Best Practices with the DMM
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Copyright 2013 by Data Blueprint
Welcome: Data Management Maturity - Achieving Best Practices using DMM
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements. Key Takeaways: • Our profession is advancing its knowledge and has a wide
spread basis for partnerships • New industry assessment standard is based on successful
CMM/CMMI foundation • Clear need for data strategy • A clear and unambiguous call for participationDate: August 9, 2016Time: 2:00 PM ETPresented by: Melanie Mecca & Peter Aiken
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Presented by Melanie Mecca & Peter Aiken, Ph.D.
Data Management Maturity
Achieving Best Practices using DMM
Copyright 2013 by Data Blueprint
Your PresentersMelanie Mecca • CMMI Institute/
Director of Data Management Products and Services
• 30+ years designing and implementing strategies and solutions for private and public sectors
• Architecture/Design experience in:
– Data Management Programs
– Enterprise Data Architecture
– Enterprise Architecture
• DMM primary author from Day 1
Peter Aiken • 30+ years data mgt. • Multiple Int. awards/recognition • Founding Director,
Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu) • Past, President, DAMA
International (dama.org) • 9 books and dozens of articles • 500+ empirical practice
descriptions • Multi-year immersions w/
organizations as diverse as US DoD, Nokia, Deutsche Bank, Wells Fargo, Walmart, and the Commonwealth of Virginia
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Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Design/Manage Data Structures
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DMMSM Structure
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Data Governance
Data Management
Strategy
Data Quality
Data Operations
PlatformArchitecture
SupportingProcesses
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DMM - Guided Navigation to Lasting Solutions
• Reference model framework of fundamental data management best practices
• Measurement instrument for organizations to evaluate capability maturity, identify gaps, and incorporate guidelines for improvements
• Developed by CMMI Institute with corporate sponsors and 50+ expert authors
• Answers: “How are we doing?” • Guides: “What should we do next?” • Baseline for: Integrated program, high
value initiatives and improvements
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DMM Themes
• Architecture and technology neutral – applicable to legacy, DW, SOA, unstructured data environments, mainframe-to-Hadoop, etc.
• Industry independent – usable by every size and type of organization with data assets, applicable to every industry
• Zeros in on the Current State of Capabilities – organization is assessed on existing DM processes and the implemented data layer
• Launch collaborative and sustained capability improvement – for the life of the DM program [aka, forever].
If you manage data, the DMM will benefit you
Data Management Practices HierarchyYou 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)
Advanced Data
Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA
Foundational Data Management Practices
12Copyright 2015 by Data Blueprint Slide #
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
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Foundation for Business Results
• Trusted Data – demonstrated, independently measured capability to ensure customer confidence in the data
• Improved Risk and Analytics Decisions –comprehensive and measured DM strategy ensures decisions are based on accurate data
• Cost Reduction/Operational Efficiency –identification of current and target states supports elimination of redundant data and streamlining of processes and systems
• Regulatory Compliance – independently evaluated and measured DM capabilities to meet and substantiate industry and regulator requirements
Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
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Copyright 2013 by Data Blueprint
Motivation
• "We want to move our data management program to the next level" – Question: What level are you at now?
• You are currently managing your data, – But, if you can't measure it, – How can you manage it effectively?
• How do you know where to put time, money, and energy so that data management best supports the mission?
"One day Alice came to a fork in the road and saw a Cheshire cat in a tree. Which road do I take? she asked. Where do you want to go? was his response. I don't know, Alice answered. Then, said the cat, it doesn't matter."
Lewis Carroll from Alice in Wonderland
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Copyright 2013 by Data Blueprint
DoD Origins• US DoD Reverse Engineering
Program Manager
• We sponsored research at the CMM/SEI asking
– “How can we measure the performance of DoD and our partners?”
– “Go check out what the Navy is up to!”
• SEI responded with an integrated process/data improvement approach
– DoD required SEI to remove the data portion of the approach
– It grew into CMMI/DM BoK, etc.
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Copyright 2013 by Data Blueprint
Acknowledgements
0018-9162/07/$25.00 © 2007 IEEE42 Computer P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t y
version (changing data into other forms, states, orproducts), or scrubbing (inspecting and manipulat-ing, recoding, or rekeying data to prepare it for sub-sequent use).
