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C* Summit 2013: Data Driven Retail: How One Mega-Retailer Drove Down Energy Costs Across 7,000 stores by David Leimbrock
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C* Summit 2013: Data Driven Retail: How One Mega-Retailer Drove Down Energy Costs Across 7,000 stores by David Leimbrock

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How do you keep up with the velocity and variety of data streaming in from all the smart devices that run the physical environments of 7,000+ stores? What about getting analytics that tell you exactly …

How do you keep up with the velocity and variety of data streaming in from all the smart devices that run the physical environments of 7,000+ stores? What about getting analytics that tell you exactly where energy waste is happening in real-time? In this talk, Riptide IO, describes their blueprint for collecting, organizing and deriving real-time operational intelligence from smart devices such as lighting, HVAC, sensors and more. Learn how this retailer gained a dramatic boost to their sustainability program, and solved some of the major bottlenecks in managing countless devices across thousands of stores.

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  • 1. DATA-DRIVENRETAIL#CASSANDRA13  CASSANDRASUMMIT2013  HOW ONE MEGA-RETAILER IS DRIVING DOWN ENERGY COSTS22% ACROSS 8,000 STORES
  • 2. { A B O U T U S }SMARTDEVICEMANAGEMENTFOR VERYLARGEENTERPRISOURPARTNER#CASSANDRA13  CASSANDRASUMMIT2013  
  • 3. MARKET TRENDSGOAL: 20% LESS ENERGYCHALLENGESOUR APPROACHVALUE DELIVERED12345DATA-DRIVENRETAIL{ W H AT W E W I L LC O V E R }“Experts oftenpossess moredata thanjudgment.”Colin Powell#CASSANDRA13  CASSANDRASUMMIT2013  
  • 4. 1 MARKET TRENDS#CASSANDRA13  CASSANDRASUMMIT2013  
  • 5. 2EXPLOSION OFSMARTDEVICES3 BIG DATAECONOMICS1LEAN & GREENBUSINESSTHREE MARKET TRENDS{ D R I V I N G D E M A N D }CASSANDRASUMMIT2013  #CASSANDRA13  
  • 6. INFODETAILSWEBSITELEAN GREEN1&
  • 7. BRICK & MORTORCHALLENGESR E TA I LBRICK &MORTARRETAILERSFACEINTENSECOSTPRESSURECASSANDRASUMMIT2013  #CASSANDRA13  
  • 8. BEING “GREEN” IS DRIVING NEWBEHAVIOR & DATA REQUIREMENTSCASSANDRASUMMIT2013  #CASSANDRA13  
  • 9. SMARTDEVICESEXPLOSION2
  • 10. ON THE ROOF(~600PTS)DEVICEDATA FROM THE LIGHTING(~600PTS)FROM THEMETERS (~400PTS)FROMREFRIGERATION(~200PTS)CASSANDRASUMMIT2013  
  • 11. ENERGY SPEND:SPREAD ACROSSHUNDREDS OFDEVICESTYPICAL  ENERGY  USE  IN  RETAIL  LIGHTINGHVACMISC#CASSANDRA13  
  • 12. SOLUTIONECONOMICS3
  • 13. Metering, HVAC,Lighting ControllersTemperature,sensor readings 15 min. intervals15  DEVICES  1,800DATA  POINTS  ~58MPER  HOUR  VOLUME ACROSS 8,071 STORESDefine “time series”: a sequence of data points, measured typically atsuccessive points in time, spaced at uniform time intervals.CASSANDRASUMMIT2013  #CASSANDRA13  
  • 14. 2 GOAL: 20% LESS ENERGY#CASSANDRA13  
  • 15. 3 CHALLENGES#CASSANDRA13  
  • 16. INITIAL SUCCESS –THEN,….0  500  1000  1500  2000  2500  3000  3500  2010   2011   2012  Number  of  Stores  Online  Number  of  Stores  Online  SAVINGS  PERFORMANCE  PROBLEMS  DRIFT  { A D D I N G 5 0 + S T O R E S P E RW E E K }CASSANDRASUMMIT2013  #CASSANDRA13  
  • 17. APPLICATION SILOSVendor  A  Store-­‐X  Vendor  D  Vendor  C  Vendor  B  Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X   Store-­‐X  Even within same vendor architecture, multiple serversRequired, with limited data sharing capabilities.{ M U LT I - V E N D O RE N V I R O N M E N T }CASSANDRASUMMIT2013  #CASSANDRA13  
  • 18. 4 HOW WE SOLVED IT#CASSANDRA13  
  • 19. DASHBOARDSEXECUTIVEENERGYBUILDINGOPERATIONS§  Financial  §  Environmental  §  Equipment  Status  §  Alerts  &  Alarms  §  Policy  §  Performance  §  ConsumpKon  §  Usage  Profile  BRIGHTWORKS  DATA  MANAGEMENT  LAYER  VERTICAL APPLICATIONSRIPTIDE  IO  &  3RD  PARTY  §  AFDD  §  ADR  §  Building  Management  §  CriKcal  Systems  Monitoring  §  Dynamic  Pricing  §  Safety  &  Security  §  Work  Order  Management  DEVICE  INTEGRATION  PLATFORM  PROPOSED SOLUTIONCASSANDRASUMMIT2013  #CASSANDRA13  
  • 20. Solution optimized for time-series.Performance at scale.Broad language support.Fault tolerance.Commercial support & healthy community; not overly specialized.TECHNICALCONSIDERATIONS123456 Keen on executing – avoid analysis paralysis.CASSANDRASUMMIT2013  #CASSANDRA13  
  • 21. EVALUATIONRESULTS1 6 12 18 24 30 36 Months of DataReads WritesCASSANDRASUMMIT2013  
  • 22. NEWARCHITECTUREB R I G H T W O R K S C L U S T E RP O W E R E D B Y C A S S A N D R ACASSANDRABrightWorksApplicationsWeatherUtility DataStores Stores StoresVendor Siloes Vendor SiloesCASSANDRASUMMIT2013  
  • 23. 5 RESULTS#CASSANDRA13  
  • 24. INSIGHTS GAINEDCASSANDRASUMMIT2013  #CASSANDRA13  
  • 25. INSIGHTSPRIORITIZEDCASSANDRASUMMIT2013  #CASSANDRA13  
  • 26. OPERATIONAL INPROVEMENTS{ B O N U S : M O R E T H A N J U S TE N E R G Y S AV I N G S }TROUBLESHOOTING TOOLFOR THEFIELDGLOBALREAL ESTATEVIEW FOREXECUTIVESIMPROVEDWORK FLOWFAULTDETECTIONNEXTCASSANDRASUMMIT2013  #CASSANDRA13  
  • 27. Reliable high performance data store.RE: data modeling perspective, pay more attention toCQL3.Datastax security features – additional layers is agood thing.Happy with decision to pre-compute as much aspossible.KEY TAKE AWAYS12345Data extract & validate rules; slow & with inevitablesurprises.CASSANDRASUMMIT2013  #CASSANDRA13  
  • 28. QUESTIONSANSWERSCASSANDRASUMMIT2013  #CASSANDRA13  
  • 29.  Dave LeimbrockPhone: +1 805 588 4580Email: dleimbro@riptideio.comt twitter.com/dleimbroCONTACT ME#CASSANDRA13  CASSANDRASUMMIT2013  

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