Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
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
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
The document discusses different techniques for building a Customer Data Hub (CDH), including registry, co-existence, and transactional techniques. It outlines the CDH build methodology, including data analysis, defining the data model and business logic, participation models, governance, and deliverables. An example enterprise customer data model is also shown using a hybrid-party model with relationships, hierarchies, and extended attributes.
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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!
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
1) MDM is the process of creating a single point of reference for highly shared types of data like customers, products, and suppliers. It links multiple data sources to ensure consistent policies for accessing, updating, and routing exceptions for master data.
2) Successful MDM requires defining business needs, setting up governance roles, designing flexible platforms, and engaging lines of business in incremental programs. Common challenges include lack of clear business cases and roadmaps.
3) Key aspects of MDM include modeling shared data, managing data quality, enabling stewardship of data, and integrating/propagating master data to operational systems in real-time or batch processes.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Data Quality Management: Cleaner Data, Better Reportingaccenture
This document discusses Accenture's regulatory reporting framework and offerings around data quality management. It provides an overview of Accenture's high-performance financial reporting framework, which aims to consolidate frameworks, processes, and technology to create efficiencies across reporting functions. It also summarizes Accenture's regulatory reporting offerings, including data quality management, capability design, target operating models, and regulatory reporting vendor implementation support. Finally, it covers key aspects of data quality management, such as issue classification, management processes, governance structures, root cause analysis, and issue prioritization. The goal is to help financial institutions improve data quality, reporting accuracy and efficiency.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
The document discusses strategies for managing master data through a Master Data Management (MDM) solution. It outlines challenges with current data management practices and goals for an improved MDM approach. Key considerations for implementing an effective MDM strategy include identifying initial data domains, use cases, source systems, consumers, and the appropriate MDM patterns to address business needs.
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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!
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
1) MDM is the process of creating a single point of reference for highly shared types of data like customers, products, and suppliers. It links multiple data sources to ensure consistent policies for accessing, updating, and routing exceptions for master data.
2) Successful MDM requires defining business needs, setting up governance roles, designing flexible platforms, and engaging lines of business in incremental programs. Common challenges include lack of clear business cases and roadmaps.
3) Key aspects of MDM include modeling shared data, managing data quality, enabling stewardship of data, and integrating/propagating master data to operational systems in real-time or batch processes.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Data Quality Management: Cleaner Data, Better Reportingaccenture
This document discusses Accenture's regulatory reporting framework and offerings around data quality management. It provides an overview of Accenture's high-performance financial reporting framework, which aims to consolidate frameworks, processes, and technology to create efficiencies across reporting functions. It also summarizes Accenture's regulatory reporting offerings, including data quality management, capability design, target operating models, and regulatory reporting vendor implementation support. Finally, it covers key aspects of data quality management, such as issue classification, management processes, governance structures, root cause analysis, and issue prioritization. The goal is to help financial institutions improve data quality, reporting accuracy and efficiency.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
The document discusses strategies for managing master data through a Master Data Management (MDM) solution. It outlines challenges with current data management practices and goals for an improved MDM approach. Key considerations for implementing an effective MDM strategy include identifying initial data domains, use cases, source systems, consumers, and the appropriate MDM patterns to address business needs.
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
The document discusses the importance of developing a data strategy before building a data warehouse. It defines a data strategy as a unified, organization-wide plan for using corporate data as a vital asset. The data strategy should address critical data issues like quality, metadata, performance, distribution, ownership, security and privacy. Developing a data strategy requires identifying strategic and operational decisions, aligning the strategy with business goals, and answering many questions across various data-related topics.
SAS DATAFLUX DATA MANAGEMENT STUDIO TRAININGbidwhm
SAS DataFlux Management studio training,Technical support ,Outsourcing ,DataFlux Data Management Platform
Overview of DataFlux Data Management Studio
DataFlux Methodology: Plan, Act, and Monitor
Managing Repositories
Different types of Data Connections
Creating and Managing Data Collections
Creating , Setting , Working with Data Explorations
Introduction ,Creating Business Rules and Custom Metrics
Overview, Creating , Preparing of Data Profiles
The document describes the Gartner Identity and Access Management (IAM) Program Maturity Model which outlines 5 levels of maturity for an organization's IAM program:
1. Initial - Ad hoc processes with little awareness or value.
2. Defined - Certain business drivers identified and tactical priorities set with informal roles and processes.
3. Managed - IAM vision and strategy defined and aligned with business, formal processes and governance established.
4. Operational Excellence - IAM architecture refined, processes integrated and contribution to business imperatives is high.
