Data Governance Keystone of Information Management Initiatives

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By: MOINGP (121 month(s) ago)

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Data Governance: Keystone of Information Management Initiatives:

Data Governance: Keystone of Information Management Initiatives Alan McSweeney

Objectives:

January 9, 2011 2 Objectives To provide an overview of the importance and relevance of data governance as part of an information management initiative

Agenda:

January 9, 2011 3 Agenda Data Management Issues Data Governance and Data Management Frameworks Approach to Data Governance State of Information and Data Governance

Data Governance:

January 9, 2011 4 Data Governance Provides an operating discipline for managing data and information as a key enterprise asset Includes organisation, processes and tools for establishing and exercising decision rights regarding valuation and management of data Elements of data governance Decision making authority Compliance Policies and standards Data inventories Full lifecycle management Content management Records management, Preservation and disposal Data quality Data classification Data security and access Data risk management Data valuation

Data Management Issues:

January 9, 2011 5 Data Management Issues Discovery - cannot find the right information Integration - cannot manipulate and combine information Insight - cannot extract value and knowledge from information Dissemination - cannot consume information Management – cannot manage and control information volumes and growth

Data Management Problems – User View:

January 9, 2011 6 Data Management Problems – User View Managing Storage Equipment Application Recoveries / Backup Retention Vendor Management Power Management Regulatory Compliance Lack of Integrated Tools Dealing with Performance Problems Data Mobility Archiving and Archive Management Storage Provisioning Managing Complexity Managing Costs Backup Administration and Management Proper Capacity Forecasting and Storage Reporting Managing Storage Growth

Information Management Challenges:

January 9, 2011 7 Information Management Challenges Explosive Data Growth Value and volume of data is overwhelming More data is see as critical Annual rate of 50+% percent Compliance Requirements Compliance with stringent regulatory requirements and audit procedures Fragmented Storage Environment Lack of enterprise-wide hardware and software data storage strategy and discipline Budgets Frozen or being cut

Information Management Issues:

January 9, 2011 8 Information Management Issues 52% of users don’t have confidence in their information 59% of managers miss information they should have used 42% of managers use wrong information at least once a week 75% of CIOs believe they can strengthen their competitive advantage by better using and managing enterprise data 78% of CIOs want to improve the way they use and manage their data Only 15% of CIOs believe that their data is currently comprehensively well managed

Data Quality:

January 9, 2011 9 Data Quality Poor data quality costs real money Process efficiency is negatively impacted by poor data quality Full potential benefits of new systems not be realised because of poor data quality Decision making is negatively affected by poor data quality

Information:

January 9, 2011 10 Information Information in all its forms – input, processed, outputs – is a core component of any IT system Applications exist to process data supplied by users and other applications Data breathes life into applications Data is stored and managed by infrastructure – hardware and software Data is a key organisation asset with a substantial value Significant responsibilities are imposed on organisations in managing data

Data, Information and Knowledge:

January 9, 2011 11 Data, Information and Knowledge Data is the representation of facts as text, numbers, graphics, images, sound or video Data is the raw material used to create information Facts are captured, stored, and expressed as data Information is data in context Without context, data is meaningless - we create meaningful information by interpreting the context around data Knowledge is information in perspective, integrated into a viewpoint based on the recognition and interpretation of patterns, such as trends, formed with other information and experience Knowledge is about understanding the significance of information Knowledge enables effective action

Data, Information, Knowledge and Action:

January 9, 2011 12 Data, Information, Knowledge and Action Data Action Knowledge Information

Information is an Organisation Asset:

January 9, 2011 13 Information is an Organisation Asset Tangible organisation assets are seen as having a value and are managed and controlled using inventory and asset management systems and procedures Data, because it is less tangible, is less widely perceived as a real asset, assigned a real value and managed as if it had a value High quality, accurate and available information is a pre-requisite to effective operation of any organisation Information is a high-value asset of any enterprise What do you do when you have something valuable Retain it Protect it Manage it

Data Management and Project Success:

January 9, 2011 14 Data Management and Project Success Data is fundamental to the effective and efficient operation of any solution Right data Right time Right tools and facilities Without data the solution has no purpose Data is too often overlooked in projects Project managers frequently do not appreciate the complexity of data issues

Generalised Information Management Lifecycle:

