Data Infrastructure for Your Retail Digital Strategy
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Transcript of Data Infrastructure for Your Retail Digital Strategy
Data Architecture
Data InfrastructureFor your Retail Digital StrategyAtif Rahman@atifshaikh
07.12.2016
The lion
Slow and Fast decision making
What comes to your mind when you see this?
Running or Analytics?
SYSTEM ISYSTEM IIRunningKnee-JerkFlight or FightLong TermAnalysisBig Picture
More ChannelsMore NoiseMore of EverythingIncrementalReplacementsNew Shiny ToolNew Way of WorkNew ProductsNew ExperiencesTypical Digital Strategy Journey
DIGITAL TRANSFORMATIONS AROUND USEnablers are all there Technological, Social, Regulatory. Barriers to entry are virtually non existent
Amazon Go Back to the Human Experience
Blending Digital with the Physical
PEOPLE
BUSINESS MODEL
Building Blocks of a Digital StrategyCLOUDMOBILESOCIALIOTANALYTICSDATA
Leading Digital, Westerman et al. 2014SYSTEM I VS SYSTEM II THINKERS
FOMO, FUD OR SOMETHING REAL?
SIMPLY THROWING MONEY AT A PROBLEM RARELY WORKED
Somethings money cannot buy?STORESDISTRIBUTIONBRANDEXPERIENCEPROCESSESORG MATURITYDATA POINTSANALYTICS
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Areas to Transform
Tenets of a (good) Data Infrastructure
Elasticity:Both Scale up and downSpiky businessBreaking SilosAgileFlexible ArchitectureProduct CatalogsPlug and Play ComponentsOnDemand Service ProvisioningAnalytical FrameworkControlled ExperimentationsNudges Growth Hacking
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Compared to Waterfall
ELASTIC
Scale Up / Down (Handling Spikes)OnDemand BackupLinearly Scalable
HOLISTIC
DATA LAKES ALLOW FOR NEARLY ALL THE DATA TO BE USED FOR KEY ACTIVITIESData Diversity and Comprehensiveness
FLEXIBLE
Not bound in traditional rigid structures (relational etc.).NOSQL
ANALYTICAL
Culture of (Scientific) experimentationSegmentation FrameworksData & Analytic Products (e.g. Segmentation) Most Analytics newcomers expect a definite end goal whereas top analytics teams deliver incremental value over rapid iterations in a safe work enviornment.
Iteration 1Iteration 2Iteration 3Iteration 4Iteration NFirst time a lot of experiments must be undertaken with variationsClearly irrelevant experiments will filter outThe problem statement starts to become more prominentPotential close solutions are in sightWinning recipes are converged!
Growth HackingHow to play your cards well
HackathonsCommunity (eCommerceSea)DYOB (Destroy Your Own Business)WWAD (What Would Amazon Do)
DONT BE AFRAID OR OVERWHELMED; FOLLOW THE TENETS AND SHORTLIST
Disclaimer: The talk is aimed at people new to Data and Analytics and hence prefers simplification over rigor.QUESTIONS?