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Selecting Data Management Tools - A practical approach

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Selecting Data Management Tools - A practical approach

  1. 1. P / 1 Considerations For Selecting Data Modelling Tools C H R I S T O P H E R B R A D L E Y I N F O R M A T I O N S T R A T E G Y A D V I S O R
  2. 2. P / 2 Considerations For Selecting Data Modelling Tools
  3. 3. P / 3 Introduction: My blog: Information Management, Life & Petrol http://infomanagementlifeandpetrol.blogspot.com @InfoRacer uk.linkedin.com/in/christophermichaelbradley/ Christopher Bradley Information Strategy Advisor +44 7973 184475 chris@chrismb.co.uk
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  5. 5. P / 6 Introduction To Chris Bradley Chris is a well known Information Strategy Advisor with 34 years experience in the Information Management field, Chris works with leading organisations including Celgene, Bristish Gas, HSBC, Total, Barclays, ANZ, GSK, Shell, BP, Statoil, Riyad Bank & Aramco in Data Governance, Information Management Strategy, Data Quality & Master Data Management, Metadata Management and Business Intelligence. He is a Director of DAMA- I, holds the CDMP Master certification, examiner for CDMP, a Fellow of the Chartered Institute of Management Consulting (now IC) member of the MPO, and SME Director of the DM Board. A recognised thought-leader in Information Management Chris is creator of sections of DMBoK 2.0, a columnist, a frequent contributor to industry publications and member of standards authorities. He leads an experts channel on the influential BeyeNETWORK, is a regular speaker at major international conferences, and is the co- author of “Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models”. He also blogs frequently on Information Management (and motorsport).
  6. 6. P / 7 Recent Presentations DAMA UK Webinar: February 2015; “An Introduction to the Information Disciplines of the DAMA DMBoK” Petroleum Information Management Summit 2015: February 2015, Berlin DE, “How to succeed with MDM and Data Governance” Enterprise Data & Business Intelligence 2014: (IRM), November 2014, London, UK “Data Modelling 101 Workshop” Enterprise Data World: (DataVersity), May 2014, Austin, Texas, “MDM Architectures & How to identify the right Subject Area & tooling for your MDM strategy” E&P Information Management Dubai: (DMBoard),17-19 March 2014, Dubai, UAE “Master Data Management Fundamentals, Architectures & Identify the starting Data Subject Areas” DAMA Australia: (DAMA-A),18-21 November 2013, Melbourne, Australia “DAMA DMBoK 2.0”, “Information Management Fundamentals” 1 day workshop” Data Management & Information Quality Europe: (IRM Conferences), 4-6 November 2013, London, UK “Data Modelling Fundamentals” ½ day workshop: “Myths, Fairy Tales & The Single View” Seminar “Imaginative Innovation - A Look to the Future” DAMA Panel Discussion IPL / Embarcadero series: June 2013, London, UK, “Implementing Effective Data Governance” Riyadh Information Exchange: May 2013, Riyadh, Saudi Arabia, “Big Data – What’s the big fuss?” Enterprise Data World: (Wilshire Conferences), May 2013, San Diego, USA, “Data and Process Blueprinting – A practical approach for rapidly optimising Information Assets” Data Governance & MDM Europe: (IRM Conferences), April 2013, London, “Selecting the Optimum Business approach for MDM success…. Case study with Statoil” E&P Information Management: (SMI Conference), February 2013, London, “Case Study, Using Data Virtualisation for Real Time BI & Analytics” E&P Data Governance: (DMBoard / DG Events), January 2013, Marrakech, Morocco, “Establishing a successful Data Governance program” Big Data 2: (Whitehall), December 2012, London, “The Pillars of successful knowledge management” Financial Information Management Association (FIMA): (WBR), November 2012, London; “Data Strategy as a Business Enabler” Data Modeling Zone: (Technics), November 2012, Baltimore USA “Data Modelling for the business” Data Management & Information Quality Europe: (IRM), November 2012, London; “All you need to know to prepare for DAMA CDMP professional certification” ECIM Exploration & Production: September 2012, Haugesund, Norway: “Enhancing communication through the use of industry standard models; case study in E&P using WITSML” Preparing the Business for MDM success: Threadneedles Executive breakfast briefing series, July 2012, London Big Data – What’s the big fuss?: (Whitehall), Big Data & Analytics, June 2012, London, Enterprise Data World International: (DAMA / Wilshire), May 2012, Atlanta GA, “A Model Driven Data Governance Framework For MDM - Statoil Case Study” “When Two Worlds Collide – Data and Process Architecture Synergies” (rated best workshop in conference); “Petrochemical Information Management utilising PPDM in an Enterprise Information Architecture” Data Governance & MDM Europe: (DAMA / IRM), April 2012, London, “A Model Driven Data Governance Framework For MDM - Statoil Case Study” AAPG Exploration & Production Data Management: April 2012, Dead Sea Jordan; “A Process For Introducing Data Governance into Large Enterprises” PWC & Iron Mountain Corporate Information Management: March 2012, Madrid; “Information Management & Regulatory Compliance” DAMA Scandinavia: March 2012, Stockholm, “Reducing Complexity in Information Management” (rated best presentation in conference) Ovum IT Governance & Planning: March 2012, London; “Data Governance – An Essential Part of IT Governance” American Express Global Technology Conference: November 2011, UK, “All An Enterprise Architect Needs To Know About Information Management” FIMA Europe (Financial Information Management):, November 2011, London; “Confronting The Complexities Of Financial Regulation With A Customer Centric Approach; Applying IPL’s Master Data Management And Data Governance Process In Clydesdale Bank “ Data Management & Information Quality Europe: (DAMA / IRM), November 2011, London, “Assessing & Improving Information Management Effectiveness – Cambridge University Press Case Study”; “Too Good To Be True? – The Truth About Open Source BI” ECIM Exploration & Production: September 12th 14th 2011, Haugesund, Norway: “The Role Of Data Virtualisation In Your EIM Strategy” Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL; “How Do You Want Yours Served? – The Role Of Data Virtualisation And Open Source BI” Data Governance & MDM Europe: (DAMA / IRM), March 2011, London, “Clinical Information Data Governance” Data Management & Information Management Europe: (DAMA / IRM), November 2010, London, “How Do You Get A Business Person To Read A Data Model? DAMA Scandinavia: October 26th-27th 2010, Stockholm, “Incorporating ERP Systems Into Your Overall Models & Information Architecture” (rated best presentation in conference) BPM Europe: (IRM), September 27th – 29th 2010, London, “Learning to Love BPMN 2.0” IPL / Composite Information Management in Pharmaceuticals: September 15th 2010, London, “Clinical Information Management – Are We The Cobblers Children?” ECIM Exploration & Production: September 13th 15th 2010, Haugesund, Norway: “Information Challenges and Solutions” (rated best presentation in conference) Enterprise Architecture Europe: (IRM), June 16th – 18th 2010, London: ½ day workshop; “The Evolution of Enterprise Data Modelling”