5. Transformational - IAM vision, strategy, processes, architecture and governance optimized for maximum business value.
A Business-first Approach to Building Data Governance ProgramsPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
Slide deck from a webinar presented by Earley Information Science on "MDM - The Key to Successful Customer Experience Management." Featured speaker is EIS Director of Delivery Services, Tim Barnes.
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
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.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
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
How to Build Data Governance Programs That Lasts: A Business-First ApproachPrecisely
Data analytics and Artificial Intelligence play an increasingly pivotal role in most modern organizations. To keep those initiatives on track, enterprises must roll out data governance programs to ensure optimal business value. Data governance has become a fundamental element of success, a key to establishing the component of the data integrity framework in any business. The most successful data governance programs use a business-first approach, delivering quick wins and cultivating sustained success throughout the organization. Unfortunately, many organizations neglect to implement such programs until they experience a negative event that highlights the absence of good data governance. That could be a data breach, a breakdown in data quality, or a compliance action that highlights the lack of effective controls. Once that happens, there are several different paths a data governance initiative might take. A typical scenario often plays out this way: The executive team calls for implementation of a company-wide data governance program. The newly-minted data governance team forges ahead, engaging business users throughout the organization and expecting that everyone will be aligned around a common purpose.
Data Virtualization for Business Consumption (Australia)Denodo
This document discusses data virtualization and its benefits for business users. It summarizes that data virtualization can create a connected data landscape that is easily shared, empower business users with self-service BI tools, develop trusted high quality data, and support flexibility. It notes data virtualization provides a logical data layer that improves decision making, broadens data usage, and offers performant access to integrated data without moving or replicating source systems.
A Business-first Approach to Building Data Governance ProgramPrecisely
Traditional data governance programs struggle to make the connection between critical policies and processes and its impact on business value and results. This leaves data management and governance practitioners having to continually make the case for data governance to secure business adoption.
Watch this on-demand webinar to learn about the proven methods to identify the data that matters, connect governance policies to business objectives, and quickly deliver value through the life of the program.
Data governance is a data strategy that incorporates disciplines like data quality, data management, data standards, policies, and business process management to support the business strategy. It applies these disciplines enterprise-wide with everyone involved to proactively govern data and maximize its value in supporting business goals.
Governance as a "painkiller": A Business First Approach to Data GovernancePrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long term.
Top 4 Priorities in Building Insurance Data Governance Programs That WorkPrecisely
The document discusses building successful data governance programs that take a business-first approach. It recommends linking data governance to business goals, prioritizing critical data that drives key business metrics and outcomes, building engagement across operational, tactical, and strategic levels, and clearing a path for success by removing friction for stakeholders. Taking this approach can accelerate program roll-out by 18-40%, increase reinvestment likelihood by over 75%, and generate 2-7x greater ROI.
The Business Value of Metadata for Data GovernanceRoland Bullivant
In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change.
Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.
TekMindz Master Data Management CapabilitiesAkshay Pandita
This document provides an overview of Master Data Management (MDM) offerings and benefits from TekMindz. MDM is an approach that centralizes master information such as customers, products, and suppliers to ensure consistent, up-to-date data across business systems. MDM addresses issues like data governance, quality and consistency. TekMindz' MDM capabilities include collaborative authoring, data quality management, event management, and integration with data quality tools. MDM implementations require data governance to construct trusted views of master data needed by business processes. TekMindz offers MDM solutions across four editions to meet different customer needs.
