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Transcript of Data-Ed Webinar: The Importance of MDM
Unlock Business Value
Through Reference & Master Data Management
10124 W. Broad Street, Suite C Glen Allen, Virginia 23060
804.521.4056
Peter Aiken, Ph.D.• 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu)
• DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data
management practices • Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset
Peter Aiken andMichael Gorman
2Copyright 2016 by Data Blueprint Slide #
We believe ...
Data Assets
Financial Assets
RealEstate Assets
Inventory Assets
Non-depletable
Available for subsequent
use
Can be used up
Can be used up
Non-degrading √ √ Can degrade
over timeCan degrade
over time
Durable Non-taxed √ √Strategic
Asset √ √ √ √
3
Copyright 2015 by Data Blueprint
• Today, data is the most powerful, yet underutilized and poorly managed organizational asset
• Data is your – Sole – Non-depleteable – Non-degrading – Durable – Strategic
• Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon!
• Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships
Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]
Copyright 2013 by Data Blueprint
• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Unlocking Business Value Through Reference & Master Data Management
Tweeting now: #dataed
4
UsesUsesReuses
What is data management?
5Copyright 2016 by Data Blueprint Slide #
Sources
Data Engineering
Data Delivery
Data
Storage
Specialized Team Skills
Data Governance
Understanding the current and future data needs of an enterprise and making that data effective and efficient in supporting business activitiesAiken, P, Allen, M. D., Parker, B., Mattia, A., "Measuring Data Management's Maturity: A Community's Self-Assessment" IEEE Computer (research feature April 2007)
Data management practices connect data sources and uses in an organized and efficient manner • Engineering • Storage • Delivery • Governance
When executed, engineering, storage, and delivery implement governance
Note: does not well-depict data reuse
Data Management
6Copyright 2016 by Data Blueprint Slide #
Sources
Data Engineering
Data Delivery
Data
Storage
Specialized Team Skills
Resources
(optimized for reuse)
Data Governance
Ana
lytic
Insi
ght
Specialized Team Skills
Maslow's Hierarchiy of Needs
7
Copyright 2015 by Data Blueprint
You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present
greaterrisk(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced Data
Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA
Foundational Data Management Practices
8
Copyright 2015 by Data Blueprint
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
Data$Management$Strategy
Data Management GoalsCorporate CultureData Management FundingData Requirements Lifecycle
DataGovernance
Governance ManagementBusiness GlossaryMetadata Management
DataQuality
Data Quality FrameworkData Quality Assurance
DataOperations
Standards and ProceduresData Sourcing
Platform$&$Architecture
Architectural FrameworkPlatforms & Integration
Supporting$Processes
Measurement & AnalysisProcess ManagementProcess Quality AssuranceRisk ManagementConfiguration Management
Component Process$Areas
DMM℠ Structure of 5 Integrated DM Practice Areas
Data architecture implementation
Data Governance
Data Management
Strategy
Data Operations
PlatformArchitecture
SupportingProcesses
Maintain fit-for-purpose data, efficiently and effectively
9Copyright 2016 by Data Blueprint Slide #
Manage data coherently
Manage data assets professionally
Data life cycle management
Organizational support
Data Quality
Copyright 2013 by Data Blueprint
The DAMA Guide to the Data Management Body of Knowledge
10
Data Management Functions
Published by DAMA International • The professional
association for Data Managers (40 chapters worldwide)
DMBoK organized around • Primary data
management functions focused around data delivery to the organization
• Organized around several environmental elements
Copyright 2013 by Data Blueprint
• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Unlocking Business Value Through Reference & Master Data Management
Tweeting now: #dataed
11
+ 1 Year
12
Copyright 2015 by Data Blueprint
• Confusion as to the system's value – Users lack confidence – Business did not know how to use
"the MDM"
• General agreement – Restart the effort
• "Root cause" analysis – Consensus – Poor quality data
• Response – Get data quality-ing!
• Inexperienced – Immature data quality practices – Tool/technological focus – Purchased a data quality tool
Copyright 2013 by Data Blueprint
Summary: Reference and MDM
13
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
– as opposed to mobile device management
• Gartner holds that MDM is a discipline or strategy – "… where the business and the IT organization work
together to ensure the uniformity, accuracy, semantic persistence, stewardship and accountability of the enterprise's official, shared master data"
• Sold as solution • Official, consistent set of identifiers - examples of these core
entities include: – Parties (customers, prospects, people, citizens, employees, vendors, suppliers,
trading partners, individuals, organizations, citizens, patients, vendors, supplies, business partners, competitors, students, products, financial structures *LEI*)
– Places (locations, offices, regional alignments, geographies) – Things (accounts, assets, policies, products, services)
• Provide context for transactions • From the term "Master File"
Master Data Management Definition
14Copyright 2015 by Data Blueprint
Wikipedia: Golden Version• In software development:
– The Golden Master is usually the RTM (Released to Manufacturing) version, and therefore the commercial version. It represents the development stage of "RTM" (Released To Manufacturing), often referred to as "going gold", or "gone golden".
