Mike2.0 Information Governance Overview

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An overview on information governance and the concept of "Governance 2.0"

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Mike2.0 Information Governance Overview

  1. 1. Information Governance Solution Offering Overview Introducing MIKE2.0 (Method for Integrated Knowledge Environment) Sean McClowry IM Solution Suite Architecture and Delivery Lead BearingPoint November 2007
  2. 2. Contents This presentation covers the following Information Management ─ Our View ─ Why Governance is Important Information Governance ─ Where it fits in the Overall Model ─ Guiding Principles ─ Key Activities Getting Started: Information Maturity (IM) QuickScan Information Governance Organisational Models Advanced Techniques: Networked Information Governance 2 MIKE2.0 Methodology A Methodology for Information Development
  3. 3. Our View: IM is a “Complexity Problem” Exponential growth of raw data and information Complexity of data and information is not appreciated; they are in constant flux across the enterprise 24 hours a day Efforts to increase visibility and access to relevant data and information are expensive, with insufficient ROI Better standards and transparency are needed to increase confidence and enable opportunities Federation is a significant factor in complexity True business insight still very hard to attain and quality is a huge problem 3 MIKE2.0 Methodology A Methodology for Information Development
  4. 4. Our View: The Solution Demands a Standard Processes and standards for managing and reporting data and information have not kept pace – everyone has “their” way Many problems are solved through informal networks – we need to link formal structures to these networks We want organisations to begin to develop a competency for “Information Development” We aim to re-shape the industry by creating the standard An open and collaborative approach is the key to delivering a standard for such a complex problem 4 MIKE2.0 Methodology A Methodology for Information Development
  5. 5. Our View: Time to Act The problem has been growing for years. Here is why IM is now a Mainstream Issue: High Impact: What is a business without its customers, its products and its employees? Federation: Organisations are becoming increasingly federated and even minor issues with data cause viral problems when propagated across the enterprise. Globalisation: multi-lingual and multi-character set issues, 24x7 data availability, support for multi-channels Compliance initiatives: the War on Terror and corporate scandals in the US have put additional pressures on the enterprises. It’s a big, complex problem: There is much to be gained for vendors of applications, information/integration technology and systems integrators. Information is an Asset: Organisations increasingly see the importance of Information Development. Its not just functions and infrastructure. 5 MIKE2.0 Methodology A Methodology for Information Development
  6. 6. Our View: What is Information Governance? What is Information Governance? “Governancequot; is what information management is mostly all about. Information management is the process by which those who set policy guide those who follow policy. Governance concerns power, and applying an understanding of the distribution and sharing of power to the management of information technologies” [i] What is the right way to apply it? Governance can involve “centralised” power, but traditional push-down models of architecture and standards only provide part of the solution. Implemented the wrong way, governance can hamper innovation and agility. Some standards are needed or we cannot be agile or innovative – we’re always fighting fires. With a foundation of standards, we can distribute power and empower a community to be far more productive. [1] Strausmann, Paul A. Information, Information Management and Governance. (2001) 6 MIKE2.0 Methodology A Methodology for Information Development
  7. 7. Our Approach: Integrated Solutions Info Mgmt Business Info Asset Access, Search Enterprise Data Enterprise Info Architecture, Intelligence Management and Delivery Management Content Mgmt Strategy & Gov Corp Performance Information Lifecycle Enterprise Portals Document Data Warehousing Information Governance Management Management & Info Delivery Management Metric & Dashboard Master Records, Contracts, Service Oriented, EII & Information Security Enterprise Search Design Data Mgmt and IP Management Model Driven Architecture Profitability, Value & Metadata, Taxonomy Customer Data ERP Document Mgmt Enterprise Data Mobile Device Access Pricing Mgmt Cataloging Integration Integration Management Strategy Real Time Workflow Information Data Quality Enterprise Content Digital Asset Customer Decisioning Management Improvement Management Strategy Management Operational Access Monitoring Content Management- Enterprise Information Data Migration Performance Mgmt & Control Web Content Assessment Balance Business Data Center Collaboration Information Mgmt COE Scorecard Management Environments, COI Organisation and Knowledge Capture Shared Service Model HR Performance Mgmt Information System Rewards Usability Data Mining, Analytics Modeling & Simulation Business Activity Monitoring Composite Solution Offerings Info Mgmt Data Driven Information Networked Info Agile Info Enterprise 2.0 Strategy IT Transformation Sharing Governance Development 7 MIKE2.0 Methodology A Methodology for Information Development