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/
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
Empired convergence 2017 - Data as your Most Strategic AssetEmpired
The document discusses how Empired helped organizations analyze customer data to better understand customers and tailor communications to drive growth. Empired's solutions allow using data intelligently to improve customer intimacy, retention, and advocacy. The document then discusses how Empired helped Pacific Smiles Group transform their business by implementing a data warehouse solution to gain insights from their large amount of patient data and improve business performance and scalability.
Customer-Centric Data Management for Better Customer ExperiencesInformatica
With consumer and business buyer expectations growing exponentially, more businesses are competing on the basis of customer experience. But executing preferred customer experiences requires data about who your customers are today and what will they likely need in the future. Every business can benefit from an AI-powered master data management platform to supply this information to line-of-business owners so they can execute great experiences at scale. This same need is true from an internal business process perspective as well. For example, many businesses require better data management practices to deliver preferred employee experiences. Informatica provides an MDM platform to solve for these examples and more.
Customer-Centric Data Management for Better Customer ExperiencesInformatica
This document discusses the need for a customer 360 solution to provide a complete view of customer data across an organization. It describes how a customer 360 solution can integrate data from various sources to create a single customer profile with contact information, preferences, relationships and interactions. It provides an overview of the key components of a customer 360 reference architecture including data ingestion, governance, delivery and analytics capabilities. Finally, it demonstrates Informatica's customer 360 solution capabilities such as predefined customer data models, workflows, enrichment and integration with other master data domains.
Data Governance That Drives the Bottom LinePrecisely
The financial services sector is investing heavily in data governance solutions to find, understand and trust customer data, while also managing compliance risk around an ever-evolving regulatory landscape more effectively.
But do you still find it difficult to get management support for data governance budgets? Do you have the tools you need to determine the “business cost of data” accurately? Can you show the CFO an ROI projection he can count on? Are you able to answer, “Will I see results on the top line or the bottom line?” Are your business line leaders able to identify areas that are losing money due to data problems?
If you answered no to any of these questions, join Precisely in our upcoming webinar that will focus on how Financial Services companies can monetize the return on investment for data governance and how to relate it to business results that every senior leader understands.
Join this on-demand webinar to learn about:
- How to select data initiatives based on corporate goals and strategy
- How to connect the dots from data challenges (quality, availability, accuracy, currency) to specific business metrics around
- How to quantify the data contribution to improving business performance around
- How to leverage metadata and linage to get a 360-degree understanding of your data
- How to evaluate data assets by assigning measures and defining scores.
- How to assign accountability to assets and processes
- How to define and execute the workflows needed to implement corrective actions
- How to highlight the benefits of data governance
The document outlines best practices across 7 domains for global enterprise CRM success: vision and strategy, business metrics, adoption, sponsorship and governance, roadmap, processes, and technology and data. It provides an overview of sessions at a conference that will cover each of these domains, with presentations from various companies.
Telangana State, India’s newest state that was carved from the erstwhile state of Andhra
Pradesh in 2014 has launched the Water Grid Scheme named as ‘Mission Bhagiratha (MB)’
to seek a permanent and sustainable solution to the drinking water problem in the state. MB is
designed to provide potable drinking water to every household in their premises through
piped water supply (PWS) by 2018. The vision of the project is to ensure safe and sustainable
piped drinking water supply from surface water sources
Thingyan is now a global treasure! See how people around the world are search...Pixellion
We explored how the world searches for 'Thingyan' and 'သင်္ကြန်' and this year, it’s extra special. Thingyan is now officially recognized as a World Intangible Cultural Heritage by UNESCO! Dive into the trends and celebrate with us!
This comprehensive Data Science course is designed to equip learners with the essential skills and knowledge required to analyze, interpret, and visualize complex data. Covering both theoretical concepts and practical applications, the course introduces tools and techniques used in the data science field, such as Python programming, data wrangling, statistical analysis, machine learning, and data visualization.
Just-in-time: Repetitive production system in which processing and movement of materials and goods occur just as they are needed, usually in small batches
JIT is characteristic of lean production systems
JIT operates with very little “fat”
Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation.
Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.
1. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Mastering Master Data
Presented by
Bill Wise, Enterprise Data Architect NCR
Mary Levins, Principal Sierra Creek Consulting
February 26, 2015
2. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Introduction
Mary Levins, PMP
Principal, Sierra Creek Consulting
Value through Governed DataTM
• 20 years experience in Process and Data Re-engineering
• Proven success in bringing Sustainable Business Value and bridging the gap
between the Business and IT
• BS and MS in Industrial & Management Engineering, Project Management & 6
Sigma Certified
• Highly accomplished Data Management expert across multiple industries including
Healthcare, Finance, Manufacturing, Electronics, Automotive, Energy, and Retail
3. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
• Bill Wise, Enterprise Data Architect with NCR and lead for the
Customer MDM
• 25+ Years in Data Management
• Instructor of Data Modeling, Encyclopedia Management and
Methodology for KnowledgeWare
• Heavily involved with development of B2B standard messages for
RosettaNet and Open Applications Group
• Implemented the Information Framework (IFW) at IBM for internal use
- based on extension of Zachman Model called Evernden Model
• Developed method for deployment of the canonical object for ‘Invoice’
at IBM – owner of US patent for method of deployment
Introduction – Bill Wise, Enterprise Architect at NCR
4. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
‘Everyday made easier.’
5. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Master Data
Management
Overview
Value and
Benefits of
Master Data
Best Practice
Approach for
Mastering
Master Data
Summary and
Questions
Mastering Master Data Agenda
6. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
MASTER DATA MANAGEMENT
OVERVIEW
7. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Master Data is data that is a critical company
asset used by multiple businesses, functions,
and users across one or many systems.
‘Asset’ is an economic resource owned
by an organization to produce value
Master Data should be managed under the
Data Governance Umbrella
Mastering Master Data – What is Master Data?
Master Data is an Asset and should be managed under
the Data Governance Umbrella
Metadata
Reference
Data
Master Data
Transaction Data
PerValueData
QualityImportance
DataReuse
VolumeofData
ShorterLifespan
8. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Master Data Examples
NCR Focused Across common subject areas
Data Subject Area Core Master Data Reference Data
Customer Channel Partner
Customer
Accounts
Customer Classifications
Customer Types
NAICS, DUNS, Hierarchies, Status
Codes
Supplier Vendor Classifications, Vendor Types,
DUNS, Hierarchies, Status Codes
Product Product, Item, Service, SKUs,
Raw Materials
GS1, ISO, UOM, Taxonomies,
Status Codes, Types
Location Addresses
Locations
GEO Codes, Country Codes,
URL
Note:
Master
Data will
depend on
your
business
9. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Data Governance and
MDM go hand in hand
Business Strategy
People Process
Data Strategy
Data Technologies
• Governance Organization
• Data Stewardship
• Policies and Procedures
• Data Quality Assurance
• Data Quality and Compliance
• Change Management Capabilities
• Metadata & Data Standards
• Data Lifecycle Management
• Compliance and Risk
Management
• Tools and Technologies
• Data Architecture
• Data Model and Architecture
Mastering Master Data – Data Governance is Foundational
Data
Governance
is the
Framework to
align the
business vision,
by
supporting the
Business
Strategy and
ensuring
adaption
through
communication
and change
management.
10. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
• Advanced Data Management
Practice
• A set of processes and
technologies used to
federate key data assets to
provide a single view across
the enterprise
Master Data Management (MDM) Definition
11. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Successful MDM initiative at NCR
All 4 components Considered
People
Process
Data
Technologies
33%
34%
35%
39%
40%
55%
Lack of MDM Experience or Skills
Lack of Business Case
Poor Data Quality
Lack of Cross-Functional
Cooperation
Lack of Executive Sponsorship
Lack of DG or Stewardship
Challenges to MDM Success
Source: TDWI Best Practices Report Q2 2012
12. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Master Data Management –
High Level Processes used at NCR
• All sources
• All formats
Acquire
• Identical
• Alike
• Related
Reconcile • Correct
• Standardize
Cleanse
• Add external
• “Golden”
record
Enhance • To source
• Other Use
Publish
All MDM tools will do these activities to some degree. You need to
understand what is important to your business.
13. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
VALUE AND BENEFITS
14. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Master Data Management Benefits
Why Care about Data?
