SlideShare a Scribd company logo
1 of 5
Download to read offline
strategy+business
ISSUE 75 SUMMER 2014
REPRINT 00250
BY DAVID MEER
When Big Data
Isn’t an Option
Companies that only have access to “little data” can still use that
information to improve their business.
leadingideasleadingideas
1
leadingideas
1
data-driven decision making—is
already under way, and accelerating.
The shift is so profound that compa-
nies lacking complete or clean mar-
ket data can no longer use this defi-
cit as an excuse to rely on the status
quo. They must make a concerted
effort to use the data that is available
to them (imperfect as it may be) or
to explore innovative, low-cost ways
to create new data.
In one example, a large beverage
manufacturer wanted to improve its
sales to bars, restaurants, and enter-
tainment venues. For years, this
company had been buying syndi-
cated data from an established
source, which covered more than
100,000 establishments. Unfortu-
nately, the data was collected and
structured to serve a broad set of cli-
ents and featured a standard seg-
mentation scheme that did not pro-
vide enough insight for the beverage
company into how to serve different
segments. So the company decided
to adopt a series of little data tech-
niques to come up with a solution
customized to its needs.
It started with observational re-
search, visiting bars and restaurants
and qualitatively cataloging the cli-
entele and their consumption pat-
terns. Synthesizing this information
resulted in more actionable segment
definitions. The next step was to
quantify the segmentation—deter-
mining how many establishments
were in each segment. The beverage
manufacturer developed an algo-
rithm based on observable charac-
teristics, then asked its sales profes-
sionals to classify all the bars and
ingly mining these data sources to
understand more about their cus-
tomers’ behavior and preferences,
and even to anticipate stock market
movements. Early successes by a few
companies have caused others to
start investing in the infrastructure,
software, and talent required to
mine big data.
There is, however, one impor-
tant caveat. Many companies—
probably most—work in relatively
sparse data environments, without
access to the abundant information
needed for advanced analytics and
data mining. For instance, point-of-
sale register data is not standard in
emerging markets. In most B2B in-
dustries, companies have access to
their own sales and shipment data
but have little visibility into overall
market volumes or what their com-
petitors are selling. Highly special-
ized or concentrated markets, such
as parts suppliers to automakers,
have only a handful of potential cus-
tomers. These companies have to be
content with what might be called
little data—readily available infor-
mation that companies can use to
generate insights, even if it is sparse
or of uneven quality. For these
companies, the U.S. Marine colo-
nel’s words will resonate more than
the latest data-mining algorithm or
social listening platform.
Several commentators have
made the point that the implications
of big data go beyond new data
sources, analytical techniques, and
technology. Rather, a paradigm
shift—away from management
based on gut feelings and toward
When Big
Data Isn’t
an Option
Companies that only
have access to “little
data” can still use that
information to improve
their business.
by David Meer
A
n advertising agency met
with a client—who hap-
pened to be a U.S. Marine
Corps colonel—and the conversa-
tion turned to the topic of reliable
data. “Look,” said the colonel,
“if I’m on a battlefield trying to de-
fend a hill and I get a piece of intel-
ligence, even if I’m not 100 percent
sure that it’s accurate, I will make
decisions based on that intelligence.”
He strongly believed that it’s better
to have some information than
none—and that you’d be a fool to
disregard it just because it falls short
of being definitive. One could say
that the colonel was a proponent of
“little data.”
There is, of course, a great deal
of discussion about the potential of
“big data,” the high-volume, high-
velocity, high-variety information
assets that require new forms of data
processing to enable companies to
make better decisions and operate
more efficiently. Giant data sets are
being created by aggregates of indi-
viduals’ behavior (on social media
sites such as Twitter and Instagram,
for example), by transaction logs,
and by automated information-sens-
ing devices. Companies are increas-
With the right mind-set, virtually all sources
of information can be exploited to improve
products, the customer experience, or profits.
strategy+businessissue75
IllustrationbyOptoDesign
restaurants in their territories based
on the algorithm. (This is a classic
little data technique: filling in the
data gaps internally.) Finally, for
each major segment, the company
designed tailored product assort-
ments, pricing, and marketing pro-
grams. Pilot projects in two large
cities have shown significant lifts in
total sales and share penetration,
and the company is now rolling out
the initiative nationwide.
Other companies have used lit-
tle data successfully as well. In one
case, a maker of industrial coating
products had limited data on pricing
broken down by customer and re-
gion. As a result, it couldn’t build
robust price elasticity models using
classical regression analysis. By us-
ing other analytical techniques,
however, the company was able to
identify specific areas in which it
could improve pricing and service
policies. It moved to a value-based
