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Big data, real time data, too much data...
The 4 V’s of Smart Data
SAP Forum 2015
Marcelo Coutinho
EAESP-FGV
marcelo.coutinho@fgv.br @mcoutinho
Source: Capgemini, Digital Shopper Relevancy, 2014
Brazilians like omnichannel
Retail “Big Data”: creating Value from variety
(of consumer touch points), volume (from
their interactions) and velocity (from their
desires)
Fonte: A.T. Kearney, Connected Consumer 2014
Brazilians are “always-on” consumers
Number of
smartphones
sold PER
MINUTE in
Brazil
between July
/September
2014
115
Source: Data Never Sleeps 2.0 – Domosphere (Google, NY Times, Apple, Cisco, The Guardian)
We are producing 9.300 buildings of information / hour
“In an information-rich world, the wealth of
information means a dearth of something else: a
scarcity of whatever it is that information
consumes. What information consumes is rather
obvious: it consumes the attention of its
recipients. Hence a wealth of information creates
a poverty of attention”.
Herbert Simon, 1971
Technology helps to capture, share
and save attention
“Data is the new oil”
Really?
Risks = Data Blindness + Analysis Paralysis
Quantity of information
Impactover
decision
efficiency/timing
Source: Kettinger and Rollins, MIT Sloan Mngt. Review
Precision beats volume
Oil has little value
Oil + Pressure + Heat + Chemicals = Big Value
Pressure = Force/Area
• Ensure data integrity (correctly report
the event)
• Garbage in, garbage out
• Problems on data capture often
involve human resources (training ,
processes, understanding the
importance of proper recording, etc. )
• If the Area is large ( several sources,
points of contact with the consumer,
geography, etc ) and you do not have
enough resources (Force), start
"pressing" one segment at a time
Tracking technologies allow us not only show segmented ads,
but segmented invitations for consumer research
• Application of the scientific
method : inferences,
deductions, hypothesis
testing, statistical methods,
multivariate analysis (cluster,
conjoint , factorial , etc )
– Attention: correlation is no
causation and linear regression
is not forecast
• Always related to business
objectives
Heat
Big Data Catalysts:
People, Process, Platform
“Data” don’t buy. People do
Personalization = data translated in behavior
“Smart Data” = measured value for the CEO/CFO
Revenue
Expenses
Cash Flow
Consumer
Satisfaction
It's hard to be simple
Big data, real time data, too much data...
The 4 V’s of Smart Data
SAP Forum 2015
Marcelo Coutinho
EAESP-FGV
marcelo.coutinho@fgv.br @mcoutinho

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Smart Data Brazil Retail_ SAPForum2015_CoutinhoFGV

  • 1. Big data, real time data, too much data... The 4 V’s of Smart Data SAP Forum 2015 Marcelo Coutinho EAESP-FGV marcelo.coutinho@fgv.br @mcoutinho
  • 2.
  • 3. Source: Capgemini, Digital Shopper Relevancy, 2014 Brazilians like omnichannel
  • 4. Retail “Big Data”: creating Value from variety (of consumer touch points), volume (from their interactions) and velocity (from their desires)
  • 5. Fonte: A.T. Kearney, Connected Consumer 2014 Brazilians are “always-on” consumers
  • 6. Number of smartphones sold PER MINUTE in Brazil between July /September 2014 115
  • 7.
  • 8. Source: Data Never Sleeps 2.0 – Domosphere (Google, NY Times, Apple, Cisco, The Guardian)
  • 9. We are producing 9.300 buildings of information / hour
  • 10. “In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention”. Herbert Simon, 1971
  • 11. Technology helps to capture, share and save attention
  • 12. “Data is the new oil” Really?
  • 13.
  • 14. Risks = Data Blindness + Analysis Paralysis
  • 15.
  • 16. Quantity of information Impactover decision efficiency/timing Source: Kettinger and Rollins, MIT Sloan Mngt. Review Precision beats volume
  • 17. Oil has little value Oil + Pressure + Heat + Chemicals = Big Value
  • 18. Pressure = Force/Area • Ensure data integrity (correctly report the event) • Garbage in, garbage out • Problems on data capture often involve human resources (training , processes, understanding the importance of proper recording, etc. ) • If the Area is large ( several sources, points of contact with the consumer, geography, etc ) and you do not have enough resources (Force), start "pressing" one segment at a time
  • 19. Tracking technologies allow us not only show segmented ads, but segmented invitations for consumer research
  • 20. • Application of the scientific method : inferences, deductions, hypothesis testing, statistical methods, multivariate analysis (cluster, conjoint , factorial , etc ) – Attention: correlation is no causation and linear regression is not forecast • Always related to business objectives Heat
  • 21.
  • 22. Big Data Catalysts: People, Process, Platform
  • 23. “Data” don’t buy. People do Personalization = data translated in behavior
  • 24. “Smart Data” = measured value for the CEO/CFO Revenue Expenses Cash Flow Consumer Satisfaction
  • 25. It's hard to be simple
  • 26. Big data, real time data, too much data... The 4 V’s of Smart Data SAP Forum 2015 Marcelo Coutinho EAESP-FGV marcelo.coutinho@fgv.br @mcoutinho