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Business Semantics for Data Governance and Stewardship

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Data quality and regulations are perpetual drivers for Data Governance and Stewardship solutions that systematically monitor the execution of data policy. And yet, there is a long road ahead to achieve Trust in Data. It is still a relatively unknown topic or comes with trauma from past failed attempts; there is no political framework with executive champions, leading to reactive rather than proactive behavior, and software support is marginal.

Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve a wide adoption and confluence of Data Trust between business and IT communities in the organization.

In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of data pain as a company grows, and we map their situation on this spectrum of semantics.

Next, we introduce the principles and framework for business semantics management to support data governance and stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with stories from the field.

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Business Semantics for Data Governance and Stewardship

  1. 1. Business Semantics For Data Governance & Stewardship Dr. Pieter De Leenheer Sloan Hall Stanford University Feb 4 - 2015
  2. 2. Overview • ICT: from Truth to Trust • The Spectrum of Business Semantics • Situation Map • Business Semantics Governance & Stewardship – Principles – Operating Framework • Reflection and Questions
  3. 3. La Trahison des Images (Magritte, 1929)
  4. 4. La Trahison des Images (2) https://deleenheer.wordpress.com/2009/12/15/magrittes-flirting-with-semantics/
  5. 5. What we talk about when we talk about no Data Governance Who approved this? I wish these guys spoke our language I can’t understand this report ! I’ve never seen this code! Who introduced this ? This doesn’t seem right. Are we sure this data is correct ? The Problem This rule is different in our country ! This is an exception to the rule !
  6. 6. Glossary Search • How frequently do you look up a word for your business? • To what purpose? – Clarification – Differentiation • What are your main sources? • Hierarchy-based navigation or key-word based search? • Authoritative Truth or trust?
  7. 7. From Truth to Trust: Behind the Curtains https://www.research.ibm.com/visual/projects/history_flow/results.htm
  8. 8. Overview • ICT: from Truth to Trust • The Spectrum of Business Semantics • Situation Map • Business Semantics Governance & Stewardship – Principles – Operating Framework • Reflection and Questions
  9. 9. Spectrum of Business Semantics Welty, C., Lehmann, F., Gruninger, G., and Uschold, M. (1999). Ontology: Expert systems all over again? In Invited panel at AAAI-99: The National Conference on Artificial Intelligence, Austin, Texas, USA.
  10. 10. The Big ‘Metadata’ Bang Catalogue and text files • The start of an organization’s data management • Represented by shared folders with lists of things such as product, customer, templates • First ‘clouds’ of metadata – Naturally emerge as by-product – For human consumption – Locally understood • From this point exponential expansion: • in volume • in consumers (receiver) • in producers (sender) • in entropy
  11. 11. Glossary • List of terms and definitions e.g., http://web.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/data-governance-and-stewardship-materials/
  12. 12. Thesaurus • add homo-, syno, mero-, hyper- and hyponymous relations
  13. 13. Taxonomy • Formalized representation of a “thesaurus” • Generalize and specialize properties and relations – generalize Vendor and Customer with similar properties into Party – specialize Location into Home Address and Office Address because of different properties • Classifying a thing as a Term, Data Element or System – E.g., “customer” vs. “CUST_TBL” vs. “CRM” to determine ownership • Inheritance-based reasoning such as syllogisms – Premise: “John doe” is a lead – Premise: All leads receive a mortgage offering – Conclusion : “John Doe” receives a mortgage offering
  14. 14. Frames
  15. 15. Logical constraints • Modal Logic: – context determines meaning, truthfulness, validity – plausibility vs. necessity • Modalities determine: – who owns a term per region, process, function – where and how enforce terms – What the definition is of a term
  16. 16. Hierarchical Context in ACORD
  17. 17. Multidimensional Context
  18. 18. Overview • ICT: from Truth to Trust • The Spectrum of Business Semantics • Situation Map • Business Semantics Governance & Stewardship – Principles – Operating Framework • Reflection and Questions
  19. 19. Situating an organization’s level of glossary need size characterizing events business needs technology support status 1 to 50 first term-and-condition templates, first products, customers a catalogue of items like customers, products and offerings spreadsheet database 51 to 100 first customer segmentation lead engine setup business functions defined as the catalogues grow in size, transform loose descriptions and definitions in text files into a glossary of terms shared file folders (for lead, prospect, customer, product, offering) 101 to 500 business functions populated inter-functional business processes develop product and customer data volumes grow the need for a thesaurus for comparing glossaries, differentation of customer types, pricing models, reporting templates local data analytics and storage Spreadsheet, mediawiki, functional processes like salesforce, SDLC, servicenow; forecasting tools, reporting tools, databases 501 to 1000 invested growth mergers and acquisition take place first signs of corrupt data reports on the board table the need to transforming thesauri into taxonomies and data models and architecture frames ISO/ACORD/BCBS standardization mediawikis go viral