  8. 8. Our Approach: An Open Source Methodology MIKE2.0 (Method for an Integrated Knowledge Environment) MIKE2.0 (Method for an Integrated Knowledge Environment) Information Management Framework A comprehensive approach to Enterprise Information Management Much more than a classic methodology: architecture, tools, code Helping to shape new theories on Information Management Core methodology with formal release cycle Governance council Framework for any open method Web / Enterprise 2.0 Developed as part of an open community Can be integrated to internally held and shared content The goal is to develop “the standard” that everyone can map to and help create Open Source (software and content): All content is freely available under the Create Commons (Attribution) License MediaWiki based Have extended MediaWiki and contributed to the community Providing an organizing framework for www.openmethodology.org development of open source IM technologies 8 MIKE2.0 Methodology A Methodology for Information Development
  9. 9. Our Approach: Open Source + Internal Assets Enterprise 2.0 Mashups Open Methodology site Assessment Tools Integrated Approach 9 MIKE2.0 Methodology A Methodology for Information Development
  10. 10. Our Approach: Collaborative Solutions Information Management Solution Suite Delivered through a Collaborative Approach Enterprise Information Management Commercial & Open Source Core Solution Offerings by Solution Capabilities Business Solutions Product Solutions Information Access, Search and Business Intelligence Asset Management Content Delivery Enterprise Data Management Enterprise Content Management Information Strategy, Architecture and Governance Sets the new standard for Information Development through an Open Source Offering 10 MIKE2.0 Methodology A Methodology for Information Development
  11. 11. Our Approach: Supported through a Foundation Information Management Solution Suite Delivered through a Collaborative Approach Enterprise Information Management Commercial & Open Source Solution Capabilities that provide a foundation for Suite Delivery Architecture Framework Business Solutions Governance Framework Product Solutions Overall Information Access, Search and Business Intelligence Usage Model ImplementationAsset Management Guide Content Delivery Enterprise Data Management Content Management Enterprise Foundational Solutions Information Strategy, Architecture and Governance Supporting Assets Sets the new standard for Information Development through an Open Source Offering 11 MIKE2.0 Methodology A Methodology for Information Development
  12. 12. Information Governance: Guiding Principles Build an Information Centric Organisation 1. Accountability. Due the nature of information capture and how it flows across the enterprise, everyone has a role to play in how it is governed. Key roles are filled by senior executives such as the CIO, Information Architects and Data and Content Stewards. 2. Efficient Operating Models. Common standards, methods, architecture and collaborative techniques allow the Governance model to be implemented in a physically central, virtual or offshore model. 3. Senior Leadership. Senior Leaders must align and work towards a common goal of improved information, while appreciating Information Management is still immature as a discipline and be ready for challenges. 12 MIKE2.0 Methodology A Methodology for Information Development
  13. 13. Information Governance: Guiding Principles Treat Information as an Asset 4. Historical Quantification. Common architectural models and tools- based quantitative assessments of data and content are key aspects of establishing a known baseline to move forward. 5. Information Value Assessment. Organizations should provide a mechanism to assign an economic value to the information assets and the resulting impacts of Information Governance practices. 6. A Common Methodology. An Information Governance programme should include a common set of activities, tasks and deliverables to build a competency 7. Standard Models A common definition of terms, domain values and their relationships is one of the fundamental building blocks of Information Governance. 8. Governance Tools. Measuring the effectiveness of an Information Governance program requires tools to capture assets and performance. 13 MIKE2.0 Methodology A Methodology for Information Development
  14. 14. Information Governance: Guiding Principles Be Pragmatic in a Strategic Context 9. Strategic Approach. Improvements will typically be measured over months and years, not days. This model must allow for tactical improvements. 10.Comprehensive Scope. An Information Governance approach should be comprehensive in its scope, covering structured data, unstructured content and the whole lifecycle of information. 11.Architecture. An Information Management architecture should be defined for the current-state, transition points and target vision. 12.Continuous Improvement. It is not always cost-effective to fix all issues in a certain area, but to instead follow the “80/20 rule”. It should re-factor a baseline through audits, monitoring, technology re-factoring and personnel training. 13.Flexibility for Change. While an Information Governance program involves putting standards in place, it must have an inbuilt pragmatism and flexibility for change. 14 MIKE2.0 Methodology A Methodology for Information Development
  15. 15. Networked Information Governance Apply Web2.0/Enterprise.2.0 Principles for Better Governance 14. Collaborative Community. Collaborative technologies can streamline communications to capture content in informal network as well as build the formal. 15. Organizing the Informal Network. Build a content model that is easily populated through user-driven categorization, informal collaboration begins to take on more formal structures. 16. Aggregation of Ideas. Not all good ideas have to come from the inside. Social Computing techniques provide an easy way to bring linked content together. 17. Linking the Informal to Formal. The same principle of applying content categories can be applied to formal governance processes. 18. Searching the Knowledge Network. Enterprise Search techniques should be implemented to make this information easily accessible. 19. Collaborative Asset Management. The maturity of your business and technology assets should be a known quantity and this information easily shared across the organization. 20. Global Standards Bodies. Having an external perspective through a central authority can help to balance competing interests and work to a similar approach. 15 MIKE2.0 Methodology A Methodology for Information Development
