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Introduction to Master Data Management PROFESSIONAL PETROLEUM DATA MANAGEMENT ASSOCIATION Introduction to Master Data Management Master Data Management Data Quality Integration with PPDM 3.8 Data Management with PPDM 3.8 1 Copyright 2013, PPDM Association. All Rights Reserved LEARNING OBJECTIVES Understand and discuss Effective Data Management Strategies Understand and discuss Data Governance Understand and discuss Master Data Management Understand and discuss Data Quality Understand and discuss Data Organization and Architecture Understand how to use PPDM 3.8 for MDM 2 Copyright 2013, PPDM Association. All Rights Reserved PROFESSIONAL PETROLEUM DATA MANAGEMENT ASSOCIATION How Does Data Management Fit into Corporate Life? Mainstream vs “burden” 3 Copyright 2013, PPDM Association. All Rights Reserved EFFECTIVE DATA MANAGEMENT STRATEGIES • Effective data management is essentially being able to deliver the data that people need, when they need it, and in the format that they need it • Any data management strategy needs to start with the needs of the business and the key decisions that they make • What information is used to make the decisions? • What data is used to derive that information? • This can then be used to assess the value of data: • Who uses it and what do they use it for? • What impact does it have on business decisions? • This, in turn, will drive decisions around governance, architecture, organization, quality, and security. 4 Copyright 2013, PPDM Association. All Rights Reserved EFFECTIVE DATA MANAGEMENT STRATEGIES 5 Copyright 2013, PPDM Association. All Rights Reserved EFFECTIVE DATA MANAGEMENT STRATEGIES 6 Copyright 2013, PPDM Association. All Rights Reserved EFFECTIVE DATA MANAGEMENT STRATEGIES 7 Copyright 2013, PPDM Association. All Rights Reserved EFFECTIVE DATA MANAGEMENT STRATEGIES Data Value Data Master Data Governance Data Mgmt Quality Data Data Data Organization Architecture Security People Process Technology Effective Data Management 8 Copyright 2013, PPDM Association. All Rights Reserved ELEMENTS OF EFFECTIVE DATA MANAGEMENT STRATEGIES Data Management Capabilities Data Data Data Master Data Data Governance Organization Architecture & Metadata Quality . Data Sizing, . Data Policies . Data Taxonomy Storage & . Data Quality Rules . Data Definitions . Data Standards . Logical Data Movement & Policies . Master Data . Business Data Models Architecture . Data Cleansing . Metadata Ownership . Business Process . Data Retention & Standards Process . Reference Data . Data Workflow Flows Deletion Policies . Compliance Rules . Phys Data Models . Data . Data Stewards . Solution Architects Administration . Business Data Enterprise Data . Data Quality . Business Data . Storage/ Administration Owners Architects Technical Services Team . Data Stewards . Data Czar or Mgt. Data Modelers Architects . Data Governance People . Data Owners Committee . DBAs . DBAs . Archiving Tools . Master Data Mgt. Data Profiling, Data Rules Library Data Modeling Tools . Storage Quality & . Automated Tools Management & . Reference Data Monitoring Tools Notifications . Design/CASE Hardware Architecture . ETL Tools (Workflow) Tools . Technical . Metadata . Audit Reports Technology Architecture Repository Copyright 2006 Accenture All Rights Reserved 9 Copyright 2013, PPDM Association. All Rights Reserved PROFESSIONAL PETROLEUM DATA MANAGEMENT ASSOCIATION Data Governance 10 Copyright 2013, PPDM Association. All Rights Reserved DATA GOVERNANCE Data Governance is how an organization manages its data assets. • The business rules, policies, practices, procedures, roles, and responsibilities to create a consistent enterprise view of an organization’s data • Data Governance is the foundation of effective data management but is too often neglected • It involves a great deal of work to establish and maintain a comprehensive Data Governance program • The PPDM 3.7/3.8 data management module provides a strong framework for Data Governance 11 Copyright 2013, PPDM Association. All Rights Reserved DATA GOVERNANCE Legislation Shareholders PPP Policy Upper mgmt Associations Practice Standards Mid mgmt People Procedure Docs SW 12 Copyright 2013, PPDM Association. All Rights Reserved DATA GOVERNANCE • POLICY – We need accountability each master data store to establish a focal point for responsibility. • PRACTICE – We must document what job functions are responsible for data creation, updates and deletes. • PRACTICE – We must document what software applications are responsible for data creation, updates and deletes. • PRACTICE – We must document where in the life cycle of a business asset it is created and used by various functional groups in our organization. • PROCEDURE – all replicated (or derived) copies will be derived from the master store, using an appropriate mechanism for replication management. 