Data Management Maturity (DMM) SM Model Ecosystem & Deep Dive: Business Glossary

Transcription

1 Data Management Maturity (DMM) SM Model Ecosystem & Deep Dive: Business Glossary Portland DAMA Chapter April 21, 2015 SM DMM model, CMM Integration, SCAMPI, SCAMPI Lead Appraiser, TSP, and IDEAL are service marks of Carnegie Mellon University. CMMI, Capability Maturity Model, Capability Maturity Modeling, CMM, and Carnegie Mellon are registered in the US Patent and Trademark Office by Carnegie Mellon University. For more information on CMU/SEI Trademark use, please visit

2 Presentation Objectives Learn about the DMM Model drivers, themes, concepts and structure How an organization s program is evaluated Complementarity with bodies of knowledge and standards Use cases for the DMM. Learn about the DMM ecosystem How the DMM Helps the DM Professional Training / Certifications DMM Partners / DMM Community Take a core sample of the DMM Deep Dive: Business Glossary (interactive) 2

3 Agenda Introduction to the DMM What is it - why did we develop it Why our industry needs it What is its structure and approach DMM in Action DMM Assessments Use Cases DMM Ecosystem Adoption / Case Studies Training / Certifications Partner Program Business Glossary Exercise 3

4 Data Management Maturity Model History, Description, Structure

5 CMMI Worldwide Capability Building CMMI Quick Stats: Over 10,000 organizations 94 Countries 12 National governments 10 languages 500+ Partners Appraisals in

6 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 involved 320+ practice statements 520+ functional work products 6

7 Fly-Over 7

8 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 our corporate sponsors - Booz Allen Hamilton, Microsoft Corporation, Lockheed Martin, and Kingland Systems Microsoft was CMMI Institute s first Pioneer of the DMM Assessment Microsoft IT, February

9 Foundation for advanced solutions You can accomplish Advanced Data Solutions without proficiency in Basic Data Management Practices, but solutions will: Take longer Cost more Not be extensible Deliver less Present greater risk Fundamental Data Management Practices Data Management Strategy Advanced Data Solutions MDM Analytics Big Data IOT Warehousing SOA Data Integration Metadata Management Data Governance Data Management Function Data Quality Program Copyright 2013 by Data Blueprint 9 9 9

10 DMM Themes Architecture and technology neutral applicable to legacy, DW, SOA, unstructured data environments, mainframe-to-hadoop, etc. Industry independent usable by every organization with data assets, applicable to every industry Emphasis on current state organization is assessed on the implemented data layer and existing DM processes Launch collaborative and sustained capability improvement for the life of the DM program [aka, forever]. If you manage data, the DMM will benefit you 10

11 DMM Structure 11

12 DMM Capability Levels Quality Reuse Clarity Level 5 Level 4 Optimized Measured Level 1 Level 2 Performed Level 3 Managed Defined Risk Ad hoc Stress 12

13 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, lower costs, happier staff Process Areas were designed to stand alone for evaluation Reflects real-world organizations Simplifies the data management landscape for all parties Flexible for multiple purposes 13

14 DMM Structure Core Category Process Area Purpose Introductory Notes Goal(s) of the Process Area Core Questions for the Process Area Related r Process Areas Functional Practices (Levels 1-5) Example Work Products Infrastructure Support Practices Explanatory e Model Components Required for Model Compliance 14

15 The DMM in Action

16 How the DMM SM Helps an Organization Gradated path - step-by-step improvements Common language Shared understanding of progress Acceleration Unambiguous practice statements for clear understanding Functional work products to aid implementation 16

17 How the DMM SM helps the DM Professional Help me to help you quick education for roles, shared concepts, complexity, connectedness Integrated 360 degree view Program level - energizes collaboration, increased involvement of lines of business Actionable and implementable initiatives, grounded in business strategy and organization s imperatives Strong support for business cases for funding of rapid achievements Certification path defined skillset and industry recognition

18 When Should I Employ 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 18

19 Foundation for Business Results Trusted Data demonstrated and independently measured capability to ensure customer confidence in the data assets Improved Risk and Analytics Decisions executing a comprehensive and measured DM strategy ensures decisions are made based on accurate data Cost Reduction/Operational Efficiency identification of current and target states enables elimination of redundant data and streamlining of DM processes and systems Regulatory Compliance independently evaluated and measured DM capabilities to meet industry and regulator requirements 19