  16. 16. The 5 Phases of MIKE2.0 Information Development through the 5 Phases of MIKE2.0 Continuous Implementation Phases Strategic Programme Blueprint is done once 2 3 1 ent ent ent crem Increm Increm In Design Develop Roadmap & Phase 1 Phase 2 Foundation Business Assessment Technology Assessment Activities Deploy Improve Begin Next Increment Phase 3, 4, 5 Improved Governance and Operating Model 16 MIKE2.0 Methodology A Methodology for Information Development
  17. 17. Key Governance Activities The MIKE2.0 approach for improving Data Governance goes across all 5 phases of the methodology. The most critical activities for improving Data Governance are as follows: Activity 1.4 Organisational QuickScan Activity 1.6 Information Governance Sponsorship and Scope Activity 1.7 Initial Information Governance Organisation Activity 2.7 Information Governance Policies Activity 2.8 Information Standards Activity 3.5 Business Scope for Improved Information Governance Activity 3.6 Enterprise Information Architecture Activity 3.7 Root Cause Analysis on Information Governance Issues Activity 3.8 Data Governance Metrics Activity 3.11 Data Profiling Activity 3.12 Data Re-Engineering Activity 5.11 Continuous Improvement - Compliance Auditing Activity 5.12 Continuous Improvement - Standards, Policies and Processes Activity 5.13 Continuous Improvement - Data Quality Activity 5.14 Continuous Improvement - Infrastructure Activity 5.15 Continuous Improvement - Information Development Organization Activity 5.16 Continuous Improvement – MIKE2.0 Methodology Other MIKE2.0 Activities are also relevant, but these are particularly focused on Data Governance 17 MIKE2.0 Methodology A Methodology for Information Development
  18. 18. Key Governance Activities Phase 1. Business Assessment and Strategy Definition Blueprint Quickly Understand Issues Establish Leadership Establish Team Organisational IG Sponsorship Initial IG QuickScan and Scope Organisation • Conduct Information Maturity • Confirm scope of Data Governance • Establishment Data Governance Assessment Program Council • Build Inventory of Information • Confirm in-scope data subject • Assignment of roles and Assets areas responsibilities • Determine Economic Value of • Assign Data Stewards to each • Definition of communications model Information subject area and tracking mechanism • Assess organizational structure, • Re-alignment of Business and people and their skills Technology Strategy An initial gap analysis is developed by assessing the organisation’s current-state issues and vision for the future- state. Data Governance scope driven by high-level information requirements and complemented by the definition of a strategic conceptual architecture. 18 MIKE2.0 Methodology A Methodology for Information Development
  19. 19. Key Governance Activities Phase 2. Technology Assessment and Selection Blueprint Deliver Policy Framework Standards for Implementation Metadata Management Info Governance Info Governance Initiate Metadata- Policies Standards Driven Approach • Definition of Information • Info Specification Standards Metadata Management goes across Governance Policy Requirements multiple activities in MIKE2, through a • Info Modelling Standards metadata-driven architecture • Definition of Information Governance Policies • Info Capture Standards • Get some form of repository and base meta-model in place from the • Approval and Distribution of • Info Security Standards onset Information Governance Policies • Info Reporting Standards • Metadata management for improved DG is more than a data dictionary • The goal is Active Metadata Integration Driven by information management guiding principles, a Policy Framework and common set of Data Standards are created that will be used throughout the implementation program. MIKE2 starts with a reference model for metadata management 19 MIKE2.0 Methodology A Methodology for Information Development
  20. 20. Key Governance Activities Phase 3. Roadmap and Foundation Activities Determine Key Data Elements Overall KDE Architecture Determine Process Issues Business Scope Enterprise Root Cause for Improved Information Analysis of DG Information Architecture Issues Governance • Prevent Issues related to Source • Define Business Process Scope for • Overlay System Architecture on Increment Enterprise Data Model System Edits • Determine KDEs and Prioritize by • Define Master Data Management • Prevent Issues related to Business Business Impact Architecture Process • Capture Recommend Business • Define BusinessTime Model for • Prevent Issues related to Process Changes KDEs Technology Architecture • Define Data Definitions and • Summarize Root Cause Issues and Business Rules Recommend Changes The MIKE2.0 governance approach focused around Key Data Elements (KDEs). These are the subset of data elements that are used to make the most critical business decisions. The Enterprise Information Architecture is built out over time using these KDEs to define a framework for Master Data Management. 20 MIKE2.0 Methodology A Methodology for Information Development
