Data Management Maturity Model. Overview

Transcription

1 Data Management Maturity Model Overview SEPG Tysons Corner May 6, 2014

2 Who Needs Better Data Management? 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 Data not universally viewed as a critical infrastructure component. 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

3 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.

4 DMM Fly-over Level 5 Level 4 Optimized Measured Level 1 Level 2 Performed Level 3 Managed Defined

5 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 Governance Data Quality Data Operations Platform & Architecture Supporting Processes Data Management Strategy Communications Data Management Function Business Case Funding Governance Management Business Glossary Metadata Management Data Quality Strategy Data Profiling Data Quality Assessment Data Cleansing Data Requirements Definition Data Lifecycle Management Provider Management Architectural Approach Architectural Standards Data Management Platform Data Integration Historical Data, Archiving and Retention Measurement and Analysis Process Management Process Quality Assurance Risk Management Configuration Management

6 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 timeboxed 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.

7 Data Management Maturity One-Page Notional Organization Data Management Strategy Risk Management Process Quality Assurance 5 Communications Data Management Function Process Management Measurement and Analysis Configuration Management Data Cleansing Funding Model Business Case Governance Management Business Glossary Data Quality Assessment Metadata Management Data Profiling Data Quality Strategy Data Requirements Data Lifecycle Management Historical Data Data Integration Data Management Platform Provider Management Architectural Approach Architectural Standards Data Strategy Data Operations Data Platform Data Governance Data Quality Supporting Processes

8 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 welleducated certified experts in the DMM.

9 How can the DMM help the organization? Gradated path - step-by-step improvements Common language Shared understanding of progress Galvanization Unambiguous practice statements for clear understanding Functional work products to aid implementation

10 How 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 Qualifications the A Team for the global standard

11 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 DMM Launch Party! Information Quality and Data Governance conference - San Diego, June 23-27

12 DMM Process Areas Data Management Strategy Name Data Management Strategy Data Management Strategy Communications Data Management Function Business Case Data Management Funding Description Goals, objectives, principles, business value, prioritization, metrics, and sequence plan for the data management program Communications strategy for data management initiatives and mechanisms, ensures business, IT, and data management stakeholders are aligned with bi-directional feedback Structure of data management organization, responsibilities and accountability, interaction model, staffing for data management resources, executive oversight Decision rationale for determining what data management initiatives should be funded based on benefits to the organization and financial considerations Funding justification for the data management program and initiatives, operational and financial metrics

13 DMM Process Areas Data Governance and Data Quality Data Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Strategy Data Profiling Data Quality Assessment Data Cleansing Structure of data governance, governance processes and leadership, metrics development and monitoring Creation, change management, and compliance for terms, definitions, and properties Strategy, classification, capture, integration, and accessibility of business, technical, process, and operational metadata Plan and initiatives for the data quality program, aligned with business objectives and impacts Analysis of semantic data content in physical data stores for meaning and defect detection Assessment and improvement of data quality, business rules and known issues analysis, measuring impact and costs Mechanisms to clean data, reporting and tracking of data issues for correction with impact and cost analysis

14 DMM Process Areas Platform & Architecture and Data Operations Platform & Architecture Architectural Approach Architectural Standards Data Management Platform Data Integration Historical Data, Archiving and Retention Data Operations Data Requirements Definition Data Lifecycle Management Provider Management Architectural strategy, frameworks, and standards for implementation planning Data standards for representation, access, and distribution Technology and capability platforms selection for data distribution and integration into consuming applications Integration and reconciliation of data from multiple sources into target destinations, standards and best practices, data quality processes at point of entry Management of historical data, archiving, and retention requirements Process and standards for developing, prioritizing, evaluating, and validating data requirements Mapping of data to business processes as data flows from one process to another Standardization of data sourcing process, SLAs, and management of data provisioning from internal and external sources

15 DMM Process Areas Supporting Processes Supporting Processes Measurement and Analysis Process Management Process Quality Assurance Risk Management Configuration Management Adapted from CMMI Establishing and reporting metrics and statistics for each process area within the data management program, supports managing to performance milestones Management and enforcement of policies, processes, and standards, from creation to dissemination to sun-setting Evaluation and audit to ensure quality execution in all data management process areas Identifying, categorizing, managing and mitigating business and technical risks for the data management program Establishing and maintaining the integrity of data management artifacts and products, and management of releases Incorporates CMMI infrastructure processes e.g., Policy, Training

16 For more information Feel free to me: To participate in the peer review of the DMM, type Review DMM in the subject line Our web site :