Building a strong Data Management capability with TOGAF and ArchiMate

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This is the deck that I used for my presentation at the EAM conference in 2013. It gives a high-level overview of the need for a solid data management capability before giving and overview of how enterprise architecture methods can be used to build this capability.

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  • This slide (and the next) appeal to BiZZdesign’s vision on using open approach: don’t re-invent the wheel but use what is already there. DAMA = DAta Management AssociationDMBOK = Data Management BOdy of KnowledgeThis framework is broad and descriptive. It can be used as a reference model to help (a) standardize terminology, (b) decide which parts to implement in the organization, and so on. There is some talk online of TOGAF and DMBOK getting more and more aligned. Another reason for going down that road.
  • Slide 8 was our proposition. That’s the “what” part. This slide is the “how” part. In order to help build a strong DM capability we use 3 ingredients:TOGAF: provides the general frame. Start with a vision, build a model of where we are (baseline) and where we want to be (target) and find a way to get there several steps. Implementation governance keeps us on track (stay aligned with business vision)CBP is one of the techniques from TOGAF. In the early phases (vision mostly) we figure out the current level of capbility along the elements of the DMBOK framework: how good is our process? how good are our tools? our people? This also helps to establish the desired increment which drives the initiative. Note: Dick made a first version of tool-support for CBP!For everything that relates to modeling at the enterprise level, we’ll use ArchiMate. When discussing this, be sure to mention that detailed models (ERD, UML) may still be necessary. We’re working on ERD support
  • We’re now diving into a set of ArchiMate-styled diagrams with extra eye-candy. For now that is all powerpoint. At some point we will actually have to build similar visuals in the tool. When presenting, point out that these are all views on an integral model. We’re starting with governance and linking it to the other aspects.The data council is a key group of people in the organization – at least from a data perspective. A steward (modeled as a business role in ArchiMate) is responsible from a business perspective for the information in a specific information area (see the work of James Martin). Stewards talk to their IT-counterparts and together they figure out the best way of realizing data requirements.
  • Next step: build the bridge to IT. This is where the strength of ArchiMate kicks in: Entities (modeled as BusinessObjects) are realized in various places in the IT landscape. A common source of data quality (DQ) issues is mis-match in data definitions when building IT systems. This is inherent to the way we build our systems: write up requirements, and design a system specific to those requirements without looking at the bigger picture.Stress another strength of ArchiMate here: this is the perfect spot for building and maintaining a conceptual data model (enterprise data model) that can be re-used across IT implementation projects
  • With many manifestations of a business entity in the application landscape, there is often a need for an integral view of key entities. Typically customers, products and components, etc. MDM (Master Data Management) is about creating a golden record, a single version of the truth. There are many architectures for achieving this (system of record / system of reference, using a service oriented approach, etc)There are many levels for modeling MDM, such as:which data objects are used for the creation of a golden record  that’s visualized herewhich attributes of DO’s are used in the golden record how data flows into an MDM system etc.
  • Another interesting topic in this field is BI and DW. This is an area that is typically associated with a lot of specialist modeling techniques such as:data vaultsnowflakestarschemaetc.At the enterprise level we tend to keep things at a higher level. The actual details of staging and data transformations in the EDW are not relevant here. From an enterprise perspective it is useful to see the lineage of data: how does data flow through the application landscape and end up in BI products. Here we can see how the EDW takes data from the MDM environment (which is integrated, and most likely cleansed) as well as the ERP system. More detailed analysis up to the attribute level is possible if necessary.
  • Metadata management is a big discipline. Meta-data is every where:business metadata says something about stewardship, definitions, quality, where data is used etc.technical metadata says something about column definitions, field specifications etc. in the IT systemnote that there’s also specific metadata for things like BI and MDM. Here we see a subset of the metadata about the Customer entity in the middle:the stewardprocesses that it is involved in (as a label view)data quality requirementsmanifestations in the IT landscape including an indication of which one is the golden record
  • In this slide I’ve attempted to bring together part of the details from the previous slides. Don’t go over all the details again. Instead, explain the strength of ArchiMate: integral modeling within a domain as well as across domains. Also emphasize the fact that the fact that this is a formal model helps in doing all sorts of analysis (impact of change, analyze damages and so on).
  • Not all clients will want to see this. Keep it handy when someone asks: this shows the formal metamodel for doing DM with ArchiMate. On the left is a predicate model (visualized as an ORM diagram), on the right is its ArchiMate counterpart.
  • Building a strong Data Management capability with TOGAF and ArchiMate