  21. 21. Key Governance Activities Phase 3. Roadmap and Foundation Activities (continued) Assess issues with KDEs Quantitatively Understand DQ Iteratively fix DQ issues Data Governance Data Re- Data Profiling Metrics Engineering • Define Metric Categories and • Prepare for Assessment • Prepare for Re-Engineering Measurement Techniques • Perform Column Profiling • Perform Data Standardization • Gather Current-State Metrics on • Perform Table Profiling • Perform Data Correction each KDE • Perform Multi-Table Profiling • Perform Data Matching and • Define Target Metrics on each KDE Consolidation • Finalize Data Quality Report • Perform Data Enrichment • Finalize Business Summary of Data Quality Impacts Metrics are defined for how data will be measured initially as well as target measures. Data Profiling is used for quantitative estimates and data is re-engineered in an iterative fashion. Artifacts stored in a metadata model. 21 MIKE2.0 Methodology A Methodology for Information Development
  22. 22. Key Governance Activities Phase 5. Develop, Test, Deploy and Improve Continuous Improvement Continuous Improvement Continuous Improvement Standards, Compliance Policies and Data Quality Auditing Processes • Attain Sponsorship of Data • Review and Revise Data • Conduct Ongoing Data Quality Governance Policies Monitoring Governance Board • Review and Revise Data • Associate Data Quality Issues with • Define Compliance Auditing Governance Metrics Root Causes Processes • Review and Revise Data • Execute Issue Prevention Process • Train Staff on Compliance Governance Standards Standards • Review and Revise Data • Conduct Auditing Processes Governance Processes •Present Auditing Results and • Implement Changes as Required Recommendations The MIKE2.0 Methodology is based around the Continuous Improvement. That means that we are continually re- factoring towards the strategic vision and there are planned activities to revisit the existing implementation. 22 MIKE2.0 Methodology A Methodology for Information Development
  23. 23. Key Governance Activities Phase 5. Develop, Test, Deploy and Improve (continued) Continuous Improvement Continuous Improvement Continuous Improvement Information Contribute to Infrastructure Development Open MIKE2.0 Organization Methodology • Re-factor Integration • Move to a Central Architecture and Help improve the overall approach to Infrastructure Delivery Model Data Governance used by our community: • Progressively Automate Processes • Develop Staff and their Skills • Help complete wanted assets • Review and Recommend Physical • Implement Data Governance Infrastructure Changes Incentives • Assist with Peer reviews • Move to a Metadata-Driven • Review and Revise • Propose new core supporting assets Architecture Communications Model • Recommend extensions to overall methodology Be an active collaborator Users of MIKE2.0 are encouraged to be part of an active community. The collaborative environment for MIKE2 allows the core method to be improved over time, whilst within a release cycle and product roadmap for stability. 23 MIKE2.0 Methodology A Methodology for Information Development
  24. 24. Getting Started: QuickScan Assessment Information Development through the 5 Phases of MIKE2.0 Continuous Implementation Phases Strategic Programme Responsible Status Activity 1.4 Organisational QuickScan for Information Blueprint is done once Development nt 2 nt 3 nt 1 eme In creme In creme In cr 1.4.1 Assess Current-State Application Portfolio 1.4.2 Assess Information Maturity Design 1.4.3 Assess Economic Value of Information Develop Roadmap & Phase 1 Phase 2 Foundation 1.4.4 Assess Infrastructure Maturity Business Assessment Technology Assessment Activities Deploy 1.4.5 Assess Key Current-State Information Improve Processes Begin Next Increment 1.4.6 Define Current-State Conceptual Phase 3, 4, 5 Architecture Improved Governance and Operating Model 1.4.7 Assess Current-State People Skills 1.4.8 Assess Current-State Organisational Structure Phase 1 – Business Assessment and Strategy Definition Blueprint 1.4.9 Assemble Findings on People, Organization 1.1Strategic 1.2 Enterprise 1.3 Overall Business and its Capabilities Mobilisation Information Strategy for Management Information Awareness Development 1.4 Organisational 1.5 Future State 1.6 Data Governance QuickScan for Vision for Sponsorship and Information Information Scope Development Management 1.7 Initial Data 1.9 Programme 1.8 Business Governance Review Blueprint Completion Organisation 24 MIKE2.0 Methodology A Methodology for Information Development
  25. 25. Getting Started: QuickScan Assessment Task 1.4.2 is used to conduct an object Information Governance Assessment Task 1.4.2 is used to conduct an object Information Governance Assessment 25 MIKE2.0 Methodology A Methodology for Information Development
  26. 26. Getting Started: QuickScan Assessment Information Maturity Model: Measure Your Data Governance Maturity Level Information Maturity Model: Measure Your Data Governance Maturity Level META Group developed a 5-level Information Maturity Model (IMM) to use as an information maturity guideline. We have extended this model as part of MIKE2.0. High Level 5 It is similar to the Software Capability Maturity Model Optimised (CMM) and focuses initially on data quality. Information Development maturity The key criteria for assessing information maturity is Level 4 being able to measure it. Information Development is Managed a strategic initiative, issues are either prevented or Level 3 corrected at the source, and best-in-class solution Proactive Information managed as architecture is enterprise asset implemented. Focus is on Level 2 and well-developed engineering processes and continuous improvement. Reactive Information organization structure Development is part of exists. the IT charter and Level 1 enterprise management Aware Awareness and processes & exist. action occur in response to issues. Action is either There is awareness system- or that problems exist department-specific. MIKE2.0 uses an objective assessment of your current but the organization and desired information maturity levels to construct a has taken little action program for improving Data Governance. regarding how data is managed. Low Information Accuracy & Organizational Confidence High 26 MIKE2.0 Methodology A Methodology for Information Development
