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DATA MATURITY – A BALANCED APPROACH
JULY 16, 2019
Ron Huizenga
Senior Product Man...
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PRE-FLIGHT...
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Logical Da...
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COMPANIES ...
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MATURITY S...
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WHAT IS MA...
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Technology...
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DATA MODEL...
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Low Produc...
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PROCESS ...
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SUMMARY ...
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ORGANIZA...
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ENTERPRI...
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ADDRESSI...
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DATA VAL...
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DATA - L...
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MODELING...
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BASIC PR...
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EXPANDED...
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GOVERNAN...
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HOW DO W...
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POST FLI...
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Data Maturity - A Balanced Approach

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Most organizations need to awaken to a sobering reality: their data maturity level is much lower than they realize. Organizational maturity is a journey requiring a balanced focus on both data and business process, with checkpoints along the way to ensure you’re on the right path. Ron Huizenga will discuss a continuous improvement approach that balances data and process alignment to achieve breakthrough results for data architecture and governance, using the Data Maturity Model as a benchmark.

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Data Maturity - A Balanced Approach

  1. 1. 1© 2019 IDERA, Inc. All rights reserved. DATA MATURITY – A BALANCED APPROACH JULY 16, 2019 Ron Huizenga Senior Product Manager, Enterprise Architecture & Modeling @DataAviator
  2. 2. 2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2019 IDERA, Inc. All rights reserved. PRE-FLIGHT BRIEFING ▪ Data Ecosystem Complexity ▪ Information capability ▪ Maturity standards ▪ Maturity indicators • Data Maturity • Process Maturity ▪ Supporting considerations • Data value chain • Data lifecycle ▪ Enterprise architecture & governance ▪ Modeling specifics ▪ Summary
  3. 3. 3© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 3© 2019 IDERA, Inc. All rights reserved. Logical Data Lake DATA ECOSYSTEM COMPLEXITY RDBMS DataIngestion Approved Raw Data Sandboxes (Data Science) Raw Transient Data Refinery Refined Data Trusted Data MDM Store Self-serve Analytics & Reporting
  4. 4. 4© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 4© 2019 IDERA, Inc. All rights reserved. COMPANIES ARE FAILING IN THEIR EFFORTS TO BECOME DATA DRIVEN ▪ The percentage of firms identifying themselves as being data-driven has declined in each of the past 3 years • 37.1% in 2017, 32.4% in 2018, 31.0% this year • in spite of increasing investment in big data and AI initiatives • Source: Harvard Business Review, Feb 5, 2019 (Randy Bean and Thomas Davenport) ▪ Very few organizations utilize information to its full potential • Deficiencies in technical capability, skills, lacking data culture • Lack of investment in value-driven information strategies • Very few know how to derive maximum value from information • Source: 2015 PwC/Iron Mountain study: Seizing the Information Advantage
  5. 5. 5© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 5© 2019 IDERA, Inc. All rights reserved. MATURITY STANDARDS AND BOKS! ▪ CMM: Capability Maturity Model (1988) • Sponsored by US Department of Defense • Carnegie Mellon University, Software Engineering Institute • Used as the basis to derive many other standards ▪ Other Standards • DMM: Data Maturity Model • BPMM: Business Process Maturity Model - Object Management Group • COBIT: Control OBjectives for Information and Technology 2000 • ITIL: Information Technology Infrastructure Library 2002 • TQM: Total Quality Management • SPC: Statistical Process Control ▪ The Bodies of Knowledge • DMBOK: Data Management Body of Knowledge • BABOK: Business Analysis Body of Knowledge • PMBOK: Project Management Body of Knowledge