  7. 7. P / 8 Recent Publications Book: “Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models”; Technics Publishing; ISBN 978-0-9771400-7-7; http://www.amazon.com/Data-Modeling-Business-Handbook-High-Level White Paper: “Information is at the heart of ALL Architecture disciplines”,; March 2014 Article: The Bookbinder, the Librarian & a Data Governance story ; July 2013 Article: Data Governance is about Hearts and Minds, not Technology January 2013 White Paper: “The fundamentals of Information Management”, January 2013 White Paper: “Knowledge Management – From justification to delivery”, December 2012 Article: “Chief INFORMATION Officer? Not really” Article, November 2012 White Paper: “Running a successful Knowledge Management Practice” November 2012 White Paper: “Big Data Projects are not one man shows” June 2012 Article: “IPL & Statoil’s innovative approach to Master Data Management in Statoil”, Oil IT Journal, May 2012 White Paper: “Data Modelling is NOT just for DBMS’s” April 2012 Article: “Data Governance in the Financial Services Sector” FSTech Magazine, April 2012 Article: “Data Governance, an essential component of IT Governance" March 2012 Article: “Leveraging a Model Driven approach to Master Data Management in Statoil”, Oil IT Journal, February 2012 Article: “How Data Virtualization Helps Data Integration Strategies” BeyeNETWORK (December 2011) Article: “Approaches & Selection Criteria For organizations approaching data integration programmes” TechTarget (November 2011) Article: Big Data – Same Problems? BeyeNETWORK and TechTarget. (July 2011) Article “10 easy steps to evaluate Data Modelling tools” Information Management, (March 2010) Article “How Do You Want Your Data Served?” Conspectus Magazine (February 2010) Article “How do you want yours served (data that is)” (BeyeNETWORK January 2010) Article “Seven deadly sins of data modelling” (BeyeNETWORK October 2009) Article “Data Modelling is NOT just for DBMS’s” Part 1 BeyeNETWORK July 2009 and Part 2 BeyeNETWORK August 2009 Web Channel: BeyeNETWORK “Chris Bradley Expert Channel” Information Asset Management http://www.b-eye-network.co.uk/channels/1554/ Article: “Preventing a Data Disaster” February 2009, Database Marketing Magazine
  8. 8. P / 9 Contents _Context _Approaches _Evaluation Method _Vendors and Products _Summary
  9. 9. P / 10 1. Context
  10. 10. page 11 Is there more to life than this? is ais ais ais ais ais ais ais ais a owns is assigned is owner of is owned by belongs to has registered at classifies has classifies transacted in parent of assigns assigns performs determines payee-correspondent relationship is granted has has requires Business Party Business Person Business Partner Legal Entity Address Type Address Bank Account Contact Counterparty Transportation Provider Financial Institution Exchange Inspector Broker Clearing HouseRegulatory Agency Rating Agency Currency Industry Classification Industry Classification Scheme Legal Entity Identification Legal Entity Identifying Scheme Bank Account Type Internal Operating Unit LE Ownership Indirect Tax Registration Tax Exemption License Contact Usage Contact Role Payment Security Type
  11. 11. page 12 DATA ARCHITECTURE MANAGEMENT DATA DEVELOPMENT DATABASE OPERATIONS MANAGEMENT DATA SECURITY MANAGEMENT REFERENCE & MASTER DATA MANAGEMENT DATA QUALITY MANAGEMENT META DATA MANAGEMENT DOCUMENT & CONTENT MANAGEMENT DATA WAREHOUSE & BUSINESS INTELLIGENCE MANAGEMENT DATA GOVERNANCE › Enterprise Data Modelling › Value Chain Analysis › Related Data Architecture › External Codes › Internal Codes › Customer Data › Product Data › Dimension Management › Acquisition › Recovery › Tuning › Retention › Purging › Standards › Classifications › Administration › Authentication › Auditing › Analysis › Data modelling › Database Design › Implementation › Strategy › Organisation & Roles › Policies & Standards › Issues › Valuation › Architecture › Implementation › Training & Support › Monitoring & Tuning › Acquisition & Storage › Backup & Recovery › Content Management › Retrieval › Retention › Architecture › Integration › Control › Delivery › Specification › Analysis › Measurement › Improvement Where does Data Modeling fit?
  12. 12. P / 13 Environmental Elements Used with kind permission of DAMA-I ACTIVITIES • Phases, Tasks, Steps • Dependencies • Sequence and Flow • Use Case Scenarios • Trigger Events ORGANIZATION & CULTURE • Critical Success Factors • Reporting Structures • Management Metrics • Values, Beliefs, Expectations • Attitudes, Styles, Preferences • Rituals, Symbols, Heritage DELIVERABLES • Inputs and Outputs • Information • Documents • Databases • Other Resources TECHNOLOGY • Tool Categories • Standards and Protocols • Section Criteria • Learning Curves PRACTICES & TECHNIQUES • Recognized Best Practices • Common Approaches • Alternative Techniques ROLES & RESPONSIBILITIES • Individual Roles • Organizational Roles • Business and IT Roles • Qualifications and Skills GOALS & PRINCIPLES • Vision and Mission • Business Benefits • Strategic Goals • Specific Objectives • Guiding Principles
  13. 13. P / 14 Data Management Tools provide support for the data management functions, including databases, modelling, governance, quality and virtualisation. Data Management Repositories are a class of Data Management Tool used to store, version and disseminate the metadata required to support the data management functions. What are Data Management Tools and Repositories?