Data Decays at a rate of 2% to 4% per month (industry estimates) which can impact mail deliverability, email campaigns, sales follow-up
calls, record completeness
“More than half of US companies work with unreliable contact data”, 2013 NetProspex Marketing Data Benchmark Report
“The cost of bad data could be as much as 15 to 20% of corporate operating revenues”, D&B
“A CRM with bad data is like a pair of glasses with an outdated prescription: they’re expensive, clunky, and keep you from seeing
opportunities until it’s too late”. D&B
Quality Data is critical for business success
• Improves customer perception and customer experience
• Decreases costs due to rework
• Increases revenue by providing trustworthy data to make business decisions, complete
transactions, leverage business opportunities, and drive improvements in lead
generation
15. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Example Benefits / Business Drivers
Customer Master Data Management
Legal/
Compliance
Regulatory Operational Sales/Marketing Financial
Privacy Mandates SOX Efficiency Marketing and
Sales Promotions
Increased
Revenue and
Profitability per
Customer
Fraud Prevention Watch List Effectiveness Branding Audit
Contracts Reporting Customer
Support
Customer retention Risk Management
Data Breach
Protection
Organic Growth BIG Data Analytics Acquisitions
16. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Insight
Knowledge
Information
Data
• Data is the
Foundation
and must be
managed to
run, improve,
and expand
the business
Transactional
Data
Why is Mastering Master Data important?
Meaningful Information depends on Quality Data
Discrete facts
Definition
Format
Raw
Growth
Strategic Direction
Business Value
Community Impact
Value
Inference
Predictive
Decision-making
Patterns
Trends
Relationships
Assumptions
Necessary for the Business =
Key Business Asset
Operational Intelligence to
Run the business
Analytical Intelligence to
Improve the Business
Strategic and Predictive
Intelligence to Expand
the Business
Master and
Reference Data
Reporting
Data
Big Data
17. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Meaningful Information Depends on Quality Data
• Who are our top customers by revenue?
• Can we rollup accounts consistently across
systems for revenue and costs?
• Can we look at credit on an Enterprise
Level?
• Can we Understand customer satisfaction
across all accounts?
• Can we Easily Match Accounts from
Acquisitions?
NCR received many benefits
from the Customer Master Data
initiative …
18. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
BEST PRACTICE APPROACH FOR
MASTERING DATA
19. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
To Ensure MDM
Success
• Use manageable
steps
• Show short term
successes
• Build towards a
vision to support
the Business
• Ensure Business
Readiness
ValuetoOrganization
Assess
Needs &
Current
State
Develop an
Enterprise
Approach
Execute on
Tangible
Projects
Develop as a
Core
Company
Competency
Enable
Business
1
2
3
4
5
Mastering Master Data
Best Practice Approach to Build toward a Vision
Complexity/ Cultural Shift/ Level of Buy in
Understand where you are now,
Where you want to be, and the
Business Readiness to get there
(Cultural Shift)
20. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Mastering Master Data
Best Practice Approach
• Document current
maturity to identify
and prioritize all
enterprise-level data
needs Based on
Business Benefit
• Document Enterprise
Stakeholder
Model/Mapping
• Identify Subject
Area important to
business
• Define Types of
Master data (data
domains)
• Use Repeatable
Processes,
Methodologies, Tools
• Identify data
sources and current
System of Record
• Document Data
Lifecycle – Captured,
Created, Maintained,
Applied, Disposed
• Organizational
Design
• Have Dedicated Data
Stewards/ Team
• Ongoing
Improvements
(Operations,
Reporting, Metrics)
Data Governance Organization
Assess
Needs &
Current
State
Develop an
Enterprise
Approach
Execute on
Tangible
Projects
Develop as a
Core
Company
Competency
Enable
Business
1 2 3 4 5
21. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
• Is there a glossary of
terms, definitions, and
rules defined and
published?
• Are the right tools and
technologies in place to
support the business and
customer?
• Are data processes aligned
across the organization?
• Are there processes to
consistently manage the
data across it’s lifecycle?
• Is the Business and IT
aligned in managing the
Data?