January 9, 2011 15 Generalised Information Management Lifecycle Design, define and implement framework to manage information through this lifecycle Generalised lifecycle that differs for specific information types Enter, Create, Acquire, Derive, Update, Capture Store, Manage, Replicate and Distribute Protect and Recover Archive and Recall Delete/Remove Manage, Control and Administer

Generalised Information Management Lifecycle:

January 9, 2011 16 Generalised Information Management Lifecycle Need to implement management frameworks and associated solutions to automate the information lifecycle Data Governance Framework Data Architecture to Implement Data Governance Data Infrastructure to Implement Data Architecture Data Operations to Manage Data Infrastructure

Expanded Generalised Information Management Lifecycle:

January 9, 2011 17 Expanded Generalised Information Management Lifecycle Enter, Create, Acquire, Derive, Update, Capture Store, Manage, Replicate and Distribute Protect and Recover Archive and Recall Delete/Remove Design, Implement, Manage, Control and Administer Implement Underlying Infrastructure Plan, Design and Specify Include phases for information management lifecycle design and implementation of appropriate hardware and software to actualise lifecycle

Objectives of Implementing Solutions to Deliver Generalised Information Management Lifecycle:

January 9, 2011 18 Objectives of Implementing Solutions to Deliver Generalised Information Management Lifecycle Establish effective policies for lifecycle enterprise information management to control data growth and lower information management costs Meet service level goals to ensure the timely completion of key business processes for mission-critical applications Support appropriate data retention compliance initiatives and mitigate risk for compliance, audits and legal discovery requests Support appropriate data retention compliance requirements and mitigate risk for compliance, audits and legal discovery requests that keep historical transaction records accessible until legal retention periods expire Implement scalable archiving strategies that easily adapt to ongoing business requirements Improve application portfolio management to decommission redundant applications and simplify the IT infrastructure Manage application information growth and its impact on service levels, operational costs and risks as well as storage requirements Manage data quality, consistency, security, privacy and accuracy

Data and Information Management:

January 9, 2011 19 Data and Information Management Data and information management is a business process consisting of the planning and execution of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets

Data and Information Management:

January 9, 2011 20 Data and Information Management To manage and utilise information as a strategic asset To implement processes, policies, infrastructure and solutions to govern, protect, maintain and use information To make relevant and correct information available in all business processes and IT systems for the right people in the right context at the right time with the appropriate security and with the right quality To exploit information in business decisions, processes and relations

Data Management Goals:

January 9, 2011 21 Data Management Goals Primary goals To understand the information needs of the enterprise and all its stakeholders To capture, store, protect, and ensure the integrity of data assets To continually improve the quality of data and information, including accuracy, integrity, integration, relevance and usefulness of data To ensure privacy and confidentiality, and to prevent unauthorised inappropriate use of data and information To maximise the effective use and value of data and information assets

Data Management Goals:

January 9, 2011 22 Data Management Goals Secondary goals To control the cost of data management To promote a wider and deeper understanding of the value of data assets To manage information consistently across the enterprise To align data management efforts and technology with business needs

Triggers for Data Management Initiative:

January 9, 2011 23 Triggers for Data Management Initiative When an enterprise is about to undertake architectural transformation, data management issues need to be understood and addressed Structured and comprehensive approach to data management enables the effective use of data to take advantage of its competitive advantages

Data Management Principles:

January 9, 2011 24 Data Management Principles Data and information are valuable enterprise assets Manage data and information carefully, like any other asset, by ensuring adequate quality, security, integrity, protection, availability, understanding and effective use Share responsibility for data management between business data owners and IT data management professionals Data management is a business function and a set of related disciplines

Organisation Data Management Function:

January 9, 2011 25 Organisation Data Management Function Business function of planning for, controlling and delivering data and information assets Development, execution, and supervision of plans, policies, programs, projects, processes, practices and procedures that control, protect, deliver, and enhance the value of data and information assets Scope of the data management function and the scale of its implementation vary widely with the size, means, and experience of organisations Role of data management remains the same across organisations even though implementation differs widely

Scope of Complete Data Management Function:

January 9, 2011 26 Scope of Complete Data Management Function

Data Governance:

January 9, 2011 27 Data Governance Capstone of Data Management initiatives Database Architecture Management Data Warehousing and Business Intelligence Management Data Quality Management Data Security Management Metadata Management Data Development Data Operations Management Reference and Master Data Management Document and Content Management Data Governance

Objectives of Data Governance:

January 9, 2011 28 Objectives of Data Governance Guide information management decision-making Ensure information is consistently defined and well understood Increase the use and trust of data as an organisation asset Improve consistency of projects across the organisation Ensure regulatory compliance Eliminate data risks

Shared Role Between Business and IT:

January 9, 2011 29 Shared Role Between Business and IT Data management is a shared responsibility between data management professionals within IT and the business data owners representing the interests of data producers and information consumers Business data ownership is the concerned with accountability for business responsibilities in data management Business data owners are data subject matter experts Represent the data interests of the business and take responsibility for the quality and use of data

Why Develop and Implement a Data Management Framework?:

January 9, 2011 30 Why Develop and Implement a Data Management Framework? Improve organisation data management efficiency Deliver better service to business Improve cost-effectiveness of data management Match the requirements of the business to the management of the data Embed handling of compliance and regulatory rules into data management framework Achieve consistency in data management across systems and applications Enable growth and change more easily Reduce data management and administration effort and cost Assist in the selection and implementation of appropriate data management solutions Implement a technology-independent data architecture

Data Governance and Data Management Frameworks:

January 9, 2011 31 Data Governance and Data Management Frameworks

Data Governance and Data Management Frameworks:

January 9, 2011 32 Data Governance and Data Management Frameworks DMBOK - Data Management Book of Knowledge TOGAF - The Open Group Architecture Framework COBIT - Control Objectives for Information and related Technology

DMBOK, TOGAF and COBIT:

January 9, 2011 33 DMBOK, TOGAF and COBIT TOGAF Defines the Process for Creating a Data Architecture as Part of an Overall Enterprise Architecture COBIT Provides Data Governance as Part of Overall IT Governance DMBOK Provides Detailed for Definition, Implementation and Operation of Data Management and Utilisation Can be a Precursor to Implementing Data Management Can Provide a Maturity Model for Assessing Data Management DMBOK Is a Specific and Comprehensive Data Oriented Framework

DMBOK, TOGAF and COBIT – Scope and Overlap:

January 9, 2011 34 DMBOK, TOGAF and COBIT – Scope and Overlap DMBOK COBIT TOGAF Data Governance Data Architecture Management Data Management Data Migration Data Development Data Operations Management Reference and Master Data Management Data Warehousing and Business Intelligence Management Document and Content Management Metadata Management Data Quality Management Data Security Management

Data Management Book of Knowledge (DMBOK):

January 9, 2011 35 Data Management Book of Knowledge (DMBOK) DMBOK is a generalised and comprehensive framework for managing data across the entire lifecycle Developed by DAMA (Data Management Association) DMBOK provides a detailed framework to assist development and implementation of data management processes and procedures and ensures all requirements are addressed Enables effective and appropriate data management across the organisation Provides awareness and visibility of data management issues and requirements

Data Management Book of Knowledge (DMBOK):

January 9, 2011 36 Data Management Book of Knowledge (DMBOK) Not a solution to your data management needs Framework and methodology for developing and implementing an appropriate solution Generalised framework to be customised to meet specific needs Provide a work breakdown structure for a data management project to allow the effort to be assessed No magic bullet

Data Management-Related Frameworks:

January 9, 2011 37 Data Management-Related Frameworks TOGAF (and other enterprise architecture standards) define a process for arriving an at enterprise architecture definition, including data TOGAF has a phase relating to data architecture TOGAF deals with high level DMBOK translates high level into specific details COBIT is concerned with IT governance and controls: IT must implement internal controls around how it operates The systems IT delivers to the business and the underlying business processes these systems actualise must be controlled – these are controls external to IT To govern IT effectively, COBIT defines the activities and risks within IT that need to be managed COBIT has a process relating to data management Neither TOGAF nor COBIT are concerned with detailed data management design and implementation

TOGAF and Data Management:

January 9, 2011 38 TOGAF and Data Management Phase C1: Data Architecture Phase C2: Solutions and Application Architecture Phase C1 (subset of Phase C) relates to defining a data architecture

TOGAF Phase C1: Information Systems Architectures - Data Architecture - Objectives:

January 9, 2011 39 TOGAF Phase C1: Information Systems Architectures - Data Architecture - Objectives Purpose is to define the major types and sources of data necessary to support the business, in a way that is: Understandable by stakeholders Complete and consistent Stable Define the data entities relevant to the enterprise Not concerned with design of logical or physical storage systems or databases