  14. 14. P / 15 What is Metadata?
  15. 15. P / 16 Logical Data Structures Business Data Items Concepts Database Schemas Modelling Patterns Reports Screens Data Lineage Non-DBMS files XML Schemas What types of Information Metadata can you name? Information Metadata Lines represent shared metadata elements, and/or links between metadata elements.
  16. 16. P / 17 The metadata archipelago, simplified Information Application Architecture Technical Architecture Organisation Location People AssetsLocation People Assets IT Service Management Project and Resource Management Business Rules Business Processes Web Services Development Methods SOA IT Assets Software HardwareSoftware Hardware
  17. 17. P / 18 None of this is new In the early1980s, I used the ICL Data Dictionary System (DDS) to model and generate complete applications. Data Items were central to this – they were used in E-R models, screen dialogues, IDMSX tables etc. All this metadata was linked and reusable. Business Model Computer Model DataProcess Adapted from a diagram on page 27 of the ICL Technical Journal, Volume 8 Issue 1, May 1992
  18. 18. P / 19 “Data / Information” related Metadata e.g . “Process” related Metadata e.g. ENTITY EVENT PROCESS TRANSFORMATION FILE SYSTEM TABLE DTD FIELD INDEX SCHEMA XSD PACKAGE MODULE TRIGGER ROLE SERVERCOLUMN STORED PROCEDURE Types Of Metadata ATTRIBUTE FUNCTION WORKFLOW PROJECT PROGRAM BUSINESS RULE RELATION- SHIP “Real Business” World Metadata “IT / Systems” World Metadata
  19. 19. P / 20 What is a data modelling tool? _Is it a metadata management tool, or a modelling tool? _Modelling tools usually have pre-defined scope in terms of: ›The types of metadata supported ›The types of links it can create between metadata, within and between models _They may support: ›Model-driven architecture ›Model-driven analysis and design
  20. 20. P / 21 Possible metadata scope for data modelling _Business Requirements _Business Rules _Business Processes _Enterprise Architecture _Glossary of Terms _Object-Oriented Analysis and Design _Conceptual And Logical Data Structures _Physical Data Structures ›Databases ›XML Schemas _Data movements, transformations and replications
  21. 21. P / 22 Tool Genres _Modelling › Data Modelling Tools › Model Management Tools › Object Modelling Tools › Process Modelling Tools › XML Development Tools _Development and Maintenance › System Management Tools › Data Development and Administration Tools › Performance Management Tools › Data Security Tools › Collaboration Tools › Application Frameworks › Change Control Systems _Database Systems › Database Management Systems › Data Integration Tools › Database Administration Tools › Identity Management Technologies _Repositories › Meta-data Repositories › Document Management Tools › Configuration Management Tools › Reference and Master Data Management Tools › Issue Management Tools › Event Management Tools › Image and Workflow Management Tools › Records Management Tools _Analytical and Reporting › Business Intelligence Tools › Statistical Analysis Tools › Analytic Applications › Data Profiling Tools › Data Quality Tools › Data Cleansing Tools › Report Generating Tools › Data Governance KPI Dashboard
  22. 22. P / 23 We All Use Models
  23. 23. P / 24 We All Use Models
  24. 24. P / 25 Data Model Levels E N T E R P R I S E C O N C E P T U A L L O G I C A L P H Y S I C A L S Y S T E M IMPLEMENTATIONFOCUS COMMUNICATIONFOCUS
  25. 25. P / 26 Why Produce A Data Model? T O P R E A S O N S * 1. Capturing Business Requirements 2. Promotes Reuse, Consistency, Quality 3. Bridge Between Business and Technology Personnel 4. Assessing Fit of Package Solutions 5. Identify and Manage Redundant Data 6. Sets Context for Project within the Enterprise 7. Interaction Analysis: Complements Process Model 8. Pictures Communicate Better than Words 9. Avoid Late Discovery of Missed Requirements 10. Critical in Managing Integration Between Systems 11. Pre-cursor to DBMS design / generate DDL * DAMA-I Survey
  26. 26. P / 27 Enterprise Conceptual Logical Physical Enterprise vs. Conceptual vs. Logical Agree basic concepts and rules Detail, may lead to physical design Big picture Optimised for specific technical environment DIFFERENTPERSPECTIVESANDLEVELS OFDETAILFORDIFFERENTUSES › Common understanding before progressing too far into detail › Used to communicate with the Business › Overview: main entities, super types, attributes, and relationships › Lots of Many to Many & multi meaning relationships › Relationships frequently show multiplicity of meaning › May be denormalised › Non-atomic & multi-valued attributes allowed; no keys › Should fit on one page › 20% of the modelling effort › Detailed: ~ 5x Entities vs Conceptual model › Detailed: Frequently pre-cursor to 1st cut physical (database) design › Detailed: Key input to requirements specification › M:M relationships resolved: Intersection entities mostly have meaning › Relationship optionality added › Primary, foreign, alternate keys included › Reference entities included › Fully normalized – no multi-valued, redundant, non-atomic attributes › May be partitioned (sub-models) › 80% of the modelling effort CONCEPTUAL/LOGICALKEYDIFFERENCES
  27. 27. P / 28 Different Data Models For Different Audiences: Business
  28. 28. P / 29 Different Data Models For Different Audiences: Technical
  29. 29. P / 30 Why Data Modelling Is Critical BUSINESS ARCHITECTURE Business Objectives & Goals Motivations & Metrics Functions, Roles, Departments INFORMATION ARCHITECTURE Enterprise Data Model Conceptual Data Models Logical Data Models Physical Data Models PROCESS ARCHITECTURE Overall Value Chain High-Level Business Processes Workflow Models APPLICATION / SYSTEMS ARCHITECTURE Systems within Scope High-Level Mapping Business Services Presentation Services (use cases)
  30. 30. P / 31 Why Data Modelling Is Critical BUSINESS ARCHITECTURE Business Objectives & Goals Motivations & Metrics Functions, Roles, Departments INFORMATION ARCHITECTURE Enterprise Data Model Conceptual Data Models Logical Data Models Physical Data Models PROCESS ARCHITECTURE Overall Value Chain High-Level Business Processes Workflow Models APPLICATION / SYSTEMS ARCHITECTURE Systems within Scope High-Level Mapping Business Services Presentation Services (use cases) The company is undertaking a radical approach to enhance Customer experience, service and satisfaction by providing seamless multi-channel Customer access to all core services B U S I N E S S O B J E C T I V E S I N F O R M A T I O N S E R V I C E S B U S I N E S S S E R V I C E S PRESENTATION SERVICES B U S I N E S S P R O C E S S ALL of the Architecture disciplines use the language (and rules) of the data model
