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Agile BI success factors


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Agile BI : why and how should you consider ?
What are the success factors (people, organisation, methodologies, tools, infrtsaructure...)

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Agile BI success factors

  1. 1. Success FactorsJean-Michel FrancoInnovation & Solutions Directorjean-michel.franco@businessdecision.comTelephone, : +33 6 67 70 01 32Twitter : @jmichel_francoAgile Business Intelligence
  2. 2. Business & Decision is a globalConsulting & Systems Integrator2012 : 221,9 M€22 500 Employees 16 Countries Multi-SpecialistBIPMCRMEIME-busExpertise recognized by thought leaders, Software vendors and industry analysts• Business Intelligence & EPM “European Marketscope for BI Services”. Gartner• Customer Relationship Mgt & MDM “CRM Wordwide Magic Quadrant”. Gartner• E-Business “Interactive Design Agency Overview, Europe, 2013 ”. Forrester
  3. 3. 3BI: raising expectations from Lines of Business …Source : GartnerSurvey Analysis: CFOs TopImperatives Fromthe 2013 Gartner FEI CFOTechnology Study
  4. 4. 4…while IT ’s ability to deliver on promises is being challengedSource : GartnerSurvey Analysis: CFOs Top Imperatives Fromthe 2013 Gartner FEI CFO Technology Study
  5. 5. Innovating through IT, close to the field• Discover : raising awareness on emergingtechnologies and use cases• Incubate : a proof of concept basedapproach to experiment IT in context ofeach business process• Productize once proof of concept hasbeen made• Continuously improve : extend existingenvironment rather than replace them -> alean approach to innovation, by increments…• Shares lessons learned, turn « nextpractices » into « best practices ».•5
  6. 6. 6Top downapproach:EnterpriseBIBottom upapproach :PersonalBIManagement teamsIs Business Intelligence in midstream ?
  7. 7. Enterprise BI as we know itOccasional user 70+ %“advanced” user: 30- %7
  8. 8. Enterprise BI as we want it8
  9. 9. 9BI as we want it: Success factorsPeopleOrgani-zationMetho-dologiesToolsInfra-structureBusiness/ processesAnalyticsData governanceInformation ManagementData DiscoverySelf Service BISelf Service InformationManagementData Lab : environment forprototyping and self serviceaccess to dataClose to the field : a frontoffice to collect ideas,experiment and design+ back office to roll out ona wide scaleUpstream collection ofbusiness needsTemplate based agilemethodologies
  10. 10. The technology layer10
  11. 11. 11The people dimensionSocialize Business Intelligenceor Changer gravity of Business IntelligenceTo engage Lines of business beyond the project blueprint phase(Model design, shared system of measurement, business glossaries…)
  12. 12. 12Infrastructure dimension : the Data Lab principleEnterprise BIData WarehouseData MartPackagedapps,Dash-boardsSelf ServiceData LabEphemeral storesApplicationprototypesSelf-ServiceSanctioneddataSharedanalyticsEnterprise levelmodelsSanctionedData sourcesUnsanctio-ned data
  13. 13. 13Project dimension: Rethinking the entire BI lifecycleWhen Challenge SolutionBefore the BI project Identify emerging business needs.Formalize business cases.Prove the concept.Bring the tools close to the usecases at early steps.Incubate new technologies.Identify key user and empowerthemDuring the BI project White page syndromeDifficult to validate design, toanticipate problems (ex : dataquality).Agile methodologiesTemplate based designAfter the BI Projectroll-outEvolve the system « on the fly »Establish a self service usageEmpower a certain category ofbusiness users to:- accompany and coach- Manage data governance- Identify change of businessneeds
  14. 14. Business ObjectivesCompany is best in class in terms of waterquality and aspires to strengthen thisleadershipProject Water Quality Performance aims toprovide the platform to drive futureperformance in that areaChosen approach• IT empowers business users(Statisticians) to get knowledgeout of external data and allowcross analysis with internal data• Agile approach :Establishing agile BI before projects ; example inutilities– Ability to sourceexternal “multi-structured “ data14 million rows atthat time– Allow datacrunching(including qualitychecks) andanalytics– Timing : 1 monthbefore first results– Proof the concepton a small scalebefore wider roll-out– “show the data”first, then learnand refine thedesign to adjustthe solution to thebusiness need
  15. 15. Business objectiveRe-engineer the marketing systemfoundations :Chosen approach• Leverage a standardized data model(Acord) covering the 17 businessdomains of insurance• iterative and incremental designapproach on three areas:Agile during the BI project:Example in insurance– Customer master data andmarketing data warehouse– Customer analytical DataMart (scoring,segmentations…)– Packaged software formulti-channel marketingcampaigns (Neolane)– Data Modeling(2 weeks sprints foreach considered datadomains)– Data integration– Data qualityassessments andaudits15
  16. 16. Agile BI all along the BI initiative :eexample in Life SciencesBusiness objectivesRelaunch Business Intelligenceinitiatives :Chosen approach– Solidify the information backoffice (data models, sharedmaster data, data quality &governance)– Closely match BusinessIntelligence to the need ofeach line of business– Better catch business needsupstream and downstream(before and after projectlaunch)– Take advantage of datadiscovery and datavisualization toolsCatchBusinessneedsDesignProductizeKey user, at each lines ofbusiness, to collect business needsand autonomously discover thedataPrototyping at very early steps ofeach projectA center of expertise and sharedstandards to quickly roll out andglobalize BI initiativesDriveusageWell defined organizations toaccompany BI usages and makesure of the efficient usage of data16
  17. 17. Success FactorsJean-Michel FrancoInnovation & Solutions Directorjean-michel.franco@businessdecision.comTelephone, : +33 6 67 70 01 32Twitter : @jmichel_francoAgile Business Intelligence