The foundation of managing data security and big data is implementing data governance. Data Owners, Metadata tagging, Customer feedback and Continuous Improvement are critical facets to provide the transparency and consistency so that customer's can trust the data, and make informed decisions.
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...Denodo
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
Linking Data Governance to Business GoalsPrecisely
The importance of data to businesses has increased exponentially over recent years as companies seek benefits such as gains in efficiency, the ability to respond to growing privacy regulations scale quickly and increased and increase customer loyalty.
Despite being a vital part of any Data Transformation, Data Governance has sometimes been misrepresented as a restrictive and controlling process leaving governance leaders having to continually make the case for business buy-in.
In this on-demand webinar we will explore the concept of business-first Data Governance, an approach that promotes adoption by the organisation, lays the foundation for data integrity and consistently delivers business value in the long term.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Governing and Preparing Data for Analytics and BusinessMark Smith
As business becomes more self-sufficient in accessing and putting data to work for analytics, there are many steps that are circumvented that can jeopardize the quality of business decisions. While it might seem easy to do one-off data preparation cycles that create analytic silos, the importance of placing governance on the data and users is essential to ensure accuracy of information used by business. The solution for these challenges can be addressed by applying effective processes and systems that are shared across business and IT. In this presentation, you'll will learn the latest best practices and steps to increase data governance and preparation processes that will shorten the time to efficiently connect users and data at any time
The Data Management challenges each organization faces are unique in their priority and severity. Therefore the structure and composition of a Data Organization is one of the major success factors for establishing a successful and sustainable data program. In this presentation, we will review the developmental stages of a data organization, the models and the choices for establishing the right structure to the organization in addition to the process for selecting the team members that will produce high-performance business results.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
Enterprise Data Management Framework OverviewJohn Bao Vuu
A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...Denodo
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
Linking Data Governance to Business GoalsPrecisely
The importance of data to businesses has increased exponentially over recent years as companies seek benefits such as gains in efficiency, the ability to respond to growing privacy regulations scale quickly and increased and increase customer loyalty.
Despite being a vital part of any Data Transformation, Data Governance has sometimes been misrepresented as a restrictive and controlling process leaving governance leaders having to continually make the case for business buy-in.
In this on-demand webinar we will explore the concept of business-first Data Governance, an approach that promotes adoption by the organisation, lays the foundation for data integrity and consistently delivers business value in the long term.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Governing and Preparing Data for Analytics and BusinessMark Smith
As business becomes more self-sufficient in accessing and putting data to work for analytics, there are many steps that are circumvented that can jeopardize the quality of business decisions. While it might seem easy to do one-off data preparation cycles that create analytic silos, the importance of placing governance on the data and users is essential to ensure accuracy of information used by business. The solution for these challenges can be addressed by applying effective processes and systems that are shared across business and IT. In this presentation, you'll will learn the latest best practices and steps to increase data governance and preparation processes that will shorten the time to efficiently connect users and data at any time
The Data Management challenges each organization faces are unique in their priority and severity. Therefore the structure and composition of a Data Organization is one of the major success factors for establishing a successful and sustainable data program. In this presentation, we will review the developmental stages of a data organization, the models and the choices for establishing the right structure to the organization in addition to the process for selecting the team members that will produce high-performance business results.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
Enterprise Data Management Framework OverviewJohn Bao Vuu
A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
DAMA Australia: How to Choose a Data Management ToolPrecisely
The explosion of data types, sources, and use cases makes it difficult to make the right decisions around the best data management tools for your organisation. Why do you need them? Who is going to use them? What is their value?
Watch this webinar on-demand to learn how to demystify the decision making process for the selection of Data Management Tools that support:
· Data governance
· Data quality
· Data modelling
· Master data management
· Database development
· And more
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
To ensure that Decision Management Systems are analytic and adaptive you must embed the results of data mining and predictive analytics in them. In this webinar you will learn what can be discovered using data mining and predictive analytic techniques and how this can be applied to the decision-making embedded in Decision Management Systems. The role of analytics in predicting risk, fraud and opportunity and the importance of continuous improvement and learning is also be covered.
Enterprise Data World Webinar: A Strategic Approach to Data Quality DATAVERSITY
We will also explore how to apply the 12 Directives, through a set of tactics to help you assess organizational readiness for data quality strategy. The purpose of such an assessment is to surface priorities for strategic action and to formulate a long-term approach to an organization’s data quality improvement.
Improve IT Security and Compliance with Mainframe Data in SplunkPrecisely
Avoid security blind spots with an enterprise-wide view.
If your organization relies on Splunk as its security nerve center, you can’t afford to leave out your mainframes.
They work with the rest of your IT infrastructure to support critical business applications–and they need to be
viewed in that wider context to address potential security blind spots.
Although the importance of including mainframe data in Splunk is undeniable, many organizations have left it out
because Splunk doesn’t natively support IBM Z® environments. Learn how Precisely Ironstream can help with a
straight-forward, powerful approach for integrating your mainframe security data into Splunk, and making it actionable
once it’s there.