  6. 6. 6© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 6© 2019 IDERA, Inc. All rights reserved. WHAT IS MATURITY? ▪ Mature • “Having reached an advanced stage of development” ▪ Organizational maturity requires: • Data Maturity • Process Maturity • One cannot be achieved without the other! ▪ They are fundamental to: • Enterprise architecture • Governance Data Process
  7. 7. 7© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 7© 2019 IDERA, Inc. All rights reserved. Technology & Infrastructure Information & Strategic Business Enablement HIGH LOW LOW HIGHValue Generation Primary IT Focus Risk Level 0 1 2 3 4 5 Description None Initial Managed Standardized Advanced Optimized Data Governance None Project Level Program Level Division Level Cross Divisional Enterprise Wide Master Data Management no formal master data clasification Non-integrated master data Integrated, shared master data repository Data Management Services Master data stewards established Data stewardship council Data Integration ad-hoc, point to point Reactive, point-to- point interfaces, some common tools, lack of standards common integration platform, design patterns Middleware utilization: service bus, canonical model, business rules, repository Data Excellence Centre (education and training) Data Excellence embedded in corporate culture Data Quality Silos, scattered data, inconsistencies accepted Recognition of inconsistecies but no management plan to address Data cleansing at consumption in order to attempt data quality improvement Data Quality KPI's and conformance visibility, some cleansing at source. Prevention approach to data quality Full data quality management practice Behaviour Unaware / Denial Chaotic Reactive Stable Proactive Predictive Data Maturity Introduction Expansion Transformation
  8. 8. 8© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 8© 2019 IDERA, Inc. All rights reserved. DATA MODEL UTILIZATION Documentation and/or Physical Database Generation (project focused) Conceptual, Logical, Physical (Design) Enterprise including canonical, lineage, governance metadata Full governance metadata, business glossary integration, lifecycle, value- chain Fully integrated modeling, glossaries, metadata, self serve analytics DataMaturity Evolution
  9. 9. 9© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 9© 2019 IDERA, Inc. All rights reserved. Low Productivity High Low Quality High High Risk Low High Waste Low Cost cutting Efficiency Value generation Chaos Management Leadership Introduction Expansion Transformation Level 1 2 3 4 5 Description Initial Managed Standardized Advanced Optimized Focus Individual: people rely on personal methods to accomplish work Proactive: management take responsibility for work unit operations and performance Integrated: standard processes based on best practices in work units Stable: variation reduced - re-use, mentoring, statistical management Systematic: improvements evaluated and deployed using organizational change management Work management Inconsistent: little or no preparation for managing a work unit Managed: balance commitments with resources Adaptable: standard processes tailored for best use in different circumstances Empowered: staff have the process data to evaluate and manage their own work Continual: individuals and workgroups continuously improve capabilities Efficiency Inefficient: few measures for analyzing effectiveness Repeatable: work units use procedures that have proven to be effective Leveraged: common measures and processes. Promote organization wide learning. Multi-functional: advance from functional processes to role based business processes. (ownership) Aligned: performance aligned across the organization to attain strategic objectives Culture Stagnant: no identifiable foundation for commitment and improvement Responsible: work units manage capability to meeting their own commitments. (Silos) Professional: organizational culture emerges from common practices across work units Predictable: metrics in place to predict capability & performance Preventative: Systematic elimination of defects and problem causes Business Process Few activities explicitly defined. Processes lack current state documentation. Basic management processes and controls established to track progress. Processes planned, documented, tactically performed. Process is documented and standardized. Cross functionality understood. Detailed measures of process and output quality. Processes managed, controlled and forecasted using quantitative techniques (and statistical algorithms) Continuous process improvement enabled by quantitative feedback. Processes fully integrated, fluid, highly predictable Decision making Tribal Knowledge, gut-feel decisions, hierarchical structure. Functional process orientation, data driven decisions, quality by inspection Integrated processes, performance metrics, data driven decisions Self service dashboards & analytics, exception management Competitive advantage through best practice innovation. Architecture Disparate IT systems Random services adoption Full service adoption Service Oriented Architecture (SOA) Process driven enterprise Process Maturity