13 Copyright 2013, PPDM Association. All Rights Reserved DATA GOVERNANCE • Business Rules describe database or application procedures that are used to enforce Policies, Practices, and Procedures • These rules help ensure that the data within the database: • Conforms to convention in terms of meaning and loading practice • Is the most trusted data available; the source is known, the history is known, and the quality is consistent and understood • Is maintained consistently over its lifecycle 14 Copyright 2013, PPDM Association. All Rights Reserved BUSINESS RULES Business rules • Create rules and associate with tables, columns, procedures etc. PPDM_RULE • Rules enforcement methods R_PPDM_RULE_CLASS Kinds of business rules R_PPDM_RULE_PURPOSE • Policies, Practices, Procedures PPDM_RULE_XREF PPDM_RULE_DETAIL • Quality (trustworthiness) PPDM_RULE_COMPONENT • Entitlements and security R_PPDM_XREF_TYPE • Relationships PPDM_GROUP • Constraints (database and data) PPDM_RULE_ENFORCEMENT • Conversions or transformations R_PPDM_ENFORCE_METHOD R_PPDM_FAIL_RESULT • Calculations 15 Copyright 2013, PPDM Association. All Rights Reserved PROFESSIONAL PETROLEUM DATA MANAGEMENT ASSOCIATION Master Data Management 16 Copyright 2013, PPDM Association. All Rights Reserved WHAT DO WE HOPE TO ACHIEVE? Any kind of data management is a lot of work. Data management is not always recognized as a good value proposition by management. - As a company - As an industry 17 Copyright 2013, PPDM Association. All Rights Reserved THE CHALLENGE TIP: SOURCE SYSTEMS LIE • Source systems may not be able to accurately reflect the business! • Legacy systems • Assume out dated processes • May flatten or denormalize your data • May not account for regional variations • May not account for temporal variations • May not capture all the important information • May replicate information that they don’t “own” • …. • Just because data is in a legacy system does not mean you should load it into an MDM! • Ask lots of questions; ask everybody! • Don’t be afraid to recommend against loading certain data types! 19 Copyright 2013, PPDM Association. All Rights Reserved COST VS BENEFIT What is the cost of bad data? • Use the fear factor to engage management • Threat management succeeds in the short term – sometimes. • Does it really help you to avoid costly errors? What is the value of good data management? • Is data management really a “core” function? • Does it really save time and effort? • Can’t I just leave this to my vendors? Is data an asset? • How much is it worth? • Can data be put “on the books” as a tangible asset? - Will the IFRS help us? 20 Copyright 2013, PPDM Association. All Rights Reserved INTEGRATED ARCHITECTURE Ontologies Meta Data Taxonomies Physical Spatial/GIS PPDM SOR MDM SOR SOR Business Rules Policies, Practices & Procedures VALUE OF INTEGRATED ARCHITECTURES The asset value of core data is Life cycle based management recognised • You know who the “real owners” of • By the users data are • By the data managers Data flows can be identified and managed • By IT staff • Beware the “rogue” user! • By managers Maintenance effort should be Business rules can be applied reduced consistently Integration effort is probably higher! Key data is stored once, used many times! • Managed replication • May require translation, granularity shifts, equivalences … 22 Copyright 2013, PPDM Association. All Rights Reserved SOR ORDER FROM CHAOS MDM / SOR SOR Warehouse SOR = System of Record Gold Standard MDM = Master Data Management 23 Copyright 2013, PPDM Association. All Rights Reserved MASTER DATA MANAGEMENT Financial Data Physical Data Spatial Data HSE Data Unstructured / Semi-structured Data Operational Structured Data Data 24 Copyright 2013, PPDM Association. All Rights Reserved AN OBJECTIVE: THE DIGITAL OILFIELD 001111000111011101100101011011000110110001011111010010010100010000111110001100010011001000110011001101000011010100110110001101 110011100000111001001100000011110000101111011101110110010101101100011011000101111101001001010001000011111000101100001111000101 001101010000010101010100010001011111010001000100000101010100010001010011111000110010001100000011000000110111010111110011000000 110110010111110011001000110111001111000010111101010011010100000101010101000100010111110100010001000001010101000100010100111110 <well_ID>1234567890</well_ID>,<SPUD_DATE>2007_06_27</SPUD_DATE>… 25 Copyright 2013, PPDM Association. All Rights Reserved OBJECTIVES REALITY Correct Complete Current 26 Copyright 2013, PPDM Association. All Rights Reserved WORK SMARTER – NOT HARDER! 2005 study at Institute of Psychiatry • University of London IQ test with distraction is 10 points lower than with no distractions. 100 95 90 85 80 IQ test with distraction is No Smoking Pot Distractions
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