  27. 27. Getting Started: QuickScan Assessment Level 1 Data Governance Organisation – Aware. An Aware Data Governance Organisation knows that the organisation has issues around Data Governance but is doing little to respond to these issues. Awareness has typically come as the result of some major issues that have occurred that have been Data Governance-related. An organisation may also be at the Aware state if they are going through the process of moving to state where they can effectively address issues, but are only in the early stages of the programme. Level 2 Data Governance Organisation – Reactive. A Reactive Data Governance Organisation is able to address some of its issues, but not until some time after they have occurred. The organisation is not able to address root causes or predict when they are likely to occur. quot;Heroesquot; are often needed to address complex data quality issues and the impact of fixes done on a system-by-system level are often poorly understood. Level 3 Data Governance Organisation – Proactive. A Proactive Data Governance Organisation can stop issues before they occur as they are empowered to address root cause problems. At this level, the organisation also conducts ongoing monitoring of data quality to issues that do occur can be resolved quickly. Level 4 Data Governance Organisation – Managed. A Managed Data Governance Organisation has a mature set of information management practices. This organisation is not only able to proactively identify issues and address them, but defines its strategic technology direction in a manner focused on Information Development. Level 5 Data Governance Organisation – Optimal. An Optimal Data Governance Organisation is also referred to as the Information Development Centre of Excellence. In this model, Information Development is treated as a core competency across strategy, people, process, organisation and technology. a 27 MIKE2.0 Methodology A Methodology for Information Development
  28. 28. Data Governance Maturity Moving Up the Maturity Model To formulate, communicate, pilot and deploy a centralised organisation for Information Development is a significant undertaking. The following artifacts from MIKE2.0 can be used to assist in this effort: A comprehensive Role Inventory across aspects of the organisation with associated competencies and metrics A set of Position Descriptions based upon the Role Inventory Organisational Structures populated with these Position Descriptions Create assessment material to support manager and staff assessment of individual competencies Formulate a Gap Analysis based on target Organizational Structure and Role competencies vs. current capabilities To validate the processes and structures of the organization via a pilot script A Training profile for staff A Recruiting profile recommending to fill typical recruiting needs An Organisational Transition Plan across the Data Governance Maturity Model 28 MIKE2.0 Methodology A Methodology for Information Development
  29. 29. Data Governance Organisational Model Level 2 Data Governance Team (FS Institution Example) There is a minimum team structure that should be used for data governance on any project. The example model shows this data governance structure for a Data Warehouse implementation, where the core focus is for risk management. Executive Sponsor Program Manager Source Data Risk Modeling IT System Warehouse Coordinator Team Rep. Managers Delivery Manager Data Quality Data Quality Manager Working Group 29 MIKE2.0 Methodology A Methodology for Information Development
  30. 30. Data Governance Organisational Model Level 2 Data Governance Team – Roles and Responsibilities Role Responsibility Executive Sponsor Strategic oversight of program and related data issues Stakeholders Governance Sponsorship of business cases for remediation efforts Ownership of legacy system-specific issue resolution Legacy System Manager Provision of system SMEs for issue remediation Management of issue escalations to business executives and source system owners program Manager Provision of resources for issue verification and remediation IT Coordinator Overall guidance for technical issue resolution Ensures remediation efforts align with overall data asset architecture Management of internal trouble ticket process for source system remediation Governance Working Group Data Modeling Team Rep Overall guidance for issue prioritisation and functional resolution Provision of risk modeling SMEs for data issue management Management level oversight of data environment, data cleansing activities and deployment Data Asset Delivery Manager Provision of technical data resources Management responsibility for technical deliverables Data Quality Manager Definition of the overall approach for short and long term DQ activities Identification and management of critical DQ issues Coordination of DQ resources Oversight of the execution of DQ testing and reporting 30 MIKE2.0 Methodology A Methodology for Information Development