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions.
Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold?
Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources.
The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance.
Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.
Master Data Management's Place in the Data Governance Landscape CCG
For many organizations, Master Data Management is a necessity to ensure consistency and accuracy of essential business entities. It further plays alongside data architecture, metadata management, data quality, security & privacy, and program management in the Data Governance ecosystem.
Join CCG's data governance subject matter experts as they overview the fundamentals of Master Data Management at our Atlanta-based Data Analytics Meetup. This event will discuss how to enable components of data governance within your organization and review how to best leverage Microsoft's SQL Server Master Data Services.
Change management success for data governanceReid Elliott
As a data management professional you know that improving data governance is a top priority for many organisations. We know that data governance frameworks, processes and tools only enable benefits to the extent that our stakeholders adopt and use them effectively.
As well as technical proficiency and good project management and delivery, data governance success also requires effective change management. Preparing for change, managing change, and sustaining change are critical steps on the journey to effective data governance. So how can data management professionals best use change management principles and techniques to contribute to the success of our data governance initiatives?
This presentation was prepared to accompany a Data Management Association Australia webinar on change management success for data governance initiatives.
Aims of the facilitated discussion in the webinar were to explore:
How change management can enable the success of your data governance, reporting and analytics initiatives.
Common people change related challenges that many data governance, reporting and analytics initiatives need to navigate.
Change management techniques you can use to drive successful project delivery, change adoption and sustainable use of data governance, and reporting and analytics solutions.
How to identify the top change management priority for your own current project, and the change management techniques that you can use to address it.
Financial Services Technology Leader Turns Mainframe Logs into Real-Time Insi...Precisely
A global financial services leader in payment, banking, and investment solutions, stays ahead of the competition by relying on powerful mainframe processing and real-time analytics. The company delivers an exceptional – and secure – digital experience to their global customer base by using Splunk and Precisely Ironstream.
View this on-demand webinar to hear how this customer turns mainframe security and operational log data into real-time insights with Ironstream and Splunk to:
- Proactively detect fraud and monitor security
- Meet SLAs with near-instant application response times
- Save time, effort and resources while lowering MTTI & MTTR
Increasing Your Business Data and Analytics MaturityDATAVERSITY
For a few years now, companies of all sizes have been looking at data as a lever to increase revenues, reduce costs or improve efficiency. However, we believe the power of using data as a strategic asset is still in its early stages. One of the main reasons for that is business leaders still do not understand that the data & analytics maturity should be seen as a long time journey and an evolving enterprise learning. This webinar will present some key points on how data management leaders can succeed in their mission by sharing some practical experiences.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
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
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
DAMA Australia: How to Choose a Data Management ToolPrecisely
The explosion of data types, sources, and use cases makes it difficult to make the right decisions around the best data management tools for your organisation. Why do you need them? Who is going to use them? What is their value?
Watch this webinar on-demand to learn how to demystify the decision making process for the selection of Data Management Tools that support:
· Data governance
· Data quality
· Data modelling
· Master data management
· Database development
· And more
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
To ensure that Decision Management Systems are analytic and adaptive you must embed the results of data mining and predictive analytics in them. In this webinar you will learn what can be discovered using data mining and predictive analytic techniques and how this can be applied to the decision-making embedded in Decision Management Systems. The role of analytics in predicting risk, fraud and opportunity and the importance of continuous improvement and learning is also be covered.
Enterprise Data World Webinar: A Strategic Approach to Data Quality DATAVERSITY
We will also explore how to apply the 12 Directives, through a set of tactics to help you assess organizational readiness for data quality strategy. The purpose of such an assessment is to surface priorities for strategic action and to formulate a long-term approach to an organization’s data quality improvement.
Improve IT Security and Compliance with Mainframe Data in SplunkPrecisely
Avoid security blind spots with an enterprise-wide view.
If your organization relies on Splunk as its security nerve center, you can’t afford to leave out your mainframes.
They work with the rest of your IT infrastructure to support critical business applications–and they need to be
viewed in that wider context to address potential security blind spots.
Although the importance of including mainframe data in Splunk is undeniable, many organizations have left it out
because Splunk doesn’t natively support IBM Z® environments. Learn how Precisely Ironstream can help with a
straight-forward, powerful approach for integrating your mainframe security data into Splunk, and making it actionable
once it’s there.
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions.
Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold?
Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources.
The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance.
Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.
Master Data Management's Place in the Data Governance Landscape CCG
For many organizations, Master Data Management is a necessity to ensure consistency and accuracy of essential business entities. It further plays alongside data architecture, metadata management, data quality, security & privacy, and program management in the Data Governance ecosystem.
Join CCG's data governance subject matter experts as they overview the fundamentals of Master Data Management at our Atlanta-based Data Analytics Meetup. This event will discuss how to enable components of data governance within your organization and review how to best leverage Microsoft's SQL Server Master Data Services.
Change management success for data governanceReid Elliott
As a data management professional you know that improving data governance is a top priority for many organisations. We know that data governance frameworks, processes and tools only enable benefits to the extent that our stakeholders adopt and use them effectively.
