data modelling - bcs modelling 1970-1990 enterprise data management coordination enterprise data...
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Data Modelling
Where did it all go wrong?DAMA London, 15th June 2007Ch i t h B dl
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Contents
1. Background2. Seven deadly sins3. Our part in fixing this
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Audience PollWhat’s your role within your organization?
Data Architect
DBA
Manager or Executive Sponsor
Business Analyst
Consultant
Marketing
Other
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1. Background
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Background:Data Management growth:
Database developmentDatabase operation
1950-1970
Data requirements analysisData modelling
1970-1990
Enterprise data management coordinationEnterprise data integrationEnterprise data stewardshipEnterprise data use
1990-2000
Explicit focus on data qualitySecurityComplianceOther responsibilities
1990-2000
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Background:Data Modelling’s promise …."a single consistent definition of data" "master data records of reference" “reduced development time”“improved data quality”“impact analysis”…….
So why is it that in many organisations the benefits of data modelling still need to be “sold” and in others the big benefits simplyfail to be delivered?
No brainers?
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2. Seven deadly sins
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i: Not focusing on benefits
Project requirements vs Big pictureReward drives behaviourWIIFMMetricsEvidenceSustained improvement
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What’s the value of Data Modelling to BP?
x body of knowledge - models repository.
Consistency of cross domain data concepts.
Eases Master Data Take-on, Legacy Migration, MI/BI, Application interoperability
Reuse of common models & definitions (including standard industry models)
Interoperability, & efficiency through common approaches
Reduction in maintenance.
Company X benefits
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Ston
gly
Agre
e
Dis
agre
e
79% 77%70%
55% 60%
4%0%
10%20%30%40%50%60%70%80%
What benefits are you gaining from the Data modelling service?
We are not obtaining any benefits
We are obtaining benefit through use of a common modelling tool
Company X: User Survey; Benefits
We are obtaining benefit through utilisation of a common repository
We are obtaining benefit through use of common standards, guidelines & processes
We are obtaining benefit through re-use of models & artefacts
We are obtaining benefit through provision of central support & help
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What’s the $ value of Data Modelling to BP?
A) Complete representation of requirementsMeasures• Number of definitions the client takes ownership of. If the client is willing to assume responsibility for the maintenance of the definitions, then it is safe to assume the definitions are accurate.• Number of modifications to the model after each review. This is more of a rolling "how well is the modelling process going" measure than an end-state measure of how complete the model is. A lower number of post-review modifications is an indicator of a higher degree of completeness.
B) Retention of collected information (including re-use)Measures• Number of times portions of a model are referenced (on a web page for example). If the model has been published (which all should be) and the repository information is easily accessible, the "number of hits" on each entity (for example) can be a gauge of theusefulness of the originally collected information.• Number of entities re-used in subsequent projects. This is as much a measure of the quality of the original analysis (and potentially design) as it is a measure of the amount of re-use. Costs savings for this measure can be calculated based on a "days per entity" number. Total time savings (and related cost savings) would be equal to the "days per entity" multiplied by the number of entities re-used• Time to market for projects. Assuming we were able to re-use an existing database for a second application, the time savings could simply be "days per entity" multiplied by the number of tables in the existing database.
C) Consistent interfaceMeasures• Review time by entity. The time required to review each entity (or definition) should decrease as the reviewers become familiar with the consistent style of the model. A side benefit to following a consistent style is that subsequent projects will be able to accurately reflect the amount of time required to review a data model in project plans based on the results of past reviews.• Amount of time spent during subsequent referral to the model. Just as the number of times the model is subsequently referenced is a measure of the retention theme, the amount of time spent when referencing a specific portion of the model is a measure of the consistency. If the model has followed a consistent interface, subsequent users of the model should be able to find the requiredinformation quickly.