• Approximately two-thirds of organizational datamanagers have formal data management training;slightly more than two-thirds of organizations useor plan to apply formal metadata management tech-niques; and slightly fewer than one-half manage theirmetadata using computer-aided software engineer-ing tools and repository technologies.3
When combined with our personal observations, theseresults suggest that most organizations can benefit fromthe application of organization-wide data managementpractices. Failure to manage data as an enterprise-, cor-porate-, or organization-wide asset is costly in terms ofmarket share, profit, strategic opportunity, stock price,and so on. To the extent that world-class organizationshave shown that opportunities can be created throughthe effective use of data, investing in data as the onlyorganizational asset that can’t be depleted should be ofgreat interest.
Increasing data management practice maturity levels can positively impact the
coordination of data flow among organizations, individuals, and systems. Results
from a self-assessment provide a roadmap for improving organizational data
management practices.
Peter Aiken, Virginia Commonwealth University/Institute for Data Research
M. David Allen, Data Blueprint
Burt Parker, Independent consultant
Angela Mattia, J. Sergeant Reynolds Community College
A s increasing amounts of data flow within andbetween organizations, the problems that canresult from poor data management practicesare becoming more apparent. Studies haveshown that such poor practices are widespread.
For example,
• PricewaterhouseCoopers reported that in 2004, onlyone in three organizations were highly confident intheir own data, and only 18 percent were very con-fident in data received from other organizations.Further, just two in five companies have a docu-mented board-approved data strategy (www.pwc.com/extweb/pwcpublications.nsf/docid/15383D6E748A727DCA2571B6002F6EE9).
• Michael Blaha1 and others in the research communityhave cited past organizational data management edu-cation and practices as the cause for poor databasedesign being the norm.
• According to industry pioneer John Zachman,2 orga-nizations typically spend between 20 and 40 percentof their information technology budgets evolving theirdata via migration (changing data locations), con-
Measuring Data ManagementPractice Maturity: A Community’s Self-Assessment
MITRE Corporation: Data Management Maturity Model • Internal research project: Oct ‘94-Sept ‘95 • Based on Software Engineering Institute Capability
Maturity Model (SEI CMMSM) for Software Development Projects
• Key Process Areas (KPAs) parallel SEI CMMSM KPAs, but with data management focus and key practices
• Normative model for data management required; need to: – Understand scope of data management – Organize data management key practices
• Reported as not-done-well by those who do it
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CMMI Institute Background
• Evolved from Carnegie Mellon’s Software Engineering Institute (SEI) - a federally funded research and development center (FFRDC)
• Continues to support and provide all CMMI offerings and services delivered over its 20+ year history at the SEI o Industry leading reference models - benchmarks and guidelines for improvement –
Development, Acquisition, Services, People, Data Management o Training and Certification program, Partner program
• Dedicated training, partner and certification teams to support organizations and professionals
• Now owned by ISACA (CISO/M, COBIT, IT Governance, Cybersecurity) and joint product offerings are planned
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CMMI – Worldwide Process Improvement
CMMI Quick Stats:
• Over 10,000 organizations
• 94 countries
• 12 National governments
• 10 languages
• 500 Partners
• 1900+ Appraisals in 2015
Copyright 2013 by Data Blueprint
Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member Survey Results, Washington D.C.: Corporate Executive Board 2006, p. 23.
Percentage of Projects on Budget By Process Framework Adoption
…while the same pattern generally holds true for on-time performancePercentage of Projects on Time By Process Framework Adoption
Key Finding: Process Frameworks are not Created EqualWith the exception of CMM and ITIL, use of process-efficiency frameworks does not predict higher on-budget project delivery…
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Copyright 2013 by Data Blueprint
CMMI Model Portfolio
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Establish, Manage, and Deliver Services
Product Development / Software Engineering
Acquire and integrate products / supply chain
Workforce development and management
Rearchitecting to present a more unified/modular offering
‹#›
DMM Drivers and Bio
• An effective DM program requires a planned strategic effort – not a Project, or a separate Program – a lifestyle.
• Organizations needed a comprehensive reference model to precisely evaluate data management capabilities
• DMM unifies understanding and priorities of business, IT, and data management.
• Foundation for collaborative and sustained capability building.
Late 2009 – Gleam in the eye
Jan 2011 – Launch development
Sep 2012 – CMMI Transformation
Apr 2014 – Industry Peer Review
Aug 2014 – DMM 1.0 Released
DMM Timeline
Now–2016 – DMM Ecosystem
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Who Wrote It and Why
• 50+ authors with years of experience in implementing data management programs and projects• Industry skills includes - MDM, DQ, EDW, BI, SOA, big data, governance,
enterprise architecture, data architecture, business and data strategy, platform implementation, business process engineering, business rules, software engineering, etc.