  10. 10. 10© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 10© 2019 IDERA, Inc. All rights reserved. PROCESS MODEL UTILIZATION Documentation Business Process Management (BPM) Process Improvement Process Design Fully Mature (Lean, Six Sigma) ProcessMaturity Evolution
  11. 11. 11© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 11© 2019 IDERA, Inc. All rights reserved. SUMMARY DESCRIPTORS Introduction Expansion Transformation 1 2 3 4 5 Initial Managed Standardized Advanced Optimized Individual initiative, heroics Reactive Integrated Stable Systematic Inconsistent Repeatable Adaptable Empowered Continuous improvement Inefficient Responsible common processes Multi-functional Strategically aligned Stagnant Tactical processes Documented and standardized Predictable Preventative Lacking current state documentation Functional process orientation Integrated processes Detailed quantitative measures Quantitative feedback Tribal knowledge Quality by inspection Performance metrics Self service analytics Fully Integrated Gut-feel decisions Random services adoption Data driven Exception management Competitive advantage Disparate IT systems Basic controls Full service adoption Service Oriented Architecture (SOA) Best practice innovation Cost cutting Project monitoring Efficiency Capability management Value generation Ad hoc Reduced rework Management Automated tactical process steps Leadership Low alignment Typically meet schedules Automated exception reporting Flexible Full governance Unpedictable No business architecture Business silos still exist Measured Business rules Reactive Immature or no data architecture Data management services Controlled Organizational change management Point-to-point interfaces Integrated master data repository Middleware (service bus) Master data stewards Optimizing Non-integrated master data Common integration platform Canonical model Data Excellence center Data Stewardship council Lack of standards Recognize data quality problems Model/metadata repository Prevention approch to data quality Data culture Inconsistencies recognized Data cleansing at consumption Data quality KPI's Proactive Full data quality management Chaotic Design patterns Some data cleansing at source Confident forecasts Predictive Project data governance Program data governance Divisional data governance Cross divisional data governance Enterprise data governance Organizational Maturity
  12. 12. 12© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 12© 2019 IDERA, Inc. All rights reserved. ORGANIZATIONAL MATURITY JOURNEY Not Feasible Not Feasible
  13. 13. 13© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 13© 2019 IDERA, Inc. All rights reserved. ENTERPRISE ARCHITECTURE Enterprise Enablement ApplicationArchitecture BusinessArchitecture TechnicalArchitecture Data Architecture Governance
  14. 14. 14© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 14© 2019 IDERA, Inc. All rights reserved. ADDRESSING GOVERNANCE THROUGH MODELS Data Governance Data Architecture Data Modeling & Design Data Storage & Operations Data Security Data Integration & Interoperability Documents & Content Reference & Master Data Data Warehousing & Business Intelligence MetaData Data Quality
  15. 15. 15© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 15© 2019 IDERA, Inc. All rights reserved. DATA VALUE CHAIN Data Data is the representation of facts as text, numbers, graphics, images sound or video Information Definition Format Timeframe Relevance =+ Information is Data in context. Without context, data is meaningless. Knowledge Patterns & Trends Relationships Assumptions =+ Knowledge is information in perspective, integrated into a viewpoint based upon the recognition and interpretation of patterns (i.e. trends) formed with other information and experience.
  16. 16. 16© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 16© 2019 IDERA, Inc. All rights reserved. HOW DO WE GET FROM DATA TO KNOWLEDGE •Identify data stores •Reverse engineer Where is the data? •Naming standards •Universal mappings to link entity instances What is it? •Visual data lineage •Business process models Where did it come from and how is it used? •Data dictionary •Business Glossary What does it mean? •Reference & master data management •Data Classification •Security classifications •Regulatory policies How do I govern it?