As well as technical proficiency and good project management and delivery, data governance success also requires effective change management. Preparing for change, managing change, and sustaining change are critical steps on the journey to effective data governance. So how can data management professionals best use change management principles and techniques to contribute to the success of our data governance initiatives?
This presentation was prepared to accompany a Data Management Association Australia webinar on change management success for data governance initiatives.
Aims of the facilitated discussion in the webinar were to explore:
How change management can enable the success of your data governance, reporting and analytics initiatives.
Common people change related challenges that many data governance, reporting and analytics initiatives need to navigate.
Change management techniques you can use to drive successful project delivery, change adoption and sustainable use of data governance, and reporting and analytics solutions.
How to identify the top change management priority for your own current project, and the change management techniques that you can use to address it.
Financial Services Technology Leader Turns Mainframe Logs into Real-Time Insi...Precisely
A global financial services leader in payment, banking, and investment solutions, stays ahead of the competition by relying on powerful mainframe processing and real-time analytics. The company delivers an exceptional – and secure – digital experience to their global customer base by using Splunk and Precisely Ironstream.
View this on-demand webinar to hear how this customer turns mainframe security and operational log data into real-time insights with Ironstream and Splunk to:
- Proactively detect fraud and monitor security
- Meet SLAs with near-instant application response times
- Save time, effort and resources while lowering MTTI & MTTR
Increasing Your Business Data and Analytics MaturityDATAVERSITY
For a few years now, companies of all sizes have been looking at data as a lever to increase revenues, reduce costs or improve efficiency. However, we believe the power of using data as a strategic asset is still in its early stages. One of the main reasons for that is business leaders still do not understand that the data & analytics maturity should be seen as a long time journey and an evolving enterprise learning. This webinar will present some key points on how data management leaders can succeed in their mission by sharing some practical experiences.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
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
Session présentée lors du SQLSaturday Paris 2014
-----
Cette session traitera de la Gouvernance des Données, principalement autour de la plate-forme Power BI (sans négliger les considérations on-prem). Comment éviter « l’enfer des datasets » ? Quelles sont les bonnes pratiques pour partager des requêtes ? Qui est ce fameux Data-Steward ? Quel est son rôle ? Comment choisir la bonne personne ? … Nous essaierons de répondre à ces questions et de vous donner des orientations avec quelques exemples pendant cette session
Gateways to Power BI, Connect PowerBI.com to your On-Prem DataJean-Pierre Riehl
--session donnée lors du SQLSaturday Madrid 2016--
PowerBI.com is a cloud-based BI platform, enabling from personal to corporate BI. But often, your data lives on-premises, on your desktop, on a shared folder or in your enterprise datawarehouse. Microsoft team built gateways to deal with that.
In this session, we will see how to connect, lively or scheduled, your dahsboards to your on-prem data. You'll learn about Personal Gateway and Enterprise Gateway. How does it work. How to configure it. How to maintain it.
Business Semantics for Data Governance and StewardshipPieter De Leenheer
Data quality and regulations are perpetual drivers for Data Governance and Stewardship solutions that systematically monitor the execution of data policy. And yet, there is a long road ahead to achieve Trust in Data. It is still a relatively unknown topic or comes with trauma from past failed attempts; there is no political framework with executive champions, leading to reactive rather than proactive behavior, and software support is marginal.
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve a wide adoption and confluence of Data Trust between business and IT communities in the organization.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of data pain as a company grows, and we map their situation on this spectrum of semantics.
Next, we introduce the principles and framework for business semantics management to support data governance and stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with stories from the field.
In that session we will discuss about Data Governance, mainly around that fantastic platform Power BI (but also around on-prem concerns).
How to avoid dataset-hell ? What are the best practices for sharing queries ? Who is the famous Data Steward and what is its role in a department or in the whole company ? How do you choose the right person ?
Keywords : Power Query, Data Management Gateway, Power BI Admin Center, Datastewardship, SharePoint 2013, eDiscovery
Level 200
Data governance at belgacom - presentation for DAMA Belux 7 nov 2013Peter Simoens
the drivers, plan and approach used to deploy data governance at Belgacom. Includes use of our BEIM enterprise data model, metadata management, data stewardship,...
Implementing a Data Lake with Enterprise Grade Data GovernanceHortonworks
Hadoop provides a powerful platform for data science and analytics, where data engineers and data scientists can leverage myriad data from external and internal data sources to uncover new insight. Such power is also presenting a few new challenges. On the one hand, the business wants more and more self-service, and on the other hand IT is trying to keep up with the demand for data, while maintaining architecture and data governance standards.
In this webinar, Andrew Ahn, Data Governance Initiative Product Manager at Hortonworks, will address the gaps and offer best practices in providing end-to-end data governance in HDP. Andrew Ahn will be followed by Oliver Claude of Waterline Data, who will share a case study of how Waterline Data Inventory works with HDP in the Modern Data Architecture to automate the discovery of business and compliance metadata, data lineage, as well as data quality metrics.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
DAMA DMBoK 2.0 keynote presentation at DAMA Australia November 2013.
