanagement
Mobileapplications/technologies
InformationSharing
Implementingplans/initatives/achievingresults
Acquisition/projectmgt
Process/system
integration
Strategicplanning
CDO Reporting
1. Dedicated solely to
data asset leveraging
2. Unconstrained by an
IT project mindset
3. Reporting to the
business
Top
Operations
Job
Top Job
Top Finance
Job
Top
Information
Technology
Job
Top
Marketing
Job
Data Governance Organization
Chief
Data
Officer
43. Copyright 2014 by Data Blueprint
43
ACS: Business Process
What we are looking for…
• Process flows (Diagrams) from a business perspective
• Process actors, including data creators and data consumers
• Pain points in the existing business processes
• Existing performance measures of business processes
Why we want to look at business processes…
• Business value of data is realized via a business process
• Most important events in the life of data – when it is created
and when it is used (Dr. Tom Redman)
• Describes the activities underpinning the competitive
advantage
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ACS: Automating Business Process Discovery
Benefits
• Obtain holistic perspective on roles
and value creation
• Customers understand and value
outputs
• All develop better shared understanding
Results
• Speed up process
• Cost savings
• Increased compliance
• Increased output
• IT systems documentation
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45
ACS: Data Management Practices-1
What we want to look at…
• Level of importance of data and information in
organizational strategy – is it explicitly identified as an
asset to be leveraged?
• How data requirements are derived
• Degree to which data is shared across organization
• How data quality issues identified and remediated.
• How data assets are designed and implemented
• How data assets are controlled, protected and maintained
once they are operational – e.g. compliance, security,
business continuity
46. Copyright 2014 by Data Blueprint
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ACS: Data
Management
Practices
Analyzing your
Data Management
Practices will be
critical in
developing
achievable
solutions
47. Copyright 2014 by Data Blueprint
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4
7
Copyright 2013 by Data Blueprint
<- CMM Level 2
48. Copyright 2014 by Data Blueprint
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4
8
Copyright 2013 by Data Blueprint
Assessment Components
Data Management Practice Areas
Data program
coordination
DM is practiced as a
coherent and coordinated
set of activities
Organizational data
integration
Delivery of data is support
of organizational objectives
– the currency of DM
Data stewardship
Designating specific
individuals caretakers for
certain data
Data development
Efficient delivery of data via
appropriate channels
Data support
Ensuring reliable access to
data
4
Capability Maturity Model
Levels
Examples of practice maturity
1 – Initial
Our DM practices are ad hoc and
dependent upon "heroes" and heroic
efforts
2 - Repeatable
We have DM experience and have the
ability to implement disciplined processes
3 - Documented
We have standardized DM practices so
that all in the organization can perform it
with uniform quality
4 - Managed
We manage our DM processes so that the
whole organization can follow our
standard DM guidance
5 - Optimizing
We have a process for improving our DM
capabilities
49. Copyright 2014 by Data Blueprint
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4
9Copyright 2013 by Data Blueprint
49
50. Copyright 2014 by Data Blueprint
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Data Management Practices Hierarchy
You can accomplish
Advanced Data Practices
without becoming
proficient in the Basic
Data Management
Practices but this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
5
0Copyright 2013 by Data Blueprint
Basic Data Management Practices
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
50
Data Program Management
Data Stewardship Data Development
Data Support Operations
Organizational Data Integration
51. Copyright 2014 by Data Blueprint
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ACS: Data Assets-1
What we are looking for…
• Broad view of the data assets (Structure and unstructured)
– Business Entity Inventory
– Business Entity Diagram
– Data Ecosystem
– Enterprise data architecture
– Application architecture describing systems and their relationships
• Narrow view of data assets in the context of the business needs
– Not all data impacts the business needs equally
– Data Dictionary
– Data Profiling
– Data models
– Tools to automate discovery such as Global ID’s
52. Copyright 2014 by Data Blueprint
52
ACS: Data Assets – Data Quality Considerations
Prevention at Source
Find and Fix
Ad-Hoc Processes
An interpretation from Dr. Tom Redman’s ‘Three Approaches to Data Quality’
54. Copyright 2014 by Data Blueprint
54
ACS: Data Assets-2
Why we want to look at data assets…
• Trying to find pain points
• Set the boundaries for what data and information is
possible under current conditions
• Understand how well the organization understands what
data exists
• Compartmentalize and decouple data from systems
• Provides a data-centric business perspective that cannot
be seen easily from business processes
• Provides a measure of complexity and potential risk of the
current operating environment
55. Copyright 2014 by Data Blueprint
55
ACS: Technology Assets-1
What we want to look at…
• Broad view of technology assets
– Enterprise and application architecture artifacts
– Inventory of technology, software, tools and environments
– Current standards vs. legacy vs. “bolt-on” technology
– Process for buying technology
– Pain points and constraints
• Narrow view of technology assets in the context of business
needs
– Identify specific systems, technology, etc… in scope
– Assess capabilities and constraints
– Implementation approach – e.g. customized or off the shelve
– Ability to non-functional requirements – e.g. performance, capacity
Why we want to look at technology assets…
• ….
