MANAJEMEN DATA MAGISTER TEKNOLOGI INFORMASI INTRODUCTION TO DATA MANAGEMENT.

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1 MANAJEMEN DATA MAGISTER TEKNOLOGI INFORMASI INTRODUCTION TO DATA MANAGEMENT

2 OBJECTIVES Informing about the nature and importance of data management. To provide an overview of a structured approach to developing and implementing a detailed data management policy including frameworks, standards, project, team and maturity Introduction to Data Management [2017] 2

3 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 Processes PeoplePeopleInfrastructure Information Applications IT Systems Introduction to Data Management [2017] 3

4 DATA, INFORMATION, & 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 Introduction to Data Management [2017] 4

5 DATA, INFORMATION, AND KNOWLEDGE Introduction to Data Management [2017] 5

6 INFORMATION IS AN ORGANIZATION 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 Introduction to Data Management [2017] 6

7 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 SMART – specific, measurable, achievable (or actionable), realistic, and timely, with a specified target timeframe 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 Introduction to Data Management [2017] 7

8 GENERALIZED 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 Man age, Co ntro l and Adm inis ter Introduction to Data Management [2017] 8

9 EXPANDED GENERALIZED MANAGEMENT INFORMATION LIFECYCLE Protect and Recover Archive and Recall Delete/Remove D esign, Im plem ent, Ma nage, Co ntro l and Adm inis ter Plan, Design and Specify Implement Underlying Infrastructure Enter, Create, Acquire, Derive, Update, Capture Store, Manage, Replicate and Distribute Include phases for information management lifecycle design and implementation of appropriate hardware and software to actualise lifecycle Introduction to Data Management [2017] 9

10 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 Introduction to Data Management [2017] 10

11 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 Introduction to Data Management [2017] 11

12 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 Introduction to Data Management [2017] 12

13 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 Introduction to Data Management [2017] 13

14 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 Introduction to Data Management [2017] 14

15 ORGANIZATION 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 Introduction to Data Management [2017] 15

16 SCOPE OF COMPLETE DATA MANAGEMENT FUNCTION Introduction to Data Management [2017] 16 Data Management Functions Data GovernanceData Architecture Management Data DevelopmentData Operations Management Data Security ManagementData Quality Management Reference and Master Data Management Data Warehousing and Business Intelligence Management Document and Content ManagementMetadata Management

17 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 Introduction to Data Management [2017] 17

18 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 Introduction to Data Management [2017] 18

19 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 Introduction to Data Management [2017] 19

20 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 Introduction to Data Management [2017] 20

21 DATA MANAGEMENT-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 21 Introduction to Data Management [2017]

22 DMBOK, TOGAF AND COBIT COBIT Provides Data Governance as Part of Overall IT Governance Can be a Precursor to Implementing Data Management TOGAF Defines the Process for Creating a Data Architecture as Part of an Overall Enterprise Architecture Can Provide a Maturity Model for Assessing Data Management DMBOK Is a Specific and Comprehensive Data Oriented Framework DMBOK Provides Detailed for Definition, Implementation and Operation of Data Management and Utilisation 22 Introduction to Data Management [2017]

23 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 23 Introduction to Data Management [2017]

24 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 Introduction to Data Management [2017] 24

25 DATA MANAGEMENT BODY 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 Introduction to Data Management [2017] 25

26 SCOPE AND STRUCTURE OF DATA MANAGEMENT BOOK OF KNOWLEDGE (DMBOK) Data Management Functions Data Management Environmental Elements Introduction to Data Management [2017] 26

27 DMBOK DATA MANAGEMENT FUNCTIONS Data Management Functions Data GovernanceData Architecture Management Data DevelopmentData Operations Management Data Security ManagementData Quality Management Reference and Master Data Management Data Warehousing and Business Intelligence Management Document and Content ManagementMetadata Management Introduction to Data Management [2017] 27