• Have Roles and Decision
Rights been defined?
PEOPLE PROCESS
DATA
TECHNO
LOGY
Assess Current State
How well does your organization Manage Data as an Enterprise Asset?
A Data Governance Maturity assessment can help to identify opportunities to drive
Value!
• Document current
maturity to identify and
prioritize all enterprise-
level data needs
• Document Stakeholder
Model/ Mapping
• Create a Roadmap
based on Business
Benefit
22. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Data Governance and MDM Maturity
Aware Reactive Managed Proactive Governed
Level – 1 Level – 2 Level – 3 Level – 4 Level - 5
• High level of dependency
on "Tribal Knowledge"
across the organization
• Data is created on an as
needed basis with no or
few rules/standards
• Multiple creators
• Data quality issues are
addressed after they occur
(reactive)
• Decision making
dependent on consensus
and/or multiple systems
• Heroic culture
(performance measured
by "fixing" problems)
• Leadership is aware of the
importance of Data
Governance and the
impact on the
performance of the
organization
• Enterprise Data
Governance organizational
structure defined and
sponsored (including
defined Data Stewards)
• Data Standards and rules
defined
• Governance program has
been implemented at an
enterprise level
• Meta-data management
processes are in place
across the enterprise
• Proactive monitoring for
data quality controls feeds
into the governance
program
• Governance policies are
used to set, communicate,
and enforce business and
IT management
• Agility and responsiveness
is greatly increased due to
a single unified view of
enterprise data
• Enterprise data
governance enables high-
quality information sharing
across the enterprise
• Single unified view of the
enterprise
Focus on improving the maturity as your organization and amount of data grows
Use the Level of Maturity across each
Component and Sub-Component as an
Improvement Metric
23. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
What are the Business Needs?
• The business needs to…
– Trust their data
– Understand its meaning
– Know where and what data
is available
– Know how it flows and is
related across different
departments
– Know how it is secured
– Know how well it is
consolidated or integrated
Business Needs
drive...
• The Data Governance Committee,
Enterprise Data Stewards, and Business
Data Stewards are advocates for business
needs
Information Needs,
which drive...
• The Data Governance Office
serves as an advocate for
information needs
Technology and
Tools
• IT is responsible
for implementing
technology
strategies to
support business
and information
needs
Business
Driven, IT
Enabled
24. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Before MDM Customer Data at NCR ...
• Was not well defined
• had decentralized method of data creation
• had the least-defined governance and rules
A
- Had over 500,000 customer records, but business said there should
only be 1,000 customers
- 20,000 customer accounts were already marked as ‘inactive’
- 350,000 customer accounts had not been used for any transactions
in the previous three years
- 40,000 customer accounts had no revenue
- 10,000 customer accounts could be grouped into 1,000 customers
which represented 92% of our revenues
A Consistent Definition of “Customer” is needed
NCR Business Need: Understand Our Customer
25. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Parties to whom we
cannot sell NCR
products
Parties who finance purchases
of NCR products
Parties who sell NCR
products to others
Parties who buy directly
from NCR
Parties who repair NCR
products in the field
Parties who use NCR
products
Parties who buy NCR
products from others
Parties who own
non-NCR products
that we service
Parties who
install NCR
products
Parties who advise
others to buy NCR
products
Parties to whom we
would like to sell NCR
products
Parties who
customize NCR
products for others
NCR Business Need: Define Our Customer
26. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Mastering Master Data
Best Practice Approach
• Document current
maturity to identify
and prioritize all
enterprise-level data
needs Based on
Business Benefit
• Document Enterprise
Stakeholder
Model/Mapping
• Identify Subject
Area important to
business
• Define Types of
Master data (data
domains)
• Use Repeatable
Processes,
Methodologies, Tools
• Identify data
sources and current
System of Record
• Document Data
Lifecycle – Captured,
Created, Maintained,
Applied, Disposed
• Organizational
Design
• Have Dedicated Data
Stewards/ Team
• Ongoing
Improvements
(Operations,
Reporting, Metrics)
Data Governance Organization
Assess
Needs &
Current
State
Develop an
Enterprise
Approach
Execute on
Tangible
Projects
Develop as a
Core
Company
Competency
Enable
Business
1 2 3 4 5
27. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Recognize Different Stakeholder Levels and their
Concerns
• Recognize Different Stakeholder Groups have different needs and
expectations
– Sponsors: “C” level or top sponsor who stands behind the initiative.