  31. 31. P / 32 Master & Reference Data Management Data Warehousing Business Intelligence Transaction Data Management Semi- Structured Data Management Content & Document Management Data Integration & Interoperability Data Quality Management Information Lifecycle Management Data Governance Data Asset Planning EIM Goals & Principles Big Data Analytics Data Models & Taxonomy's Metadata Management Geospatial Data Management Enterprise Information Management Framework Business-Driven Goals Drive a Robust Enterprise Information Management Practice EIM goals and strategies are business-driven for the entire enterprise, underpinned by guiding principles supported by senior management Proactive planning for the information lifecycle including the acquisition, manipulation, access, use, archiving & disposal of information The identification of appropriate data integration approach for business challenges e.g. ETL, P2P, EII, Data Virtualisation or EAI. The full lifecycle control and management of information from acquisition to retention & destruction Organise information to align with business & technical goals using Enterprise, Conceptual, Logical & Physical models Roles, responsibilities, structures and procedures to ensure that data assets are under active stewardship Processes, procedures and policies to ensure data is fit for purpose and monitored Metadata capture, management, & manipulation to place data in business & technical context Manage diverse data sources across the organisation from transaction data management, to data warehousing and business intelligence, to Big Data analytics
  32. 32. P / 33
  33. 33. P / 34 The Internet Of Things?
  34. 34. P / 35 Use a drawing tool?
  35. 35. P / 37 Why not use a drawing tool? Drawing tools communicate ideas visually, and may capture some additional information about some of the objects represented each diagram stands alone: if a process appears on five diagrams, there is no connection between those five symbols; you cannot ensure that those five diagrams are consistent in how they depict that process, nor is there a way of knowing whether or not the process also appears on a sixth diagram that you haven’t been told about
  36. 36. P / 38 Why not use a drawing tool? There is no link from that process to any objects that were generated from it, and no link to any associated objects Objects appear to live in a disconnected world. When it comes to managing change, that disconnected world gets very uncomfortable _In conclusion: Drawing tools do not manage metadata
  37. 37. P / 39 A typical Business Process Model This was created in a modelling tool, but could equally have been drawn in a ‘drawing tool’.
  38. 38. P / 40 Can a drawing tool be customised? Stereotypes have been used in the process model, to add new types of objects, and specialisations of standard objects, such as Resources, complete with custom symbols
  39. 39. P / 42 0,n Each New Entity must have one and only one Authorship. Each Authorship may have one or more New Entity. 1,1 1,1 Each Book Role may have one or more New Entity. Each New Entity must have one and only one Book Role. 0,n 0,1 Each Gender may classify one or more Person. Each Person may claim at most one Gender. 0,n 0,1 Each Person must play one or more Book Role. Each Book Role may be played by at most one Person. 1,n 1,1 Each Book Role must be recognised via one or more Authorship. Each Authorship must be attributed to one and only one Book Role. 1,n Authorship Person Name Book Title Royalty Percentage Characters (100) Characters (100) Number (2) Primary ID <pi> Primary Identifier Relationship 'Author-Authorship' Relationship 'Relationship_6' Book Role Person Name Book Role Type Pseudonym Characters (100) Number (2) Characters (100) Primary ID <pi> Primary Identifier Relationship 'Person Role' Relationship 'Author-Authorship' Relationship 'Relationship_5' Person Person Name Gender Code Characters (100) Characters (60) Primary ID Alt <pi> <ai> Primary Identifier Relationship 'Reference_3' Relationship 'Person Role' Gender Gender Code Gender Name Characters (60) Characters (256) Key_1 <pi> Primary Identifier Relationship 'Reference_3' New Entity Aut_Person Name Book Title Person Name Characters (100) Characters (100) Characters (100) Identifier_1 <pi> Primary Identifier Relationship 'Relationship_5' Relationship 'Relationship_6' Can a drawing tool mimic a different tool? Customised to look like it was produced by a different tool
  40. 40. P / 43 Can a drawing tool create matrices? Like this editable matrix showing links between LDM attributes and domains Or this editable matrix showing links between LDM attributes and Business Rules
  41. 41. P / 44 Can a drawing tool link models? This visual representation shows you the ‘generation links’ between a Logical Data Model and a Physical Data Model
  42. 42. P / 45 Can a drawing tool trace dependencies? Like the impact and lineage analysis for this column, which is: • used by two indexes and one key • linked to a Business Term and a Requirement • governed by a domain shared from another model • a foreign key and was generated from a LDM attribute
  43. 43. P / 46 Can a drawing tool create an OLAP Cube? Like converting the tables and references at the top to the design of an OLAP Cube at the bottom. Can it also generate the OLAP Cube, complete with the scripts used to transfer the data from the relational database? Can it reverse-engineer an OLAP Cube? FK_MONTH_RELATIONS_YEAR FK_BOOK_SAL_RELATIONS_MONTH FK_BOOK_SAL_RELATIONS_PUBLICAT Publication Book Title Publication Media Type Code Publication Date Dollar List Price ISBN Page Count Primary Author Name Primary Author Pseudonym Primary Author Royalty Percent age CHAR(100) NUMBER(2) DATE NUMBER(5,2) NUMBER(13) NUMBER(4) CHAR(100) CHAR(100) NUMBER(2) <pk> <pk> <i> <i> not null not null null not null not null null not null null null Book Sales Year Code Month Code Book Title Publication Media Type Code Gross Sales Value Amount NUMBER(2) NUMBER(2) CHAR(100) NUMBER(2) NUMBER(5,2) <pk,fk1> <pk,fk1> <pk,fk2> <pk,fk2> <i1,i2> <i1,i2> <i1,i3> <i1,i3> not null not null not null not null not null Month Year Code Month Code Month Description CHAR(60) CHAR(60) CHAR(256) <pk,fk> <pk> <i1,i2> <i1> not null not null null Year Year Code Year Description CHAR(60) CHAR(256) <pk> <i> not null not null Book Sales - Year_Month Book Sales - Publication Measure Book Sales Gross Sales Value Amount Year Code Month Code Book Title Publication Media Type Code Year_Month Year Year Code Year Description Month Year Code Month Code Month Description <h:1> <h:2> <h:3> Hierarchy_1 <Default> <h> Publication Book Title Publication Media Type Code Publication Date Dollar List Price ISBN Page Count Primary Author Name Primary Author Pseudonym Primary Author Royalty Percentage <h:1> <h:2> Hierarchy_1 <Default> <h> Attributes Hierarchy
  44. 44. P / 47 Can a drawing tool manage versions, branching and configurations? Repositories can support multiple versions of models, branching to support multiple environments, and the grouping of related models to form configurations. Permissions can be varied by repository folders, branches, projects, and models.
