Data Management Maturity Model
Overview
SEPG
Tysons Corner May 6, 2014
Who Needs Better Data Management?
Virtually everything in business today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose.
– Bill Gates
•
Every organization in business. Why?
– Collection of data assets developed over time – legacy application data stores, repositories, interfaces, services
– Confusion over where to obtain data – multiple sources, redundant data, impenetrable siloes
– Lack of clear roles – creating, nurturing, building, sustaining, and controlling data assets
– Lack of trust in information quality and usefulness
Guided Navigation to Lasting Solutions -
The Data Management Maturity Model
• Reference model framework of foundational data management capabilities – measurement tool for organizations to evaluate capability maturity, identify gaps, and incorporate guidelines for improvements
• Developed by CMMI Institute with our corporate sponsors - Booz Allen Hamilton, Lockheed Martin, Microsoft Corporation, and Kingland Systems
• From initial content created from contributions of many experts, we enhanced, refined and structured the DMM to leverage the strengths and proven
approach of CMMI
• Activity based, evidence tested - not an encyclopedia or compendium
• We have conducted DMM Assessments for: Microsoft Corporation; Fannie Mae; the Federal Reserve System Statistics Function; and the Ontario
Teachers Pension Plan; now completing for Freddie Mac.
• Our Sponsors have conducted assessments for: the Securities and Exchange Commission; Treasury, Office of Financial Research; and CISCO.
DMM Fly-over
Performed Managed Defined Measured Optimized Level1
Level2
Level3
Level4
Level5
What’s in the Model?
25 Process Areas
• Purpose – Introduction - Goals – Questions - Capability Level
Criteria – Work Products
• Policies – Processes – Standards
– Governance – Metrics – Enabling Technology – Implementation Tips
300+ Practice Statements
300+ Work Products
Data Management Strategy Data Management Strategy Communications
Data Management Function Business Case
Funding
DataGovernance Governance Management Business Glossary Metadata Management
DataQuality Data Quality Strategy Data Profiling
Data Quality Assessment Data Cleansing
Data Operations Data Requirements Definition Data Lifecycle Management Provider Management
Platform & Architecture Architectural Approach Architectural Standards Data Management Platform Data Integration
Historical Data, Archiving and Retention
Supporting Processes Measurement and Analysis Process Management Process Quality Assurance Risk Management Configuration Management
Starting the Journey - DMM Assessment Method
• The DMM can be used as a standalone guide, however, to maximize its value as a catalyst for forging a shared perspective and cultural evolution, our time-boxed facilitated method:
– Provides interactive launch collaboration event with a broad range of stakeholders
– Evaluates capabilities collectively by consensus affirmations
– Naturally facilitates unification of factions - everyone has a role
– Solicits key input through supplemental interviews
– Verifies the evaluation with work product reviews (evidence)
– Report and executive briefing presents Scoring, Findings, Observations, Strengths, and offers targeted specific Recommendations.
• Audit-level rigor will be introduced in the future to serve as a benchmark of maturity, similar to CMMI SCAMPI A.
To date, over 200 individuals from business, IT, and data management in early adopter organizations have employed the DMM - practice by practice, work product by work product - to evaluate their capabilities.
Data Management Maturity One-Page
Notional Organization
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5Data Management Strategy
Communications
Data Management Function Funding Model Business Case Governance Management Business Glossary Metadata Management Data Requirements
Data Lifecycle Management Provider Management
Architectural Approach Architectural Standards
Data Management PlatformData Integration Historical Data
Data Quality Strategy Data Profiling Data Quality Assessment
Data Cleansing Configuration Management
Measurement and Analysis Process Management
Process Quality Assurance
Risk Management
Are there DMM ‘Data Wranglers’?
• Our initial cadre of 12 certified Enterprise Data Management Experts (EDMEs) is available to assist you
• Certified individuals from our sponsoring companies have worked for 18 months to develop and refine concepts and
content, and are ready to conduct DMM Assessments employing our approach • Our courses will train and develop
How can the DMM help the organization?
Gradated path -step-by-step improvements Unambiguous practice statements for clear understanding Functional work products to aid implementation Common language Shared understanding of progress GalvanizationHow can the DMM help the DM Professional?
“Help me to help you” – platform for your customers –
conveys roles, shared concepts, complexity, connectedness Perspective enhancement – 360 degree view, energized collaboration, increased involvement of lines of business Enhanced support for funding and rapid achievements
Certification path – defined skillset and recognition
Product Suite Timeline
•
Peer review through May 19 (115 individuals and counting)
•
Partner program will be launched in June 2014
•
Target release DMM 1.0 Summer 2014
•
Full suite of courses –
– Three sequential courses leading to certification and licensing of EDMEs to facilitate assessments against the framework and assist organizations in implementing data management process improvements.
– First course released Summer 2014, final course in our initial suite Fall 2014
– Future audit-level course Summer 2015.
DMM Launch Party!
DMM Process Areas
Data Management Strategy
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, ensures 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
DMM Process Areas
Data Governance and Data Quality
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
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
DMM Process Areas
Platform & Architecture and Data Operations Platform & Architecture
Architectural Approach Architectural strategy, frameworks, and standards for implementation
planning
Architectural Standards Data standards for representation, access, and distribution
Data 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
Data Operations
Data Requirements Definition Process and standards for developing, prioritizing, evaluating, and validating data requirements
Data Lifecycle Management 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
DMM Process Areas
Supporting Processes
Supporting Processes Adapted from CMMI
Measurement 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
