Data-Ed: Data Systems Integration & Business Value PT. 1: Metadata

1. Copyright 2013 by Data Blueprint Data Systems Integration & Business Value Part 1: Metadata Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation. Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies. Date: July 9, 2013 Time: 2:00 PM ET/11:00 AM PT Presenter: Peter Aiken, Ph.D. 1

2. Copyright 2013 by Data Blueprint Get Social With Us! Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed Like Us on Facebook www.facebook.com/datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals 2

3. Copyright 2013 by Data Blueprint 3 Peter Aiken, PhD • 25+ years of experience in data management • Multiple international awards & recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • President, DAMA International (dama.org) • 8 books and dozens of articles • Experienced w/ 500+ data management practices in 20 countries • Multi-year immersions with organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, and the Commonwealth of Virginia

4. Data Systems Integration & Business Value Part 1: Metadata Presented by Peter Aiken, Ph.D. 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056

5. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. What is metadata and why is it important? 3. Major metadata types & subject areas 4. Metadata benefits, application & sources 5. Metadata strategies & implementation 6. Metadata building blocks 7. Guiding Principles 8. Specific teachable example 9. Take Aways, References and Q&A Tweeting now: #dataed Outline 1. Data Management Overview 2. What is metadata and why is it important? 3. Major metadata types & subject areas 4. Metadata benefits, application & sources 5. Metadata strategies & implementation 6. Metadata building blocks 7. Guiding Principles 8. Specific teachable example 9. Take Aways, References and Q&A 5

6. Copyright 2013 by Data Blueprint 1. Data Management Overview 2. What is metadata and why is it important? 3. Major metadata types & subject areas 4. Metadata benefits, application & sources 5. Metadata strategies & implementation 6. Metadata building blocks 7. Guiding Principles 8. Specific teachable example 9. Take Aways, References and Q&A Tweeting now: #dataed Outline 6

7. Data Program Coordination Feedback Data Development Copyright 2013 by Data Blueprint Standard Data Five Integrated DM Practice Areas Organizational Strategies Goals Business Data Business Value Application Models & Designs Implementation Direction Guidance 7 Organizational Data Integration Data Stewardship Data Support Operations Data Asset Use Integrated Models Leverage data in organizational activities Data management processes and infrastructure Combining multiple assets to produce extra value Organizational-entity subject area data integration Provide reliable data access Achieve sharing of data within a business area

8. Copyright 2013 by Data Blueprint Five Integrated DM Practice Areas Manage data coherently. Share data across boundaries. Assign responsibilities for data. Engineer data delivery systems. Maintain data availability. Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations 8

9. • 5 Data management practices areas / data management basics ... • ... are necessary but insufficient prerequisites to organizational data leveraging applications that is self actualizing data or advanced data practices Copyright 2013 by Data Blueprint Hierarchy of Data Management Practices (after Maslow) Basic Data Management Practices – Data Program Management – Organizational Data Integration – Data Stewardship – Data Development – Data Support Operations http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png Advanced Data Practices • Cloud • MDM • Mining • Big Data • Analytics • Warehousing • SOA 9

11. • Data Management Body of Knowledge (DMBOK) – Published by DAMA International, the professional association for Data Managers (40 chapters worldwide) – Organized around primary data management functions focused around data delivery to the organization and several environmental elements • Certified Data Management Professional (CDMP) – Series of 3 exams by DAMA International and ICCP – Membership in a distinct group of fellow professionals – Recognition for specialized knowledge in a choice of 17 specialty areas – For more information, please visit: • www.dama.org, www.iccp.org Copyright 2013 by Data Blueprint DAMA DM BoK & CDMP 11

15. Copyright 2013 by Data Blueprint Meta-data or metadata • In the history of language, whenever two words are pasted together to form a combined concept initially, a hyphen links them • With the passage of time, the hyphen is lost. The argument can be made that that time has passed • There is a copyright on the term "metadata," but it has not been enforced • So, term is "metadata" 15

