DATA-DRIVEN RETAIL #CASSANDRA13 CASSANDRASUMMIT2013 HOW ONE MEGA-RETAILER IS DRIVING DOWN ENERGY COSTS 22% ACROSS 8,000 STORES
C* Summit 2013: Data Driven Retail: How One Mega-Retailer Drove Down Energy Costs Across 7,000 stores by David Leimbrock
Dec 05, 2014
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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DATA-DRIVEN RETAIL
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HOW ONE MEGA-RETAILER IS DRIVING DOWN ENERGY COSTS 22% ACROSS 8,000 STORES
{ A B O U T U S }
SMART DEVICE MANAGEMENT FOR VERY LARGE ENTERPRISES
OUR PARTNER
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MARKET TRENDS GOAL: 20% LESS ENERGY CHALLENGES OUR APPROACH VALUE DELIVERED
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DATA-DRIVEN RETAIL
{ W H AT W E W I L L C O V E R }
“Experts often possess more data than judgment.”
Colin Powell
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1 MARKET TRENDS
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2 EXPLOSION OF SMART DEVICES
3 BIG DATA ECONOMICS 1 LEAN & GREEN
BUSINESS
THREE MARKET TRENDS { D R I V I N G D E M A N D }
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INFO
DETAILS
WEBSITE
LEAN GREEN
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&
BRICK & MORTOR CHALLENGES
R E TA I L
BRICK & MORTAR RETAILERS FACE INTENSE COST PRESSURE
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BEING “GREEN” IS DRIVING NEW BEHAVIOR & DATA REQUIREMENTS
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SMART DEVICES EXPLOSION
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ON THE ROOF (~600PTS)
DEVICE
DATA FROM THE LIGHTING (~600PTS)
FROM THE METERS (~400PTS)
FROM REFRIGERATION
(~200PTS)
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ENERGY SPEND: SPREAD ACROSS HUNDREDS OF DEVICES
TYPICAL ENERGY USE IN RETAIL
LIGHTING
HVAC
MISC
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SOLUTION ECONOMICS
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Metering, HVAC, Lighting Controllers
Temperature, sensor readings 15 min. intervals
15 DEVICES
1,800DATA POINTS
~58M PER HOUR
VOLUME ACROSS 8,071 STORES
Define “time series”: a sequence of data points, measured typically at successive points in time, spaced at uniform time intervals.
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2 GOAL: 20% LESS ENERGY
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3 CHALLENGES
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INITIAL SUCCESS – THEN,….
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1000
1500
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2500
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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 R W E E K }
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APPLICATION SILOS
Vendor 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 servers Required, with limited data sharing capabilities.
{ M U LT I - V E N D O R E N V I R O N M E N T }
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4 HOW WE SOLVED IT
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DASHBOARDS
EXECUTIVE �
ENERGY �BUILDING �
OPERATIONS�§ Financial § Environmental
§ Equipment Status § Alerts & Alarms
§ Policy § Performance
§ ConsumpKon § Usage Profile
BRIGHTWORKS DATA MANAGEMENT LAYER
VERTICAL APPLICATIONS RIPTIDE IO & 3RD PARTY
§ AFDD § ADR § Building Management § CriKcal Systems Monitoring § Dynamic Pricing § Safety & Security § Work Order Management
DEVICE INTEGRATION PLATFORM
PROPOSED SOLUTION CASSANDRASUMMIT2013
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Solution optimized for time-series.
Performance at scale.
Broad language support. Fault tolerance.
Commercial support & healthy community; not overly specialized.
TECHNICAL CONSIDERATIONS
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6 Keen on executing – avoid analysis paralysis.
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EVALUATION RESULTS
1 6 12 18 24 30 36 Months of Data
Reads Writes
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NEW ARCHITECTURE
B R I G H T W O R K S C L U S T E R P O W E R E D B Y C A S S A N D R A
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BrightWorks Applications
Weather
Utility Data
Stores Stores Stores
Vendor Siloes Vendor Siloes
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5 RESULTS
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INSIGHTS GAINED CASSANDRASUMMIT2013
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INSIGHTS PRIORITIZED
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OPERATIONAL INPROVEMENTS { B O N U S : M O R E T H A N J U S T
E N E R G Y S AV I N G S }
TROUBLESHOOTING TOOL
FOR THE FIELD
GLOBAL REAL ESTATE
VIEW FOR EXECUTIVES
IMPROVED WORK FLOW
FAULT DETECTION
NEXT
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Reliable high performance data store.
RE: data modeling perspective, pay more attention to CQL3. Datastax security features – additional layers is a good thing.
Happy with decision to pre-compute as much as possible.
KEY TAKE AWAYS
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5 Data extract & validate rules; slow & with inevitable surprises.
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QUESTIONS ANSWERS
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Dave Leimbrock Phone: +1 805 588 4580 Email: [email protected]
t twitter.com/dleimbro
CONTACT ME
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