  31. 31. Data Governance Organisational Model Level 3 Data Governance Team (FS Institution Example) Focused on Data Investigation and Re-Engineering Focused on Data Investigation and Re-Engineering Executive Data Governance Council Sponsor Enterprise Data Warehouse Steering Committee Data Quality Leader Executive Steering Data Strategy & Queue Technical Analysts Overall Coordination of DQM Management (DSQ) Committee Strategy Program Department 5 Function 1 Department 1 Department 4 Department 3 Department 2 (IBD) (eg. Risk) (eg. Equities) (MCD) (IMD) (eg. FID) Data Stewards (End-to-end Responsibility for these Subject Areas) Technical Analysts DQ Analysts Business Analysts Define Standards Compliance Auditing Define Standards Compliance Auditing • Specification • Data Standard Source Data Collaboration • Specification • Data Standard Source Data Collaboration • Data Capture • Business Rule • Data Capture • Source Analysis • Business Rule • Reporting • Source Analysis • Data Management Process • Reporting • Data Management Process • Target Analysis • Target Analysis Define Business Rules Establish Metrics Define Business Rules Establish Metrics Data Modelling • Define • Metric Categories Data Modelling • Define • Metric Categories Collaboration • Test Compliance • Target Ratings Collaboration • Test Compliance • Target Ratings • Source to Logical • Source to Logical Business Process Definition • Volume and performance Issue Management Business Process Definition • Volume and performance Issue Management • Document & Model • Monitor & Report • Document & Model • Monitor & Report Physical Design Physical Design Collaboration Definitions Collaboration Profile & Measure Definitions Profile & Measure • Entities • Track Results • Performance • Entities • Track Results • Performance Characteristics • Attributes • Facilitate Root Cause Analysis Characteristics • Attributes • Facilitate Root Cause Analysis 31 MIKE2.0 Methodology A Methodology for Information Development
  32. 32. Data Governance Organisational Model Level 3 Data Governance Team – Roles and Responsibilities Role Description Time Commitment Full time Executive Sponsor The Executive Sponsor sets initial direction and goals for the program. In an ongoing basis, the Executive Sponsor approves information policy and tracks the progress of quality initiatives compared to target plan. Full time Data Strategy & Queue The DSQ has responsibility for developing Data Quality strategy and policies, as well as Management (DSQ) leadership and supervision for the overall program. Additional responsibilities include approval of identified business process improvements and the communication plan. Full time Data Quality Leader (DQL) The DQL provides day to day leadership over the DQM program. The DQL has significant DQM expertise and is deeply involved in all aspects of the program while also participating in the DQM Executive Steering Committee (which includes considerably approval responsibility). The DQL is also responsible for managing business process improvement and the communication plan. Full time Data Steward Data stewards act as the conduit between IT and the business and accept accountability for data definition, data management process definition, and information quality levels for specific data subject areas. Data stewardship involves taking responsibility for data elements for their end-to-end usage across the enterprise. Full time Technical Analyst Technical Analysts are members of existing project teams that are assigned to the DQM project when specific activities in their project areas are impacted. They provide the technical expertise required to implement new tools or to improve existing systems. Full time Business Analyst Business Analysts are members of the existing Business Units that are assigned to the DQM project when specific activities in their business areas are impacted. They provide the business expertise required to define the usage of key data elements and to improve business processes. Full time Data Quality Analyst Data Quality Analysts are fully dedicated to the DQM project. Their responsibility is to provide expertise on quality improvement best practices and to perform auditing to ensure projects are complying with data quality management processes and standards. Full time Data Owner Data Owners are responsibility for the accuracy of the data in their area of responsibility. For credit-related data, the Account Officers are the data owners. Ideally the data owners would have a single interface into the source systems where key data elements reside. 32 MIKE2.0 Methodology A Methodology for Information Development
  33. 33. Data Governance Organisational Model Level 4 Data Governance Team (FS Institution Example) View Focused on Data Stewardship and Ownership, other teams would include technology and Roles from Level 3 Org View Focused on Data Stewardship and Ownership, other teams would include technology and Roles from Level 3 Org Executive Sponsor DG Steering Committee (Finance, Credit, Enterprise Data Architect, Audit, Retail, C-Level Wholesale, etc. DATA GOVERNANCE COUNCIL Chief Architect XBR Program Manager MDM Enterprise Data Warehouse SYSTEM & PROCESS SYSTEM OF RECORD OWNERS OWNERS BUS DATA CONCEPT IT OWNERS DATA STEWARDS MDM BUS: tbd BUS: tbd BUS: tbd BUS: tbd Business IT: tbd IT: tbd IT: tbd IT: tbd Owner Classification Product Classification Product PRMS CRS New Position New Position BUS: tbd tbd tbd BUS: tbd BUS: tbd BUS: tbd #4 #4 IT: tbd MDM Business IT: tbd IT: tbd IT: tbd Analyst Involved Party Hierarchy Involved Party Hierarchy New Position New Position BUS: tbd BUS: tbd BUS: tbd BUS: tbd tbd tbd #5 #4 IT: tbd IT: tbd IT: tbd IT: tbd Business Analyst – Arrangement Resource Item Arrangement Credit Reports Resource Item New Position New Position tbd BUS: tbd BUS: tbd BUS: tbd BUS: tbd #5 #5 IT: tbd IT: tbd IT: tbd IT: tbd IT Steward Event Event New Position (To Be BUS:tbd BUS: tbd BUS: tbd #5 Assigned) IT: tbd IT: tbd IT: tbd Data Quality Lead Business and Technical Analysts (Pool of Data Quality Analysts Business and Technical Analysts (Pool of resources to be assigned) (Pool of resources to be assigned) resources to be assigned) 33 MIKE2.0 Methodology A Methodology for Information Development