Company Y metrics
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Value of Data Modelling - Company Z
• Increased reuse & development efficiency >>> Reduced development time (*based upon £10k per new Entity & 46% re-use)
$300m
• Increased consistency >>> Decreased maintenance (*based upon 22% reduction in # bespoke tables & messages)
$75m
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ii: Forgetting the purpose
Top down only?Bottom up & middle outIt’s not simply for RDBMS development
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Why Produce a Data Model?Company Z Top Ten Reasons
1. Capturing Business Requirements 2. Promotes Reuse, Consistency, Quality3. Bridge Between Business and Technology
Personnel4. Assessing Fit of Package Solutions5. Identify and Manage Redundant Data6. Sets Context for Project within the Enterprise 7. Interaction Analysis: Compliments Process Model8. Pictures Communicate Better than Words9. Avoid Late Discovery of Missed Requirements 10. Critical in Managing Integration Between Systems
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Not only for “new” Data Base Systems?SOA:Important in an SoA World. Definition of data & consequently calls to / results from services is vital.Straight through processing can exacerbate the issue
• what does the data mean?• which definition of X (e.g. “cost of goods”)?• need to utilise the logical model and ERP models definitions
Data Lineage:Repository based Data migration design - ConsistencySource to target mappingReverse engineer & generate ETLImpact analysis
ERP:Model Data requirements – aid configuration / fit for purpose evaluationData IntegrationLegacy Data take onMaster Data integration
BI / DW:Model Data requirements in Dimensional ModelReverse engineer BW Info Cubes, BO Universes, …….Generate Star / Snowflake / Starflake schemas
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iii: Language & intellectual snobbery
The term “Modelling” often has baggage associated with it
Use appropriate language & terms for different audiences
Banish methodology bigots & dogmaBarker / ERD /UML / OR / etc etc
Banish methodology bigots & dogmaNEVER air methodology issues in front of users
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iv: Discipline
NASA Mars Climate Orbiter
Dumbing down - It’s not just about picture drawing!Don’t forget the metadataTraining & appropriate personnelIdentify relevant standards & guidelinesCommunicateHonesty – it’s not easy!
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v: Inappropriate positioning
Don’t do it just for modelling's sake!
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Data modelling performed in isolation – silos DM, PM, DBA ...Left until too late in the lifecycleSpeed – too much focus on final 20% to be “theoretically perfect”DM considered an overheadCharging for Modelling infrastructureHidden / unpublished models – what’s the point!Limited re-useProjects left to own devices – “the train has departed”DM function not resourced appropriately thus models not subject to peer / cross-domain review
v: Inappropriate positioning
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vi: Failing to adaptPlethora of tools – good usage is more important
than choosing the “best”Forgetting the overall information architecture
Master Data, Transaction data, MI/BI, Unstructured, BDD …
Disservice by ERP package vendorsCOTS Logical Data Model with package?
Lack of soft skillsHero seeking
cowboys
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vii: Square pegs & round holes
TLA factory – DM, MDM, EDM, EII, CDI, SOA …….The right people in the role?
Is being a good modeller enough?Certification coming at last ☺
Engaging with the businessNobody owes us a living
Communicating our successesDo people know why this is undertaken?
Creating communities of interestLack of “Selling” skills
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3. Our part in fixing this
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3NF
DBA• Cautious• Analytical• Structured• Doesn’t like to
talk• “Just let me
code!”
Data Architect• Analytical• Structured• Passionate• “Big Picture” focused• Likes to Talk• “Let me tell you about
my data model!”
Business Executive• Results-Oriented• “Big Picture” focused• Little Time• “How is this going to help
me?”• “I don’t care about your
data model.”• “I don’t have time.”
Industry CultureDBAs, Data Architects and Executives are different creatures
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Role of the Data ArchitectHow to gain Traction, Budget and Executive buy-in
• Be Visible about the program:• Identify key decision-makers in your organization and update them on your project and its value to the organization• Focus on the most important data that is crucial to the business first! Publish that and get buy in before moving on. (e.g. start small with a core set of data)
•Monitor the progress of your project and show its value:• Define deliverables, goals and key performance indicators (KPIs) • Start small—focus on core data that is highly visible in the organization. Don’t try to “boil the ocean” initially.• Track and Promote progress that is made• Measure Metrics where possible
“Hard data” is easy (# data elements, #end users, money saved, etc.)“Softer data” is important as well (data quality, improved decision-making, etc.) Anecdotal examples help with business/executive users
“Did you realize we were using the wrong calculation for Total Revenue?”(based on data definitions)
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Communicate EffectivelyProvide Information to uses in their “Language”
• Repurpose information into various tools: BI, ETL, DDL, etc.• Publish to the Web• Exploit collaboration tools / SharePoint / Wiki …….• Business users like Excel, Word, Web tools
Document Metadata• Data in Context (by Organization, Project, etc.)• Data with Definitions
Provide the Right Amount of Information• Don’t overwhelm with too much information. For business users, terms and
definitions, might be enough.• Cater to your audience. Don’t show DDL to a business user or Business
definitions to a DBA.Market, Market, Market!
• Provide Visibility to your project.• Talk to teams in the organization that are looking for assistance• Provide short-term results with a subset of information, then move on.
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Model publishing
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Case Study: Web-based information sharing
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Maturity @ your company
Visibility
TechnologyTrigger
Peak of inflatedexpectations
Trough of disillusionment Slope of enlightenment Plateau of productivity
Typical Gartner “hype cycle”
Avoid the abyss via investment in “sustain”activities
Current position
Make it sustainable:
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Thank you
Contact details:Email: [email protected]: +44 (0)7973 184475MSN: [email protected]: www.ipl.com