pricing approach to ensure its most
profitable customers were receiving
the highest service levels. Implemen-
tation in one business unit in one
region alone yielded a 4 percent in-
crease in sales.
In another instance, a regional
health insurance company trying to
differentiate itself through outstand-
ing customer experience realized
that its call center was a potential
source of data about customer pain
points and potential solutions. The
company took full transcripts of
the calls—not just the summaries
entered by service representatives—
and applied available text-mining
algorithms. From this data, the
company was able to improve the
format and language of its written
communications, and streamline
the call-center process. In addition,
it uncovered an opportunity to in-
troduce storefront locations in cer-
tain neighborhoods in order to im-
prove its customer interactions and
increase customer retention rates.
Even large companies are able
to make use of little data techniques.
The Chinese large-appliance giant
Haier uses information gathered by
service technicians to drive innova-
tion. In the late 1990s, some techni-
cians, for example, found that rural
customers were using their washing
machines to wash vegetables, lead-
ing to clogs. Haier used this infor-
mation to develop a new type of
washer, which the company says is
“mainly for washing clothes, sweet
potatoes, and peanuts.”
With the right mind-set, virtu-
ally all sources of information can
be exploited to improve products,
the customer experience, or a com-
pany’s profits. Little data techniques,
therefore, can include just about any
method that gives a company more
insight into its customers without
breaking the bank. As the examples
above illustrate, mining little data
doesn’t mean investing in expensive
data acquisition, hardware, soft-
ware, or technology infrastructure.
Rather, companies need three
things:
• The commitment to become
more fact-based in their decision
making. This commitment is often
spurred by a sense that competition
is heating up or the company is fall-
ing behind changing customer hab-
its and preferences. But fact-based
decision making can be an impor-
tant source of competitive advantage
for market-leading companies.
• The willingness to learn by
doing. Since little data applications
are not commercially available via
third parties, companies have to use
trial and error. However, once a few
priorities have surfaced, a series of
pilot projects will give the company
useful experience and, with a little
luck, some early successes that can
inspire the rest of the organization.
• A bit of creativity. To generate
richer data, companies need to get
creative, in part by tapping into the
customer interactions that take place
naturally. For instance, retailers can
intercept shoppers in store locations
for quick iPad-assisted surveys. Any
website with a registration form can
add questions that reveal preferences
beyond the basic data usually col-
lected. Call-center conversations are
another opportunity to gather data
on a particular topic, and the text
can be mined for greater insight into
the customer. Some companies cre-
ate advanced user panels of savvy
customers to get input during the
R&D process for new products.
Others rely on their sales representa-
tives to report trends in customer
2
leadingideas
leadingideas
3
leadingideas
3
preferences and competitors’ activi-
ties. The bottom line: Companies
have to put in the extra effort re-
quired to capture and interpret data
that is already being generated.
Companies often start the jour-
ney by picking a product, a region,
and a problem that needs attention
and running one or more pilot proj-
ects. This allows executives to dem-
onstrate to themselves and the rest
of the organization that the return
on effort and cost is justified. Once
companies start investing in analyt-
ics, they almost never stop, because
the things they learn drive improve-
ments in the business that more
than pay for the analysis. The activ-
ity becomes self-funding. In some
cases, companies that start with lit-
tle data end up recognizing the value
of the resulting insights and expand-
ing their investment to incorporate
larger data sets and more advanced
analytics. For others, little data is all
that’s needed. In either case, the
benefits are clear: Executives get in-
sight into what they can do to im-
prove their competitive position,
or—to put it in terms that a Marine
Corps colonel might appreciate—
identify what might be charging up
the hill to surprise them. It’s hard to
put a price tag on that. +
Reprint No. 00250
David Meer
david.meer@strategyand.pwc.com
is a partner with Strategy&’s consumer
and retail practice, and is based in
New York.
strategy+businessissue75
Articles published in strategy+business do not necessarily represent the views of PwC Strategy& Inc. or any
other member firm of the PwC network. Reviews and mentions of publications, products, or services do not
constitute endorsement or recommendation for purchase.
© 2014 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member firms,
each of which is a separate legal entity. Please see www.pwc.com/structure for further details.
strategy+business magazine
is published by PwC Strategy& Inc.
To subscribe, visit strategy-business.com
or call 1-855-869-4862.
For more information about Strategy&,
visit www.strategyand.pwc.com
• strategy-business.com
• facebook.com/strategybusiness
• http://twitter.com/stratandbiz
101 Park Ave., 18th Floor, New York, NY 10178