TOGAF Phase C1: Information Systems Architectures - Data Architecture - Overview:

January 9, 2011 40 TOGAF Phase C1: Information Systems Architectures - Data Architecture - Overview

TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture:

January 9, 2011 41 TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture Data Management Important to understand and address data management issues Structured and comprehensive approach to data management enables the effective use of data to capitalise on its competitive advantages Clear definition of which application components in the landscape will serve as the system of record or reference for enterprise master data Will there be an enterprise-wide standard that all application components, including software packages, need to adopt Understand how data entities are utilised by business functions, processes, and services Understand how and where enterprise data entities are created, stored, transported, and reported Level and complexity of data transformations required to support the information exchange needs between applications Requirement for software in supporting data integration with external organisations

TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture:

January 9, 2011 42 TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture Data Migration Identify data migration requirements and also provide indicators as to the level of transformation for new/changed applications Ensure target application has quality data when it is populated Ensure enterprise-wide common data definition is established to support the transformation

TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture:

January 9, 2011 43 TOGAF Phase C1: Information Systems Architectures - Data Architecture - Approach - Key Considerations for Data Architecture Data Governance Ensures that the organisation has the necessary dimensions in place to enable the data transformation Structure – ensures the organisation has the necessary structure and the standards bodies to manage data entity aspects of the transformation Management System - ensures the organisation has the necessary management system and data-related programs to manage the governance aspects of data entities throughout its lifecycle People - addresses what data-related skills and roles the organisation requires for the transformation

TOGAF Phase C1: Information Systems Architectures - Data Architecture - Outputs:

January 9, 2011 44 TOGAF Phase C1: Information Systems Architectures - Data Architecture - Outputs Refined and updated versions of the Architecture Vision phase deliverables Statement of Architecture Work Validated data principles, business goals, and business drivers Draft Architecture Definition Document Baseline Data Architecture Target Data Architecture Business data model Logical data model Data management process models Data Entity/Business Function matrix Views corresponding to the selected viewpoints addressing key stakeholder concerns Draft Architecture Requirements Specification Gap analysis results Data interoperability requirements Relevant technical requirements Constraints on the Technology Architecture about to be designed Updated business requirements Updated application requirements Data Architecture components of an Architecture Roadmap

COBIT Structure:

January 9, 2011 45 COBIT Structure

COBIT and Data Management:

January 9, 2011 46 COBIT and Data Management COBIT objective DS11 Manage Data within the Deliver and Support (DS) domain Effective data management requires identification of data requirements Data management process includes establishing effective procedures to manage the media library, backup and recovery of data and proper disposal of media Effective data management helps ensure the quality, timeliness and availability of business data

COBIT and Data Management:

January 9, 2011 47 COBIT and Data Management Objective is the control over the IT process of managing data that meets the business requirement for IT of optimising the use of information and ensuring information is available as required Focuses on maintaining the completeness, accuracy, availability and protection of data Involves taking actions Backing up data and testing restoration Managing onsite and offsite storage of data Securely disposing of data and equipment Measured by User satisfaction with availability of data Percent of successful data restorations Number of incidents where sensitive data were retrieved after media were disposed of

COBIT Process DS11 Manage Data:

January 9, 2011 48 COBIT Process DS11 Manage Data DS11.1 Business Requirements for Data Management Establish arrangements to ensure that source documents expected from the business are received, all data received from the business are processed, all output required by the business is prepared and delivered, and restart and reprocessing needs are supported DS11.2 Storage and Retention Arrangements Define and implement procedures for data storage and archival, so data remain accessible and usable Procedures should consider retrieval requirements, cost-effectiveness, continued integrity and security requirements Establish storage and retention arrangements to satisfy legal, regulatory and business requirements for documents, data, archives, programmes, reports and messages (incoming and outgoing) as well as the data (keys, certificates) used for their encryption and authentication DS11.3 Media Library Management System Define and implement procedures to maintain an inventory of onsite media and ensure their usability and integrity Procedures should provide for timely review and follow-up on any discrepancies noted DS11.4 Disposal Define and implement procedures to prevent access to sensitive data and software from equipment or media when they are disposed of or transferred to another use Procedures should ensure that data marked as deleted or to be disposed cannot be retrieved. DS11.5 Backup and Restoration Define and implement procedures for backup and restoration of systems, data and documentation in line with business requirements and the continuity plan Verify compliance with the backup procedures, and verify the ability to and time required for successful and complete restoration Test backup media and the restoration process DS11.6 Security Requirements for Data Management Establish arrangements to identify and apply security requirements applicable to the receipt, processing, physical storage and output of data and sensitive messages Includes physical records, data transmissions and any data stored offsite