  34. 34. Data Governance Organisational Model Level 4 Data Governance Team – Roles and Responsibilities Role Responsibilities Time Commitment – The Executive Sponsor will set the initial direction and goals for the program. On an ongoing basis, the Executive – <5% Executive Sponsor approves budgets, establishes highest level policies, and monitors information policy Sponsor setting and tracks progress of quality initiatives compared to target plan. – Develop and monitor an overall strategic plan for data quality improvement encompassing all affected Data – Quarterly systems. Plan to include linkage and convergence of existing data warehouse’s and data marts. Governance – Adhoc – Sponsor and champion for data quality initiatives for all systems, LOBs and functions. Ensure scheduling Council meetings as and resource allocation across LOBs needed – Provide data quality feedback and progress across all LOBs, systems and functions – Provide approval, prioritization, sign-off of major data quality initiatives. – Communicate with business segments to ensure expectations for data quality initiatives are in-line with what can be delivered. – Oversight of business planning and requirements process to ensure data quality needs are appropriately addressing the needs of the users. – Resolution of escalated issues. – Responsible for developing Data Governance strategy and policies, as well as leadership and supervision Data – Monthly for the overall program. Governance initially – Active working committee of the Data Governance board. Accountability for executing Board Steering – Move to responsibilities. Committee quarterly – Provide periodic data quality updates to the ITEC and policy committee basis for the – Definition and signoff of project scope, requirements and test results. future – Estimates high level funding needs, requests budget from the executive sponsor. – Approval of identified data quality improvement initiatives. – Will include members of the Lines of Business (Wholesale, Mortgage, Retail, PCS), Finance, IT, Credit Risk Mgt, Company Quality Mgt, Audit, and the Enterprise Data Architect. 34 MIKE2.0 Methodology A Methodology for Information Development
  35. 35. Data Governance Organisational Model Level 4 Data Governance Team – Roles and Responsibilities Role Responsibilities Time Commitment Data Quality – Provides day to day leadership over the data quality program. – Full time Program – Focal point for coordinating System of Record (SOR) owners. – Staff support Manager will be – Guide and support requirements and testing of data quality initiatives needed as – Owner of scorecard process and execution. Provide scorecard feedback to all involved parties including data SOR owners, data concept owners, data stewards and to the Board governance – Ensures execution of policies and strategies of the Data Governance Board and Steering Committee. grows – Review and prioritizes projects, determine funding needs and requests funding approval from the Data Quality Steering Committee – Coordinate the release management program with LOBs and scheduling of data quality and technical projects. – Facilitates the development and training of best practice data quality policies, procedures and methodologies. – Monitors enterprise data quality milestones and performance measures to ensure enterprise-level data quality. Provides feedback to ITEC and all LOBs Enterprise Data – Provides single point of architectural coordination for all Enterprise Data Warehouse related approved – Full time Architect initiatives – Focuses on planning for infrastructure efficiencies, and linkage, cleansing and usage of data, ensures implementation of remediation and the priority of issues – Ensures the compliance and execution of the data governance program policies, processes and procedures across data stewards – Reconciliation, re-creation, metadata design and maintenance Enterprise Data – Ensures the Enterprise Data Warehouse collectively meets the requirements of the business – Full time Warehouse – Coordinates the resolution of issues identified by data concept owners and data stewards. System and – Identifies new funding requirements, assists in prioritizing requests and submits to the data Process Owner governance board for approval – Coordinates on-going data integrity and linkage/usage with source system changes – Coordinates efficient infrastructure investments 35 MIKE2.0 Methodology A Methodology for Information Development
  36. 36. Data Governance Organisational Model Level 4 Data Governance Team – Roles and Responsibilities Role Responsibilities Time Commitment – The data concept owners initially will be senior credit risk management representatives responsible for enforcement of Data Concept – Full time common, enterprise wide business concepts for credit risk data. Owner – Provide business side leadership of data quality improvement initiatives. (Business) – Responsible for business concept definition, requirements definition and sign off, and testing review and sign off. – They are responsible for prioritizing data quality projects and the appropriate use of data elements. – Facilitate coordination required to resolve cross LOB naming and definition issues. – Focuses on administering data policies, defining business rules, defining procedures for the data processes – Responsible for on-going settlement of the Enterprise Data Warehouse with the SOR data. – Oversight of one or more areas of an organization’s information models Data Steward – 50% – Will focus on a particular subject areas (IT) – Provide leadership on the IT side of data quality improvement initiatives by leading combined teams of technical, business and quality analysts – Participate, influence and sign off on data requirements and design of data