28 DMBOK DATA MANAGEMENT FUNCTIONS Data Governance - planning, supervision and control over data management and use Data Architecture Management - defining the blueprint for managing data assets Data Development - analysis, design, implementation, testing, deployment, maintenance Data Operations Management - providing support from data acquisition to purging Data Security Management - Ensuring privacy, confidentiality and appropriate access Data Quality Management - defining, monitoring and improving data quality Reference and Master Data Management - managing master versions and replicas Data Warehousing and Business Intelligence Management - enabling reporting and analysis Document and Content Management - managing data found outside of databases Metadata Management - integrating, controlling and providing metadata Introduction to Data Management [2017] 28

29 DMBOK DATA MANAGEMENT FUNCTIONS – SCOPE SUMMARY Introduction to Data Management [2017] 29

30 DMBOK DATA MANAGEMENT FUNCTIONS – SCOPE SUMMARY Data Governance – The exercise of authority, control and shared decision-making (planning, monitoring and enforcement) over the management of data assets. Data Governance is high-level planning and control over data management. Data Architecture Management – The development and maintenance of enterprise data architecture, within the context of all enterprise architecture, and its connection with the application system solutions and projects that implement enterprise architecture. Data Development – The data-focused activities within the system development lifecycle (SDLC), including data modeling and data requirements analysis, design, implementation and maintenance of databases data-related solution components. Database Operations Management – Planning, control and support for structured data assets across the data lifecycle, from creation and acquisition through archival and purge. Data Security Management – Planning, implementation and control activities to ensure privacy and confidentiality and to prevent unauthorized and inappropriate data access, creation or change. Introduction to Data Management [2017] 30

31 DMBOK DATA MANAGEMENT FUNCTIONS – SCOPE SUMMARY Reference & Master Data Management – Planning, implementation and control activities to ensure consistency of contextual data values with a “golden version” of these data values. Data Warehousing & Business Intelligence Management – Planning, implementation and control processes to provide decision support data and support knowledge workers engaged in reporting, query and analysis. Document & Content Management – Planning, implementation and control activities to store, protect and access data found within electronic files and physical records (including text, graphics, image, audio, video) Meta Data Management – Planning, implementation and control activities to enable easy access to high quality, integrated meta data. Data Quality Management – Planning, implementation and control activities that apply quality management techniques to measure, assess, improve and ensure the fitness of data for use. Introduction to Data Management [2017] 31

32 DMBOK DATA MANAGEMENT ENVIRONMENTAL ELEMENTS Data Management Environmental Elements Goals and PrinciplesActivities Primary DeliverablesRoles and Responsibilities Practices and TechniquesTechnology Organisation and Culture Introduction to Data Management [2017] 32

33 DMBOK DATA MANAGEMENT ENVIRONMENTAL ELEMENTS Goals and Principles - directional business goals of each function and the fundamental principles that guide performance of each function Activities - each function is composed of lower level activities, sub-activities, tasks and steps Primary Deliverables - information and physical databases and documents created as interim and final outputs of each function. Some deliverables are essential, some are generally recommended, and others are optional depending on circumstances Roles and Responsibilities - business and IT roles involved in performing and supervising the function, and the specific responsibilities of each role in that function. Many roles will participate in multiple functions Practices and Techniques - common and popular methods and procedures used to perform the processes and produce the deliverables and may also include common conventions, best practice recommendations, and alternative approaches without elaboration Technology - categories of supporting technology such as software tools, standards and protocols, product selection criteria and learning curves Organisation and Culture – this can include issues such as management metrics, critical success factors, reporting structures, budgeting, resource allocation issues, expectations and attitudes, style, cultural, approach to change management Introduction to Data Management [2017] 33

34 DMBOK DATA MANAGEMENT ENVIRONMENTAL ELEMENTS Introduction to Data Management [2017] 34 Goals and Principles Activities Primary Deliverables Roles and Responsibilities Practices and Techniques Technology Organisation and Culture Data Governance Data Architecture Management Data Development Data Operations Management  Scope of Each Data Management Function  Data Security Management Data Quality Management Reference and Master Data Management Data Warehousing and Business Intelligence Management Document and Content Management Metadata Management