  45. 45. P / 48 Can a drawing tool provide access via a Portal?
  46. 46. P / 49 Can a drawing tool merge or compare models?
  47. 47. P / 51 Can a drawing tool present a different UI to different users? Can a drawing tool give you control of naming standards?Can a drawing tool tell you which diagrams an object is in? Does a drawing tool allow you to add functionality? Can a drawing tool generate HTML or RTF reports?
  48. 48. P / 52 2. Approaches _3 different approaches to selecting tools
  49. 49. P / 53 Approaches
  50. 50. P / 54 TacticalEnterprise Best of Breed Approaches
  51. 51. P / 55 Tactical Tool (E.g. Visio/PowerPoint) _Advantages ›Best quick fit for the job ›Just-in-time, meets immediate need ›Quick (often zero) selection and procurement process ›Already have expertise (“Use what you know”) _Disadvantages ›Does not meet strategic, long-term needs ›No metadata repository ›Lack of interoperability – difficulty in integrating data for holistic view ›Limited re-use / leverage possibilities
  52. 52. P / 56 Enterprise Toolset _Benefits ›Multiple modelling capabilities (e.g. Data, Process, Enterprise, Technology …..) ›Artefacts linked & shared in Enterprise class repository ›Skills can be shared across the enterprise ›Modelling artefacts stored and used consistently ›Aligned with strategic goals _Disadvantages ›One size does not fit all, one “modelling” area capability may be sub- optimal ›Easy to over-provision, unused functionality ›Protracted, expensive selection and procurement process ›May force unfamiliar techniques on users the needs of the many outweigh the needs of the few
  53. 53. P / 57 Best of Breed Tooling _Benefits ›Best fit for a single modelling discipline ›Skills can be shared across the enterprise ›Volume license discounts ›Modelling artefacts in one type stored and used consistently _Disadvantages ›Exchange of artefacts between modelling tools ›Potentially requires a 3rd party repository ›Potentially requires support from multiple vendors for full modelling reach ›Great care required in selection and procurement process
  54. 54. P / 58 Some enterprise capable, others suitable for small-scale modelling only Some specific to data modelling, others cover other aspects of modelling, e.g. process modelling, business architecture, enterprise architecture Some provide data modelling capability only as part of other functionality, e.g. part of database programming IDE Some targeted at particular database platforms, whilst others are cross- platform A wide range of tools available Data Modelling Tools
  55. 55. P / 59 Each tool has strengths and weaknesses Consider capabilities in non- data modelling areas too Differences may not be obvious from vendor marketing Take time to evaluate capabilities to select tool that meets your needs Summary Data Modelling Tools
  56. 56. P / 60 3. Evaluation method
  57. 57. P / 61 Outline Evaluation Method
  58. 58. P / 62 Steps 1-6 1. Establish TOR & Identify requirements 2. Identify constraints 3. Tailor evaluation criteria 4. Assign weightings to evaluation criteria 5. Compile an initial list of candidate products 6. Evaluate products • Evaluation Terms of Reference • Statement of client requirements: • High level functional requirements • Performance & technical requirements • Commercial requirements • Key decision making criteria. Key documents and deliverables : • Statement of client constraints: E.g. hardware; existing software; operating system; cost; migration effort & timescale, risk, staff skill • Tailored, product & vendor evaluation questionnaires • Tailored, unweighted product & vendor evaluation spreadsheet(s) • Weighted product & vendor evaluation spreadsheet(s) • Short list of candidate products • Product elimination or exclusion reasons • Supplier pre-qualification questionnaires and/or briefings prepared and issued • Progress log for tracking status of communications with suppliers. • Issued vendor questionnaires, • Vendor visit reports, • User visit reports Step From 9 (sensitivity analysis)
  59. 59. P / 63 Steps 7-10 To 4 (assign weightings) 7. Apply the evaluation criteria against the products 8. Score and rank the products 9. Perform sensitivity and "what if" analyses 10. Present the findings * Reduce product list * If necessary re-address weightings * More detailed evaluation of reduced product list • Updated product and vendor evaluation spreadsheet(s) • In house demonstration / proof of concept report • Scored & ranked spreadsheets • Recommendation and rationale for exclusion / adoption of products • Obtain further information • Confirm recommendation • Product findings management summary • Evaluation spreadsheet(s) • Vendor product literature • Product pros & cons • Recommend product + implications for use • Recommended next steps (e.g. procurement, negotiate terms, POC, installation, etc)
  60. 60. P / 64 Why follow an evaluation approach? ›Often several different tools are already in place for different user communities. ›Evaluation must be seen to be fair and equitable, and address the priority requirements. ›A selection cannot be effective if a checklist is the sole justification of whether a product or supplier is suitable or not. ›A product rating highly on say the technical evaluation criteria may not always be the best one for your particular environment, i.e. staff skill levels, attitude to risk etc. ›Selection must not be solely based on how “good” a product is, but rather on how well suited it is to business needs. ›The chosen product may not necessarily be the “best” on the market but it will be the one that best meets your requirements, and yields the optimum benefits. ›Critical that requirements are fully understood, documented and prioritised. ›Take advice to highlight the implications of requirements (or sometimes lack of them) before the evaluation progresses too far.