16. Copyright 2013 by Data Blueprint Definitions • Metadata is – Everywhere in every data management activity and integral to all IT systems and applications. – To data what data is to real life. Data reflects real life transactions, events, objects, relationships, etc. Metadata reflects data transactions, events, objects, relations, etc. – The data that describe the structure and workings of an organization’s use of information, and which describe the systems it uses to manage that information. [quote from David Hay's new book, page 4] • Data describing various facets of a data asset, for the purpose of improving its usability throughout its life cycle [Gartner 2010] • Metadata unlocks the value of data, and therefore requires management attention [Gartner 2011] • Metadata Management is – The set of processes that ensure proper creation, storage, integration, and control to support associated use of metadata 16

17. Copyright 2013 by Data Blueprint Analogy: Card catalog in a library • Card catalog identifies what books are stored in the library and where they are located in the building • Users can search for books by subject area, author, or title • Catalog shows author, subject tags, publication date and revision history of each book • Card catalog information helps determine which books will meet the reader’s needs • Without this catalog resource, finding books in the library would be difficult, time consuming and frustrating • Readers may search many incorrect books before finding the right book if a catalog does not exist 17 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

18. Copyright 2013 by Data Blueprint Definition (continued) • Metadata is the card catalog in a managed data environment • Abstractly, Metadata is the descriptive tags or context on the data (the content) in a managed data environment • Metadata shows business and technical users where to find information in data repositories • Metadata provides details on where the data came from, how it got there, any transformations, and its level of quality • Metadata provides assistance with what the data really means and how to interpret it 18 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

19. Copyright 2013 by Data Blueprint Defining Metadata Metadata is any combination of any circle and the data in the center that unlocks the value of the data! Adapted from Brad Melton Data WhereWhy What How Who When Data 19

20. Copyright 2013 by Data Blueprint Who: Author What: Title Where: Shelf Location When: Publication Date A small amount of metadata (Card Catalog) unlocks the value of a large amount of data (the Library) Library Metadata Example Libraries can operate efficiently through careful use of metadata (Card Catalog) 20 Data WhereWhy What How Who When Library Book

21. Copyright 2013 by Data Blueprint Outlook Example "Outlook" metadata is used to navigate and manage email Imagine how managing e-mail (already non-trivial) would change if Outlook did not make use of metadata 21 Data WhereWhy What How Who When Email Message

22. Copyright 2013 by Data Blueprint Who: "To" & "From" What: "Subject" How: "Priority" Where: "USERID/Inbox", "USERID/Personal" Why: "Body" When: "Sent" & "Received” • Find the important stuff/weed out junk • Organize for future access/ outlook rules Outlook Example, continued 22

23. Uses Copyright 2013 by Data Blueprint What is the structure of metadata practices? • Metadata practices connect data sources and uses in an organized and efficient manner – Storage: repository, glossary, models, lineage - currently multiple technologies are used – Engineering: identifying/harvesting/normalizing/administer evolving metadata structures – Delivery: supply/access/portal/definition/lookup search identify/ensure required metadata supplies to meet business needs – Governance: ensure proper/creation/storage/integration/control to support effective use • When executed, engineering and delivery implement governance Sources Metadata Governance Metadata Engineering Metadata Delivery Metadata Practices Metadata Storage 23 Specialized Team Skills

24. Extraction Sources Copyright 2013 by Data Blueprint Organized Knowledge 'Data' Improved Quality Data Data Organization Practices Metadata Practices will be inextricably intertwined with Data Quality and Master Data and Knowledge Management, (among other functions) Opera<onal Data Data Quality Engineering Master Data Management Prac<ces Suspected/ Iden<ﬁed Data Quality Problems Routine Data Scans Master Data Catalogs Routine Data Scans Knowledge Management Prac<ces Data that might beneﬁt from Master Management 24

25. Copyright 2013 by Data Blueprint Polling Question #1 • My organization began using or is planning to use a formal approach to metadata management a) Last year (2012) b) This year (2013) c) Next year (2014) d) Not at all 25

28. • Process Metadata is... – Data that defines and describes the characteristics of other system elements, e.g. processes, business rules, programs, jobs, tools, etc. • Examples of Process metadata: – Data stores and data involved – Government/regulatory bodies – Organization owners and stakeholders – Process dependencies and decomposition – Process feedback loop and documentation – Process name Copyright 2013 by Data Blueprint Types of Metadata: Process Metadata 28 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