More Related Content

What's hot

Atos_whitepaper_Analytics_HR_interactive
Atos_whitepaper_Analytics_HR_interactiveAtos_whitepaper_Analytics_HR_interactive
Atos_whitepaper_Analytics_HR_interactive
Nicolas Mallison
 
Hortonworks hadoop big data_retail__white_paper
Hortonworks hadoop big data_retail__white_paperHortonworks hadoop big data_retail__white_paper
Hortonworks hadoop big data_retail__white_paper
Shyam Babu
 
Big Data and Marketing: Data Activation and Management
Big Data and Marketing: Data Activation and ManagementBig Data and Marketing: Data Activation and Management
Big Data and Marketing: Data Activation and Management
Conor Duke
 
Pivotal_thought leadership paper_WEB Version
Pivotal_thought leadership paper_WEB VersionPivotal_thought leadership paper_WEB Version
Pivotal_thought leadership paper_WEB Version
Madeleine Lewis
 
Pactera Big Data Solutions for Retail
Pactera Big Data Solutions for Retail Pactera Big Data Solutions for Retail
Pactera Big Data Solutions for Retail
Pactera_US
 

What's hot (16)

The Big Deal About Big Data For Customer Engagement
The Big Deal About Big Data For Customer EngagementThe Big Deal About Big Data For Customer Engagement
The Big Deal About Big Data For Customer Engagement
 
Atos_whitepaper_Analytics_HR_interactive
Atos_whitepaper_Analytics_HR_interactiveAtos_whitepaper_Analytics_HR_interactive
Atos_whitepaper_Analytics_HR_interactive
 
THE CHIEF DATA OFFICER
THE CHIEF DATA OFFICERTHE CHIEF DATA OFFICER
THE CHIEF DATA OFFICER
 
Big Data for Marketing: When is Big Data the right choice?
Big Data for Marketing: When is Big Data the right choice?Big Data for Marketing: When is Big Data the right choice?
Big Data for Marketing: When is Big Data the right choice?
 
Ericka pionin digital transformation – defined
Ericka pionin   digital transformation – definedEricka pionin   digital transformation – defined
Ericka pionin digital transformation – defined
 
Hortonworks hadoop big data_retail__white_paper
Hortonworks hadoop big data_retail__white_paperHortonworks hadoop big data_retail__white_paper
Hortonworks hadoop big data_retail__white_paper
 
Not waving-but-drowning
Not waving-but-drowningNot waving-but-drowning
Not waving-but-drowning
 
Personalisation & Single Customer View
Personalisation & Single Customer ViewPersonalisation & Single Customer View
Personalisation & Single Customer View
 
Unlocking big data
Unlocking big dataUnlocking big data
Unlocking big data
 
Six building blocks for creating a high performing digital
Six building blocks for creating a high performing digitalSix building blocks for creating a high performing digital
Six building blocks for creating a high performing digital
 
Business Intelligence
Business IntelligenceBusiness Intelligence
Business Intelligence
 