– Often confused with "gold master" which refers to a physical recording entity such as that sent to a manufacturing plant.
• In data management:
– It is the data value representing the "correct" answer to the business question
• Definition-Reference/Master Data Management
– Planning, implementation and control activities to ensure consistency with a "golden version" of contextual data values.
15Copyright 2016 by Data Blueprint Slide #
Definition: Reference Data Management• Control over defined domain values (also known as
vocabularies), including:
• Control over standardized terms, code values and other unique identifiers;
• Business definitions for each value, business relationships within and across domain value lists, and the;
• Consistent, shared use of accurate, timely and relevant reference data values to classify and categorize data.
16Copyright 2016 by Data Blueprint Slide #
Copyright 2013 by Data Blueprint
Reference Data
• Reference Data: – Data used to classify or categorize other data, the value
domain
– Order status: new, in progress, closed, cancelled
– Two-letter USPS state code abbreviations (VA)
• Reference Data Sets
17
US United States
GB (not UK) United Kingdom
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Definition: Master Data Management
Control over master data values to enable consistent, shared, contextual use across systems, of the most accurate, timely and relevant version of truth about essential business entities.
18
Copyright 2013 by Data Blueprint
Master Data• Data about business entities providing context
for transactions but not limited to pre-defined values
• Business rules dictate format and allowable ranges – Parties (individuals, organizations, customers,
citizens, patients, vendors, supplies, business partners, competitors, employees, students)
– Locations, products, financial structures
• From the term Master File
19
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Reference Data versus Master Data
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• Reference Data: – Control over defined
domain values (vocabularies) for standardized terms, code values, and other unique identifiers
– The fact that we maintain 9 possible gender codes
• Master Data: – Control over master data
values to enable consistent, shared, contextual use across systems
– The "golden" source of the gender of your customer "Pat"
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Both provide the context for transaction data
Copyright 2013 by Data Blueprint
• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Unlocking Business Value Through Reference & Master Data Management
Tweeting now: #dataed
21
Copyright 2013 by Data Blueprint
Reference Data Facts 2012
• Home-grown reference data solutions predominate, putting institutions at risk for meeting regulatory constraints
• Risk management is seen as a more important business driver for improving data quality than cost
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Source: http://www.igate.com/22926.aspx
• Global industry-wide survey of reference data professionals
• Results show: Poor quality of reference data continues to create major problems for financial institutions.
Copyright 2013 by Data Blueprint
Reference Data Facts 2012, cont’d• Despite recommended practices of centralizing
reference data operations, 31% of the firms surveyed still manage data locally
• New and changing regulatory requirements have prompted many financial service companies to re-evaluate their reference data strategies. To prepare for new regulations, nearly 62% of survey respondents are planning to extend or customize their reference data systems during 2012 and 2013.
23
Source: http://www.igate.com/22926.aspx
Copyright 2013 by Data Blueprint
Interdependencies
24
Data Governance
Master Data Data Quality
interdependencies
25Copyright 2016 by Data Blueprint Slide #
Data Governance
Master Data Data Quality
makes the case and is
responsible for
is a necessary but insufficient prerequisite
to success
MD capabilities constrain governance
effectiveness
Solution Framework
26Copyright 2016 by Data Blueprint Slide #
SORs
SOR 1
SOR 2
SOR 3
SOR 4
SOR 5
SOR 6
SOR 7
SOR 8
Repository
IndicatorExtraction
Service (could be
segmented byday of week
month, system, etc.)