  17. 17. 17© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 17© 2019 IDERA, Inc. All rights reserved. DATA - LIFECYCLE ▪ Describes how a data element is created, read, updated, deleted (CRUD) ▪ Many factors come into play • Business rules • Business processes • Applications ▪ There may be more than 1 way a particular data element is created ▪ Need to model: • Business process • Data lineage • Data flow • Integration • Include Extract Transform and Load (ETL) for data warehouse/data marts and staging areas Create/Collect Classify Store Use/ModifyShare Retain/Archive Destroy
  18. 18. 18© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 18© 2019 IDERA, Inc. All rights reserved. MODELING: BREAK DOWN DATA ECOSYSTEM COMPLEXITY ▪ Data Models • Conceptual • Logical • Physical • Dimensional • Enterprise/Canonical ▪ Visual Data Lineage ▪ Enterprise Data Dictionaries • Naming Standards • Attachments ▪ Metadata Repository • Business Glossaries
  19. 19. 19© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 19© 2019 IDERA, Inc. All rights reserved. DATA MODELING CONTEXT Implementation Models Categories CustomerCustomerDemo CustomerDemographicsCustomers Employees EmployeeTerritories Order Details Orders Products Region Shippers Suppliers Territories Data Warehouse Enterprise Model(s) Enterprise Data Dictionaries ConceptualModels
  20. 20. 20© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 20© 2019 IDERA, Inc. All rights reserved. LINK ENTITY INSTANCES (WHAT AND WHERE) Enterprise Data Model Implementation Model 1 Implementation Model 2 Purchase Order Header Purchase Item Customer Address Address Sales Order Header Manufacture Item City Country Sales Order Line Supplier Address Purchase Order Line Vendor Supplier SuppliersProduct Item Part Customer Client CustomersProvince State State / Province Universal Mappings - Repository
  21. 21. 21© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 21© 2019 IDERA, Inc. All rights reserved. HIGH LEVEL PROCESS CONTEXT
  22. 22. 22© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 22© 2019 IDERA, Inc. All rights reserved. BASIC PROCESS
  23. 23. 23© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 23© 2019 IDERA, Inc. All rights reserved. HOW IS THE DATA USED IN BUSINESS PROCESSES?
  24. 24. 24© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 24© 2019 IDERA, Inc. All rights reserved. EXPANDED PROCESS DETAIL (NEXT LEVEL)
  25. 25. 25© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 25© 2019 IDERA, Inc. All rights reserved. Corporate Accounting Customer Service Human Resources Marketing Sales Supply Chain Governance policies GDPR HIPAA PCI PIPEDA SOX MDM catalog Reference Data Master Data DATA GOVERNANCE LIBRARY
  26. 26. 26© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 26© 2019 IDERA, Inc. All rights reserved. GLOSSARIES & TERMS
  27. 27. 27© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 27© 2019 IDERA, Inc. All rights reserved. GOVERNANCE POLICY CATALOG
  28. 28. 28© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 28© 2019 IDERA, Inc. All rights reserved. REFERENCE DATA SET CATALOG
  29. 29. 29© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 29© 2019 IDERA, Inc. All rights reserved. INTEGRATED MODELING, ENTERPRISE ARCHITECTURE, GOVERNANCE COLLABORATION PLATFORM Enterprise Data Dictionaries Logical & Physical Data Models Dimensional Models Visual Data Lineage Conceptual Data Models Business Process Models Goals & Strategies Applications Business Units Business Rules Stewards Business Glossaries Business Concepts Reference Data Sets Policies Alerts & Notifications Security Follow Capability Discussion Threads Data Sources
  30. 30. 30© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 30© 2019 IDERA, Inc. All rights reserved. HOW DO WE IMPLEMENT LASTING CHANGE? ▪ Have a defined target • Break down into small, sustainable changes • Plan, then execute • Incorporate contingencies • Without a plan, the chance of success is virtually ZERO • “Hope is not a strategy” ▪ Concrete ▪ Measurable ▪ Continuous improvement approach • Evaluate, measure, adjust • Rinse & repeat • Add additional changes in small increments
  31. 31. 31© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 31© 2019 IDERA, Inc. All rights reserved. POST FLIGHT DE-BRIEF ▪ Organizational maturity requires: • Data Maturity • Process Maturity ▪ Modeling is essential! • Data modeling • What, where • Process modeling • How, context • Data lineage • Lifecycle • More knowledge from • Metadata • Business glossaries ▪ Core to enterprise architecture and governance ▪ Approach • Continuous, incremental improvement • Celebrate success! • Repeat
  32. 32. 32© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 32© 2019 IDERA, Inc. All rights reserved. THANKS! Any questions? You can find me at: ron.huizenga@idera.com @DataAviator

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