COBIT Data Management Goals and Metrics:

January 9, 2011 49 COBIT Data Management Goals and Metrics Backing up data and testing restoration Managing onsite and offsite storage of data Securely disposing of data and equipment Activity Goals Frequency of testing of backup media Average time for data restoration Key Performance Indicators Maintain the completeness, accuracy, validity and accessibility of stored data Secure data during disposal of media Effectively manage storage media Process Goals % of successful data restorations # of incidents where sensitive data were retrieved after media were disposed of # of down time or data integrity incidents caused by insufficient storage capacity Process Key Goal Indicators Backing up data and testing restoration Managing onsite and offsite storage of data Securely disposing of data and equipment Activity Goals Occurrences of inability to recover data critical to business process User satisfaction with availability of data Incidents of noncompliance with laws due to storage management issues IT Key Goal Indicators Are Measured By Are Measured By Are Measured By Drive Drive

Approach to Data Governance:

January 9, 2011 50 Approach to Data Governance

Data Governance:

January 9, 2011 51 Data Governance Core function of Data Management Interacts with and influences each of the surrounding ten data management functions Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets Data governance function guides how all other data management functions are performed High-level, executive data stewardship Data governance is not the same thing as IT governance Data governance is focused exclusively on the management of data assets

Data Governance:

January 9, 2011 52 Data Governance Shared decision making is the hallmark of data governance Requires working across organisational and system boundaries Some decisions are primarily business decisions made with input and guidance from IT Other decisions are primarily technical decisions made with input and guidance from business data stewards at all levels Business Operating Model IT Leadership Capital Investments Research and Development Funding Data Governance Model Enterprise Information Model Information Needs Information Specifications Quality Requirements Issue Resolution Information Management Strategy Information Management Policies Information Management Standards Information Management Metrics Information Management Services Database Architecture Data Integration Architecture Data Warehousing Architecture Metadata Architecture Technical Metadata Decisions Made by Business Management Decisions Made by IT Management

Data Governance:

January 9, 2011 53 Data Governance Data governance is accomplished most effectively as an on-going program and a continual improvement process Every effective data governance program is unique, taking into account distinctive organisational and cultural issues, and the immediate data management challenges and opportunities Data governance is not the same thing as IT governance

Data Governance and IT Governance:

January 9, 2011 54 Data Governance and IT Governance IT Governance makes decisions about IT investments IT application portfolio IT project portfolio IT Governance aligns the IT strategies and investments with enterprise goals and strategies COBIT (Control Objectives for Information and related Technology) provides standards for IT governance Only a small portion of the COBIT framework addresses managing information Some critical issues, such as Sarbanes-Oxley compliance, span the concerns of corporate governance, IT governance, and data governance Data Governance is focused exclusively on the management of data assets Data Governance is at the heart of managing data assets

Data Governance – Definition and Goals:

January 9, 2011 55 Data Governance – Definition and Goals Definition The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets Goals To define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics To track and enforce regulatory compliance and conformance to data policies, standards, architecture, and procedures To sponsor, track, and oversee the delivery of data management projects and services To manage and resolve data related issues To understand and promote the value of data assets

Data Governance - Overview:

January 9, 2011 56 Data Governance - Overview Business Goals Business Strategies IT Objectives IT Strategies Data Needs Data Issues Regulatory Requirements Inputs Business Executives IT Executives Data Stewards Regulatory Bodies Suppliers Intranet Website E-Mail Metadata Tools Metadata Repository Issue Management Tools Data Governance KPI Dashboard Tools Executive Data Stewards Coordinating Data Stewards Business Data Stewards Data Professionals DM Executive CIO Participants Data Policies Data Standards Resolved Issues Data Management Projects and Services Quality Data and Information Recognised Data Value Primary Deliverables Data Producers Knowledge Workers Managers and Executives Data Professionals Customers Consumers Data Value Data Management Cost Achievement of Objectives # of Decisions Made Steward Representation / Coverage Data Professional Headcount Data Management Process Maturity Metrics Data Governance

Data Governance Function, Activities and Sub-Activities:

January 9, 2011 57 Data Governance Function, Activities and Sub-Activities

Data Governance:

January 9, 2011 58 Data Governance Data governance is accomplished most effectively as an on-going program and a continual improvement process Every data governance programme is unique, taking into account distinctive organisational and cultural issues, and the immediate data management challenges and opportunities Data governance is at the core of managing data assets

Data Governance - Possible Organisation Structure:

January 9, 2011 59 Data Governance - Possible Organisation Structure

Data Governance Shared Decision Making:

January 9, 2011 60 Data Governance Shared Decision Making Enterprise Information Model Business Operating Model Information Needs IT Leadership Information Specifications Capital Investments Quality Requirements Research and Development Funding Issue Resolution Data Governance Model Business Decisions IT Decisions Shared Decision Making Database Architecture Enterprise Information Management Strategy Data Integration Architecture Enterprise Information Management Policies Data Warehousing and Business Intelligence Architecture Enterprise Information Management Standards Metadata Architecture Enterprise Information Management Metrics Technical Metadata Enterprise Information Management Services

Data Stewardship:

January 9, 2011 61 Data Stewardship Formal accountability for business responsibilities ensuring effective control and use of data assets Data steward is a business leader and/or recognised subject matter expert designated as accountable for these responsibilities Manage data assets on behalf of others and in the best interests of the organisation Represent the data interests of all stakeholders, including but not limited to, the interests of their own functional departments and divisions Protects, manages, and leverages the data resources Must take an enterprise perspective to ensure the quality and effective use of enterprise data

Data Stewardship - Roles:

January 9, 2011 62 Data Stewardship - Roles Executive Data Stewards – provide data governance and make of high-level data stewardship decisions Coordinating Data Stewards - lead and represent teams of business data stewards in discussions across teams and with executive data stewards Business Data Stewards - subject matter experts work with data management professionals on an ongoing basis to define and control data

Data Stewardship Roles Across Data Management Functions - 1:

January 9, 2011 63 Data Stewardship Roles Across Data Management Functions - 1 All Data Stewards Executive Data Stewards Coordinating Data Stewards Business Data Stewards Data Architecture Management Review, validate, approve, maintain and refine data architecture Review and approve the enterprise data architecture Integrate specifications, resolving differences Define data requirements specifications Data Development Validate physical data models and database designs, participate in database testing and conversion Define data requirements and specifications Data Operations Management Define requirements for data recovery, retention and performance Help identify, acquire, and control externally sourced data Data Security Management Provide security, privacy and confidentiality requirements, identify and resolve data security issues, assist in data security audits, and classify information confidentiality Reference and Master Data Management Control the creation, update, and retirement of code values and other reference data, define master data management requirements, identify and help resolve issues

Data Stewardship Roles Across Data Management Functions - 2:

January 9, 2011 64 Data Stewardship Roles Across Data Management Functions - 2 All Data Stewards Executive Data Stewards Coordinating Data Stewards Business Data Stewards Data Warehousing and Business Intelligence Management Provide business intelligence requirements and management metrics, and they identify and help resolve business intelligence issues Document and Content Management Define enterprise taxonomies and resolve content management issues Metadata Management Create and maintain business metadata (names, meanings, business rules), define metadata access and integration needs and use metadata to make effective data stewardship and governance decisions Data Quality Management Define data quality requirements and business rules, test application edits and validations, assist in the analysis, certification, and auditing of data quality, lead clean-up efforts, identify ways to solve causes of poor data quality, promote data quality awareness

Data Strategy:

January 9, 2011 65 Data Strategy High-level course of action to achieve high-level goals Data strategy is a data management program strategy a plan for maintaining and improving data quality, integrity, security and access Address all data management functions relevant to the organisation

Elements of Data Strategy:

January 9, 2011 66 Elements of Data Strategy Vision for data management Summary business case for data management Guiding principles, values, and management perspectives Mission and long-term directional goals of data management Management measures of data management success Short-term data management programme objectives Descriptions of data management roles and business units along with a summary of their responsibilities and decision rights Descriptions of data management programme components and initiatives Outline of the data management implementation roadmap Scope boundaries

Data Strategy:

January 9, 2011 67 Data Strategy Data Management Scope Statement Goals and objectives for a defined planning horizon and the roles, organisations, and individual leaders accountable for achieving these objectives Data Management Programme Charter Overall vision, business case, goals, guiding principles, measures of success, critical success factors, recognised risks Data Management Implementation Roadmap Identifying specific programs, projects, task assignments, and delivery milestones