UpdateAddresses
LatencyCheckService
Ch 1
Ch 2
Ch 3
Ch 4
Ch 5
Ch 6
Channels
Ch 7
Ch 8
External Address Validation Processing
CustomerContact
Copyright 2013 by Data Blueprint
Inextricably intertwined
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Organized Knowledge 'Data'
Improved Quality Data
Data Organization Practices
Operational Data
Data Quality Engineering
Master Data Management
Practices
Suspected/ Identified
Data Quality
Problems
Routine Data Scans
Master Data Catalogs
Routine Data Scans
Knowledge Management
Practices
Data that might benefit from Master Management
Sources( (Metadata(Governance(
(
Metadata(Engineering(
(
Metadata(Delivery( Uses(
Metadata(Prac8ces((dashed lines not in existence)
Metadata(Storage(
Copyright 2013 by Data Blueprint
Interactions
28
Improved Quality Data
Master Data
Monitoring
Data Governance
Practices
Master Data Management
Practices
Governance Violations Monitoring
Data Quality Engineering
Practices
Data Quality
Monitoring
Monitoring Results:
Suspected/ Identified
Data Quality
Problems Data Quality Rules
Monitoring Results:
Suspected/ Master Data &
Characteristics
Routine Data
Scans
Master Data
Catalogs
Governance Rules
Routine Data
Scans
Monitoring Rules
Focused Data
Scans
Operational Data
Data Harvesting
Quality Rules
Copyright 2013 by Data Blueprint
Payroll Application(3rd GL)Payroll Data
(database)
R& D Applications(researcher supported, no documentation)
R & D Data (raw) Mfg. Data
(home grown database)
Mfg. Applications(contractor supported)
Finance
Data (indexed)
Finance Application(3rd GL, batch
system, no source)
Marketing Application(4rd GL, query facilities, no reporting, very large)
Marketing Data
(external database)
Personnel App.(20 years old,
un-normalized data)
Personnel Data
(database)
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Multiple Sources of (for example) Customer Data
Copyright 2013 by Data Blueprint
Vocabulary is Important-Tank, Tanks, Tankers, Tanked
30
Copyright 2013 by Data Blueprint
Reference Data Architecture
31
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Master Data Architecture
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Copyright 2013 by Data Blueprint
Combined R/M Data Architecture
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Copyright 2013 by Data Blueprint
"180% Failure Rate" Fred Cohen, Patni
34
http://www.igatepatni.com/bfs/solutions/payments.aspx
Copyright 2013 by Data Blueprint
MDM Failure Root-Causes• 30% of MDM programs are regarded as failures
• 70% of SOA projects in complex, heterogeneous environments had failed to yield the expected business benefits unless MDM is included
• Root-causes of failures: – 80% percent of MDM initiatives fail because of ineffective leadership,
underestimated magnitudes or an inability to deal with the cultural impact of the change
– MDM was implemented as a technology or as a project
– MDM was an Enterprise Data Warehouse (EDW) or an ERP
– MDM was an IT Effort
– MDM is separate to data governance and data quality
– MDM initiatives are implemented with inappropriate technology
– Internal politics and the silo mentality impede the MDM initiatives
35
Copyright 2013 by Data Blueprint
Automating Business Process Discovery (qpr.com)
36
Benefits • Obtain holistic perspective on
roles and value creation • Customers understand and value
outputs • All develop better shared
understanding
Results • Speed up process • Cost savings • Increased compliance • Increased output • IT systems documentation
Copyright 2013 by Data Blueprint
Traditional Engine
37
Copyright 2013 by Data Blueprint
Prius Hybrid Engine
38
Copyright 2013 by Data Blueprint 39
Copyright 2013 by Data Blueprint
• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Unlocking Business Value Through Reference & Master Data Management
Tweeting now: #dataed
40
Copyright 2013 by Data Blueprint
Goals and Principles
41
1. Provide authoritative source of reconciled, high-quality master and reference data.
2. Lower cost and complexity through reuse and leverage of standards.
3. Support business intelligence and information integration efforts.
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Reference & MDM Activities
42
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• Understand Reference and Master Data Integration Needs
• Identify Master and Reference Data Sources and Contributors
• Define and Maintain the Data Integration Architecture
• Implement Reference and Master Data Management Solutions
• Define and Maintain Match Rules • Establish “Golden” Records • Define and Maintain Hierarchies and Affiliations • Plan and Implement Integration of New Data Sources • Replicate and Distribute Reference and Master Data • Manage Changes to Reference and Master Data
Copyright 2013 by Data Blueprint
Specific Reference and MDM Investigations
43
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• Who needs what information?
• What data is available from different sources?
• How does data from different sources differ?
• How can inconsistencies be reconciled?
• How should valid values be shared?