Big Data and Marketing: Data Activation and Management
Big Data and Marketing: Data Activation and ManagementBig Data and Marketing: Data Activation and Management
Big Data and Marketing: Data Activation and Management
 
Pivotal_thought leadership paper_WEB Version
Pivotal_thought leadership paper_WEB VersionPivotal_thought leadership paper_WEB Version
Pivotal_thought leadership paper_WEB Version
 
Big and Fast Data: The Rise of Insight-Driven Business
Big and Fast Data: The Rise of Insight-Driven BusinessBig and Fast Data: The Rise of Insight-Driven Business
Big and Fast Data: The Rise of Insight-Driven Business
 
Pactera Big Data Solutions for Retail
Pactera Big Data Solutions for Retail Pactera Big Data Solutions for Retail
Pactera Big Data Solutions for Retail
 
Balance Internet launches second edition of Digital Transformation in B2B eCo...
Balance Internet launches second edition of Digital Transformation in B2B eCo...Balance Internet launches second edition of Digital Transformation in B2B eCo...
Balance Internet launches second edition of Digital Transformation in B2B eCo...
 

Viewers also liked

C:\fakepath\chapter 13 explorations
C:\fakepath\chapter 13   explorationsC:\fakepath\chapter 13   explorations
C:\fakepath\chapter 13 explorations
Ms. Gutierrez
 
Emerging Europe (Ch.9 Honors and Regular)
Emerging Europe (Ch.9 Honors and Regular)Emerging Europe (Ch.9 Honors and Regular)
Emerging Europe (Ch.9 Honors and Regular)
Ms. Gutierrez
 
Chapter 13 explorations
Chapter 13   explorationsChapter 13   explorations
Chapter 13 explorations
Ms. Gutierrez
 
Ancient Greek Culture
Ancient Greek CultureAncient Greek Culture
Ancient Greek Culture
Ms. Gutierrez
 
An Introduction To Ancient Greece
An Introduction To Ancient GreeceAn Introduction To Ancient Greece
An Introduction To Ancient Greece
Ms. Gutierrez
 

Viewers also liked (14)

Royal Guest 04
Royal Guest 04Royal Guest 04
Royal Guest 04
 
C:\fakepath\chapter 13 explorations
C:\fakepath\chapter 13   explorationsC:\fakepath\chapter 13   explorations
C:\fakepath\chapter 13 explorations
 
Quantify Your Enterprise Client System Virtualization and Mobility Modeling i...
Quantify Your Enterprise Client System Virtualization and Mobility Modeling i...Quantify Your Enterprise Client System Virtualization and Mobility Modeling i...
Quantify Your Enterprise Client System Virtualization and Mobility Modeling i...
 
My Image Archive
My Image ArchiveMy Image Archive
My Image Archive
 
Martindudek twitter
Martindudek twitterMartindudek twitter
Martindudek twitter
 
Service Provider Cloud Enablement and Growth
Service Provider Cloud Enablement and GrowthService Provider Cloud Enablement and Growth
Service Provider Cloud Enablement and Growth
 
Guests Arrival
Guests ArrivalGuests Arrival
Guests Arrival
 
Simple Java
Simple JavaSimple Java
Simple Java
 
Memories of My Childhood at Satara, Pecha Kucha Night, Pune
Memories of My Childhood at Satara, Pecha Kucha Night, PuneMemories of My Childhood at Satara, Pecha Kucha Night, Pune
Memories of My Childhood at Satara, Pecha Kucha Night, Pune
 
Emerging Europe (Ch.9 Honors and Regular)
Emerging Europe (Ch.9 Honors and Regular)Emerging Europe (Ch.9 Honors and Regular)
Emerging Europe (Ch.9 Honors and Regular)
 
Chapter 13 explorations
Chapter 13   explorationsChapter 13   explorations
Chapter 13 explorations
 
Sdm rekrutmen dan assesment
Sdm rekrutmen dan assesmentSdm rekrutmen dan assesment
Sdm rekrutmen dan assesment
 
Ancient Greek Culture
Ancient Greek CultureAncient Greek Culture
Ancient Greek Culture
 
