Roadmap For 21 st Century Enterprise Data Governance

Transcription

1 Infrastructure Roadmap For 21 st Century Enterprise Data Governance [Getting Enterprise Data Management Strategy Unstuck!] James M Pennington June 2011 The 4-Point Plan: 1. Metaphysical Data Architecture is the strategic and tactical foundation for the objectives of Enterprise Data Governance. Principles, Policy, Standards, and Procedures are brought to life and put into effect through Metaphysical Data Architecture. 2. Metaphysical Data Architecture governs the formation of enterprise master data objects and elements at design-time. 3. Semantic Frameworks and Controlled Value Sets govern semantics at design-time and transaction time. 4. Data Quality Services actively govern element content in enterprise data created at transaction time. The Data Governance Value Objective: Set the foundation for enterprise wide consistency, alignment, integration, interoperability, information quality, greater capability. Create and manage an enterprise data asset, not a liability. Create ease of conformance and add both efficiency and effectiveness benefits to conformance. Enterprise Data Architecture Effectiveness: This is the 21 st Century; it is time to rethink how data is formed. Business and the enterprise and technology have evolved considerably, but data management strategy is stuck. James M Pennington Formation-ISE

2 Generating IT Value Through Data Governance (DG) There are multiple business reasons for and positive impacts of data standards. They include: Data Quality Much has been written about the pervasive issues with data quality. The requisite first thing to address is data standards and standardization on an enterprise wide scale. This is done through Metaphysical Data architecture. Ratchet up the content quality through adding active data quality services and data governance protocols Code Reliability The lowest level of operation in a system involves comparison of bit strings. Lack of standards adds considerably to the risk of incorrect and unintended results. As errors are encountered, more code is written to counter the inconsistencies, but where will they show up next, and when? Complexity Reduction Adding middleware code to deal with lack of integration, situations and multiple data variances and many more physical structures that could have been avoided adds to the complexity in maintenance of systems. Inconsistencies in the data results and reports add complexity and risk to analysis and decision-making, along with confusion and risk to the business. Flexibility A cleaner and consistent code and database environment is easier to manage and modify. Responsiveness New requirements and data changes will occur. Standardization reduces the time and increases the reliability of data quality services or data use. New solutions start with clean, whole, consistent, accurate data. Security, Privacy The number of data structures is much smaller and elements don t propagate into multiple data stores. Agility: Is it easier for the enterprise to run on a solid, adaptive foundation or in quicksand? Note: agile development thinking also has to catch up to the 21 st Century. James M Pennington 2

3 Data Governance Objectives: 1. Set the foundation for enterprise wide consistency, alignment, integration, interoperability, information quality, greater capability. 2. Create and manage an enterprise data asset, not a liability. 3. Establish a solid foundation for agility. 4. Enable continuous high quality data and effective governance when business requirements and applications change. Data Governance Context: Question Who Owns the Data?: Who is Responsible for Data Quality Standards? Who Enforces The Policies and Standards? Who Decides on the Customization of Data Quality Rules? What Are The Methods of Enforcement? How Do We Determine What Data We Need? Answer The Enterprise. Enterprise Data Architecture Enterprise Data Governance Enterprise Data Architecture Policy, Controls, Standards identifying Templates and Data Quality Services Data content is aligned with Enterprise Functional Framework & Master Data The way we think about data and how it is designed and governed has to change. The above answers reflect this new thinking; the goals and target capabilities reflect these changes. DG Strategy and Tactical Capabilities: o ACTIVE UP-FRONT ENTERPRISE GOVERNANCE; NOT PASSIVE o STABLE AND HOLISTIC STANDARDS VIA METAPHYSICAL DATA ARCHITECTURE, o WHOLE ENTERPRISE, ALL DATA, ALL WORK SYSTEMS; SAME STANDARDS FOR ALL APPLICATIONS AND PROJECTS o MASTER DATA FORMS WITH FLEXIBLE (ADDITIVE) CONTENT, DATA MANAGEMENT CAPABILITY o CONSISTENCY THROUGH APPLIED TEMPLATES, PATTERNS, ENTERPRISE DQ SERVICES o GOVERNED CONTENT QUALITY WITH GOVERNANCE PROTOCOLS APPLIED BY THE ENTERPRISE DQ SERVICES James M Pennington 3

4 Figure 1 Roadmap Schematic of Pro-Active Data Governance Strategy Metaphysical Data Architecture: Elements, Context, Enterprise Architectural Objects (Architecture of Enterprise Standards) Base Fields ** (40+) Enterprise Masters (12-20) Standard Context & Semantics Data Design Time Transaction Data Create/Update Time Std Data Data Quality Services (SOA) Governance Protocols ** Base Field is a template with standard meta-data Consistency Metrics Comparison (approximate) Performance Metric This Strategy IT Project Based Number of Tables <1000 >100,000 Number of Element Variations <40+> >50,000 Structures With Business Data >50,000 to 300,000 Ungovernable Content Variations 0 Many per enterprise Point to Point Interfaces 0 Many per enterprise Mapping Translations 0 1 per element DG Manageability Simple Very Difficult ++ James M Pennington 4

5 A Holistic Model Of Element Meta-data Figure 2 Five Dimensions of Element Meta-Data Architectural Object Element Concept Representation Element Purpose Semantic Physical Format Element Concept dominates the Element Architecture; not the Application Project Entity Data Model A complete standard is necessary to be functional on a scale that integrates and enables sophisticated capabilities with rich enterprise data objects. The above architectural model identifies each of the dimensions of a data element, necessary for a comprehensive standard that is scalable enterprise wide. Each element dimension is standardized (has meta-data standards defined) before any elements are created. Those standards are then applied when defining one instance of an element. Elements are created by Enterprise Data Architecture for Master Data Objects. These forms and element patterns drive implementation(s). In the past, IT developers have used pre-defined naming standards and data types as data is created for projects. This is not sufficient for enterprise level element meta-data standards. The level of standardization is far too low, and there is much redundancy and ambiguity. Standards and Architecture are highly connected ideas. Standards create uniformity while architecture defines form, function and fit (alignment). A holistic architecture for defining element meta-data results in a comprehensive base for the meta-data standard. The scope of this document is limited to a data element (hereafter usually referred to with a shortened string of element ). Achieve a minimal set of robust standards by focusing on Enterprise Element Classes as the basis. Focus on Element patterns. Think outside of the box of data models and schema. James M Pennington 5