Data Policies:

January 9, 2011 68 Statements of intent and fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and information More fundamental, global, and business critical than data standards Describe what to do and what not to do Should be few data policies stated briefly and directly Data Policies

Data Policies:

January 9, 2011 69 Data Policies Possible topics for data policies Data modeling and other data development activities Development and use of data architecture Data quality expectations, roles, and responsibilities Data security, including confidentiality classification policies, intellectual property policies, personal data privacy policies, general data access and usage policies, and data access by external parties Database recovery and data retention Access and use of externally sourced data Sharing data internally and externally Data warehousing and business intelligence Unstructured data - electronic files and physical records

Data Architecture:

January 9, 2011 70 Data Architecture Enterprise data model and other aspects of data architecture sponsored at the data governance level Need to pay particular attention to the alignment of the enterprise data model with key business strategies, processes, business units and systems Includes Data technology architecture Data integration architecture Data warehousing and business intelligence architecture Metadata architecture

Data Standards and Procedures:

January 9, 2011 71 Data Standards and Procedures Include naming standards, requirement specification standards, data modeling standards, database design standards, architecture standards and procedural standards for each data management function Must be effectively communicated, monitored, enforced and periodically re-evaluated Data management procedures are the methods, techniques, and steps followed to accomplish a specific activity or task

Data Standards and Procedures:

January 9, 2011 72 Data Standards and Procedures Possible topics for data standards and procedures Data modeling and architecture standards, including data naming conventions, definition standards, standard domains, and standard abbreviations Standard business and technical metadata to be captured, maintained, and integrated Data model management guidelines and procedures Metadata integration and usage procedures Standards for database recovery and business continuity, database performance, data retention, and external data acquisition Data security standards and procedures Reference data management control procedures Match / merge and data cleansing standards and procedures Business intelligence standards and procedures Enterprise content management standards and procedures, including use of enterprise taxonomies, support for legal discovery and document and e-mail retention, electronic signatures, report formatting standards and report distribution approaches

Regulatory Compliance:

January 9, 2011 73 Regulatory Compliance Most organisations are is impacted by government and industry regulations Many of these regulations dictate how data and information is to be managed Compliance is generally mandatory Data governance guides the implementation of adequate controls to ensure, document, and monitor compliance with data-related regulations.

Regulatory Compliance:

January 9, 2011 74 Regulatory Compliance Data governance needs to work the business to find the best answers to the following regulatory compliance questions How relevant is a regulation? Why is it important for us? How do we interpret it? What policies and procedures does it require? Do we comply now? How do we comply now? How should we comply in the future? What will it take? When will we comply? How do we demonstrate and prove compliance? How do we monitor compliance? How often do we review compliance? How do we identify and report non-compliance? How do we manage and rectify non-compliance?

Issue Management:

January 9, 2011 75 Issue Management Data governance assists in identifying, managing, and resolving data related issues Data quality issues Data naming and definition conflicts Business rule conflicts and clarifications Data security, privacy, and confidentiality issues Regulatory non-compliance issues Non-conformance issues (policies, standards, architecture, and procedures) Conflicting policies, standards, architecture, and procedures Conflicting stakeholder interests in data and information Organisational and cultural change management issues Issues regarding data governance procedures and decision rights Negotiation and review of data sharing agreements

Issue Management, Control and Escalation:

January 9, 2011 76 Issue Management, Control and Escalation Data governance implements issue controls and procedures Identifying, capturing, logging and updating issues Tracking the status of issues Documenting stakeholder viewpoints and resolution alternatives Objective, neutral discussions where all viewpoints are heard Escalating issues to higher levels of authority Determining, documenting and communicating issue resolutions.