35 DMBOK DATA MANAGEMENT ENVIRONMENTAL ELEMENTS – SCOPE SUMMARY Introduction to Data Management [2017] 35

36 BASIC AND SUPPORTING ENVIRONMETAL ELEMENTS Introduction to Data Management [2017] 36

37 SCOPE OF DATA MANAGEMENT BODY OF KNOWLEDGE (DMBOK) DATA MANAGEMENT FRAMEWORK There are 10 functions and 102 activities. There are more than 12 activities within a function, activities have been grouped under two or more sub-functions. Three functions have sub-functions, with 8 sub-functions overall (2, 4 and 2). Functions and sub-functions are named with noun phrases, while activities are named with verb phrases. Hierarchy Function Activity Sub-Activity (not in all cases) Each activity is classified as one (or more) of: Planning Activities (P) Activities that set the strategic and tactical course for other data management activities May be performed on a recurring basis Development Activities (D) Activities undertaken within implementation projects and recognised as part of the systems development lifecycle (SDLC), creating data deliverables through analysis, design, building, testing, preparation, and deployment Control Activities (C) Supervisory activities performed on an on-going basis Operational Activities (O) Service and support activities performed on an on- going basis Introduction to Data Management [2017] 37

38 DMBOK FUNCTION AND ACTIVITY SERVICE Introduction to Data Management [2017] 38 Data Management Data Governance Data Architecture Management Data Development Data Operations Management Data Security Management Data Quality Management Reference and Master Data Management DW and BI Management Document and Content Management Metadata Management Data Management Planning Data Management Control Understand Enterprise Information Needs Develop and Maintain the Enterprise Data Model Analyse and Align With Other Business Models Define and Maintain the Database Architecture Define and Maintain the Data Integration Architecture Define and Maintain the DW / BI Architecture Define and Maintain Enterprise Taxonomies and Namespaces Define and Maintain the Metadata Architecture Data Modeling, Analysis, and Solution Design Detailed Data Design Data Model and Design Quality Management Data Implementation Database Support Data Technology Management Understand Data Security Needs and Regulatory Requirements Define Data Security Policy Define Data Security Standards Define Data Security Controls and Procedures Manage Users, Passwords, and Group Membership Manage Data Access Views and Permissions Monitor User Authentication and Access Behaviour Classify Information Confidentiality Audit Data Security Develop and Promote Data Quality Awareness Define Data Quality Requirement Profile, Analyse, and Assess Data Quality Define Data Quality Metrics Define Data Quality Business Rules Test and Validate Data Quality Requirements Set and Evaluate Data Quality Service Levels Continuously Measure and Monitor Data Quality Manage Data Quality Issues Clean and Correct Data Quality Defects Understand Reference and Master Data Integration Needs Identify Master and Reference Data Sources and Contributors Define and Maintain the Data Integration Architecture Implement Reference and Master Data Management Solutions Define and Maintain Match Rules Establish “Golden” Records Define and Maintain Hierarchies and Affiliations Design and Implement Operational DQM Procedures Monitor Operational DQM Procedures and Performance Plan and Implement Integration of New Data Sources Replicate and Distribute Reference and Master Data Understand Business Intelligence Information Needs Define and Maintain the DW / BI Architecture Implement Data Warehouses and Data Marts Implement BI Tools and User Interfaces Process Data for Business Intelligence Monitor and Tune Data Warehousing Processes Monitor and Tune BI Activity and Performance Documents / Records Management Content Management Understand Metadata Requirements Define the Metadata Architecture Develop and Maintain Metadata Standards Implement a Managed Metadata Environment Create and Maintain Metadata Integrate Metadata Manage Metadata Repositories Distribute and Deliver Metadata Query, Report, and Analyse Metadata Manage Changes to Reference and Master Data