20 Starting the Journey - DMM Assessment Method The DMM can be used by small group however, to maximize its value as a catalyst for forging shared perspective and program acceleration our method provides: Interactive launch collaboration event with a broad range of stakeholders Capabilities evaluated collectively by consensus affirmations Facilitates unification of factions - everyone has a voice / role Solicits key business input through supplemental interviews Verifies evaluation with work product reviews (evidence) Report and executive briefing presents Scoring, Findings, Observations, Strengths, and targeted specific Recommendations. Audit-level rigor will be introduced in 2016 to serve as a maturity benchmark, leveraging the CMMI SCAMPI A appraisal method To date, over 300 individuals from business, IT, and data management in early adopter organizations have employed DMM practice by practice, work product by work product - to evaluate their capabilities.

21 DMM Assessment One-Page Sample Organization 21

22 Next Step Sample DM Roadmap Comprehensive and Realistic Roadmap for the Journey 22

23 Summary - Why Do a DMM Assessment? Tour de force learning precisely how you are doing Makes gaps highly visible for all stakeholders e.g. pain awareness, step one in going to the doctor Brings factions and siloed organizations together i.e. making an organization-wide program possible Creates common concepts, perspective, and terminology Creates a shared vision and purpose i.e., lights a path for working together to improve the data assets Priorities begin to clarify Provides a baseline for monitoring progress over time Industry-wide standard begets confidence 23

24 DMM Assessment Drivers Early Adopters Microsoft Integrated Information Management supporting transition to the Real-Time Enterprise, enhance data governance Fannie Mae Validation of EDM program and governance, discovery for new business priorities Federal Reserve System Statistics Validation of inherent strengths, discovery of gaps, leverage capabilities across the Banks Ontario Teachers Pension Plan Evaluation of well-rounded program, voice of the customer, governance expansion Freddie Mac Evaluation of current state to prepare for a Single- Family-wide data management program launch Global Internet Equipment Manufacturer Enhance corporate strategy to improve Customer data Center for Army Analysis Enhance data asset strength to support analytics for logistics, targeting, deployments for warfighting effort. Every organization will have its own meta-drivers for the Assessment and results 24

25 The Real Time Enterprise Microsoft 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 Business Processes People Information Technology Processes achieve business results People make decisions Decisions are driven by Information Technology speeds the delivery of information [ 25 ] Strategic Enterprise Architecture

26 Microsoft Establishing a Common Data Management Language Data Management Maturity Model [ 26 ]

27 Velocity Microsoft Maturity Levels Related to Real Time Data Real Time Competitive Advantage Level 5 Optimized Processes are improved on a continuous basis and advocated at the executive management level. Operational Effectiveness Level 4 Measured Established metrics. Variance management across the process lifecycle. Batch Enabling Capabilities Level 1 Level 2 Performed Level 3 Managed Defined Established processes, improved over time. Tailored to meet specific needs predictably and consistently. Formalized processes. Infrastructure supports at business unit level. Clearly defined roles and responsibilities. Ad-hoc processes. Emphasis on data repair. Transitory improvements. [ 27 ] Strategic Enterprise Architecture

28 Microsoft CMMI Assessment Recommendations Unified effort to maximize data sharing and quality Monitor and measure adherence to data standards Data Management Strategy Data Management Operations Map key business processes to data Leverage Meta Data repository Integrate data governance structures Prioritize policies, processes, standards, to support corporate initiatives Data Governance Data Quality Platform & Architecture Leverage best practices for data archival and retention Maximize shared services utilization Top-down approach to prioritization Up-stream error prevention Common Data Definitions [ 28 ] Strategic Enterprise Architecture

29 Key Lessons Microsoft 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! Microsoft IT Annual Report may be found at: [ 29 ] Strategic Enterprise Architecture

30 DMM Ecosystem Organizations & Professionals

31 2015 Building the DMM Ecosystem Results / Assets Partner Program / Outreach Certifications Product Suite DMM 31

32 DMM Ecosystem - Product Suite Overview Results / Assets Partner Program / Outreach Certifications Product Suite DMM Data Management Maturity (DMM) Model o Comprehensive document with descriptions, practice statements and work products 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 32

33 DMM Ecosystem Training Results / Assets Partner Program / Outreach Certifications Product Suite DMM Training Classes Building EDM Capabilities (3 days) elearning Building EDM Capabilities (self-paced, web-based) Mastering EDM Capabilities (5 days) Enterprise Data Management Expert (5 days) Future EDM Lead Appraiser (5 days) 33