29. Copyright 2013 by Data Blueprint Business Process Metadata Who: Created the documentation? What: Are the important dependencies among the processes? How: Do the business processes interact with each other? 29 Data WhereWhy What How Who When Email Message

30. Copyright 2013 by Data Blueprint Types of Metadata: Business Metadata • Business Metadata describe to the end user what data are available, what they mean and how to retrieve them. • Included are: – Business names and definitions of subject and concept areas, entities, attributes – Attribute data types and other attribute properties – Range descriptions, calculations, algorithms and business rules – Valid domain values and their definitions 30 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

31. Copyright 2013 by Data Blueprint Types of Metadata: Technical & Operational Metadata from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International • Technical and operational metadata provides developers and technical users with information about their systems • Technical metadata includes… – Physical database table and column names, column properties, other properties, other database object properties and database storage • Operational metadata is targeted at IT operations users’ needs, including… – Information about data movement, source and target systems, batch programs, job frequency, schedule anomalies, recovery and backup information, archive rules and usage • Examples of Technical & Operational metadata: – Audit controls and balancing information – Data archiving and retention rules – Encoding/reference table conversions – History of extracts and results 31

32. • Data stewardship Metadata is about... – Data stewards, stewardship processes, and responsibility assignments • Data stewards… – Assure that data and Metadata are accurate, with high quality across the enterprise. – Establish and monitor data sharing. • Examples of Data stewardship metadata: – Business drivers/goals – Data CRUD rules – Data definitions – business and technical – Data owners – Data sharing rules and agreements/contracts – Data stewards, roles and responsibilities Copyright 2013 by Data Blueprint Types of Metadata: Data Stewardship 32 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

33. Copyright 2013 by Data Blueprint Types of Metadata: Provenance • Provenance: – the history of ownership of a valued object or work of art or literature" [Merriam Webster] – For each datum, this is the description of: • Its source (system or person or department), • Any derivation used, and • The date it was created. – Examples of Data Provenance: • The programs or processes by which it was created • Its owner • The steward responsible for its quality • Other roles and responsibilities • Rules for sharing it 33 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

34. Copyright 2013 by Data Blueprint Metadata Subject Areas Subject Areas Components 1) Business Analytics Data definitions, reports, users, usage, performance 2) Business Architecture Roles and organizations, goals and objectives 3) Business Definitions Business terms and explanations for a particular concept, fact, or other item found in an organization 4) Business Rules Standard calculations and derivation methods 5) Data Governance Policies, standards, procedures, programs, roles, organizations, stewardship assignments 6) Data Integration Sources, targets, transformations, lineage, ETL workflows, EAI, EII, migration/conversion 7) Data Quality Defects, metrics, ratings 8) Document Content Management Unstructured data, documents, taxonomies, ontologies, name sets, legal discovery, search engine indexes 34 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

35. Copyright 2013 by Data Blueprint Metadata Subject Areas, continued Subject Areas Components 9) Information Technology Infrastructure Platforms, networks, configurations, licenses 10)Conceptual data models Entities, attributes, relationships and rules, business names and definitions. 11)Logical Data Models Files, tables, columns, views, business definitions, indexes, usage, performance, change management 12)Process Models Functions, activities, roles, inputs/outputs, workflow, timing, stores 13)Systems Portfolio and IT Governance Databases, applications, projects, and programs, integration roadmap, change management 14)Service-oriented Architecture (SOA) information: Components, services, messages, master data 15)System Design and Development Requirements, designs and test plans, impact 16)Systems Management Data security, licenses, configuration, reliability, service levels 35 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

38. Copyright 2013 by Data Blueprint 7 Metadata Benefits 1. Increase the value of strategic information (e.g. data warehousing, CRM, SCM, etc.) by providing context for the data, thus aiding analysts in making more effective decisions. 2. Reduce training costs and lower the impact of staff turnover through thorough documentation of data context, history, and origin. 3. Reduce data-oriented research time by assisting business analysts in finding the information they need in a timely manner. 4. Improve communication by bridging the gap between business users and IT professionals, leveraging work done by other teams and increasing confidence in IT system data. 5. Increased speed of system development’s time-to-market by reducing system development life-cycle time. 6. Reduce risk of project failure through better impact analysis at various levels during change management. 7. Identify and reduce redundant data and processes, thereby reducing rework and use of redundant, out-of-data, or incorrect data. 38 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