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ACS: Technology Assets-2
Why we want to look at technology assets…
• What technology assets are currently available for the
solution
• What technology standards needs to be considered in the
solution
• Informs as to the complexity of the current environment
• Highlights ‘shadow’ technology solutions
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57
How to Analyze the Current State
• Interviews
• Surveys
• Document and artifact review
• Intranet and wiki reviews
• Facilitated sessions – i.e. Workshops
• Leverage existing organizational structures – i.e.
working groups, governance teams
• Requisite Skills: Critical Thinking, Inquisitiveness,
Collaboration, Tenacity, Organization and Technical
Writing
58. Copyright 2014 by Data Blueprint
58
Outline
• Data Strategy Overview
• Determining the Business Needs
• Target Measurement & Success Criteria
• Current State Analysis
• Developing a Solution to Address Business Needs
– Closing Foundational Gaps
– Solving for Specific Business Needs
• Developing a Roadmap
• Q&A
59. Copyright 2014 by Data Blueprint
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Data Strategy Framework (DSF)
Business
Need
Current
State
Solution
Target Source
Value Capabilities
DATA STRATEGY
Road Map
• People & Org.
• Bus. Processes
• Data Mgmt.
Practices
• Data Assets
• Tech Assets
• Bus. Strategy &
Objectives
• Competitive
Advantage
• Bus. Structures
• Bus. Measures
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Expected Results
Data Strategy Solution should…
• Be tailored to solve specific business needs
• Be measureable against set targets
• Develop organizational capabilities, as necessary,
to ensure the solution is sustainable
• Be achievable given the current state capabilities
• Define a solution with enough specificity to
develop an implementation road map
61. Copyright 2014 by Data Blueprint
61
Data Strategy Solution Framework (DSSF)
People &
Organization
Data Assets
Technology Assets
Data Mgmt. Practices
Business Processes
Business
Goals and
Objectives
Enables
Enables
Informs
Creates
Enables
Measures
Delivers
Enables
Enables
Provides
Context
The solution architecture and change management plans result from this framework
63. Copyright 2014 by Data Blueprint
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Outline
• Data Strategy Overview
• Determining the Business Needs
– Foundational Business Understanding
– Identify Specific Business Needs
– An Example
• Measurement & Success Criteria
– An Overview
– An Example
• Developing a Solution to Address Needs
– Closing Foundational Gaps
– Solving for Specific Business Needs
• Developing a Roadmap and Plan
• Q&A
64. Copyright 2014 by Data Blueprint
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How To: Creating a Roadmap
• Outputs from…
– Business Needs Assessment
– Current State Analysis
• In support of the Business Strategy
– Inextricably Linked
• Come up with a reasonable way to ID and close the
gaps within the solution framework
– Outline a long-term vision and implementation milestones
– Achievable, realistic plans
– Build momentum with specific, short-term win projects
• Approach: Crawl, Walk, Run
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The Approach of Crawl, Walk, Run
• Crawl:
– Identify business opportunity and determine a scope that fosters
early learning yet delivers measureable value
• Walk:
– Develop foundational &
technical data management
practices ensuring they are
repeatable. Enlarge the
scope of projects that
expand capabilities
• Run:
– Continuous improvement and expanded application of maturing
data management practices
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The Benefits of Crawl, Walk, Run
• ‘Pilot-like’ projects create a unique opportunity for
organizational learning while providing measureable
value
• Builds support for new approaches to data management
– i.e. supports change management activities
• More achievable approach to managing data as an asset
• Allows for foundational components to be developed
while concurrently executing more tactical solutions
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Road Map Framework
• High-level Road Map
• Road Map Activities
• Align Activities to Business Value Targets (i.e.
Traceability)
• Road Map Activity Details
• Level Of Effort Estimates (where possible)
• Budget Estimates (where possible)
68. Copyright 2014 by Data Blueprint
Sessions:
• Data Strategy 2.0: Focus on the
Roadmap and Implementation
• 3 hour workshop with Lewis Broome
• Addressing Data Challenges
using the Data Management
Maturity Model
• Melanie A. Mecca, CMMI Institute
Peter Aiken, Data Blueprint
• 120+ thought leaders
• 800 attending Senior IT
Managers, Architects, Analysts,
Architects & Business Executives
• 5 full days of in-depth education
and networking opportunities
• … and more!!!
• Register here:
www.edw2015.dataversity.net
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Questions?
+ =
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions now.
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Upcoming Events
Business Value from MDM
February 10, 2015
@ 2:00 PM ET/11:00 AM PT
Data Architecture Requirements
March 10, 2015
@ 2:00 PM ET/11:00 AM PT
71. 10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056