• Consortium approach – proven approaches• Broad practical wisdom - What works• DM experts combined with reference model architects and business knowledge
experts from multiple industries
• We needed it – we wrote it for us, & we wrote it for you
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DMM and DMBOK
CMMI Institute and DAMA International are collaborating to:
• Eliminate any confusion between the two tools and highlight their complementarity
• Extend and enhance data management training for organizations and professionals
• Provide benefits to DAMA members (members receive a discount for our public training classes)
Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
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Data Management Maturity (DMM)SM Model
o DMM 1.0 released August 2014 • 3.5 years in development • Sponsors – Microsoft, Lockheed
Martin, Booz Allen Hamilton • 50+ contributing authors, 70+ peer
reviewers, 80+ orgs • 6 categories, 25 process areas • 414 practice statements • 596 functional work products
‹#›
You Are What You DO
• Model emphasizes behavior • Creating effective, repeatable processes• Leveraging and extending across the
organization• Activities result in work products
• Processes, standards, guidelines, templates, policies, etc.
• Reuse and extension = maximize value, lower costs, happier staff
• Process Areas were designed to stand alone for evaluation• Reflects real-world organizations• Flexible for multiple purposes
• Whole model• Selected Category(ies)• Specific Process Areas
• Relationships are indicated because operationally, “everything is connected”
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One concept for process improvement, others include:
• Norton Stage Theory • TQM • TQdM • TDQM • ISO 9000
and focus on understanding current processes and determining where to make improvements.
Copyright 2013 by Data Blueprint
DMM Capability Maturity Model Levels
Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts
Performed (1)
Managed (2)
Our DM practices are defined and documented processes performed at
the business unit level
Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices
Defined (3)
Measured (4)
We manage our data as a asset using advantageous data governance practices/structures
Optimized
(5)DM is strategic organizational capability, most importantly we have a process for
improving our DM capabilities
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‹#›
DMM Capability Levels
Performed
Managed
Defined
Measured
Optimized
Level
1
Level
2
Level
3
Level
4
Level
5
Risk
Quality
Ad hoc
Reuse
Stress
Clarity
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‹#›
Capability and Maturity Disambiguation
Capability – “We can do this”• Specific Practices – “We’re doing it well”• Work Products – “We’ve documented the processes we are
following” (processes, work products, guidelines, standards, etc.)
Maturity – “….and we can prove it”• Process Stability – “Take it to the bank”• Ensures Repeatability
• Policy• Training• Quality Assurance, etc.
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‹#›
DMM Structure
Core Category
Process Area
Purpose
Introductory Notes
Goal(s) of the Process Area
Core Questions for the Process Area
Functional Practices (Levels 1-5)
rRelated Process Areas
Example Work Products
Infrastructure Support Practices
eExplanatory Model Components Required for Model Compliance
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Maintain fit-for-purpose data, efficiently and effectively
DMM℠ Structure of 5 Integrated DM Practice Areas
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Copyright 2015 by Data Blueprint
Manage data coherently
Manage data assets professionally
Data architecture implementation
Data lifecycle implementation
Organizational support
DMM Process AreasData Management Strategy
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Name Description
Data Management Strategy
Data Management Strategy Goals, objectives, principles, business value, prioritization, metrics, and sequence plan for the data management program
Communications
Communications strategy for data management initiatives and mechanisms to ensure business, IT, and data management stakeholders are aligned with bi-directional feedback
Data Management Function Structure of data management organization, responsibilities and accountability, interaction model, staffing for data management resources, executive oversight
Business Case Decision rationale for determining what data management initiatives should be funded based on benefits to the organization and financial considerations
Data Management Funding Funding justification for the data management program and initiatives, operational and financial metrics
Create, communicate, justify and fund a unifying vision for data management
DMM Process AreasData Governance
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Data Governance Governance Management Structure of data governance, governance processes and
leadership, metrics development and monitoring Business Glossary Creation, change management, and compliance for terms,
definitions, and properties Metadata Management Strategy, classification, capture, integration, and accessibility of
business, technical, process, and operational metadata
Active organization-wide participation in key initiatives and critical decisions essential for the data assets
DMM Process AreasData Quality
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Data Quality
Data Quality Strategy Plan and initiatives for the data quality program, aligned with business objectives and impacts
Data Profiling Analysis of semantic data content in physical data stores for meaning and defect detection
Data Quality Assessment Assessment and improvement of data quality, business rules and known issues analysis, measuring impact and costs
Data Cleansing Mechanisms to clean data, reporting and tracking of data issues for correction with impact and cost analysis
A business-driven strategy and approach to assess quality, detect defects, and cleanse data
Platform & Architecture
Architectural Approach Architectural strategy, frameworks, and standards for implementation planning
