For Data Project Leaders: This data warehouse data modeling approach enables shorter time to insights, lower cost and greater adaptability to external changes by combining my End to End Data Modelstorming concept with Data Vault modeling.

1. Shorter Time to Insight,
More Adaptable,
Less Costly
Business Intelligence
with
Modelstorming and Data Vault
Presented by Daniel Upton

2. The Business Intelligence Promise:
Smarter, more fact-baseddecision-makingas aneverydayroutine
Presented by Daniel Upton

3. WithoutaDataWarehouse,BIreportinggetsboggeddown…
* slow queryperformance
* insufficient drill-downto details, and
* mission critical reportswithconflictingdata.
ADataWarehouseremainsthegold-standardforresolving
thoseissues,buthasbeen…
* time consumingto build
* slow to adaptto rapidbusinesschanges
* thus,expensive not only to build,butsimply to maintain.
Whysoslowandexpensive?
Presented by Daniel Upton

4. StarSchemas:
Appealingsimplicitytolesstechnicalusers,buttoloadthem,thefacts,withtheirnew,hard-coded
businessrulesbecomedependentondimensions,withtheirownsetofhard-codedrules,making
thesemodels,andtheirloadingcode,time-consumingtodesignandload,and…costlytoevolveor
changeeveninsmallwaysoncedelivered. Meanwhile,sourcesystemdata,businessrules,and
reportingrequirementscontinuetoevolvefasterandfaster.
So,withadeliveredtraditional‘StraighttoStarSchema’(noEDW)architecture,evenwithouta
needformajorenhancements …businessintelligencemaintenancecostsincreaseovertime.
Presented by Daniel Upton

5. Backintheday,thepotentialopportunityto…
“Buildyourintegrateddatawarehousenow.
Easilyaddnewoperationaldata,newrules,andnewrelationshipswheneverneeded”
…wouldneverhavebeensuggestedbyanhonest,experiencedpractitioner.

6. Whydooldschooldatawarehousestakesolongtodeliver?
Datadependenciesresultintaskdependencies
Deliver Data
Presented by Daniel Upton

7. Deliver Data
Thefivemosttime-consumingtaskshavealsobeen
themostresistanttobreakdownintosmallerchunks,
duetotheirlogicalanddata-relatedinterdependencies
Presented by Daniel Upton

8. Critical Path Delays

9. DataVaultArchitecture,especially withModelstorming,shortenstasksand
theirdependenciesintomuchsmaller,time-boxedtaskiterationsbyloosely
couplingandquicklywarehousingthedatabeforeapplyingtransformationsfor
businessrules,datapresentation,reporting,oranalytics.
Presented by Daniel Upton
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data

10. Data Vault is a
platform-agnostic architecture.

11. Who is using Data Vault?~ 2,000 worldwide implementations

12. Simple Business Case: University
Alumni Development specialists must know which donors donate
how much and when, in order to ensure donors receive
recognition and benefits.
Institutional Research must know how many students enrolled
weekly to ensure University meets qualifications for student
loans.
Auditors must know what family relationships exist between
donors and students to monitor for conflicts of interest and
uphold reputation.
Presented by Daniel Upton

13. DataVaultandModelStorming
Presented by Daniel Upton

14. DataVaultandDataModelStorming:
FastProfileofSourceData
DataDependenciesForceSequentialTransactionProcessing
Presented by Daniel Upton

15. DataVaultModeling,viaModelstorming,isquicklydone
HighCohesionandLooseCoupling
Deliveredin1/4 thetimeofold-schoolmodels.
Nodatadependenciesbetweenfree-standingensembles,thus
loadensemblessimultaneously,soloadingtimeis1/2ofold-school.
Data Vault Technical Reference:
‘Super Charge Your Data Warehouse’
by Daniel Linstedt, LearnDataVault.com, 2008.
Presented by Daniel Upton

