Developing a Data Management Strategy Using CMMI Data Maturity Model

Transcription

1 Developing a Data Management Strategy Using CMMI Data Maturity Model Dr. Sanjay Shirude, Ph.D., PMP, CDMP, CBIP ACCEL B I March 18, 2015 DG01

2 Dr. Sanjay Shirude, PH.D., PMP, CDMP, CBIP, CMDM Dr. Sanjay Shirude has +20 years of experience in management of design, development, and deployment of enterprise data management systems. Dr. Shirude has significant expertise simplifying business IT integration by collecting and translating business requirements and objectives for application development, quality control, performance reporting, budgeting, and resource management into technical specifications and process management. As a data management expert, His technical expertise extends into data governance, business case analysis, business intelligence, SOA, and cloud computing. His experience covers Agile, scrum, and SDLC waterfall methodologies; with roles as a program manager, scrum master, product owner, analyst, trainer, and mentor. PhD Management [Information Systems] MS Management Science MS Statistics, Pune University Pune, India PMP, CDMP, CBIP, CMDM 2

3 What is Data Management? 3

4 Hierarchy of Needs 4 4 4

5 Understanding of Data Maturity Model? Physiolo gical Data Performed Safety Information Managed Love/ Belonging Knowledge Defined Esteem Insight Measured Self- Actualiz ation Wisdom Optimized 5

6 CMMI Worldwide Process Improvement CMMI Quick Stats: Over 10,000 organizations 94 countries 12 National governments 10 languages 500 Partners 1373 Appraisals in

7 CMMI Model Portfolio Establish, manage, and deliver services Product development / software engineering Acquire and integrate products / supply chain Workforce development and management 7

8 Data Management Maturity (DMM) SM Model The DMM was released on August 7, years in development 4 sponsoring organizations 50+ contributing authors 70 peer reviewers 80+ organizations 230 content pages 8

9 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 CMMI Institute 9

10 Why the DMM SM is Useful Collaborative Influence The CxO s best friend Lines of business forge a shared perspective Lines of business understand current strengths and weaknesses Lines of business understand their roles Reveals critical needs for the data management program Winning hearts and minds - motivates all parties to collaborate for improvements Pixabay.com DemielHadji 10

11 DMM SM Structure SEI CMMI CMMI Institute 11

12 DMM Process Areas Data Management Strategy Name Data Management Strategy Data Management Strategy Communications Data Management Function Business Case Program 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 12

13 Do I need a Data Management Strategy? Benefits Business Alignment Shared Vision Enhanced Collaboration Path Forward Sustained program support Optimal resource allocation Fosters top-down informed decisions Success Factors Secure active participation of all relevant stakeholder, especially the business Ensure visible and active executive sponsorship Determining which business process drives the DMS Agree on Prioritizations criteria and method Broad-based approval High level sequence plan not too detailed Have a strategy review process 13

14 Data Management Strategy Purpose, Definition, Goals Purpose Defines the vision, goals, and objectives for the data management program, and ensure that all relevant stakeholders are aligned on priorities and the program implementation and management Definition Rationale for the data management program, which defines the aims of the program, identifies the components of the initiative and describes how they fit together Goals Establish maintain and follow a DMS that aligned with organizational strategy approved by all stakeholders, communicated across the organization and reflected in architecture, technology and business planning. Maintains the DMS including goals, objective, priorities and scope for all business areas through data governance program. Develop, monitor, and measure an effective sequence plan for guiding data management program implementation 14

15 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 15

16 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 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 Provider Management Standardization of data sourcing process, SLAs, and management of data provisioning from internal and external sources 16

17 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 DMM also incorporates CMMI infrastructure processes e.g., Policy, Training 17

18 Measurement = Confidence Activity-focused and evidencebased evaluation of the data management program Allows organizations to gauge their data management achievements against peers Fuels enthusiasm and funding for improvement initiatives Pixabay.com stux Enhances an organization s reputation quality and progress 18

19 Guided Navigation to Lasting Solutions - The Data Management Maturity Model Reference model framework of fundamental data management capabilities Measurement instrument for organizations to evaluate capability maturity, identify gaps, and incorporate guidelines for improvements From contributions of many experts, DMM was structured and crafted to leverage the strengths and proven approach of CMMI Conducted DMM Assessments for: Microsoft Corporation; Fannie Mae; Federal Reserve System Statistics Function; Ontario Teachers Pension Plan; and Freddie Mac.,Securities and Exchange Commission; Treasury, Office of Financial Research; and CISCO. 19