Overview of DMBOK, what's different in 2.0, and how the DMBOK co-exists and successfully interoperates with other frameworks such as TOGAF and COBIT
Updated with revised DMBoK 2 release date
chris.bradley@dmadvisors.co.uk
DGIQ - Case Studies_ Applications of Data Governance in the Enterprise (Final...Enterprise Knowledge
Thomas Mitrevski, Senior Data Management and Governance Consultant and
Lulit Tesfaye, Partner and Vice President of Knowledge and Data Services
presented “Case Studies: Applications of Data Governance in the Enterprise” on December 6th, 2023 at DGIQ in Washington D.C.
In this presentation, Thomas and Lulit detailed their experiences developing strategies for multiple enterprise-scale data initiatives and provided an understanding of common data governance and maturity needs. Thomas and Lulit based their talk on real-world examples and case studies and provided the audience with examples of achieving buy-in to invest in governance tools and processes, as well as the expected return on investment (ROI).
Check out the presentation below to learn:
How Leading Organizations are Benchmarking Their Data Governance Maturity
Why End-User Training was Imperative in Seeing Scaled Governance Program Adoption
Which Tools and Frameworks were Critical in Getting Started with Data Governance
How Organizations Achieved Success with Data Governance in Under 12 Weeks
What Successful Data Governance Implementation Roadmaps Really Look Like
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
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
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Find more Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
Data Governance, Compliance and Security in Hadoop with ClouderaCaserta
In our recent Big Data Warehousing Meetup, we discussed Data Governance, Compliance and Security in Hadoop.
As the Big Data paradigm becomes more commonplace, we must apply enterprise-grade governance capabilities for critical data that is highly regulated and adhere to stringent compliance requirements. Caserta and Cloudera shared techniques and tools that enables data governance, compliance and security on Big Data.
For more information, visit www.casertaconcepts.com
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
The Missing Link in Enterprise Data Governance - Automated Metadata ManagementDATAVERSITY
So many companies and organizations are in the same boat. They’re drowning in their data — so much data, from so many different sources. They understand that data governance is hugely important for them to be able to know their data inside and out and comply with regulations. What many companies have not yet come to terms with when implementing their data governance strategy and supporting tools, is the criticality of metadata in the process. As the ‘data about data,’ metadata provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a must for BI groups dealing with analytics and business user reporting.
Octopai's CEO, Amnon Drori will discuss this critical missing link in enterprise data governance and the impact of automating metadata management for data discovery and data lineage for BI. He'll demonstrate how BI groups use Octopai to not only locate their data instantly, but to quickly and accurately visualize and understand the entire data journey to enable the business to move forward.
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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/
2. February 13, 2015 -Data Governance 2
Speakers Intro
Pamela Hulse
— Director of Data Governance &
Compliance
— Wolters Kluwer Health (formerly NDC
Health)
— Previous data management experience
with Mayo Clinic, McKessonHBOC
Paul Dyksterhouse
— Acxiom
— Data Warehouse Technical Unit Leader
— Previous data management experience
with BankOne, Schwab, Honeywell,
American Express, UPS, NDCHealth
3. February 13, 2015 -Data Governance 3
Wolters Kluwer Health
Healthcare analytics provider for pharmaceutical companies
20 years of healthcare claims data warehousing and business
intelligence
Service pharmaceutical manufacturers including Pfizer, GSK
10 million transactions per week and 50 Terabyte Data
Warehouse
Currently housed on DB2, Oracle, MSSQL and Netezza platforms
with MicroStrategy BI interfaces
In process of being migrated to Acxiom’s scalable Linux Grid
4. February 13, 2015 -Data Governance 4
Agenda
Introduction
The Path Traveled
Data Governance
Data Access and Asset Management
Data Architecture
Data Tool Selection
Outcomes
5. February 13, 2015 -Data Governance 5
Introduction
A little more than a year ago, Wolters Kluwer Health was faced
with two large seemingly insurmountable challenges.
As newer members of the Wolters Kluwer Information
Management team, Pamela Hulse & Paul Dyksterhouse faced
technical, process, and people challenges to address data access
and distribution requirement in a changing business environment.
This is the story of how over the past year we revolutionized data
governance.
The revolution was in taking the data governance process that
was out of control and getting it under control.