39 DMBOK FUNCTION AND ACTIVITY - PLANNING ACTIVITIES Data Management Data Governance Data Architecture Management Data Development Data Operations Management Data Security Management Data Quality Management Reference and Master Data Management DW and BI Management Document and Content Management Metadata Management Data Management Planning Data Management Control Understand Enterprise Information Needs Develop and Maintain the Enterprise Data Model Analyse and Align With Other Business Models Define and Maintain Define and Maintain the Data Integration Architecture Define and Maintain the DW / BI Architecture Define and Maintain Enterprise Taxonomies and Namespaces Define and Maintain the Metadata Architecture Data Modeling, Analysis, and Solution Design Detailed Data Design Data Model and Design Quality Management the DatabaseData Implementation Architecture Database Support Data Technology Management Understand Data Security Needs and Regulatory Requirements Define Data Security Policy Define Data Security Standards Define Data Security Controls and Procedures Manage Users, Passwords, and Group Membership Manage Data Access Views and Permissions Monitor User Authentication and Access Behaviour Classify Information Confidentiality Audit Data Security Develop and Promote Data Quality Awareness Define Data Quality Requirement Profile, Analyse, and Assess Data Quality Define Data Quality Metrics Define Data Quality Business Rules Test and Validate Data Quality Requirements Set and Evaluate Data Quality Service Levels Continuously Measure and Monitor Data Quality Manage Data Quality Issues Clean and Correct Data Quality Defects Understand Reference and Master Data Integration Needs Identify Master and Reference Data Sources and Contributors Define and Maintain the Data Integration Architecture Implement Reference and Master Data Management Solutions Define and Maintain Match Rules Establish “Golden” Records Define and Maintain Hierarchies and Affiliations Design and Implement Operational DQM Procedures Monitor Operational DQM Procedures and Performance Plan and Implement Integration of New Data Sources Replicate and Distribute Reference and Master Data Understand Business Intelligence Information Needs Define and Maintain the DW / BI Architecture Implement Data Warehouses and Data Marts Implement BI Tools and User Interfaces Process Data for Business Intelligence Monitor and Tune Data Warehousing Processes Monitor and Tune BI Activity and Performance Documents / Records Management Content Management Understand Metadata Requirements Define the Metadata Architecture Develop and Maintain Metadata Standards Implement a Managed Metadata Environment Create and Maintain Metadata Integrate Metadata Manage Metadata Repositories Distribute and Deliver Metadata Query, Report, and Analyse Metadata Manage Changes to Reference and Master Data Introduction to Data Management [2017] 39

40 DMBOK FUNCTION AND ACTIVITY - CONTROL ACTIVITIES Data Management Data Governance Data Architecture Management Data Development Data Operations Management Data Security Management Data Quality Management Reference and Master Data Management DW and BI Management Document and Content Management Metadata Management Data Management Planning Data Management Control Understand Enterprise Information Needs Develop and Maintain the Enterprise Data Model Analyse and Align With Other Business Models Define and Maintain the Database Architecture Define and Maintain the Data Integration Architecture Define and Maintain the DW / BI Architecture Define and Maintain Enterprise Taxonomies and Namespaces Define and Maintain the Metadata Architecture Data Modeling, Analysis, and Solution Design Detailed Data Design Data Model and Design Quality Management Data Implementation Database Support Data Technology Management Understand Data Security Needs and Regulatory Requirements Define Data Security Policy Define Data Security Standards Define Data Security Controls and Procedures Manage Users, Passwords, and Group Membership Manage Data Access Views and Permissions Monitor User Authentication and Access Behaviour Classify Information Confidentiality Audit Data Security Develop and Promote Data Quality Awareness Define Data Quality Requirement Profile, Analyse, and Assess Data Quality Define Data Quality Metrics Define Data Quality Business Rules Test and Validate Data Quality Requirements Set and Evaluate Data Quality Service Levels Continuously Measure and Monitor Data Quality Manage Data Quality Issues Clean and Correct Data Quality Defects Understand Reference and Master Data Integration Needs Identify Master and Reference Data Sources and Contributors Define and Maintain the Data Integration Architecture Implement Reference and Master Data Management Solutions Define and Maintain Match Rules Establish “Golden” Records Define and Maintain Hierarchies and Affiliations Design and Implement Operational DQM Procedures Plan and Implement Integration of New Data Sources Replicate and Distribute Reference and Master Data Understand Business Intelligence Information Needs Define and Maintain the DW / BI Architecture Implement Data Warehouses and Data Marts Implement BI Tools and User Interfaces Process Data for Business Intelligence Monitor and Tune Data Warehousing Processes Monitor and Tune BI Activity and Performance Documents / Records Management Content Management Understand Metadata Requirements Define the Metadata Architecture Develop and Maintain Metadata Standards Implement a Managed Metadata Environment Create and Maintain Metadata Integrate Metadata Manage Metadata Repositories Distribute and Deliver Metadata Query, Report, and Analyse Metadata Manage Changes to Reference and Master Data Monitor Operational DQM Procedures and Performance 40 Introduction to Data Management [2017]