quality related projects and applications. – Determine how data will be managed – Executes data quality scorecard for data subject areas across affected systems – Provides technology direction for DQ improvement initiatives – Documents and maintains data quality definitions and usage at the concept and data element level on Enterprise Data Warehouse – Accountable to the Data Governance Program Manager for planning and implementing data quality policies, strategies System of – No changes and initiatives at the application level Record (SOR) required to – Shapes, defines, manages and implements initiatives to improve data quality based upon data quality feedback Owners existing – Builds data quality projects into application strategic plan and LOB project funding plans commitment – Provides business analysts and technical analysts to support data quality analysis and implementation levels – Coordinates source system changes – Responsible to exert influence and oversee input processes that feed system ensure consistent inputs in compliance with standards and policies – Partner with Enterprise Data Warehouse System and Process Owner to perform on-going reconciliations of their systems with the Enterprise Data Warehouse 36 MIKE2.0 Methodology A Methodology for Information Development
  37. 37. Data Governance Organisational Model Level 4 Data Governance Team – Roles and Responsibilities Role Responsibilities Time Commitment CDM Business – Responsible for assessment of data quality, remediation requirements and implementation of CDM – Full time Owner – Provides requirements for extensions of Enterprise Data Warehouse data concepts and additional definitions – Identify data quality issues and interacts with the Data Governance Lead for resolution – Assessing the needs of end-users and to ensure the data is collected, aggregated, & reported accurately – Coordinates prioritization of projects for self assessment gaps with Basel Steering Committee – Responsible for on-going settlement of the cubes to the Enterprise Data Warehouse – Initially: 100% CDM IT – Improve and maintain the quality, accessibility and reusability of data and information Steward – Focuses on administering data policies, defining business rules, defining procedures for the data processes – Participate, influence and sign off on data requirements and project design on data quality related projects and application, Executes data quality scorecard for data subject areas across affected systems Data Quality – Manage the data quality analysts and coordinates the tasks for the business and technical analysts. – Full time Lead – Point of contact to the data stewards/owners and the system owners. Will identify the data quality, business and technical analysts needed to execute the data quality policies, processes, etc. – Act as point of contact to the CDM, Enterprise Data Warehouse Stewards, and Systems of Record for small and everyday changes required. Provide expertise on quality improvement best practices and to perform auditing to ensure projects are complying with data quality management processes and standards. Business – Business Analysts are members of the existing LOBs that are assigned to the Governance team when specific activities – As requested Analysts in their business areas are impacted. – Articulate the usage of data elements based on definitions and guidelines by data concept owners – Validate and maintain business rules with the appropriate lines of business – Define data field names, definitions, standards, will be assigned to work with the Data Stewards as necessary. Accountable to the concept owners and/or the system owners Technical – Technical Analysts are members of existing project teams that are assigned to the Governance team when specific – As requested Analysts activities in their project areas are impacted. – Understand data structure – Provide technical expertise required to implement new tools and improve existing systems 37 MIKE2.0 Methodology A Methodology for Information Development
  38. 38. Data Governance Organisational Model Roles of Data Stewards and Data Owners Issue Escalation DG Steering Issue Escalation Committee DATA CONCEPT OWNERS AND Input and STEWARDS coordination (TRAFFIC COPS) with LOB’s on precise data Feedback to definitions Data Concept System Owners Business Owners Close COMPANY Business-IT SOR Owners coordination LOBs on data definitions, quality and standards DQ Lead Data Concept IT Stewards DG Improvement Opportunities New Opportunity Definition 38 MIKE2.0 Methodology A Methodology for Information Development
  39. 39. Data Governance Organisational Model Level 5 - Information Development Centre of Excellence Organisation Framework: Balance of Power In moving to the centralized model for information and infrastructure development, Leadership, Architecture and Delivery must represented on the team. Leadership The key team members across the areas must actively collaborate through formal and informal reporting relationships Architecture Delivery to guide a strategic idea to its realization. It is an organizational model that provides a “balance of power” whilst providing an enabler to: • Align Business and Technology Strategy • Align Strategic and Tactical Objectives • Technology procurement efficiencies • Justify spend based on business case • Balance risk with speed of delivery • A common set of technology standards and policies • Reuse at an enterprise level This has shown to be a very successful model for contemporary IT organizations and complements a centralized approach for the Technology Backplane. It is a model focused on providing solutions for the Business, driven by the needs of the Business. 39 MIKE2.0 Methodology A Methodology for Information Development

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