Copyright 2013 by Data Blueprint
Primary Deliverables
• Data Cleansing Services • Master and Reference
Data Requirements • Data Models and Documentation • Reliable Reference and Master Data • "Golden Record" Data Lineage • Data Quality Metrics and Reports
44
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Roles and Responsibilities
45
Consumers: • Application Users • BI and Reporting Users • Application Developers and
Architects • Data integration Developers and
Architects • BI Vendors and Architects • Vendors, Customers and Partners
Participants: • Data Stewards • Subject Matter Experts • Data Architects • Data Analysts • Application Architects • Data Governance Council • Data Providers • Other IT Professionals
Suppliers: • Steering Committees • Business Data Stewards • Subject Matter Experts • Data Consumers • Standards Organizations • Data Providers
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Technology
46
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• ETL • Reference Data Management
Applications • Master Data Management
Applications • Data Modeling Tools • Process Modeling Tools • Meta-data Repositories • Data Profiling Tools • Data Cleansing Tools • Data Integration Tools • Business Process and Rule Engines • Change Management Tools
Copyright 2013 by Data Blueprint
• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Unlocking Business Value Through Reference & Master Data Management
Tweeting now: #dataed
47
Copyright 2013 by Data Blueprint
Guiding Principles
1. Shared R/M data belong to the organization.
2. R/M data management is an on-going data quality improve-ment program – goals cannot be achieved by 1 project alone.
3. Business data stewards are the authorities accountable at determining the golden values.
4. Golden values represent the "best" sources. 5. Replicate master data values only from golden
sources. 6. Reference data changes require formal change
management
48
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
10 Best Practices for MDM1. Active, involved executive sponsorship
2. The business should own the data governance process and the MDM or CDI project
3. Strong project management and organizational change management
4. Use a holistic approach - people, process, technology and information:
5. Build your processes to be ongoing and repeatable, supporting continuous improvement
49
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
Copyright 2013 by Data Blueprint
10 Best Practices for MDM, cont’d6. Management needs to recognize the
importance of a dedicated team of data stewards
7. Understand your MDM hub's data model and how it integrates with your internal source systems and external content providers
8. Resist the urge to customize
9. Stay current with vendor-provided patches
10.Test, test, test and then test again.
50
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
Copyright 2013 by Data Blueprint
• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Unlocking Business Value Through Reference & Master Data Management
Tweeting now: #dataed
51
Copyright 2013 by Data Blueprint
15 MDM Success Factors1. Success is more likely and
more frequently observed once users and prospects understand the limitations and strengths of MDM.
2. Taking small steps and remaining educated on where the MDM market and technology vendors are will increase longer-term success with MDM.
3. Set the right expectations for MDM initiative to help assure long-term success.
4. Long-term MDM success requires the involvement of the information architect.
5. Create a governance framework to ensure that individuals manage master data in a desirable manner.
6. Strong alignment with the organization's business vision, demonstrated by measuring the program's ongoing value, will underpin MDM success.
7. Use a strategic MDM framework through all stages of the MDM program activity cycle — strategize, evaluate, execute and review.
52
[Source: unknown]
Copyright 2013 by Data Blueprint
15 MDM Success Factors
53
8. Gain high-level business sponsorship for the MDM program, and build strong stakeholder support.
9. Start by creating an MDM vision and a strategy that closely aligns to the organization’s business vision.
10.Use an MDM metrics hierarchy to communicate standards for success, and to objectively measure progress.
11.Use a business case development process to increase business engagement.
12.Get the business to propose and own the KPIs; articulate the success of this scenario.
13.Measure the situation before and after the MDM implementation to determine the change.
14.Translate the change in metrics into financial results.
15.The business and IT organization should work together to achieve a single view of master data.
[Source: unknown]
Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]
Copyright 2013 by Data Blueprint 54
Copyright 2013 by Data Blueprint
Summary: Reference and MDM
55
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Copyright 2013 by Data Blueprint
Questions?
56
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
+ =
Copyright 2013 by Data Blueprint
References
57
Copyright 2013 by Data Blueprint
Additional References• http://www.mdmsource.com/master-data-management-tips-best-practices.html • http://www.igate.com/22926.aspx • http://www.itbusinessedge.com/cm/blogs/lawson/just-the-stats-master-data-management/?
cs=50349 • http://searchcio-midmarket.techtarget.com/news/2240150296/Smart-grid-systems-expert-
devises-business-transformation-template • http://www.itbusinessedge.com/cm/blogs/lawson/free-report-shows-businesses-fed-up-
with-bad-data/?cs=50416 • http://www.itbusinessedge.com/cm/blogs/lawson/whats-ahead-for-master-data-
management/?cs=50082 • http://www.itbusinessedge.com/cm/blogs/vizard/master-data-management-reaches-for-the-
cloud/?cs=49264 • http://www.information-management.com/channels/master-data-management.html • http://www.dataversity.net/applying-six-sigma-to-master-data-management-mdm-
framework-for-integrating-mdm-into-ea-part-2/ • http://www.dataqualityfirst.com/getting_master_data_facts_straight_is_hard.htm
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