6. February 13, 2015 -Data Governance 6
Experience gained and lessons learned
Successes
— Large number of people involved reduced pushback and
propagated vision
— Experience level of external resources
— Package solution acquisition
— Vision is carried into new initiatives that will further the
impact
— Maintained external compliance certification
— Project came in under budget and within a 12 month period
— Further the maturity of the organization
7. February 13, 2015 -Data Governance 7
Experience gained and lessons learned
Things to do different next time
— Proof of concept/vendor participation
— Further education of internal resources
Governance & Data Management
Technology vision
8. February 13, 2015 -Data Governance 8
Experience gained and lessons learned
Other considerations
—Immaturity of package solutions and available
consultants
—Progress slowed by new large initiatives
—Availability of key staff
Technical skills required
Data Management & Governance experience required
9. February 13, 2015 -Data Governance 9
Revolution in Data Governance
“Whether occurring spontaneously, which is rare, or through
careful planning, revolutions depend for their success on crucial
timing, the fostering of popular support, and the nucleus of a
new governmental organization.” Encarta
Foundation of the Revolution
— Attributes
— Established Environment/Culture resistant to change
— People with a vision
Catalyst for Revolution
— External events that change perspectives
— A key event that consolidates the supporters
10. February 13, 2015 -Data Governance 10
Attributes of the Revolution
•Must be swift
•Must be strong
•Must be driven
•Require outside support
11. February 13, 2015 -Data Governance 11
Established Environment/Culture Resistant to Change
No management investment or priority on process improvement
Tactical approach to data management issues
Brittle legacy systems from too many short term fixes
Complex web of processes, systems, and platforms
Silos of departments and individuals with
key knowledge of data assets
Established suite of products with a very
established customer base
12. February 13, 2015 -Data Governance 12
People with a vision
Executive Sponsor –
primary data & large
project owner
Dedicated individuals
to drive the project
and own the future
process
— Business Sponsor
— Technology Sponsor
14. February 13, 2015 -Data Governance 14
Key Event: Business not able to meet challenges
Risk of non-compliance
Risk of not inventorying data assets, transforms and
products in an accessible repository
Lack of organizational resource priority to manage
risks
Product quality and service issues
Increased costs and missed opportunities
Inability to measure risks
Inability to secure sensitive data assets
15. February 13, 2015 -Data Governance 15
Role of the revolutionary
Deliver a message
That states the reality of the losses of not changing;
And provides a vision to people
that foments support for transformation
16. February 13, 2015 -Data Governance 16
We are here to share with you the path
we followed.
17. February 13, 2015 -Data Governance 17
The path…to revolution…
1 Education
Educate the business owners to their risks and needs.
18. February 13, 2015 -Data Governance 18
Pharmaceuticals R Us
Compounds
Formulas
Pharmaceutical
Products
19. February 13, 2015 -Data Governance 19
Data Warehouse
The ability to store and easily
retrieve attribute level information
on data assets, access, transforms,
and deliverables is essential for
asset management, quality products
and responsive customer service.
Compounds = Data Assets
Formulas = Business Rules & Transformations
Products = Information Deliverables
20. February 13, 2015 -Data Governance 20
2 Resources
Obtain champion, funding, leadership team
— Essential that the business own defining the solution and
implementing it.
Assess internal capacity vs. resource needs
— Availability
— Skills, Experience, Knowledge
Procured professional resources to meet the need
— Business
— Technology
21. February 13, 2015 -Data Governance 21
3 Define parallel project work teams
(security, controls,
HIPAA compliance,
contractual
obligations)
Architecture
(Data and Metadata)
Metadata / ETL
Tools and Processes
Governance
Data Asset & Access
Management
22. February 13, 2015 -Data Governance 22
Launch
Resources
— Hired a Director of Data Access Management
— Procured experienced vendor – 5 vendors
Analysis
— Compiled requirements and use cases
— Evaluated available options
Build / Buy – existing solutions
— Enterprise Metadata Solutions
— Integrator Metadata Solutions
RFP process – 5 vendors
Proof of concept – 2 vendors
23. February 13, 2015 -Data Governance 23
Project Work Teams
Data Governance –
Development of roles, responsibilities, communication strategies, policies,
processes, and procedures, as well as assistance in implementing them.
Data Asset & Access Management –
Definition of Data Flows, Common Data Model, and Metadata for information
management and the documentation of these data assets. Identification and
documentation surrounding sensitive HIPAA & ArcLight Contractual data elements
including business process and business rules / requirements for a data integration
tool.
Data Architecture –
The validation and recommendation of a architecture that is aligned with business
requirements
Data Tool Selection –
Evaluate a short list of Data Integration / Metadata Tools that includes a Proof-Of-
Concept pilot, results collection and the creation of a Wolters Kluwer Health
Recommendations Document
24. February 13, 2015 -Data Governance 24
New Vision
The old paradigm: “Just do it!”
The post-compliance paradigm:
“Do it. Control it. Document it. Prove it!”
Data Governance
25. February 13, 2015 -Data Governance 25
Data Governance Deliverables
Data Governance framework design
— Roles & responsibilities
— Policies
— Key procedures
Defined key roles & processes
— Governance steering committee
Plan for complete implementation
Data Governance
26. February 13, 2015 -Data Governance 26
Data Governance Groups
Staff
perspective
Management
perspective
Executive
perspective
Managers
and other
influencers
Staff
Corporate
Leadership
Stewards
Exec
Council
GRCS
Board
Data Gov
Mgmt Team
Lead
Stewards
Small group that runs
the Governance Program
Larger group of Subject
Matter Experts, Super-
users, Directors/Managers
of Functional Areas
VPs in various
Business
and IT groups
Staff that works with data
Management or staff
that communicates
with or gives direction
to stewards
Data Governance
27. February 13, 2015 -Data Governance 27
Scores: 0 – Non-existent 1 – Initial / Ad Hoc 2 – Repeatable but Intuitive 3 – Defined Process
4 – Managed and Measurable 5 - Optimized
Data Governance
28. February 13, 2015 -Data Governance 28
Project Teams
Data Governance –
Development of roles, responsibilities, communication strategies, policies,
processes, and procedures, as well as assistance in implementing them.