An Introduction To Ancient Greece
An Introduction To Ancient GreeceAn Introduction To Ancient Greece
An Introduction To Ancient Greece
 

Similar to When big data is not an option

360i Report: Big Data
360i Report: Big Data360i Report: Big Data
360i Report: Big Data
360i
 
Barry Ooi; Big Data lookb4YouLeap
Barry Ooi; Big Data lookb4YouLeapBarry Ooi; Big Data lookb4YouLeap
Barry Ooi; Big Data lookb4YouLeap
Barry Ooi
 
1 Five Big Data Trends Revolutionizing Retail Summary.docx
1  Five Big Data Trends Revolutionizing Retail Summary.docx1  Five Big Data Trends Revolutionizing Retail Summary.docx
1 Five Big Data Trends Revolutionizing Retail Summary.docx
jeremylockett77
 
Malaysia Presentation
Malaysia PresentationMalaysia Presentation
Malaysia Presentation
Alan Royal
 

Similar to When big data is not an option (20)

360i Report: Big Data
360i Report: Big Data360i Report: Big Data
360i Report: Big Data
 
Data-Analytics-Resource-updated for analysis
Data-Analytics-Resource-updated for analysisData-Analytics-Resource-updated for analysis
Data-Analytics-Resource-updated for analysis
 
Capitalize On Social Media With Big Data Analytics
Capitalize On Social Media With Big Data AnalyticsCapitalize On Social Media With Big Data Analytics
Capitalize On Social Media With Big Data Analytics
 
Mejorar la toma de decisiones con Big Data
Mejorar la toma de decisiones con Big DataMejorar la toma de decisiones con Big Data
Mejorar la toma de decisiones con Big Data
 
The Quantified Self Goes Corporate
The Quantified Self Goes CorporateThe Quantified Self Goes Corporate
The Quantified Self Goes Corporate
 
Data set Improve your business with your own business data
Data set   Improve your business with your own business dataData set   Improve your business with your own business data
Data set Improve your business with your own business data
 
Barry Ooi; Big Data lookb4YouLeap
Barry Ooi; Big Data lookb4YouLeapBarry Ooi; Big Data lookb4YouLeap
Barry Ooi; Big Data lookb4YouLeap
 
1 Five Big Data Trends Revolutionizing Retail Summary.docx
1  Five Big Data Trends Revolutionizing Retail Summary.docx1  Five Big Data Trends Revolutionizing Retail Summary.docx
1 Five Big Data Trends Revolutionizing Retail Summary.docx
 
Data Driven Marketing: the DNA of customer orientated companies
Data Driven Marketing: the DNA of customer orientated companiesData Driven Marketing: the DNA of customer orientated companies
Data Driven Marketing: the DNA of customer orientated companies
 
Is Your Company Braced Up for handling Big Data
Is Your Company Braced Up for handling Big DataIs Your Company Braced Up for handling Big Data
Is Your Company Braced Up for handling Big Data
 
Big Data: Where is the Real Opportunity?
Big Data: Where is the Real Opportunity?Big Data: Where is the Real Opportunity?
Big Data: Where is the Real Opportunity?
 
Age Friendly Economy - Improving your business with data
Age Friendly Economy - Improving your business with dataAge Friendly Economy - Improving your business with data
Age Friendly Economy - Improving your business with data
 
Big & Fast Data: The Democratization of Information
Big & Fast Data: The Democratization of InformationBig & Fast Data: The Democratization of Information
Big & Fast Data: The Democratization of Information
 
Module 2 - Improving current business with your own data - Online
Module 2 - Improving current business with your own data - Online Module 2 - Improving current business with your own data - Online
Module 2 - Improving current business with your own data - Online
 
Data set module 2
Data set   module 2Data set   module 2
Data set module 2
 
Big Data in Retail (White paper)
Big Data in Retail (White paper)Big Data in Retail (White paper)
Big Data in Retail (White paper)
 
Malaysia Presentation
Malaysia PresentationMalaysia Presentation
Malaysia Presentation
 
