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Establishing Data Architecture & Governance


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High-level framework and approach for establishing data architecture and governance.

Published in: Technology, Business

Establishing Data Architecture & Governance

  1. 1. Establishing Data Architecture & Governance Rod Dickerson
  2. 2. Presentation Overview Current State Challenges & Impact Proposed Solution & Approach Critical Success Factors Next Steps 2
  3. 3. Current State Data Challenges From the Business Perspective… Impacted Data Challenges Capabilities Organization not aware of all data at its disposal Some data not even inventoried Unknown Data Existence Documentation is lacking Confusion around where to go for certain data Operations Content and meaning of data not fully known Unknown Data Meaning Data not thoroughly understood Agility and Reporting Record key integrity Referential integrity Cardinality integrity Inadequate / Inflexible Data Structures Insertion/deletion anomalies Duplicate or lost entities Current design does not meet information requirements Data is defined differently across applications Data Inconsistencies Multiple formats for same data elements Different meanings for the same code value Multiple codes values with the same meaning Risk Management Missing data and Security Invalid Data Content Wrong data (no constraints applied) Data outside defined domain (overloaded columns) Security / Access Control Data propagation increases risk of exposure & data leakage 3
  4. 4. Current State Data Challenges From the IT Perspective… Impacted Data Challenges Capabilities No common vision and prioritization for data Big picture / long-term view not well understood Lack of Planning and Roadmap No data sourcing (authoritative source) strategy Prioritization and investment model not defined Waste of space to have duplicate data Causes more maintenance head aches Data Redundancy Data changes in one file could cause inconsistencies Compromises data integrity Lack of coordination and central control IT Efficiency Inadequate data responsibility clearly defined No single point of contact for issue resolution Lack of Governance & Stewardship Different applications handle data differently (Process & Data) Ambiguous business rules defined Non-standard file formats Limited data sharing Each application maintains it’s own data Each application program needs to include code for it’s own metadata Program-Data Dependence Each application program must have its own processing routines for reading, inserting, updating, and deleting data 4
  6. 6. The Cause… Disparate Data Cycle A self-perpetuating cycle where disparate data continue to be produced at an ever-increasing rate because people do not know what data exist, or do not want to use it because they don’t understand it, or can’t trust it. 6
  7. 7. Breaking the Cycle… A framework for enabling the Business Why Change is Needed The Way Forward The Payoff Data is correct #1 Data is accurate Data is consistent Data is complete Data is integrated Data values follow the business rules Data corresponds to established BUSINESS DRIVERS domains Agility Data is well defined and understood Ability to introduce new functionality / capability in a timely manner Program-data independence Risk Management Planned data redundancy Gain better control over data environment Improved data consistency IT Efficiency Improved data sharing Reduce / eliminate inefficiencies + decrease Increased application development complexity productivity Enforcement of standards Improved data quality Improved data accessibility and responsiveness Reduced program maintenance Improved decision support 7
  8. 8. High-Level Process 1 Obtain 2 Executive Buy-In and 8 Establish Support Management Maintain the Structure Architecture and Control Define an 3 Governance Architecture Use and Process & Monitor the (Control & Oversight) Approach Architecture 7 Develop Develop the Baseline Transition Data Develop Plan Architecture Target Data 4 Architecture 6 5 8
  9. 9. Defining the Data Architecture What process, people, technology, standards, and Key Components of Data Architecture GOVERNANCE governance do we need to leverage our data asset? What data assets do we have and how are they being used (context) today (by whom and when), with what METADATA MANAGEMENT tools? How should data be organized, persisted, and/or OPERATIONAL distributed in support of business operations? ENABLEMENT What are the key business questions that drive decision DECISION SUPPORT making, and what data is needed to answer them? 9
  10. 10. Addressing the Right Things… Vision View Components Primary Concerns Key Artifacts What process, people, technology, standards, and 1 Data Strategy & Roadmap governance do we need to leverage the data asset? Process Framework GOVERNANCE What cross-organizational structure is required to ensure Governance Framework & 2 data decisions are being made consistently (in alignment Process with the Bank’s strategy)? What data assets (per classification) do we have and how 3 Data Catalog are they being used (context) today (by whom and when)? Inventory Sourcing 4 Data Store Classification What (and where) is the authoritative source of data? METADATA Roadmap 5 Data Steward Directory Ownership Who is responsible (steward) for what data? Stewardship Enterprise Data Model & 6 How should data be organized (designed / modeled)? Standards Organization Distribution Persistence 7 Data Management Plan Where should data be persisted (stored)? OPERATIONAL 8 Data Distribution Strategy How should data be distributed (replicated)? ENABLEMENT 9 Operational Update Patterns In what order should replicated data be updated? Access & Security Data Access Policy and Standards 10 How should data be accessed/secured (in different locations)? What are the key business questions that drive the Bank, Business Intelligence Roadmap 11 and what data is needed to answer them? DECISION Analysis Reporting SUPPORT Enterprise Reporting Strategy 12 Where should data be reported from (with what tools)? Governance 10