34 Building EDM Capabilities Schedule DAY 1 DAY 2 DAY 3 Module 1 Course Introduction Module 2 DMM Model and Method Module 3 Category: Data Management Strategy Module 4 Category: Data Governance Module 5 Category: Data Quality Module 6 Category: Data Operations Module 7 Category: Platform and Architecture Module 8 Category: Supporting Processes Module 9 Capability Implementation & Process Improvement Module 10 Wrap-up 34

35 Advanced Schedule DAY 1 DAY 2 DAY 3 DAY 4 DAY 5 Module 1 Course Introduction Module 2 Interpreting the Model Module 3 Work Products Module 4 Governance Management: Deep Dive Module 6 Level 3 Module 7 Business Glossary: Deep Dive Module 8 Metadata Management Module 9 Key Process Areas Module 10 Related Process Areas Module 11 Data Quality Program: Team Module 12 Measurement and Metrics Module 13 DM Strategy Concepts Module 14 DQ Program: Team Presentations Module 15 Consulting Skills Module 16 Change Management Module 17 DMS DMF COM Module 18 DMM Future State Module 19 EDME Class Preview & Application Module 5 Data Management Responsibilities 35

36 EDME Schedule DAY 1 DAY 2 DAY 3 DAY 4 DAY 5 Module 1 Introduction Module 2 EDME Role /Assessment Drivers / Benefits Module 3 Scoping the Assessment Module 4 Client Preparation Module 5 Communications, Negotiations, Client Management Module 6 Assessment Preparation Module 7 Presentation Skills Module 8 Workshop Prep Module 9 Conducting the Workshop Module 10 Facilitation Skills Module 11 Interviewing Skills Module 12 Work Product Review Module 13 Schedule Options Module 14 Mock Assessment Prep Module 15 Mock Assessment Execution (Individual) Module 16 Synthesizing Results Module 17 Final Report & Executive Briefing Module 18 Briefing Presentation / Prepare Report and Briefing Module 19 Present Executive Briefing (Team) Module 20 Post-Assessment Consulting Module 21 Consulting Skills / Change Management Module 22 EDME Certification / Wrap-up 36

37 DMM Ecosystem - Certifications Results / Assets Partner Program / Outreach Certifications Product Suite Certifications: Credentials and Credibility Enterprise Data Management Expert (EDME) Assessing and Launching the DM Journey DMM Lead Appraiser (DMM LA) Benchmarking and Monitoring Improvements DMM 37

38 DMM Ecosystem Partner Program Results Reporting Partner Program / Outreach Certifications Product Suite DMM 38

39 DMM Ecosystem Results and Assets Results Results / Assets Partner Program / Outreach Certifications Product Suite DMM Benchmarking Web publication of approved appraisals Case studies Best Practice Examples DMM Assets Translations (#1 Portuguese) Seminars Self-Assessment Tool Profiles White Papers Academic Courses 39

40 DMM Events thru Jul 2015 First EDME Course Apr DC Building EDM Capabilities elearning April 14 Enterprise Data World Mar 30 Apr 2 DMM Seminar with Peter Aiken DMM Case Studies Freddie Mac, FRS Statistics Building EDM Capabilities - May Seattle Building EDM Capabilities May Toronto DGIQ Jun 8-11 Assertive Data Quality Seminar Mastering Capabilities Jun DMM Intro Jul Dublin / Trinity College EDME Jul Webinar CMMI Institute, May Webinar - DataVersity, July DMM White Paper series - DataVersity 40

41 Deep Dive / Exercise Business Glossary

42 When You Focus on Your Shared Data.. You may have discovered that there are: No authoritative data stores, just many data stores with overlapping data Various views about critical data element sets, differing values for the same element, etc. challenging to be definitive Lack of agreement on names, definitions Varying calculations used around the firm Disagreements about what, how much, and the extent of data description and lineage needed for reporting and audit Etc., etc. etc. (complexity and issues) 42

43 the business may not agree about the data We ll explore one selected DMM process area as an example of what an organization needs to do Business Glossary A common understanding of terms and definitions about data supporting business processes for all stakeholders Provides a shared, approved foundation for understanding and integration of data across the organization. Each term refers to a specific, atomic fact; each definition is unique Properties (facts) are standardized and applied to each term. 43