41 DMBOK FUNCTION AND ACTIVITY – DEVELOPMENT ACTIVITIES Data Management Data Governance Data Architecture Management Data Development Data Operations Management Data Security Management Data Quality Management Reference and Master Data Management DW and BI Management Document and Content Management Metadata Management Data Management Planning Data Management Control Understand Enterprise Information Needs Develop and Maintain the Enterprise Data Model Analyse and Align With Other Business Models Define and Maintain the Database Architecture Define and Maintain the Data Integration Architecture Define and Maintain the DW / BI Architecture Define and Maintain Enterprise Taxonomies and Namespaces Define and Maintain the Metadata Architecture Data Modeling, Analysis, and Solution Design Detailed Data Design Data Model and Design Quality Management Data Implementation Database Support Data Technology Management Understand Data Security Needs and Regulatory Requirements Define Data Security Policy Define Data Security Standards Define Data Security Controls and Procedures Manage Users, Passwords, and Group Membership Manage Data Access Views and Permissions Monitor User Authentication and Access Behaviour Classify Information Confidentiality Audit Data Security Develop and Promote Data Quality Awareness Define Data Quality Requirement Profile, Analyse, and Assess Data Quality Define Data Quality Metrics Define Data Quality Business Rules Test and Validate Data Quality Requirements Set and Evaluate Data Quality Service Levels Continuously Measure and Monitor Data Quality Manage Data Quality Issues Clean and Correct Data Quality Defects Understand Reference and Master Data Integration Needs Identify Master and Reference Data Sources and Contributors Define and Maintain the Data Integration Architecture Implement Reference and Master Data Management Solutions Define and Maintain Match Rules Establish “Golden” Records Define and Maintain Hierarchies and Affiliations Design and Implement Operational DQM Procedures Plan and Implement Integration of New Data Sources Replicate and Distribute Reference and Master Data Understand Business Intelligence Information Needs Define and Maintain the DW / BI Architecture Implement Data Warehouses and Data Marts Implement BI Tools and User Interfaces Process Data for Business Intelligence Monitor and Tune Data Warehousing Processes Monitor and Tune BI Activity and Performance Documents / Records Management Content Management Understand Metadata Requirements Define the Metadata Architecture Develop and Maintain Metadata Standards Implement a Managed Metadata Environment Create and Maintain Metadata Integrate Metadata Manage Metadata Repositories Distribute and Deliver Metadata Query, Report, and Analyse Metadata Manage Changes to Reference and Master Data Monitor Operational DQM Procedures and Performance Introduction to Data Management [2017] 41