16. SummaryofDataVaultDesignFundamentals
Hub: OneBusiness Key(uniquelistofvalues); SurrogateKeyasPrimaryKey. AHubisfree-standing,neverfunctionally
dependentonanothertable. HasoneormoredependentSatellites.
Satellite: PrimarykeyisHubsSurrogateKey+DateTimeStamptocapturehistoricversionsofallfieldsfromsourcetable
orfile. Foreignkeyconstraintsareremoved. DependentonexactlyoneHub.
Link: HistoricrelationshipofrecordsbetweentwoormoreEnsembles,usingnewHubSurrogateKeys. Usedinsteadof
sourcesystem’sforeignkeys,thuspreservingcorrect cardinalityrelationshipsamongrecordsbetweentables,while
removingcascadingdatadependenciesbetweenEnsembles,allowingfasterdataloadsandallowingforfuture
enhancementwithoutre-workinexistingtables.

17. OldSchoolDataWarehousevs.DataVault:
Abbreviationsdenotesametasks,nowbrokendownintosmallerchunks.
Assumesameresources,same skilllevels,sameeffort.
…whatiswronghere?
Deliver Data
Presented by Daniel Upton

18. Modelstormed DataVaultArchitecturalProcess reducestaskdependencies
toafraction,byquicklywarehousingandlooselycouplingthedatapriorto
applyingbusinessrulesforanalyticsorreporting.
Presented by Daniel Upton
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data

19. Now, in the 11th Hour of Development,
Two User Stories Change
Institutional Research must know how many students enrolled
weekly, with what relationship to a Counselor, to ensure
University meets qualifications for student loans.
Auditors must know what family relationships exist between
donors and students and Counselors to monitor for conflicts of
interest and uphold reputation.
_______________________
In a straight-to-star schema architecture, such requirement
changes require time-consuming, costly re-engineering within
the data warehouse. Here, a fact table, first built without
reference to Counselor, now needs re-engineering.
Presented by Daniel Upton

20. DataVault Model: Prior to 11th HourChange
Presented by Daniel Upton

21. DataVaultModel: After11th HourChange
LeanModelStormAdditionsComplete
Zerochangestoanyexistingobjects,thusfastandnointerferencewithother
codingefforts. Deliveredthistimein¼thetimeoftraditionalmethods.
Presented by Daniel Upton

22. Data Vault Architecture
Robust Integration with Big Data from
Social Media, Machine Sensors, etc.

23. WithDataVaultarchitectureandModelstorming,thepromiseto…
“Buildyourintegrateddatawarehousenow.
Easilyaddnewoperationaldata,newrules,andnewrelationships
wheneverneeded” …isnowaroutineexpectation.

24. DataVaultArchitecture,especially withLeanModelstorming, reducestask
dependenciestoafraction,warehousingandlooselyintegratingthedata
priortoapplyingyourbusinessrulesforanalyticsorreporting.
Presented by Daniel Upton
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data
Deliver
Data

25. DataVaultModelstorming
Presented by Daniel Upton

26. The Business Intelligence Promise:
Smarter, more fact-baseddecision-makingas aneverydayroutine
Presented by Daniel Upton

27. Shorter Time to Insight,
More Adaptable,
Less Costly
Business Intelligence
with
Data Vault
Presented by Daniel Upton

28. PerformanceG2, Inc. is a full service performance management consultancy and advisory experts who specialize
in helping business leaders drive the value of their data and or analytic environment to ultimately allow them to
think smarter, act faster, and grab a competitive edge. Our goal is to guarantee that our clients have the right
knowledge and intelligence of their business – customers, products, markets, and people, and we do so by
partnering with the best technology companies and hiring the top consultants and trainers.
www.performanceg2.com
Daniel Upton, owner and principal consultant at DecisionLab LLC, is a Certified Data Vault Data Modeler. A
frequent speaker, blogger, and advocate for agile methods in the business intelligence and analytics space, he
has used his unique EDW model-storming method to quickly implement Data Vaults for many organizations
nationwide. Daniel is pleased to partner with PerformanceG2 to deliver the benefits of a data warehouse that is
more robust, faster to deliver, easier to adapt, and less costly to maintain than previously considered possible.
www.decisionlab.net