  61. 61. P / 65 Evaluation Criteria _Sources of knowledge _Weighting approach _Rating & Scoring approach
  62. 62. P / 66 Evaluation criteria - sources I N F O R M A T I O N N E C E S S A R Y T O S C O R E T H E D E T A I L E D Q U E S T I O N S F O R E A C H P R O D U C T W I L L B E D E R I V E D F R O M M A N Y P L A C E S I N C L U D I N G : - ›Actual experience of the evaluation team; ›Experience of other client staff; ›Reference visits, talking to existing users; ›Liaison and discussion with the vendors; ›Trying out the vendor training programs; ›Feedback from the press and the general marketplace; ›Evaluation reports such as those from Gartner, Ovum and Forrester; ›Consultancy support /previous evaluation projects; ›Benchmarking / trial licence use; ›Gut feel; ›……..
  63. 63. P / 67 Evaluation criteria - weighting A S S I G N W E I G H T S S U C H T H A T T H E S U M O F T H E W E I G H T S F O R A L L O F T H E C R I T E R I A I N T H E S U B - S U B - S E C T I O N E Q U A L S 1 0 0 ; ›The same technique is applied at the sub-section level to indicate the relative importance of the sub-section within a section; ›The same technique is then applied at the section level to indicate the relative importance of that section for a particular product type. ›If the evaluation is not just concerned with a single product type but with a complete package of products from a single vendor, an additional level of weighting will be used to indicate the relative importance of each product type within the complete evaluation package. ›Each of the team members, in isolation, should apply weightings to the criteria. ›Review the complete set of weightings to identify any major discrepancies, these should be discussed and finally agreed to give an overall team weighting. ›Undertake the weighting approach before any detailed investigation is undertaken.
  64. 64. P / 68 Section Heading Sub section Question Question weight Sub section weight Section weight 1. Vendor 40 1.1 Co details (general) Several questions / possibly sub-sub sections 10 1.2 Co details (product) Several questions / possibly sub-sub sections 10 1.3 Product plans Several questions / possibly sub-sub sections 15 1.4 Support Services / training Several questions / possibly sub-sub sections 25 1.5 Commercials Several questions / possibly sub-sub sections 40 100 2. Data Modelling Functional Capabilities 20 2.1 Model levels & types supported 40 Enterprise & Conceptual model support 30 Logical model support 30 Physical model support 20 Dimensional model support 20 100 2.2 Methods & notations Several questions / possibly sub-sub sections 20 2.3 Sub model support Several questions / possibly sub-sub sections 20 2.4 Validation & standards Several questions / possibly sub-sub sections 20 100 3. Interfaces & Integration Several questions & sub sections 10 4. Management , Collaboration & Extension Several questions & sub sections 10 5. Repository Several questions & sub sections 10 6. Non functional Several questions & sub sections 10 TOTAL 100
  65. 65. P / 69 Evaluation criteria - rating 9-10 The product adequately satisfies the criterion and, in addition, has significant usable advantages. A nine or ten is assigned based on the strength of these advantages. 8 The product adequately satisfies the criterion, but has no particular strong points or weak points. 5-7 The product meets the criterion but has some weak points. In certain cases, the weak points can be somewhat counterbalanced against identified strong points. In other instances, the solution could provide functional capabilities but will be penalized for the ease of use of the features. 1-4 The product meets the criterion on a marginal basis only, and, in addition, has significant weak points. In some instances, a weakness in a particular area can be viewed as a technical constraint in the use of the product. It will also imply that user programming or other non trivial work rounds will be required to meet a particular requirement. 0 The product does not meet the criterion. Total user implementation will be required. Each member of the team, in isolation, performs the rating of the products. Review to identify any discrepancies. Discuss amongst the evaluation team & averaged to give a team rating. Rating for each product is multiplied by weighting yielding a score for that product. Scores are computed for all criteria; summarized to the sub-section, section, product type and total level.
  66. 66. P / 70 Evaluation Categories _Example data modelling tool evaluation categories
  67. 67. P / 71 Evaluation criteria (vendor) T E X T _Company Details (General) ›Size, Turnover, Geographic reach …. _Company Details (Product Division) ›Product family tree, history, acquisition …. _Product Plans / Philosophy ›Product direction, enhancement approach, vision …. _Support, Services, Training ›Problem solving, fixes, professional services, training …. _Contractual Terms / Commercial ›Licensing types, concurrent, named users, discount….
  68. 68. P / 72
  69. 69. P / 73
  70. 70. P / 74 Master & Reference Data Management Data Warehousing Business Intelligence Transaction Data Management Semi- Structured Data Management Content & Document Management Data Integration & Interoperability Data Quality Management Information Lifecycle Management Data Governance Data Asset Planning EIM Goals & Principles Big Data Analytics Data Models & Taxonomy's Metadata Management Geospatial Data Management Reference Architecture W H A T I S T H E C O M P L E T E P A C K A G E R E Q U I R E D T O B E H I G H L Y E F F I C I E N T W . R . T . D A T A M O D E L L I N G A C R O S S T H E E N T E R P R I S E ? EIM goals and strategies are business-driven for the entire enterprise, underpinned by guiding principles supported by senior management Proactive planning for the information lifecycle including the acquisition, manipulation, access, use, archiving & disposal of information The identification of appropriate data integration approach for business challenges e.g. ETL, P2P, EII, Data Virtualisation or EAI. The full lifecycle control and management of information from acquisition to retention & destruction Organise information to align with business & technical goals using Enterprise, Conceptual, Logical & Physical models Roles, responsibilities, structures and procedures to ensure that data assets are under active stewardship Processes, procedures and policies to ensure data is fit for purpose and monitored Metadata capture, management, & manipulation to place data in business & technical context Manage diverse data sources across the organisation from transaction data management, to data warehousing and business intelligence, to Big Data analytics
  71. 71. P / 75 Data Modeling Tool Features _With life and data modeling tools, you get what you pay for. _The more you pay, the more features are provided by the product. _The wider your use of data models within the organization, the more features you tend to need.