    1. 1. Building a strong data management capabilitywith TOGAF® and ArchiMate®
    2. 2. Dr. Bas van Gils2+31-(0)6-484 320 88b.vangils@bizzdesign.nlhttp://linkedin.com/in/basvghttp://blog.bizzdesign.comhttp://www.twitter.com/basvg“Life is and will ever remain an equation incapable of solution, but it contains certain known factors.”--Nikola Tesla (1935)
    3. 3. Agenda• Why data management matters• Setting the scene:– Introduction TOGAF– Introduction ArchiMate• Using TOGAF and ArchiMate asinstruments to build a strongdata management capability• More informationComicsfrom:http://www.datagovernance.com
    4. 4. WHY DATA MANAGEMENT MATTERS
    5. 5. Does this sound familiar?Delivering answers to anintelligence questiontakes several days. Weneed the answer today!We do not have a single,unified view of ourcustomer or our productsWe don’t trust ourintelligence data. Qualityhas been an issue toooften in the past.Tracing customercomplaints, late orders,defective parts isextremely hard for us
    6. 6. 6Data Management:NoSQL, Big Data, BI, Business Policy/ Rules, MDM, Meta-data, DataWarenhouseStrategic Management:Business ModelCanvas, Innovation, BusinessIntelligence, business model
    7. 7. Data as an asset- Jill Dyché, IRM UK 2013 MDM/DG keynote “Big Data and Data Asset Management”
    8. 8. Map out the enterprise[Master] Data Management programs cause change: todata, to systems, to business process, to people and tothe enterprise. An organization should map out theirorganization to identify the data, systems, process andpeople affected by the initiative and how they will beaffected.-- Whitepaper “Why MDM projects fail and what this means for big data” by Entity
    9. 9. INTRODUCTION TOGAF AND ARCHIMATE
    10. 10. Ingredients of an EA framework10View-pointsRepository, Reference ModelsProcess LanguageArchiMateTOGAF
    11. 11. ArchiMate + TOGAF11
    12. 12. Capability based planning12
    13. 13. Applying ArchiMate® 2.0Baseline Target13
    14. 14. USING TOGAF AND ARCHIMATE AS INSTRUMENTS TOBUILD A STRONG DATA MANAGEMENT CAPABILITY
    15. 15. Framework for Data Management• Many frameworks have beenproposed for DM– Focus on an aspect of DM– Focus on tooling for DM– Focus on a process for DM– ...• This presentation is based on theDAMA DMBOK framework– Integrated approach to DM– Easy to align with the OpenGroupstandards for Enterprise Architecture
    16. 16. Building a DM capability using EA methodsTOGAF provides a structuredmethod for effectivelyrealizing a business vision:• start with a business vision forthe data management capbility• develop baseline & targetarchitecture• incremental realizationCapability based planning is akey ingredient for succesfulenterprise-wide change• DMBOK Elements map onaspects of the DM capability• Provides a basis forroadmapping: what goes first?ArchiMate is the language ofchoice for enterprise-levelmodeling• High level modeling within &across domains• Basis for analysis &visualization• Aligned with TOGAF & DMBOK
    17. 17. Data governance• Integral, enterprise widegovernance is key to success• The role of the data steward iscrucial: s/he is responsible fordata quality in a specific subjectarea• Data stewards co-ordinate theirwork in a data council which isjointly responsible for theinformation landscape
    18. 18. Information versus data• Several Entities are pertinent in thecontext of a Subject Area• Several systems may manage DataObjects that realize (part of) theinformation that is pertinent tothese Entities• In terms of decomposition:– At the architecture level wework with Business Objects andData Objects– These may be refined in an ERDdiagram at the design level
    19. 19. Master Data Management (MDM)• Many organizations have to jugglewith disparate administrations ofkey entities• Having an integral overview ofthese entities is key to businesssuccess• MDM is the discipline of providingthis integrated view. There aremany (IT) patterns to realize thisgoal. In modern architectures itoften ties in with SOA
    20. 20. Business Intelligence & Data Warehousing• Transaction systems directlysupporting business processesmaintain data at a low level ofgranularity• Business Intelligence is a query,analysis, and reporting capability ofthe organization that providesinsight in historical and aggregateddata of the organization• A data warehouse (EDW) is atechnical environment that enablesBusiness Intelligence
    21. 21. Meta-data management• Meta-data is often defined as“data about data”.• A distinction must be madebetween– Business meta-data– Technical meta-data• Everything that we model aboutdata can be seen as meta-data:– Properties– Documentation– Relations to other objects
    22. 22. OverviewStewardship of aninformation areaDecomposition of aninformation area in entitiesMastering of dataobjects in an MDMenvironmentData movement to anEDW environment forBI products such asfinancial reports,production reports,governance logs etc.Data Object counterpartsof entities are stored ininformation systems
    23. 23. Switching between visualizations
    24. 24. Alignment with the business, strong datagovernance, and grip on the informationlandscape enables the organization to getanswers quicker.Master Data Management (MDM)helps the organization to define asingle unified view of key EntitiesCentral meta data management (businessdefinitions, links to processes, lineage insystems) and strong governance results inincreased trust in data quality.Data issues often lurk underbusiness issues. A model-basedapproach covering all related enterprise-issues will enable the organization tohandle these issues effectivelyProblem solved?Delivering answers to anintelligence questiontakes several days. Weneed the answer today!We do not have asingle, unified view of ourcustomer or our productsWe don’t trust ourintelligence data. Qualityhas been an issue toooften in the past.Tracing customercomplaints, late orders,defective parts is extremelyhard for us
    25. 25. Both the information landscape, system landscape, and organization /governance structure around data management are highly complex.This complexity is managed by using ArchiMate modeling withBiZZdesign ArchitectTake AwayData management is not a project; it is a continuous process. Thiscapability will contribute to sustainable business success.An architecture-approach will help the organization to build a strongdata management capability.Organizations are increasingly dependent on quality data / informationto run their business. Therefore: data management is a business issue.
    26. 26. ©BiZZdesign. All rights reserved.BiZZdesign and BiZZdesign logos are registered trademarks of BiZZdesign Company.

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