44 Why create a business glossary? Creates clarity among lines of business, which improves communications by resolving diverse uses of terms for data models, EDW, data marts, ontologies, reports, etc. Everyone understands everyone else standardized usage Meaning is clear and unambiguous for every business process Critical for effective design of integrated data Data models are higher quality and accomplished more quickly Deepens stakeholder understanding of the data Foundation for all metadata central core Anchor point - data lineage Starting point - mapping data to business processes 44

45 And more reasons why. Improves quality and shortens time to delivery of rearchitected data layer components Repositories and master data hubs Internal and external data interfaces Shared data services Perfect project for start-up data governance groups substantive task, clear outcomes Allows development of confident, accurate analytics / reporting. 45

46 Business Glossary The business glossary provides a shared, approved foundation for understanding and integration of data across the organization. It is an important support for: Risk analysis and reporting Data integration Metadata management Data quality assessments and profiling Data store consolidation, custom-to-cots migrations Business process improvement and automation Compliance and audit and just about every other data-related initiative. 46

47 Business Glossary is the Anchor Metadata Business Glossary Business Rules Quality Rules 47

48 Business Glossary (BG) Governance Management Data Governance Metadata Management Business Glossary 48

49 Business Glossary (BG) Purpose Supports a common understanding of terms and definitions about structured and unstructured data supporting business processes for all stakeholders. Definition The business glossary is an approved, governed compendium of business term names and definitions. The process developed will define how the organization creates, approves, updates, and promulgates consistent business terms and definitions, fostering shared data usage across the organization. Consistent terms and definitions, with corresponding metadata, are essential to managing corporate data in the context of meaning. 49

50 BG Goals The language that represents the data is unambiguously aligned with the language of the business. The organization has created a comprehensive, approved business glossary The organization follows the standards for naming, definitions, and metadata associated with business terms. Organization-wide access to the business glossary allows all stakeholders to achieve a common understanding of standard business terms. 50

51 BG Goals - Continued Data governance facilitates the review, approval, and consistent usage of business terms. A compliance and enforcement process ensures consistent application of business terms as new data requirements and projects arise. The organization has a communication plan and process in place for continuous feedback on the usefulness of the glossary to data users and other stakeholders. 51

52 Business Glossary Functional Practices Level 1 Business terms are defined Logical data models refer to business terms Level 2 Business glossary management follows a process Unique business terms Business glossary is linked into new development Business terms are known and accessible Level 3 Governance monitors for compliance Business term change management Progress metrics Monitoring for correctness of business terms New development uses the business glossary Business terms are linked to logical and physical representations Level 4 Metrics-based improvements Standard industry business terms Integrated into metadata repository Level 5 Continuous improvement and contribution to industry standards & best practices Inclusion of business rules and ontology structures 52

53 Capability Levels of BG Level 1 Level 2 Level 3 Level 4 Level 5 Business terms are defined for a particular purpose; logical data model attributes are created from business terms. Business glossary processes for definition, properties, usage, and maintenance are defined; standard terms are published and available; each term has a unique name and definition; data store development, integration, and consolidations use business terms. Approved business terms used in shared repositories, data transfer mechanisms, ontologies, semantic models, etc.; organization-wide governance for glossary compliance; Business terms mapped to logical and physical names; Impact analysis prior to changes; metrics employed to measure progress and compliance. The business glossary is integrated into the organization s metadata repository; it incorporates standard industry terms and properties as appropriate. statistical analysis is performed to assess progress and adjust targets. The business glossary is enhanced with all applicable business rules, and ontology / semantic structures. Optimization techniques are employed to develop extensions; the organization publishes case studies and white papers on effective management of business terms. 53

54 How do you know you re doing a good job? Has your organization created a business glossary of standard business terms? How are business terms, definitions, and corresponding metadata created, approved, verified, and managed? Are business terms referenced as the first step in the design of application data stores and repositories? What role does data governance perform in creating, approving, managing, and updating business terms? Do you cross-reference and map standard business terms to businessspecific usages (synonyms, business unit glossaries, logical attributes, physical data elements, etc.)? Does the organization employ a defined process for stakeholders to provide feedback about business terms? How is the business glossary enhanced to reflect changes and additions? Is a compliance process implemented to ensure that business units and projects are correctly applying business terms? 54