  72. 72. P / 76 Evaluation criteria (tools) R E Q U I R E M E N T S / E V A L U A T I O N C A T E G O R I E S _Data Modelling Functional Capabilities _Interfaces & Integration _Management & Collaboration _Repository _Non-functional
  73. 73. P / 77 1. Data Modelling Functional Capabilities Model levels & type supported  Enterprise, Conceptual, Logical, Physical, Dimensional Methods / Notations  ER, UML Class Diagrams, Barker, Object Relational Sub models  Model partitioning, Model linking, Generalisation & Inheritance Validation, Standards  Model validation, Own rules
  74. 74. P / 78 “A Picture is Worth a Thousand Words” Examples of High-Level Data Models
  75. 75. P / 79 Is Notation Important? _Many Notations can be used to express a high-level data model _The choice of notation depends on purpose and audience _For data-related initiatives, such as MDM and DW: ›ER modeling using IE (Information Engineering) is our choice of notation ›It is important that your high-level model uses a tool that can generate DDL, or can import/export with a tool that can ›A repository-based solution helps with reuse and standards for enterprise- wide initiatives _We’re not producing art
  76. 76. P / 80 Blog: Information Management, Life & Petrol http://infomanagementlifeandpetrol.blogspot.com
  77. 77. P / 81 Merise Notation 0,n (1,1)0,n 1,1 0,n Inheritance_1 Entity_1 Sub Type Super Type Relationship_1 Relationship_2 Entity_4
  78. 78. P / 82 Barker Notation Relationship_2 Relationship_1Entity_1 Super Type Sub Type
  79. 79. P / 83 IDEF1X Notation Relationship_2 Relationship_1 Inheritance_1 Entity_1 Sub Type Super Type
  80. 80. P / 84 Information Engineering Relationship_2 Relationship_1 Inheritance_1 Entity_1 Sub Type Super Type
  81. 81. P / 85 1. Data Modelling Functional Capabilities: Usability _C o n t r o l o v e r t h e w i n d o w s a n d t o o l b a r s d i s p l a y e d , a n d t h e i r p o s i t i o n a n d s i z e _A u t o - l a y o u t o f s e l e c t e d p a r t s o f a d i a g r a m o r a c o m p l e t e d i a g r a m _C o n t r o l o f t h e p l a c e m e n t o f s y m b o l s o n a d i a g r a m b y t h e a n a l y s t _C o n t r o l o v e r t h e s t y l e a n d c o n t e n t o f s y m b o l s b y t h e a n a l y s t _F l e x i b l e e d i t i n g c a p a b i l i t i e s _F l e x i b l e d i a g r a m p r i n t i n g c a p a b i l i t i e s _R e p l a c e s e l e c t e d t e x t i n o b j e c t n a m e s a n d o t h e r p r o p e r t i e s _A n n o t a t e d i a g r a m s w i t h a d d i t i o n a l s y m b o l s t o i m p r o v e c o m m u n i c a t i o n _E x p e r t , t i m e l y s u p p o r t a v a i l a b l e
  82. 82. P / 86
  83. 83. P / 87
  84. 84. P / 88 2. Interfaces & Integration Generation capabilities  DBMS, XML, Cubes, Rules, Stored Procedures, Triggers, SOA,  Roll up / down Reverse Engineering  ER <> Class, XML, DBMS Integration  Business process, EA, Dev tools MetaData Exchange  XMI, Direct tool interfaces, Missing component reporting
  85. 85. P / 89 2. Interfaces and Integration _ I m p o r t e x i s t i n g d a t a m o d e l s c r e a t e d b y o t h e r t o o l s _ R e v e r s e e n g i n e e r e x i s t i n g d a t a a r t e f a c t s , s u c h a s X M L s c h e m a s a n d d a t a b a s e s c h e m a s , i n t o p h y s i c a l d a t a m o d e l s _ G e n e r a t e o r u p d a t e a n e x t e r n a l d a t a a r t i f a c t , s u c h a s a n X M L o r d a t a b a s e s c h e m a , f r o m a d a t a m o d e l _ C r e a t e o r u p d a t e a m o d e l b a s e d u p o n i n f o r m a t i o n h e l d i n s p r e a d s h e e t s _ C o m p a r e t w o d a t a m o d e l s , a n d u p d a t e o n e o r b o t h m o d e l s a s a r e s u l t ( t h i s m a y a l s o b e r e f e r r e d t o a s m e r g i n g m o d e l s ) _ C o m p a r e a d a t a m o d e l w i t h a n e x i s t i n g d a t a a r t i f a c t , s u c h a s a n X M L o r d a t a b a s e s c h e m a , a n d u p d a t e t h e m o d e l a n d / o r t h e d a t a a r t i f a c t a s a r e s u l t _ E x p o r t d a t a m o d e l s i n a f o r m a t t h a t c a n b e o p e n e d b y o t h e r t o o l s _ B u i l d c r o s s - r e f e r e n c e s o r t r a c e a b i l i t y l i n k s b e t w e e n d a t a m o d e l o b j e c t s a n d o b j e c t s d e f i n e d i n o t h e r t y p e s o f m o d e l s , s u c h a s b u s i n e s s p r o c e s s o r e n t e r p r i s e a r c h i t e c t u r e m o d e l s _ I n t e g r a t i o n w i t h p o p u l a r d e v e l o p m e n t e n v i r o n m e n t s _ I n t e g r a t i o n w i t h p r o c e s s a n d p o r t f o l i o m a n a g e m e n t w o r k f l o w s , a n d w i t h I T c o n f i g u r a t i o n m a n a g e m e n t _ G e n e r a t e s c r i p t s t o m a n a g e t h e m o v e m e n t o f d a t a t h r o u g h t h e a r c h i v i n g c y c l e , o r d a t a m o v e m e n t s
  86. 86. P / 90
  87. 87. P / 91 3. Management, Collaboration & Extension Collaboration  Multi team usage, Team working capabilities, Workflow Ease of Use  Global standards, Search, Usability Import, Merge, Compare  Types of import, Conflict resolution, Comparison types, Generate delta vs. total DDL Extensibility  User defined properties, New metadata types, Open API, Macros, Published Object Model, Published Repository Model Security & Control  Access Control, Content Control, Functionality Control, Version control, Auditing Reporting  Definition, Publishing, Web / Intranet Portal