Data Asset & Access Management –
Definition of Data Flows, Common Data Model, and Metadata for information
management and the documentation of these data assets. Identification and
documentation surrounding sensitive HIPAA & ArcLight Contractual data elements
including business process and business rules / requirements for a data
integration tool.
Data Architecture –
The validation and recommendation of a architecture that is aligned with Wolters
Kluwer Health’s business requirements
Data Tool Selection –
Evaluate a short list of Data Integration / Metadata Tools that includes a Proof-
Of-Concept pilot, results collection and the creation of a Wolters Kluwer Health
Recommendations Document
29. February 13, 2015 -Data Governance 29
Data Asset & Access Management
Analysis of all Data Warehouse assets at all points in
the lifecycle
Analysis of all Access Roles
Modeling of data access granting, data usage, and
metadata management
Extension of metadata definitions to include the type
and level of sensitivity
Data Asset & Access Management
30. February 13, 2015 -Data Governance 30
Data access and control requirements
Collection of business rules
Identification of key data elements (PHI, Contractual)
with metadata
Documentation of key data flows
Identification of key control points
High-Level Business Process Model [UML]
Infrastructure / Systems Diagram
Team Deliverables
Data Asset & Access Management
31. February 13, 2015 -Data Governance 31
Sensitive data
Data Asset & Access Management
32. February 13, 2015 -Data Governance 32
Sensitive data
•Regulatory Sensitive Data Elements:
HIPAA (PHI/IIHI)
Name, Birth Date, SSN, Demographics, other ID numbers
•Contractual Sensitive Data Elements:
Vendor License Agreements
NCPDP Number, Vendor/Pharmacy Name,
Demographics
Data Asset & Access Management
33. February 13, 2015 -Data Governance 33
3.4 Maintain Product
Delivery Options
Metadata Repository System(ERStudio)
Maintain Logical and
Physical Data Element
Descriptions and
Rules
Business User
(Can include members of
Data Services, Data
Management and Client
Services)
E-Security Administrator
Data Access Manager
/ Data Analyst
ETL Tool / ERStudio
MicroStrategy / BI tools /
Scanners
(Systems)
2.3 View Logical
Descriptions, Business
Definitions, Reports and
Product Definitions
5.1 Update
Repository
5.2 Update Metrics
4.6 Analyze
Repository Usage
4.5 Analyze Data
Usage / Lineage
4.4 Analyze Access
to Data Assets
6.1 Generate
Data Asset
Inventory Report
6.2 Set Inventory
Security Levels
Use Case Diagram
Technical User
(Can include members of
Data Services,
Data Management and Client
Services)
2.2. Maintain
Logical to Physical
Maps
2.1. Maintain
Physical Data
Descriptors and
Sensitivity Rules
Data Services /
Client Services
Workforce
2.4 Maintain Business
Definitions for Data,
Rules and Processes
2.5 Link Logical Rules
and Data to Business
Definitions
3.2 Link Product
Definitions to Business
Process Definitions
4.1 Identify and
Update Governors
and Stewards
4.7 Analyze
Repository Data
Quality
4.2 Maintain
Governance Policies
and Procedures
4.3 View Governance
Policies, Procedures,
Governors, Stewards
Workforce
Data Management
Workforce
3.1 Maintain Product
Definitions
Client Services
Workforce / Product Mgmt
2.6 Maintain Report
Definitions
Color Key: Security Logical View Physical View Business View Governance
1.2 Audit Linkage of
Logical Rules and Data to
Business Definitions
3.3 Maintain Clients
3.5 Link Clients to
Product Delivery
Options
1.3 Audit Linkage of
Logical Rules and Data to
Physical Entities
Use Case Line Key:
Thick : In scope
Thick-dashed: Some Dev.
Thin- solid : Prototype
Thin-dashed : HL Arch.
None : Deferred
1.0 Maintain Lists of
Production Servers and
Databases
1.1 Maintain
Logical Data Descriptors
and Sensitivity Rules
Data Asset & Access Management
34. February 13, 2015 -Data Governance 34
Data Asset & Access Management
Data Governance –
Development of roles, responsibilities, communication strategies, policies,
processes, and procedures, as well as assistance in implementing them.
Data Asset & Access Management –
Definition of Data Flows, Common Data Model, and Metadata for information
management and the documentation of these data assets. Identification and
documentation surrounding sensitive HIPAA & ArcLight Contractual data elements
including business process and business rules / requirements for a data
integration tool.