without proper alignemnt between them; first metadata tools in IT to align certain functions, business limited to spreadsheets 1001 plus global operations one or more red flaggs: legal (regulatory compliance breached): organizational (CxO fired), bad reputation (fraud), financial loss (penalties, debt) Reporting standards transformed into corporate data policies and rules and data quality Modalities as to who are to define them and how and where to enforce them have been set The need for the CDO function is mentioned but resistance from CIO/CTO Big Data opportunities loom beyond the data nebula (screen with universe). platform with several data management systems (infa, ibm, oracle) scared by M&A. Lineage fragmented, not properly validated by business data governance organization theorized (or failed before) so no one takes accountability, lack of functional descriptions or enterprise-wide championship Glossaries’ usefulness implodes as their numbers increase The enterprise data model is common ground for IT but useless to the business. Validation is urgent.
  20. 20. Overview • ICT: from Truth to Trust • The Spectrum of Business Semantics • Situation Map • Business Semantics Governance & Stewardship – Principles – Operating Framework • Reflection and Questions
  21. 21. Principles of Business Semantics • Democracy • Emergence • Perspective rendering • Perspective unification • Validation http://www.academia.edu/874733/Business_semantics_management_A_case_study_for_competency-centric_HRM
  22. 22. Principles at work in the Situation Map • Emergence is a continuous principle at work • Unification and rendering continuous in flux but at two different frequencies (B vs. IT) • Validation is limited to technical lineage • Democracy and Business Validation (socio- technical) are lacking • Reactive rather than pro-active governance (defining) and stewardship (enforcing) • Lack of tools
  23. 23. Overview • Communication: from Truth to Trust • The Spectrum of Business Semantics • Situation Map • Business Semantics Governance & Stewardship – Principles – Operating Framework • Reflection and Questions
  24. 24. Gradually Build Trust based on Stewardship and Validation • What? – Qualitative meta data: e.g., definition for address, codes, mappings, classifications, etc. • Who? – Roles and responsibilities for people • How ? – Collaborative workflows to orchestrate people in achieving high-quality meta-data – Start Simple, Buy-in, Council – Measure Maturity and Trust – Separate stewardship from integration Data Governance Council: Governance Operating Model Roles & Responsibilities Processes & Workflow Asset Types & Traceability Data Governance Organization Data Stewardship Activities Data Quality Development IT / Operational Data Management Activities Data Modeling Metadata Lineage Establishes& drives Aligns& Coordinates Reports& Escalates Monitors& Remediates Metadata Scanning Reference Data Authoring Data Integration Hierarchy Management Business & Data Definitions Business Traceability Semantic Modeling Mapping Specifications Policy Management Business Rules Data Quality Rules Data Quality Reporting Issue Management Reference Data Crosswalks Master Data Stewardship Data Quality Profiling DQ Defect Resolution ...
  25. 25. Example in Health Insurance http://prezi.com/ve1ws8jmpqcn/workflow/
  26. 26. Global Data Governance • Objective – n Enterprise service buses => 1 Global Information Market Place • Challenges – Data Service = data sharing agreement across organization silos, policies, regulations, semantic assumptions. E.g., Address – No clear balance between data ownership and control: • responsibilities are not set • for each data point : increasing exposure to risk regarding quality and policy compliance • Service is more about trust because truth is relative
  27. 27. Solution
  28. 28. Solution One Global Information Hub
  29. 29. Solution Phase 1 : Jun-Sept One Global Information Hub
  30. 30. Solution Phase 2 : Oct-Nov One Global Information Hub
  31. 31. Solution Phase 2 : Oct-Nov One Global Information Hub
  32. 32. Solution Phase 3 : Dec - One Global Information Hub
  33. 33. Solution One Global Information Hub
  34. 34. What is to be governed? Data Governance Questions • What does the term ”address” mean? • How is term “address" represented? • In what system are data elements on ”address” recorded? • What views does a data sharing agreement include? • To which policy does my data sharing agreement comply? • What country is my term “address” classified? • … Collibra Traceability Paths  Term has attributes definition, description, etc.  Term is represented by Data Element  Data Element has system of record System  Data sharing Agreement groups Data View  … Business Term ≠ Data Element https://compass.collibra.com/display/COOK/Asset+Types+and+Traceability+Requirements
  35. 35. Operating Model
  36. 36. Traceability Diagram
  37. 37. Who? RACI
  38. 38. How is it to be governed? • Status Types and Workflows – For Domains, Terms, Users, and later for Issues and Data Sharing Agreements BUSINESS SEMANTICS GLOSSARY Candidate In Progress Under Review Accepted In Revision Rejected Term requested on the domain page 11 1 2 2 3 3 2 3 Depricated 4 5 Workflows 1 2 Propose Business Term Edit Business Term 3 Onboarding Business Term 4 Deprecate Business Term 5 Reactivate Business Term https://compass.collibra.com/display/COOK/Lifecycle%3A+Workflows+and+Status+Types
  39. 39. How it it to be governed? Propose Workflow
  40. 40. How it it to be governed? Onboarding Workflow
  41. 41. How it it to be governed? Approval Workflow
  42. 42. Questions for the Audience We presume the starting point is glossary. • What factors would make it impossible? • Know of cases where it has been achieved without? • Is it possible to establish data governance without a glossary?

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