Thinking Small: Bringing the Power of Big Data to the Masses
Thinking Small:  Bringing the Power of Big Data to the MassesThinking Small:  Bringing the Power of Big Data to the Masses
Thinking Small: Bringing the Power of Big Data to the Masses
 
Bidata
BidataBidata
Bidata
 
Future focus 2018 by i prospect
Future focus 2018 by i prospect Future focus 2018 by i prospect
Future focus 2018 by i prospect
 

More from Nicholas Yap

Hybrid Cloud IT Transformation for Enterprise
Hybrid Cloud IT Transformation for Enterprise Hybrid Cloud IT Transformation for Enterprise
Hybrid Cloud IT Transformation for Enterprise
Nicholas Yap
 
CloudFX Enterprise ITaaS Solution
CloudFX Enterprise ITaaS SolutionCloudFX Enterprise ITaaS Solution
CloudFX Enterprise ITaaS Solution
Nicholas Yap
 
Accenture from on premise to the cloud
Accenture from on premise to the cloudAccenture from on premise to the cloud
Accenture from on premise to the cloud
Nicholas Yap
 
Accenture from-on-premise-to-the-cloud
Accenture from-on-premise-to-the-cloudAccenture from-on-premise-to-the-cloud
Accenture from-on-premise-to-the-cloud
Nicholas Yap
 

More from Nicholas Yap (11)

Hybrid Cloud IT Transformation for Enterprise
Hybrid Cloud IT Transformation for Enterprise Hybrid Cloud IT Transformation for Enterprise
Hybrid Cloud IT Transformation for Enterprise
 
Cloud IT Transformation for Enterprise
Cloud IT Transformation for EnterpriseCloud IT Transformation for Enterprise
Cloud IT Transformation for Enterprise
 
CloudFX Enterprise ITaaS Solution
CloudFX Enterprise ITaaS SolutionCloudFX Enterprise ITaaS Solution
CloudFX Enterprise ITaaS Solution
 
CLOUDFX: Addressing Challenges in Cloud Migration and Paving the Way for IT T...
CLOUDFX: Addressing Challenges in Cloud Migration and Paving the Way for IT T...CLOUDFX: Addressing Challenges in Cloud Migration and Paving the Way for IT T...
CLOUDFX: Addressing Challenges in Cloud Migration and Paving the Way for IT T...
 
Accenture from on premise to the cloud
Accenture from on premise to the cloudAccenture from on premise to the cloud
Accenture from on premise to the cloud
 
Accenture from-on-premise-to-the-cloud
Accenture from-on-premise-to-the-cloudAccenture from-on-premise-to-the-cloud
Accenture from-on-premise-to-the-cloud
 
2014 state of storage infographic
2014 state of storage infographic2014 state of storage infographic
2014 state of storage infographic
 
Determine Your Private Cloud Cost vs Benefit Analysis
Determine Your Private Cloud Cost vs Benefit AnalysisDetermine Your Private Cloud Cost vs Benefit Analysis
Determine Your Private Cloud Cost vs Benefit Analysis
 
CloudFX Strategic Cloud Consulting Services Offering
CloudFX Strategic Cloud Consulting Services Offering CloudFX Strategic Cloud Consulting Services Offering
CloudFX Strategic Cloud Consulting Services Offering
 
Optimize Your Enterprise Storage with a Phased Pragmatic Plan
Optimize Your Enterprise Storage with a Phased Pragmatic PlanOptimize Your Enterprise Storage with a Phased Pragmatic Plan
Optimize Your Enterprise Storage with a Phased Pragmatic Plan
 
Cloud Operational Maturity Model Report
Cloud Operational Maturity Model ReportCloud Operational Maturity Model Report
Cloud Operational Maturity Model Report
 

Recently uploaded

TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
panagenda
 

Recently uploaded (20)

Design and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data ScienceDesign and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data Science
 
Top 10 CodeIgniter Development Companies
Top 10 CodeIgniter Development CompaniesTop 10 CodeIgniter Development Companies
Top 10 CodeIgniter Development Companies
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdf
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps Productivity
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe
 
Introduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxIntroduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptx
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 Warsaw
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch Tuesday
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM Performance
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
 