Data Architecture –
The validation and recommendation of a architecture that is aligned with Wolters
Kluwer Health’s business requirements
Data Tool Selection –
Evaluate a short list of Data Integration / Metadata Tools that includes a Proof-
Of-Concept pilot, results collection and the creation of a Wolters Kluwer Health
Recommendations Document
35. February 13, 2015 -Data Governance 35
Team Deliverables
Metadata architecture
—Operational
—Governance
Industry-based best practice findings
Common Warehouse Metamodel
Data Architecture Design
Development Solution Diagram
Project Plan for Phase II
Data Architecture
36. February 13, 2015 -Data Governance 36
Example Metadata Architecture
Data Sources Business ApplicationsData Warehouse Environment
Context
Metadata (Business, Technical, Operational) & Security / Access Control (eTrust)
Data
Data integration architecture – Data models
Metadata Repository
ExternalDataSources
Quality Control (QC)
Master Reference Data
Collection and
Standardization
ETL
QC
ETL
3a
Client
Profile
Pharma
Data Mart
Products
IHR Data
Mart
Products
ETL Engine
Pharma
Data Mart
IHR Data
Mart
Integrated Repository
Consolidation / Aggregated Layer
ETL
Data Architecture
37. February 13, 2015 -Data Governance 37
Project Teams
Data Governance –
Development of roles, responsibilities, communication strategies,
policies, processes, and procedures, as well as assistance in
implementing them.
Data Asset & Access Management –
Definition of Data Flows, Common Data Model, and Metadata for information
management and the documentation of these data assets. Identification and
documentation surrounding sensitive HIPAA & ArcLight Contractual data elements
including business process and business rules / requirements for a data integration
tool.
Data Architecture –
The validation and recommendation of a architecture that is aligned with
business requirements
Data Tool Selection –
Evaluate a short list of Data Integration / Metadata Tools that includes a Proof-Of-
Concept pilot, results collection and the creation of a Wolters Kluwer Health
Recommendations Document
38. February 13, 2015 -Data Governance 38
Solution Requirements Matrix & Priorities
Tool Recommendation Document:
—Acceptance Criteria Matrix
—Proof of Concept Plan and Design
—Testbed Management Strategy
—Proof of Concept Test Result
Over 50 users for 4 weeks required for definition of
test cases, text execution, and review of results.
Team Deliverables
Tool
39. February 13, 2015 -Data Governance 39
Metadata ETL Proof of Concept
Three test cases that would validate highest
complexity/risk areas of functionality
Delivered requirements, test cases, test data and
acceptance criteria 3 weeks in advance
Scheduled checkpoint progress meetings
Schedule 1 week for each POC
Tool
40. February 13, 2015 -Data Governance 40
Metadata ETL Proof of Concept
Tool
41. February 13, 2015 -Data Governance 41
FALCON
Metadata Project
NDCHealth
Phoenix, Arizona
Quad Analysis Of ETL Vendor Evaluation Positioning
Business Alignment - CIBER SME’s
Rev Drawing Number Department xxx
1.2 2005.03.23.1 Information Management
DRAFT First Release Pg 1 OF 5
Low HIgh
Productivity:EaseOfUse,Integration,ChangeMgmt,Reusability,Functionality
Performance: Throughput, Scalability, Infrastructure Requirements, etc.
HighLow
Im
Legend:
Im Informatica Metadata Score
Ie Informatica ETL (Data Movement) Score
Am Ascential Metadata Score
Ae Ascential ETL (Data Movement) Score
Ie
Ae
Am
CIBER SME Analysis:
Ascential
Ø IBM Purchase Is Expected To Delay Release
Of Integrated Product Suite And Functionality
Improvements
Ø Ascential Infrastructure Requirements Lowers
Metadata Scoring
Ø Ascential’s Lack Of Integration For Their
Product Suite Negatively Affects Developer
Productivity (ETL Score)
Ø Ascential’s Lack Of Architectural Integration
Lowered The Metadata Score
lnformatica
Ø Informatica’s SuperGlue Is Best Metadata
Engine In The ETL Market
Ø ETL Tool Has Improved Their Parallel
Performance Recently (Especially On SUN
Servers)
Ø Informatica’s High Productivity Score Results
From Integrated Toolsets And Powerful Reuse
& CM Functions
Ø Informatica Parallel Technology Is Close But
Not Equal To Ascential’s.