• Benefit – Business Case
• Budget – Compare to the ROI
• Balance – Across other Priorities
– Project Team/ Contributors: Primary implementers or contributors
• Approval and Bandwidth
• Support for their business/team/role
– Operational Team: Users (creators or users of the solution)
• Training & Support
• Workload
Create an Enterprise Stakeholder Model
Develop an
Enterprise
Approach
2
28. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
When doing high level models, make sure the business
can understand it
Enterprise
Entity
Customer Entity
Customer
Account
Customer Site
Customer
Location
Customer
Grouping
Customer
Relationship
This is much
easier to explain
than this
Data Models should be Simple for the Business
29. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Mastering Master Data
Best Practice Approach
• Document current
maturity to identify
and prioritize all
enterprise-level data
needs Based on
Business Benefit
• Document Enterprise
Stakeholder
Model/Mapping
• Identify Subject
Area important to
business
• Define Types of
Master data (data
domains)
• Use Repeatable
Processes,
Methodologies, Tools
• Identify data
sources and current
System of Record
• Document Data
Lifecycle – Captured,
Created, Maintained,
Applied, Disposed
• Organizational
Design
• Have Dedicated Data
Stewards/ Team
• Ongoing
Improvements
(Operations,
Reporting, Metrics)
Data Governance Organization
Assess
Needs &
Current
State
Develop an
Enterprise
Approach
Execute on
Tangible
Projects
Develop as a
Core
Company
Competency
Enable
Business
1 2 3 4 5
30. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
A detailed
dataflow of
Master Data
usage can
be quite
complex
Data Flow Diagrams
Help to see how data is created and used
Execute on
Tangible
Projects
3
31. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
MDM Tools can provide value – if they meet your needs.
• Understand
Requirements and Use
Cases
• Consider Long Term
needs (Multi vs Single
or Silo Domains)
• Complete a Relative
Positioning Map
(Vendor Solutions
against Requirements) 400
420
440
460
480
500
520
540
560
580
600
2012 2013
MDM Software Revenue
Market Growth in $M
Customer MDM
Product MDM
Source: Gartner Magic Quadrant November 2014
12.2%
8.7%
32. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Technologies considered at NCR
• Existing systems that create or update customer data.
• New tools for the MDM needs
– Data Acquisition – ETL tools like Informatica
– Data Reconciliation, Cleansing – tools like Trillium or Oracle DQ
– Enhancement – tools like D&B
– Publishing – Web Services or ETL tools
• A dedicated MDM Tool to orchestrate the other tools, house the data and
provide a user interface to query and change the data.
33. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Mastering Master Data
Best Practice Approach
• Document current
maturity to identify
and prioritize all
enterprise-level data
needs Based on
Business Benefit
• Document Enterprise
Stakeholder
Model/Mapping
• Identify Subject
Area important to
business
• Define Types of
Master data (data
domains)
• Use Repeatable
Processes,
Methodologies, Tools
• Identify data
sources and current
System of Record
• Document Data
Lifecycle – Captured,
Created, Maintained,
Applied, Disposed
• Organizational
Design
• Have Dedicated Data
Stewards/ Team
• Ongoing
Improvements
(Operations,
Reporting, Metrics)
Data Governance Organization
Assess
Needs &
Current
State
Develop an
Enterprise
Approach
Execute on
Tangible
Projects
Develop as a
Core
Company
Competency
Enable
Business
1 2 3 4 5
34. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Organizational Design Example
Customer Data Stewardship Council (PT)
• Takes strategies from Exec Committee
• Communicates to Data Stewards
• Data Stewardship is the execution of decision-making processes
Customer Data Stewards (FT)
• Central point of contact for in SOR
• Aligned to support each Operational Data
Steward
Enterprise
Level
Subject Area
Data Steward
Operational/LOB Level
Data Steward
Executive Data Governance Committee (PT)
• Sets strategic priorities and direction (Company wide)
• Provides executive level support
• Resolves escalated issues
Data Governance Manager (FT):
Aligns program with Executive Committee
direction, communicates program components
and value
LOB Level Data Steward (PT)
• Subject matter experts in LOB
• Works with Customer Data Steward on business
definitions, quality requirements, business rules
for their area
Develop as a
Core
Company
Competency
4
35. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Mastering Master Data
Best Practice Approach
• Document current
maturity to identify
and prioritize all
enterprise-level data
needs Based on
Business Benefit
• Document Enterprise
Stakeholder
Model/Mapping
• Identify Subject
Area important to
business
• Define Types of
Master data (data
domains)
• Use Repeatable
Processes,
Methodologies, Tools
• Identify data
sources and current
System of Record
• Document Data
Lifecycle – Captured,
Created, Maintained,
Applied, Disposed
• Organizational
Design
• Have Dedicated Data
Stewards/ Team
• Ongoing
Improvements
(Operations,
Reporting, Metrics)
Data Governance Organization
Assess
Needs &
Current
State
Develop an
Enterprise
Approach
Execute on
Tangible
Projects
Develop as a
Core
Company
Competency
Enable
Business
1 2 3 4 5
36. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Enable the Business through On-going Improvements
Develop and track a set of Enterprise
Metrics for Master Data
Select What
to Measure
Plan the
Metrics
Develop the
Metrics
Test the
Metrics
Implement
and Publish
• Based on business
goals related to
Master Data
• Determined key
consistent metrics
important across
all LOBs
• Use a formal
Metrics
planning
process
• Determine the data,
process, people and
technology for
collecting, reporting,
and acting on the
metrics
• Determine dimensions
of quality to be tested
• Test to ensure the metric
can be collected and
reported accurately
• Set the baseline
• Set goals and targets
• Automate
• Publish and
communicate the
metrics
• Define RACI for clear
responsibilities related
to the metrics process
“Measurement is the first step that leads to control and
eventually to improvement. If you can’t measure something,
you can’t understand it. If you can’t understand it, you can’t
control it. If you can’t control it, you can’t improve it.”
― H. James Harrington
37. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
0.0
1.0
2.0
3.0
4.0
5.0
1.1 Data Governance
Organization
1.2 Stewardship
2.1 Policies and
Procedures
2.2 Data Quality and
Conformance
2.3 Data Quality
Assurance (profiling,
cleansing)
2.4 Information Lifecycle
Management
2.5 Data Risk
Management
2.6 Change Management
3.1 Technologies
3.2 Infrastructure/ Data
Architecture
4.1 Data Classification
4.2 Metadata
Management
Current State
Future State
Scale:
0 - Unaware
1- Aware
2- Reactive
3- Proactive
4- Managed
5- Best in Class
1. Organization
2. Process and
Procedures
4. Data
3. Technologies
LEVEL 2.0: Reactive
• Data quality issues are
addressed after they occur
(reactive)
• Multiple versions of the truth
• Multiple users have access to
make changes without clear
policies or standards
• System Centric vs Data Centric
Master Data Maturity (Example)
Use as an Improvement Metric for Roadmap
*Detailed Governance Maturity Model created and owned by Mary Levins
38. SIERRA CREEK CONSULTINGValue Through Governed DataTM Copyright 2014 by Sierra Creek Consulting
Summary
• Master Data is an Asset and should be managed under the Data
Governance Umbrella
• Develop a Stakeholder Model and Ensure there is Business
Sponsorship and Engagement
• Understand where you are now, where you want to be, and the
business readiness to get there (Cultural Shift)
• Understand existing systems and sources when defining the future
architecture
• Successful MDM initiatives must consider all Data Governance
components across People, Processes, Technologies, and Data
• Use the Level of Maturity across each Component and Sub-Component
as an Improvement Metric for your roadmap
• Measure your master data across the enterprise so you can manage it.
Manage it so you can continually improve it.
People
Process
Data
Technologies