42 DMBOK FUNCTION AND ACTIVITY – OPERATIONAL ACTIVITIES Data Management Data Governance Data Architecture Management Data Development Data Operations Management Data Security Management Data Quality Management Reference and Master Data Management DW and BI Management Document and Content Management Metadata Management Data Management Planning Data Management Control Understand Enterprise Information Needs Develop and Maintain the Enterprise Data Model Analyse and Align With Other Business Models Define and Maintain the Database Architecture Define and Maintain the Data Integration Architecture Define and Maintain the DW / BI Architecture Define and Maintain Enterprise Taxonomies and Namespaces Define and Maintain the Metadata Architecture Data Modeling, Analysis, and Solution Design Detailed Data Design Data Model and Design Quality Management Data Implementation Database Support Data Technology Management Understand Data Security Needs and Regulatory Requirements Define Data Security Policy Define Data Security Standards Define Data Security Controls and Procedures Manage Users, Passwords, and Group Membership Manage Data Access Views and Permissions Monitor User Authentication and Access Behaviour Classify Information Confidentiality Audit Data Security Develop and Promote Data Quality Awareness Define Data Quality Requirement Profile, Analyse, and Assess Data Quality Define Data Quality Metrics Define Data Quality Business Rules Test and Validate Data Quality Requirements Set and Evaluate Data Quality Service Levels Continuously Measure and Monitor Data Quality Manage Data Quality Issues Clean and Correct Data Quality Defects Understand Reference and Master Data Integration Needs Identify Master and Reference Data Sources and Contributors Define and Maintain the Data Integration Architecture Implement Reference and Master Data Management Solutions Define and Maintain Match Rules Establish “Golden” Records Define and Maintain Hierarchies and Affiliations Design and Implement Operational DQM Procedures Plan and Implement Integration of New Data Sources Replicate and Distribute Reference and Master Data Understand Business Intelligence Information Needs Define and Maintain the DW / BI Architecture Implement Data Warehouses and Data Marts Implement BI Tools and User Interfaces Process Data for Business Intelligence Monitor and Tune Data Warehousing Processes Monitor and Tune BI Activity and Performance Documents / Records Management Content Management Understand Metadata Requirements Define the Metadata Architecture Develop and Maintain Metadata Standards Implement a Managed Metadata Environment Create and Maintain Metadata Integrate Metadata Manage Metadata Repositories Distribute and Deliver Metadata Query, Report, and Analyse Metadata Manage Changes to Reference and Master Data Monitor Operational DQM Procedures and Performance Introduction to Data Management [2017] 42

43 DMBOK ENVIRONMENTAL ELEMENTS STRUCTURE Data Management Environmental Elements Goals and Principles Activities Primary Deliverables Roles and Responsibilities Technology Practices and Techniques Organisation and Culture Vision and Mission Business Benefits Strategic Goals Specific Objectives Guiding Principles Phases. Tasks, Steps Dependencies Sequence and Flow Use Cases and Scenarios Trigger Events Inputs and Outputs Information Documents Databases Other Resources Individual Roles Organisation Roles Business and IT Roles Qualifications and Skills Tool Categories Standards and Protocols Selection Criteria Learning Curves Recognised Best Practices Common Approaches Alternative Techniques Critical Success Factors Reporting Structures Management Metrics Values, Beliefs, Expectations Attitudes. Styles, Preferences Teamwork, Group Dynamics, Authority, Empowerment. Contracting Strategies Change Management Approach Introduction to Data Management [2017] 43

44 DATA MANAGEMENT SUMMARY Introduction to Data Management [2017] 44

45 REFERENCE Structured and Comprehensive Approach Data to Management and Data Management Body of Knowledge (DMBOK). Alan McSweeney. August 20, 2016. The DAMA Guide to The Data Management Body of Knowledge (DAMA-DMBOK Guide). 1 st Edition. Mark Mosley, Michael Brackett, and Susan Earley. Dama International. USA. 2009 DAMA-DMBOK Functional Framework. Mark Mosley. September 10, 2008 Introduction to Data Management [2017] 45