  88. 88. P / 92 3. Management, Collaboration & Extension _E x t e n d t h e t o o l ’ s u n d e r l y i n g d a t a m o d e l , a l l o w i n g a n a l y s t s t o c h a n g e t h e w a y i n w h i c h m o d e l o b j e c t s a r e d e f i n e d a n d t o d e f i n e n e w t y p e s o f m o d e l o b j e c t s _E x t r a c t i n f o r m a t i o n f r o m m o d e l o b j e c t s f o r p u b l i c a t i o n i n v a r i o u s f o r m a t s , s u c h a s H T M L a n d d o c u m e n t s _P r o v i d e a c c e s s t o t h e c o n t e n t o f m o d e l s v i a a p r o g r a m m a b l e i n t e r f a c e , t o p r o v i d e a m e c h a n i s m f o r t h e a u t o m a t i o n o f r e p e t i t i v e t a s k s , a n d t o e x t e n d t h e f u n c t i o n a l i t y p r o v i d e d b y t h e t o o l _I n t e g r a t e w i t h L D A P / A c t i v e D i r e c t o r y f o r u s e r a u t h e n t i c a t i o n
  89. 89. P / 93
  90. 90. P / 94 & version control & auditing …
  91. 91. P / 95 4. Repository Tool Integration & Data Definition  Model repository, Active vs Passive, Types, Validation Architecture  Stand alone tool, Repository architecture, Reporting, CWM Extensibility  Own metadata, non “data model” metadata
  92. 92. P / 96
  93. 93. P / 97 Example Repository Uses Common requirements? •Metadata of ROR’s •Models of ROR •Business definitions •Ownership •Stakeholders •Provenance •Business roles •Security •Version control •Synonym support •Reporting • ……. SoA Services Directory Data Distribution Services (eg DV) Enterprise Data Catalogue Data Modelling Repository
  94. 94. P / 98 5. Non Functional Cloud Data Modelling Service  Provision & use in Cloud, DMaaS, Cloud licensing User Group  Vendor support, How active Documentation  Product, Quick Start standards
  95. 95. P / 99 0 200 400 600 800 1000 1200 WeightedTotal Weighted Total Summary Soft Issues Data Modelling Respository Management Features Interfaces and Integration Modelling
  96. 96. P / 100 Enterprise Repository Data Modelling Business Process Technology Infrastructure E Enterprise Repository Data Modelling Business Process Technology Infrastructure A Enterprise Repository Data Modelling Business Process Technology Infrastructure P Enterprise Repository Data Modelling Business Process Technology Infrastructure S
  97. 97. P / 101 4. Vendors and Products
  98. 98. P / 102 Process Modelling Tools
  99. 99. P / 103 Data Integration Tools
  100. 100. P / 104 Object Modelling Tools
  101. 101. P / 105 Business Intelligence/Reporting Tools
  102. 102. P / 106 Data Quality/Profiling/Cleansing Tools
  103. 103. P / 107 Data Modelling Tools http://www.information- management.com/media/pdfs/MySoftForge.pdf http://en.wikipedia.org/wiki/Comparison_of_ data_modeling_tools Dezign Modelright Others See databaseanswers.com
  104. 104. P / 108 Metadata Repositories Rochade Adaptive Metadata Manager Dataflux etc
  105. 105. P / 109
  106. 106. P / 110 5. Summary
  107. 107. P / 112 Summary 3 approaches to evaluating tools An evaluation method provides auditability Weight before scoring Get stakeholders involved It’s YOUR requirements, NOT an academic study Information is at the heart of all architecture disciplines There's more to modelling than just data
  108. 108. P / 113 And finally The quality and reliability of comparative evaluations issued by vendors varies significantly  the worst we’ve seen (very recently) was ‘unofficial’, presumably produced by a sales rep for a particular customer. It was completely unprofessional, the sole intention was to rubbish a competitor, no matter how true it was. For example,  claiming that the other tool doesn't support feature Y, just because it doesn’t have a feature called Y - in this case, it does support that feature, just happens to give it a different name  missing information – “my tool supports both relational and dimensional modelling” – doesn’t mention the fact that the other tool also supports both of them  apparent hearsay – throwaway comments such as “they say that my tool can leverage colour better than tool Y” with no supporting information  it’s very difficult to produce an unbiased and detailed comparison of tools, as very few people know the target tools in sufficient detail  take everything with a huge pinch of salt – take time to come to your own conclusions H O W M U C H C A N Y O U R E L Y O N T O O L C O M P A R I S O N S F R O M V E N D O R S ?
  109. 109. P / 114 Are you going this year? It’s at October 5th-7th at Chapel Hill, North Carolina. Find out more or register at http://datamodelingzone.com Alternatively, go to Hamburg, Germany September 28th-29th. To receive a discount of 20% when you register, use this discount code – MCGEACHIE
  110. 110. P / 115 Contact: My blog: Information Management, Life & Petrol http://infomanagementlifeandpetrol.blogspot.com @InfoRacer uk.linkedin.com/in/christophermichaelbradley/ Christopher Bradley Information Strategy Advisor +44 7973 184475 chris@chrismb.co.uk

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