  11. 11. Maturing the Capability Moving the Needle… Stage I Stage II Stage III Stage IV Stage V Service Optimizing Chaotic Reactive Proactive Informal Disciplined Standard Predictable “Self” Improving Maturity Processes Processes Process Process Process Repeatable methods Manual, inconsistent Course corrections are Methods improve and gain Improvements are create opportunities for methods that are not applied in certain cases, consistency with predictable, proven, and efficiencies & economies repeatable over time understanding & use intentionally created of scale Data Driven Information Driven Knowledge Driven Focus Application Focused Departmental Focused Enterprise Focused Governance DB Operations, Physical DB Design Enterprise Model, Data Catalog Enterprise Data Program Data Development Data Support Data Stewardship Data Integration Management Roadmap Definition, coordination, Operations Requirements Identification, definition, Identification, modeling, implementation, and Tuning Analysis specification, sourcing, coordination, organization, monitoring of enterprise data Maintenance Modeling and standardization of all distribution, and management Backup/recovery Design data across all LOBs architecting of data shared vision, goals, organization, Archiving Implementation within a specific subject across business areas or processes, policies, plans, area (e.g., customer) the enterprise standards, metrics, audits, and schedules Initial Transitional Goal Asset Ignorance Asset Recognition Leveraged Assets 11
  12. 12. The Plan… Key Components Communication People Process Technology Policy Metadata 1. Stakeholder 1. Establish Decision Rights 1. Stewardship 1. Outline Acceptable 1. Align Policies, 1. Define Enterprise Metadata Communications and Checks-and-Balances Technology and Tools Requirements, and Controls 2. Manage Change 2. Specify Data Quality Usage 2. Measuring and Reporting 2. Establish Accountability Requirements 3. Resolve Issues Value 2. Data Management Tools 3. Stakeholder Support Track I Track II Track III TRACK I PLANNING ARCHITECTURE GOVERNANCE High-Level Iterative Approach Program Prioritization & Data Policies & Training Data / Information Architecture Management Roadmap Governance Standards Communication & Charter & Plan Transformation Plan Current State Future State Stewardship Asset Guidance Education Establish Identify Data Reqs ; Identify Key Identify Major Inventory Current Governance Sourcing; Master Participants; High Level Milestones and Prepare & Conduct State Assets and Structure; Identify Draft and Publish Data Data Stores; Requirements; Goals & Dependencies Crucial Training; Mentor IT & Data; Document Ownership and Management Policies & Interfacing; How Data Objectives; Desired to Implementing Future Business Staff Context & Accountability of Standards is Accessed & From Outcome and Plan State Architecture Semantics Data Assets; Where Monitor & Report Continuous Improvement Iterative Process 12
  13. 13. Data Governance Framework “Strategy” executed by “People” through a set of integrated “Processes” ensures accurate “Data” through “Policies” enabled by “Technology” 1. Strategy 2. executed by “People” 4. ensures accurate “Data” through “Policies” 2. 0 Data Governance Council 4.0 Data Assets 4.1 Rules & Standards are consistent through working with driven from to ensure 2. 1 Data Stewards 4.3 Compliance 4.2 Policies to serve and monitored through 2. 2 Data Stakeholders 4.4 Data Quality 4.5 Performance Metrics 1.1 Organization and Planning 3. through a set of integrated “Processes” 5. enabled by “Technology” 1.0 Strategy and Mission 3. 0 Meetings and Communications 5.0 Business Intelligence Applications establishing 5.1 Data Warehouses and Integration Tools 3. 1 Decision Rights and Controls 5.2 Master Data and Metadata operated via 3. 2 Roles and Responsibilities 5.3 Data Quality Tools 13
  14. 14. Data Governance Structure Executives authorize solutions Stewards and Content Managers and provide issue resolution — represent the Business community. even if they impact They work with dedicated organizational structure or Executive governance managers through project costs and timelines. processes that administer data Leadership based on business rules. • Create standard definitions for data. • Establish authority to create, read, Governance managers update and delete data. are responsible for the • Ensure consistent and appropriate Governance development and usage of data. implementation of the policies, guidelines, and • Provide SME in the resolution of standards for managing data issues the corporation’s data. Proactive & Responsive Processes Stewardship/Quality Management 14
  15. 15. Critical Success Factors Executive Management Commitment & Support Availability of Technical Resources Availability of Business/Data SMEs Data Management (Metadata) Tools and Repository Data Modeling Toolset Business Process Modeling Toolset Data Discovery and Dictionary Toolset Empowerment to Enforce Approved Data Policies & Standards 15
  16. 16. Next Steps Making it Happen… Tentative Timeline Identify Key Participants 30 Days Identify Steering Committee Identify Working Group Participants Hold First Working Session Establish Recurring Schedule Draft Program Charter 45 Days Determine business drivers & requirements Develop vision and objectives Develop guiding principles 60 Days Submit Charter to Steering Committee for Approval Document Current State (v1.0) 90 Days Draft Phase I Recommendation & Plan Present Recommendation & Plan to Steering Committee for Approval 16
  17. 17. THANK YOU! Rod Dickerson 17