39. Copyright 2013 by Data Blueprint Metadata for Semistructured Data • Unstructured data – Any data that is not in a database or data file, including documents or other media data • Metadata describes both structured and unstructured data • Metadata for unstructured data exists in many formats, responding to a variety of different requirements • Examples of Metadata repositories describing unstructured data: – Content management applications – University websites – Company intranet sites – Data archives – Electronic journals collections – Community resource lists • Common method for classifying Metadata in unstructured sources is to describe them as descriptive metadata, structural metadata, or administrative metadata 39 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

40. Copyright 2013 by Data Blueprint Metadata for Unstructured Data: Examples • Examples of descriptive metadata: – Catalog information – Thesauri keyword terms • Examples of structural metadata – Dublin Core – Field structures – Format (audio/visual, booklet) – Thesauri keyword labels – XML schemas • Examples of administrative metadata – Source(s) – Integration/update schedule – Access rights – Page relationships (e.g. site navigational design) 40

41. Copyright 2013 by Data Blueprint Specific Example • Four metadata sources: 1. Existing reference models (i.e., ADRM) 2. Conceptual model created two years ago 3. Existing systems (to be reverse engineered) 4. Enterprise data model } 41

44. Copyright 2013 by Data Blueprint Metadata History 1990-2008 • The history of Metadata management tools and products seems to be a metaphor for the lack of a methodological approach to enterprise information management: • Lack of standards and proprietary nature of most managed Metadata solutions cause many organizations to avoid focusing on metadata • This limits organizations’ ability to develop a true enterprise information management environment • Increased attention given to information and its importance to an organization’s operations and decision-making will drive Metadata management products and solutions to become more standardized • More recognition to the need for a methodological approach to managing information and metadata 44

45. Copyright 2013 by Data Blueprint Metadata History: The 1990s • Business managers began to recognize the value of Metadata repositories • Newer tools expanded the scope • Potential benefits identified during this period include: – Providing semantic layer between company’s system and business users – Reducing training costs – Making strategic information more valuable as aid in decision making – Creating actionable information – Limiting incorrect decisions 45

46. Copyright 2013 by Data Blueprint Metadata History: Mid-to late 1990s • Metadata becomes more relevant to corporations who were struggling to understand their information resources caused by: – Y2K deadline – Emerging data warehousing initiatives – Growing focus around the World Wide Web • Beginning of efforts to try to standardize Metadata definition and exchange between applications in the enterprise • Examples of standardization: – 1995: CASE Definition Interchange Facility (CDIF) – 1995: Dublin Core Metadata Elements – 1994 – 1999: First parts of ISO 11179 standard for Specification and Standardization of Data Elements were published – 1998: Common Warehouse Metadata Model (CWM) – 1995: Metadata Coalitions’ (MDC) Open Information Model – 2000: Both standards merged into CSM. Many Metadata repositories began promising adoption of CWM standard 46

47. Copyright 2013 by Data Blueprint Metadata History: 21st Century • Update of existing Metadata repositories for deployment on the web • Introduction of products to support CWM • Vendors begin focusing on Metadata as an additional product offering • Few organizations purchase or develop Metadata repositories • Effective enterprise-wide Managed Metadata Environments are rare due to: – Scarcity of people with real world skills – Difficulty of the effort – Less than stellar success of some of the initial efforts at some companies – Stagnation of the tool market after the initial burst of interest in late 90s – Still less than universal understanding of the business benefits – Too heavy emphasis on legacy applications and technical metadata 47

48. Copyright 2013 by Data Blueprint Metadata History: Current Decade • Focus on need for and importance of metadata • Focus on how to incorporate Metadata beyond traditional structured sources and include semistructured sources • Driving factors: – Recent entry of larger vendors into the market – Challenges related to addressing regulatory requirements, e.g. Sarbanes-Oxley, and privacy requirements with unsophisticated tools – Emergence of enterprise-wide initiatives, e.g. information governance, compliance, enterprise architecture, automated software reuse – Improvements to the existing Metadata standards, e.g. RFP release of new OMG standard Information Management Metamodel (IMM), which will replace CWM – Recognition at the highest levels that information is an asset that must be actively and effectively managed 48