Architectural Standards Data standards for representation, access, and distributionData Management Platform Technology and capability platforms selection for data distribution and
integration into consuming applications
Data Integration Integration and reconciliation of data from multiple sources into target destinations, standards and best practices, data quality processes at point of entry
Historical Data, Archiving and Retention
Management of historical data, archiving, and retention requirements
DMM Process AreasPlatform & Architecture
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A collaborative approach to architecting the target state with appropriate standards, controls, and toolsets
DMM Process AreasData Operations
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Data Operations
Data Requirements Definition Process and standards for developing, prioritizing, evaluating, and validating data requirements
Data Lifecycle Mapping of data to business processes as data flows from one process to another
Provider Management Standardization of data sourcing process, SLAs, and management of data provisioning from internal and external sources
Systematic approach to address business drivers and processes, building knowledge for maximizing data assets
DMM Process AreasSupporting Processes
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Supporting Processes Adapted from CMMIMeasurement and Analysis Establishing and reporting metrics and statistics for each
process area within the data management program, supports managing to performance milestones
Process Management Management and enforcement of policies, processes, and standards, from creation to dissemination to sun-setting
Process Quality Assurance Evaluation and audit to ensure quality execution in all data management process areas
Risk Management Identifying, categorizing, managing and mitigating business and technical risks for the data management program
Configuration Management Establishing and maintaining the integrity of data management artifacts and products, and management of releases
Systematic approach to address business drivers and processes, building knowledge for maximizing data assets
Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
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‹#›
Natural events for employing the DMM
• Use Cases - assess current capabilities before: • Developing or enhancing DM program / strategy• Embarking on a major architecture transformation• Establishing data governance• Expansion / enhancement of analytics • Implementing a data quality program• Implementing a metadata repository• Designing and implementing multi-LOB solutions:
• Master Data Management• Shared Data Services• Enterprise Data Warehouse• Implementing an ERP• Other multi-business line efforts.
Like an Energy audit or an executive physical
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Copyright 2013 by Data Blueprint
Assessment Components
Data Management Practice Areas
Data Management Strategy
DM is practiced as a coherent and coordinated set of activities
Data Quality
Delivery of data is support of organizational objectives – the currency of DM
Data Governance
Designating specific individuals caretakers for certain data
Data Platform/Architecture
Efficient delivery of data via appropriate channels
Data Operations Ensuring reliable access to data
Capability Maturity Model Levels
Examples of practice maturity
1 – PerformedOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts
2 – ManagedWe have DM experience and have the ability to implement disciplined processes
3 – Defined
We have standardized DM practices so that all in the organization can perform it with uniform quality
4 – MeasuredWe manage our DM processes so that the whole organization can follow our standard DM guidance
5 – Optimized We have a process for improving our DM capabilities
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Copyright 2013 by Data Blueprint
Industry Focused Results• CMU's Software
Engineering Institute (SEI) Collaboration • Results from hundreds organizations in
various industries including: ✓ Public Companies ✓ State Government Agencies ✓ Federal Government ✓ International Organizations
• Defined industry standard • Steps toward defining data management
"state of the practice"
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Data Management Strategy
Data Governance
Platform & Architecture
Data Quality
Data Operations
Focus: Implementation
and Access
Focus: Guidance and
Facilitation
Optimized (V)
Measured (IV)
Defined (III)
Managed (II)
Initial (I)
Development guidance
Data Adminstration
Support systems
Asset recovery capability
Development training
0 1 2 3 4 5
Client Industry Competition All Respondents
Data Management Practices Assessment
Challenge
Challenge
Challenge
Data Program Coordination
Organizational Data Integration
Data Stewardship
Data Development
Data Support Operations
43Copyright 2015 by Data Blueprint
High Marks for IFC's Audit
44Copyright 2015 by Data Blueprint
Leadership & Guidance
Asset Creation
Metadata Management
Quality Assurance
Change Management
Data Quality
0 1 2 3 4 5
TRE ISG IFC Industry Benchmarks Overall Benchmarks
1
2
3
4
5
Data
Prog
ram
Coor
dinati
on
Orga
nizati
onal
Data
Integ
ratio
n
Data
Stew
ards
hip
Data
Deve
lopme
nt
Data
Supp
ort O
pera
tions
2007 Maturity Levels 2012 Maturity Levels
Comparison of DM Maturity 2007-2012
45Copyright 2015 by Data Blueprint
Measurement = Confidence
• Activity-focused and evidence-based evaluation of the data management program
• Allows organizations to gauge their data management achievements against peers
• Fuels enthusiasm and funding for improvement initiatives
• Enhances an organization’s reputation – quality and progress
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Starting the Journey - DMM Assessment Method
• To maximize the DMM’s value as a catalyst for forging shared perspective and program acceleration, our method provides:
– Collaboration launch event - broad range of stakeholders
– Capabilities evaluated by consensus affirmations
– Solicits key business input through supplemental interviews
– Verifies evaluation with work product reviews (evidence)
– Report and executive briefing presents Scoring, Findings, Observations, Strengths, and customized specific Recommendations.