Productivity
Performance
National Practice Experts
Subjective Scores - CIBER
Informatica(Metadata)Wins
Tool
42. February 13, 2015 -Data Governance 42
Project Timeline
Metadata Project – Part One - Analysis
Metadata Project – Part Two - Implementation
1/3/2005 1/10/2005 1/17/2005 1/24/2005 1/31/2005 2/7/2005 2/14/2005 2/21/2005 2/28/2005 3/7/2005 3/14/2005 3/21/2005
Deliverable
Review
Librarian
Turnover
Architect. RoadmapTechnical Assessment & Requirements Phase
JANUARY FEBRUARY MARCH
Project Planning & Closure
Data Governance Framework D.G. Implementation
Architecutral High Level Design Tool Recommendation/Testbed
3/7/2005 3/14/2005 3/21/2005 3/28/2005 4/4/2005 4/11/2005 4/18/2005 4/25/2005 5/2/2005 5/9/2005 5/16/2005
Test Scripts Support
DATA GOVERNANCE FRAMEWORK IMPLEMENTATION & WORKOUT Project Closure Doc's
Knowledge Transfer & Training, Goal Setting Meetings & Deliverable Reviews
Metadata Capture Data & Bus. Rules Validation & Testing Production 5/13/2005
ETL Coding ETL Debugging, Testing, Metadata & Tuning Script Test & Validation Turnover
MARCH APRIL MAY
Tool
43. February 13, 2015 -Data Governance 43
• Inventory of data assets, sensitivity, and
data access
• Where-founds of data
• Identify controls and
owners; Apply controls
• Complement existing Change
Management with governance controls
• Ongoing management / measurement:
- Audit Project/SRE/Customer changes,
- Audit access controls and asset inventory
- Assess impact of regulatory & compliance
changes
- Measure data governance effectiveness
• Executive Council
• Data Governance Manager + Team
• GRCS Board (provides perspective on Governance,
Risk, Compliance, and Security)
• Lead Stewards (serve as communication hubs)
• Formalize stewardship
responsibilities for all staff
Data Governance plus Metadata: Solution Facets
People
Process
Info
Tools
• Inventory of data owners
• Risk management focus
– assessment,
prioritization, controls
• Technology to
facilitate
harvesting, storing,
and publishing
data about
Wolters Kluwer Health
data
• Industry-standard
frameworks for working
with controls
44. February 13, 2015 -Data Governance 44
The future of the revolution
Foundation Laid - The Data Governance, Metadata and
ETL laid the foundation for managing data at the
attribute level.
Continue the Transformation
— Wolters Kluwer has now engaged in a 2 year initiative to
convert all systems over to Data Stage
— Goal is to be able to manage data and business rules in a more
transparent and flexible manner
— Further the automation and formalization of the Data
Governance, Metadata and ETL initiatives and gain the
additional value
— Wolters Kluwer is moving it’s data processes to Acxiom’s
enterprise data grid to support the transformation.
45. February 13, 2015 -Data Governance 45
Experience gained and lessons learned
Successes
— Large number of people involved reduced pushback and
propagated vision
— Experience level of external resources
— Package solution acquisition
— Vision is carried into new initiatives that will further the
impact
— Maintained external compliance certification
— Project came in under budget and within a 12 month period
— Further the maturity of the organization
46. February 13, 2015 -Data Governance 46
Experience gained and lessons learned
Things to do different next time
—Proof of concept/vendor participation
—Further education of internal resources
Governance & Data Management
Technology vision
47. February 13, 2015 -Data Governance 47
Experience gained and lessons learned
Other issues
—Immaturity of package solutions and available
consultants
—Progress slowed by new large initiatives
—Availability of key staff
Technical skills required
Data Management & Governance experience required
49. February 13, 2015 -Data Governance 49
Contributors
Wolters Kluwer Business and IT teams
Knightsbridge
Ciber
Informatica
IBM Ascential
www.SOXonline.com
51. February 13, 2015 -Data Governance 51
Proactive Data Governance
Change Management
Process
Ø The Case for Data
Governance
Ø Data Governance Groups
Ø Data Governance Processes
Ø What Data Governance Looks
Like
Ø Next Steps
Impact is
understood.
Risks are
identified and
Managed.
Trigger:
Change
Request
5.
Communicate
Status
Notify all stakeholders
of decisions and
required actions.
Administer Process
Exec
Council
Data
Governance
Management
Team
GRCS Board,
Project or Functional
Teams, Lead Stewards,
others as appropriate
1.
Triage
Set Goals,
Assess & Communicate
Required Levels of
Involvement
GRCS
3.
Conduct Risk Analysis
Identify upstream and downstream
impacts. Consider impacts of change
on Governance, Risk, Compliance, and
Security efforts.
4.
Decide How to Proceed
Decide whether to approve
the change, and whether
adjustments are required for
any other efforts or controls.
2.
Conduct
Due
Diligence
optional loop-outs
52. February 13, 2015 -Data Governance 52
Ø Data Governance roles & policies rollout
Ø Tool Configuration
Ø extend the metadata model
Ø build ETL Connectors
Ø build user workflow and reports
Ø Repository population
Ø Testing and data validation
Ø Knowledge transfer
Ø User adoption training and execution
Implementation Approach
Implement Best Practices
53. February 13, 2015 -Data Governance 53
Revolution in Data Governance Outcomes
Data Governance formally defined, trained, established and
integrated into change management
Unified approach of Business and Technology
Recognition of Maturity Model
Executive level sponsorship and accountability
Complete assessment, procurement and implementation in under
12 months
Metadata – Daily update of metadata to repository for data
sensitivity access assessments and audit