49. Copyright 2013 by Data Blueprint Why Metadata Matters • They know you rang a phone sex service at 2:24 am and spoke for 18 minutes. But they don't know what you talked about. • They know you called the suicide prevention hotline from the Golden Gate Bridge. But the topic of the call remains a secret. • They know you spoke with an HIV testing service, then your doctor, then your health insurance company in the same hour. But they don't know what was discussed. • They know you received a call from the local NRA office while it was having a campaign against gun legislation, and then called your senators and congressional representatives immediately after. But the content of those calls remains safe from government intrusion. • They know you called a gynecologist, spoke for a half hour, and then called the local Planned Parenthood's number later that day. But nobody knows what you spoke about. – https://www.eff.org/deeplinks/2013/06/why-metadata-matters 49

50. Copyright 2013 by Data Blueprint Metadata Strategy • Metadata Strategy is – A statement of direction in Metadata management by the enterprise – A statement of intend that acts as a reference framework for the development teams – Driven by business objectives and prioritized by the business value they bring to the organization • Build a Metadata strategy from a set of defined components • Primary focus of Metadata strategy – gain an understanding of and consensus on the organization’s key business drivers, issues, and information requirements for the enterprise Metadata program • Need to understand how well the current environment meets these requirements now and in the future • Metadata strategy objectives define the organization’s future enterprise metadata architecture and recommend logical progression of phased implementation steps • Only 1 in 10 organizations has a documented, board approved data strategy 50

51. Copyright 2013 by Data Blueprint Polling Question #2 • Compliance laws have influenced my organization to pay more attention to and/or put more resources into: a) Data quality improvement efforts b) Metadata management efforts c) Database management, in general d) No impact 51

55. Copyright 2013 by Data Blueprint Goals and Principles 55 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International • Provide organizational understanding of terms and usage • Integrate Metadata from diverse sources • Provide easy, integrated access to metadata • Ensure Metadata quality and security

56. Copyright 2013 by Data Blueprint Polling Question #3 • My organization began using or is planning to use a metadata repository (purchased or homegrown) a) Last year (2012) b) This year (2013) c) Next year (2014) d) Not applicable 56

57. Copyright 2013 by Data Blueprint Activities • Understand Metadata requirements • Define the Metadata architecture • Develop and maintain Metadata standards • Implement a managed Metadata environment • Create and maintain metadata • Integrate metadata • Management Metadata repositories • Distribute and deliver metadata • Query, report and analyze metadata 57 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

58. Copyright 2013 by Data Blueprint Activities: Metadata Standards Types • Two major types: – Industry or consensus standards – International standards • High level framework can show – How standards are related – How they rely on each other for context and usage 58 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

59. Copyright 2013 by Data Blueprint • Common Warehouse Metadata (CWM): • Specifies the interchange of Metadata among data warehousing, BI, KM, and portal technologies. • Based on UML and depends on it to represent object- oriented data constructs. • The CWM Metamodel Activities: Noteworthy Metadata Standards Types Warehouse ProcessWarehouse ProcessWarehouse Process Warehouse Opera;onWarehouse Opera;onWarehouse Opera;on Transforma<onTransforma<on OLAP Data Mining Informa<on Visualiza<on Business Nomenclature Object Model Rela<onal Record Mul<dimensionalMul<dimensional XML Business Informa<on Data Types Expression Keys and Indexes Type Mapping SoOware Deployment Object ModelObject ModelObject ModelObject ModelObject ModelObject Model Management Analysis Resource Founda<on 59

60. Copyright 2013 by Data Blueprint Information Management Metamodel (IMM) • Object Management Group Project to replace CWM • Concerned with: – Business Modeling • Entity/relationship metamodel – Technology modeling • Relational Databases • XML • LDAP – Model Management • Traceability – Compatibility with related models • Semantics of business vocabulary and business rules • Ontology Definition Metamodel • Based on Core model • Used to translate from one model to another 60