55 What Obstacles Can Hamper Progress? Difficulty to persuade diverse lines of business to shift their terminology and/or map to new approved terms Often left to the logical model and doesn t get promoted Resistance to change of terms Applications always lag behind multi-year transition Difficulty in selling time, scope, and cost Effort to search for, combine, and create approved terms Governance challenge to focus and gain agreements Lack of approved, clear, prioritized phased approach No current or planned central repository Lack of approved standards for business terms and properties. 55

56 Typical Implementation Problems Whose job is it to lead? Often no core shepherd group (e.g., data management function is not sufficiently empowered or resourced) Data Management Function Data Governance Groups Getting organized Agreeing on properties beyond name and definition Agreeing on standards and approach Where to store the data so that it can be efficiently used and maintained Difficulty to persuade diverse lines of business to shift their terminology and/or map to new approved terms Seems overly technical and bookish No one wants to change their databases or reports 56

57 Common Pitfalls During Construction Failing to gain input from / align with key business processes results in: Poor / erratic quality of terms Critical blind spots Disconnection between business and data Failing to start with / align with highly shared data fosters: Disharmony / loss of will Inefficiency / confusion Little recognition for results 57

58 .and What Helps You Succeed? Executive mandate Commitments from all relevant stakeholders Clear governance activities and decisions from the start Phased plan Accounting for major initiatives Aligned with priorities in the data management strategy Scope of each phase specified Central repository - empowers the business Metrics track progress and help maintain momentum Clear issues resolution process - escalation Training for participants (And if you are so fortunate) having an Enterprise Data Model or business area data models 58

59 Flexibility Needed All Avenues will be Taken Top-Down (Subject Area by priority) Middle In (Key data stores) Bottom-Up (Reports) Which stretch of road you choose first depends on: Urgency / key priorities Available staff / data stewards Major initiatives (e.g., EDW redesign, MDM) 59

60 Discovering Terms - System Approach Pros Cons Rich descriptions Easier to validate Data lineage Bias to prior system Deja vu all over again Lots of data model changes Realize benefits quickly 60

61 Subject Area Approach Pros Cons Refined descriptions Surface strategic issues sooner Fosters collaboration Thorough Difficult to validate Data lineage is complex More preparation time Delayed benefit realization 61

62 Sample Business Term Standards - Excerpt Standard Definition Example Descriptive Consistent Unique Business Term name clearly and concisely defines an element in the enterprise Consistent wording in the Business Term name enables users looking at the term to know exactly how the element and its associated attribute are used Business Term name has only one meaning throughout the enterprise Use Issuer Mailing Address or Issuer Shipping Address instead of Address Consistent use of Code (which includes a list of valid values) or Indicator (Yes or No). The use of Flag in a business term name is not allowed. Employee Identification Number refers only to the number generated by the HR System of Record. This is different than the Party Identification Number which is used to identify an organization or individual. Minimal Acronyms No Abbreviations No Special Characters or Numbers Business Term name and/or definition should not include acronyms. However if an acronym is included it: 1) must be unique at the enterprise level; 2) needs to be approved by the Data Governance Group; 3) and must be defined in the acronym repository. Business Term and definition should not contain abbreviations Business Term name and definition cannot contain any special characters. Use of numbers should be limited as well. Valid usage could include industry standards such as EDI and EFT. Invalid usage or usage requiring Governance Group approval might include: SLA (Service Level Agreement) DOAT (Account Termination Date) AMT becomes amount PMT becomes payment CD becomes code Some of the special characters not allowed include underscore (_), number (#), dollar ($), and ampersand (&). Address 1 and Address 2 should be Primary Address and Secondary Address. 62

63 Exercise: Evaluate an Organization s Business Glossary Capabilities

64 Let s put the DMM to Work 1. Please take a few minutes to read the Business Glossary Process Area handout Purpose, Introduction, Goals, and Questions 2. Have an organization in mind (pick one and stick to it) 3. Write your answer to each Practice Statement Fully Met, Partially Met, or Not Met - (F, P, or N) 4. Future work doesn t count e.g. We plan to start our Business Glossary effort next quarter 5. We ll take a class straw poll, statement by statement a collective finger in the wind - How are organizations doing with their Business Glossary? 64

65 Resources and Contact DMM Model DMM Training Schedule and Registration DMM Partner Program Questions about the DMM (Kyle Morton) (Melanie Mecca) 65