Data Management Projects:

January 9, 2011 77 Data Management Projects Data management roadmap sets out a course of action for initiating and/or improving data management functions Consists of an assessment of current functions, definition of a target environment and target objectives and a transition plan outlining the steps required to reach these targets including an approach to organisational change management Every data management project should follow the project management standards of the organisation

Data Asset Valuation:

January 9, 2011 78 Data Asset Valuation Data and information are truly assets because they have business value, tangible or intangible Different approaches to estimating the value of data assets Identify the direct and indirect business benefits derived from use of the data Identify the cost of data loss, identifying the impacts of not having the current amount and quality level of data

State of Information and Data Governance:

January 9, 2011 79 State of Information and Data Governance Information and Data Governance Report, April 2008 International Association for Information and Data Quality (IAIDQ) University of Arkansas at Little Rock, Information Quality Program (UALR-IQ) Ponemon Institute 2009 Annual Study Cost of a Data Breach

Terms Used by Organisations to Describe the Activities Associated with Governing Data:

January 9, 2011 80 Terms Used by Organisations to Describe the Activities Associated with Governing Data

Your Organisation Recognises and Values Information as a Strategic Asset and Manages it Accordingly:

January 9, 2011 81 Your Organisation Recognises and Values Information as a Strategic Asset and Manages it Accordingly

Direction of Change in the Results and Effectiveness of the Organisation's Formal or Informal Information/Data Governance Processes Over the Past Two Years:

January 9, 2011 82 Direction of Change in the Results and Effectiveness of the Organisation's Formal or Informal Information/Data Governance Processes Over the Past Two Years

Perceived Effectiveness of the Organisation's Current Formal or Informal Information/Data Governance Processes:

January 9, 2011 83 Perceived Effectiveness of the Organisation's Current Formal or Informal Information/Data Governance Processes

Actual Information/Data Governance Effectiveness vs. Organisation's Perception:

January 9, 2011 84 Actual Information/Data Governance Effectiveness vs. Organisation's Perception

Current Status of Organisation's Information/Data Governance Initiatives:

January 9, 2011 85 Current Status of Organisation's Information/Data Governance Initiatives

Expected Changes in Organisation's Information/Data Governance Efforts Over the Next Two Years:

January 9, 2011 86 Expected Changes in Organisation's Information/Data Governance Efforts Over the Next Two Years

Focus of Information / Data Governance Efforts:

January 9, 2011 87 Focus of Information / Data Governance Efforts

Overall Objectives of Information / Data Governance Efforts:

January 9, 2011 88 Overall Objectives of Information / Data Governance Efforts

Primary Activities of Organisation's Information / Data Governance Efforts:

January 9, 2011 89 Primary Activities of Organisation's Information / Data Governance Efforts

Primary Drivers for Organisation's Information / Data Governance Efforts:

January 9, 2011 90 Primary Drivers for Organisation's Information / Data Governance Efforts

Category of Tools Currently Used in Organisation:

January 9, 2011 91 Category of Tools Currently Used in Organisation

Functional Area to Which the Leader of the Organisation's Information / Data Governance Effort Reports:

January 9, 2011 92 Functional Area to Which the Leader of the Organisation's Information / Data Governance Effort Reports

Number of Levels Between the Organisation's Most Senior Leader and the Person Most Directly in Charge of the Information / Data Governance Effort:

January 9, 2011 93 Number of Levels Between the Organisation's Most Senior Leader and the Person Most Directly in Charge of the Information / Data Governance Effort

Membership of Senior Information / Data Governance Body within an Organisation:

January 9, 2011 94 Membership of Senior Information / Data Governance Body within an Organisation

Relationship Between Information / Data Governance and Data Quality Leadership:

January 9, 2011 95 Relationship Between Information / Data Governance and Data Quality Leadership

Change In Organisation's Information / Data Quality Over the Past Two Years:

January 9, 2011 96 Change In Organisation's Information / Data Quality Over the Past Two Years

Maturity Of Information / Data Governance Goal Setting And Measurement In Your Organisation:

January 9, 2011 97 Maturity Of Information / Data Governance Goal Setting And Measurement In Your Organisation

Maturity Of Information / Data Governance Processes And Policies In Your Organisation:

January 9, 2011 98 Maturity Of Information / Data Governance Processes And Policies In Your Organisation

Maturity Of Responsibility And Accountability For Information / Data Governance Among Employees In Your Organisation:

January 9, 2011 99 Maturity Of Responsibility And Accountability For Information / Data Governance Among Employees In Your Organisation

Average Per Record Cost of a Data Breach 2005 – 2009 USD:

January 9, 2011 100 Average Per Record Cost of a Data Breach 2005 – 2009 USD

Average Organisational Cost of a Data Breach 2005 – 2009 USD:

January 9, 2011 101 Average Organisational Cost of a Data Breach 2005 – 2009 USD

More Information:

January 9, 2011 102 More Information Alan McSweeney alan@alanmcsweeney.com

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