61. • Metadata repositories • Quality metadata • Metadata analysis • Data lineage • Change impact analysis • Metadata control procedures • Metadata models and architecture • Metadata management operational analysis Copyright 2013 by Data Blueprint Primary Deliverables 61 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

62. • Suppliers: – Data Stewards – Data Architects – Data Modelers – Database Administrators – Other Data Professionals – Data Brokers – Government and Industry Regulators • Participants: – Metadata Specialists – Data Integration Architects – Data Stewards – Data Architects and Modelers – Database Administrators – Other DM Professionals – Other IT Professionals – DM Executives – Business Users • Consumers: – Data Stewards – Data Professionals – Other IT Professionals – Knowledge Workers – Managers and Executives – Customers and Collaborators – Business Users Copyright 2013 by Data Blueprint Roles and Responsibilities 62 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

63. Copyright 2013 by Data Blueprint Technology • Metadata repositories • Data modeling tools • Database management systems • Data integration tools • Business intelligence tools • System management tools • Object modeling tools • Process modeling tools • Report generating tools • Data quality tools • Data development and administration tools • Reference and mater data management tools 63 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

67. Copyright 2013 by Data Blueprint 15 Guiding Principles 1. Establish and maintain a Metadata strategy and appropriate policies, especially clear goals and objectives for Metadata management and usage 2. Secure sustained commitment, funding, and vocal support from senior management concerning Metadata management for the enterprise 3. Take an enterprise perspective to ensure future extensibility, but implement through iterative and incremental delivery 4. Develop a Metadata strategy before evaluating, purchasing, and installing Metadata management products 5. Create or adopt Metadata standards to ensure interoperability of Metadata across the enterprise 6. Ensure effective Metadata acquisition for internal and external metadata 7. Maximize user access since a solution that is not accessed or is under-accessed will not show business value 67 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

68. 8. Understand and communicate the necessity of Metadata and the purpose of each type of metadata; socialization of the value of Metadata will encourage business usage 9. Measure content and usage 10.Leverage XML, messaging and web services 11.Establish and maintain enterprise-wide business involvement in data stewardship, assigning accountability for metadata 12.Define and monitor procedures and processes to ensure correct policy implementation 13.Include a focus on roles, staffing, standards, procedures, training, & metrics 14.Provide dedicated Metadata experts to the project and beyond 15.Certify Metadata quality Copyright 2013 by Data Blueprint 15 Guiding Principles, continued 68 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

71. Copyright 2013 by Data Blueprint 6609/10/12 Example: iTunes Metadata • Example: – iTunes Metadata • Insert a recently purchased CD • iTunes can: – Count the number of tracks (25) – Determine the length of each track 71

72. Copyright 2013 by Data Blueprint 6709/10/12 Example: iTunes Metadata • When connected to the Internet iTunes connects to the Gracenote(.com) Media Database and retrieves: – CD Name – Artist – Track Names – Genre – Artwork • Sure would be a pain to type in all this information 72

74. Copyright 2013 by Data Blueprint Example: iTunes Metadata 6909/10/12 • Notice I didn't get the desired results • I already had another Miles Davis recording in iTunes • Must fine-tune the request to get the desired results – Album contains "The complete birth of the cool" • Now I can move the playlist "Miles Davis" to a folder 74

75. Copyright 2013 by Data Blueprint Example: iTunes Metadata 7009/10/12 • The same: –Interface –Processing –Data Structures • are applied to –Podcasts –Movies –Books –.pdf files • Economies of scale are enormous 75

78. Uses Copyright 2013 by Data Blueprint Metadata Take Aways • Metadata unlocks the value of data, and therefore requires management attention [Gartner 2011] • Metadata is the language of data governance • Metadata defines the essence of integration challenges Sources Metadata Governance Metadata Engineering Metadata Delivery Metadata Practices Metadata Storage 78 Specialized Team Skills

85. Data Systems Integration & Business Value Pt. 2: Cloud August 13, 2013 @ 2:00 PM ET/11:00 AM PT Data Systems Integration & Business Value Pt. 3: Warehousing September 10, 2013 @ 2:00 PM ET/11:00 AM PT Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net Copyright 2013 by Data Blueprint Upcoming Events 85

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