20 DMM Maturity Levels Quality Reuse Level 5 Level 4 Optimized Measured Level 1 Level 2 Performed Level 3 Managed Defined Risk Ad Hoc SEI CMMI 20

21 Infrastructure Support Practices = Maturity Level 2 - Institutionalize as a Managed Process Establish an Organizational Policy Plan the Process Provide Resources Assign Responsibility Train People Manage Configurations Identify and Involve Relevant Stakeholders Monitor and Control the Process Objectively Evaluate Adherence Review Status with Higher Level Management Level 3 - Institutionalize Organizational Standards Establish Standards Provide Assets that Support the Use of the Standard Process Plan and Monitor the Process Using a Defined Process Collect Process-Related Experiences to Support Future Use Adopted from CMMI: 21

22 Independent Process Areas Staudt as is cc2.5 Every organization performs data management disciplines What is emphasized is what grows changing priorities Can become piecemeal focus on highest pain, not root causes DMM Process Areas were designed to stand alone for evaluation Reflects real-world organizations Simplifies the data management landscape for all parties Because everything is connected relationships are indicated 22

23 How the DMM SM Helps the Health Care Organization Gradated path - step-by-step improvements Common language Shared understanding of progress Acceleration Unambiguous practice statements for clear understanding SEI CMMI Functional work products to aid implementation 23

24 SEI CMMI 24

25 What the DMM is Not Digknowledge Taliesin as is MorgueFilelicense Not a compendium of all data management knowledge Does not address every topic and subtopic that s important 35+ years of evolution Foundational thinkers Talented vendors Wealth of collective experience Fully mature industry practices. Too much specificity = pages Not a cookbook Doesn t identify the one best way 25

26 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 = maximum value Non-prescriptive technology, architectural approaches, organizational structures, etc. Too much specificity = pages = overwhelming and forces organization into non-optimal solutions Reuse Ad Hoc 26

27 How the DMM SM helps the DM Professional Help me to help you platform for your customers conveys roles, shared concepts, complexity, connectedness Provides an integrated 360 degree view - energizes collaboration, increased involvement of lines of business Actionable and implementable initiatives, grounded in business strategy and organization s imperatives Enhances business cases for funding of rapid achievements Qualifications the A Team for the global standard Certification path defined skillset and industry recognition 27

28 DMM Certification Enterprise Data Management Expert Prerequisites DMM advanced concepts Meet qualifications Application / Resume / Interview Complete course Pass exam Assessment observation DarkoStojanovic as is cc Certification awarded 28

29 DMM The Holistic View Key Business Elements Purpose & Value Strategy & Formulation Goal Setting Structure Control & Feedback Data Management Data Governance Data Management Strategy Data Quality Data Operations Data Platform and Archiecture Rear Tire Front Tire ID30914 Peter Sulonen Cropped CC 2.5 DMM Levels Performed Measured Managed Optimized Defined ID30914 Peter Sulonen Reversed CC

30 DMM The Holistic View Past Experience Data Future Direction Goals Provides Feedback (for rider) ID30914 Peter Sulonen Soft Edge CC 2.5 Project Management (which the organization/ rider directs) 30

31 Questions Data Performed Information Managed Knowledge Defined Insight Measured Wisdom Optimized Physiol ogical Data Performed Safety Information Managed Love/ Belongin g Knowledge Defined Esteem Insight Measured Self- Actualiz ation Wisdom Optimized 31

32 Accel BI th Ave Ste Seattle, WA PMO@accelbi.com Phone: (800)

33 Thank You for Attending! For any further questions, feel free to join the Chat Session following this presentation, or contact me outside of ERworld. Dr. Sanjay Shirude, Ph.D. PMP, CDMP, CBIP LinkedIn: Linkedin.com/company/accel-bi Facebook: Facebook.com/AcceleratedBusinessIntegration Please enjoy the rest of your time at ERworld 2015! 33

34 Legal Notice Copyright CA All trademarks, trade names, service marks and logos referenced herein belong to their respective companies. No unauthorized use, copying or distribution permitted. THIS PRESENTATION IS FOR YOUR INFORMATIONAL PURPOSES ONLY. CA assumes no responsibility for the accuracy or completeness of the information. TO THE EXTENT PERMITTED BY APPLICABLE LAW, CA PROVIDES THIS DOCUMENT AS IS WITHOUT WARRANTY OF ANY KIND, INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NONINFRINGEMENT. In no event will CA be liable for any loss or damage, direct or indirect, in connection with this presentation, including, without limitation, lost profits, lost investment, business interruption, goodwill, or lost data, even if CA is expressly advised of the possibility of such damages. 34