• Audit-level rigor will be introduced in 2016 as a maturity benchmark, leveraging the proven CMMI Appraisal method
To date, over 700 assessment participants from business, IT, and data management in early adopter organizations have employed DMM 1.0 - practice by practice, work product by work product - to evaluate their capabilities.
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If You’ve Used an Earlier Version of the DMM…
• Many organizations have reported positive accomplishments from assessments and internal evaluations using earlier versions of the DMM
• A mapping of previous to published version process areas is available for your use
• We have completed and taught three successive courses – eLearning plus 10 days advanced training - to support your organization in implementing effective data management practices.
Strategic Enterprise Architecture
Data Management Operations
Platform & Architecture
Data Quality
Data Governance
Data Management
Strategy
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CMMI Assessment Recommendations
• Unified effort to maximize data sharing and quality
• Monitor and measure adherence to data standards
• Top-down approach to prioritization • Up-stream error prevention • Common Data Definitions
• Leverage best practices for data archival and retention
• Maximize shared services utilization
• Map key business processes to data
• Leverage Meta Data repository
• Integrate data governance structures • Prioritize policies, processes,
standards, to support corporate initiatives
Microsoft
Strategic Enterprise Architecture
▪ In the world of Devices and Services, Data Management is a pillar of effectiveness
▪ DMM is a key tool to facilitate the Real-Time Enterprise journey
▪ Active participation of cross-functional teams from Business and IT is key for success
▪ Employee education on the importance of data and the impact of data management is a good investment
▪ Build on Strengths!
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Key Lessons
Microsoft IT Annual Report may be found at: http://aka.ms/itannualreport
Microsoft
How the DMMSM Helps the Organization
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Gradated path -step-by-step improvements
Unambiguous practice statements for clear understanding
Functional work products to aid implementation
Common language Shared understanding of progress
Acceleration
‹#›
How the DMMSM helps the DM Professional
“Help me to help you” – education for roles, complexity, connectedness
Integrated 360 degree program level view – launches collaboration, increased involvement of lines of business
Actionable and implementable initiatives
Strong support for business cases
Certification path – defined skillset and industry recognition
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‹#›
DMM Ecosystem - Product Suite Overview
• Data Management Maturity Model o Comprehensive document with
descriptions, practice statements and work products
o Enterprise license option
• Assessments o Structured, facilitated working sessions
resulting in detailed current/future state executive report
• Training & Certification o Introductory, Advanced and Expert
courses with associated certifications
• Formal Measurement/Appraisal (2016) o Benchmark measurement and scoring of
capability/maturity level
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‹#›
DMM Ecosystem – Training
Training Classes
• Building EDM Capabilities (3 days)
• eLearning Building EDM Capabilities (self-paced, web-based) (10 hours)
• Mastering EDM Capabilities (5 days)
• Enterprise Data Management Expert (5 days)
• Future – EDM Lead Appraiser (5 days)
On-site courses available at your location
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‹#›
DMM Ecosystem - Certifications
Certifications: Credentials and Credibility
• Enterprise Data Management Expert (EDME) – Assessing and Launching the DM Journey
• DMM Lead Appraiser (DMM LA) – Benchmarking and Monitoring Improvements
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‹#›
DMM Ecosystem – Results and Assets
Results
• Case studies • Best Practice Examples • Benchmarking • Web publication of approved
appraisals
DMM Assets
• Translations (#1 Portuguese) • Seminars (RDA, Governance,
Quality) • DMM Compass • Profiles – Regulatory • Academic Courses • White Papers / Articles
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Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
63
For more information
• Feel free to email me: • [email protected]
• And visit our web site: • http://cmmiinstitute.com/DMM
65
How to get stuff out of your Crystal Ball: Predictive Analytics September 13, 2016 @ 2:00 PM ET/11:00 AM PTwith Steffani Burd, PhD
Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net
Copyright 2013 by Data Blueprint 66
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