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
 
TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 
JavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate GuideJavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate Guide
 
Overview of Hyperledger Foundation
Overview of Hyperledger FoundationOverview of Hyperledger Foundation
Overview of Hyperledger Foundation
 
How to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfHow to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cf
 
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
 

When big data is not an option

  • 1. strategy+business ISSUE 75 SUMMER 2014 REPRINT 00250 BY DAVID MEER When Big Data Isn’t an Option Companies that only have access to “little data” can still use that information to improve their business.
  • 2. leadingideasleadingideas 1 leadingideas 1 data-driven decision making—is already under way, and accelerating. The shift is so profound that compa- nies lacking complete or clean mar- ket data can no longer use this defi- cit as an excuse to rely on the status quo. They must make a concerted effort to use the data that is available to them (imperfect as it may be) or to explore innovative, low-cost ways to create new data. In one example, a large beverage manufacturer wanted to improve its sales to bars, restaurants, and enter- tainment venues. For years, this company had been buying syndi- cated data from an established source, which covered more than 100,000 establishments. Unfortu- nately, the data was collected and structured to serve a broad set of cli- ents and featured a standard seg- mentation scheme that did not pro- vide enough insight for the beverage company into how to serve different segments. So the company decided to adopt a series of little data tech- niques to come up with a solution customized to its needs. It started with observational re- search, visiting bars and restaurants and qualitatively cataloging the cli- entele and their consumption pat- terns. Synthesizing this information resulted in more actionable segment definitions. The next step was to quantify the segmentation—deter- mining how many establishments were in each segment. The beverage manufacturer developed an algo- rithm based on observable charac- teristics, then asked its sales profes- sionals to classify all the bars and ingly mining these data sources to understand more about their cus- tomers’ behavior and preferences, and even to anticipate stock market movements. Early successes by a few companies have caused others to start investing in the infrastructure, software, and talent required to mine big data. There is, however, one impor- tant caveat. Many companies— probably most—work in relatively sparse data environments, without access to the abundant information needed for advanced analytics and data mining. For instance, point-of- sale register data is not standard in emerging markets. In most B2B in- dustries, companies have access to their own sales and shipment data but have little visibility into overall market volumes or what their com- petitors are selling. Highly special- ized or concentrated markets, such as parts suppliers to automakers, have only a handful of potential cus- tomers. These companies have to be content with what might be called little data—readily available infor- mation that companies can use to generate insights, even if it is sparse or of uneven quality. For these companies, the U.S. Marine colo- nel’s words will resonate more than the latest data-mining algorithm or social listening platform. Several commentators have made the point that the implications of big data go beyond new data sources, analytical techniques, and technology. Rather, a paradigm shift—away from management based on gut feelings and toward When Big Data Isn’t an Option Companies that only have access to “little data” can still use that information to improve their business. by David Meer A n advertising agency met with a client—who hap- pened to be a U.S. Marine Corps colonel—and the conversa- tion turned to the topic of reliable data. “Look,” said the colonel, “if I’m on a battlefield trying to de- fend a hill and I get a piece of intel- ligence, even if I’m not 100 percent sure that it’s accurate, I will make decisions based on that intelligence.” He strongly believed that it’s better to have some information than none—and that you’d be a fool to disregard it just because it falls short of being definitive. One could say that the colonel was a proponent of “little data.” There is, of course, a great deal of discussion about the potential of “big data,” the high-volume, high- velocity, high-variety information assets that require new forms of data processing to enable companies to make better decisions and operate more efficiently. Giant data sets are being created by aggregates of indi- viduals’ behavior (on social media sites such as Twitter and Instagram, for example), by transaction logs, and by automated information-sens- ing devices. Companies are increas- With the right mind-set, virtually all sources of information can be exploited to improve products, the customer experience, or profits. strategy+businessissue75
  • 3. IllustrationbyOptoDesign restaurants in their territories based on the algorithm. (This is a classic little data technique: filling in the data gaps internally.) Finally, for each major segment, the company designed tailored product assort- ments, pricing, and marketing pro- grams. Pilot projects in two large cities have shown significant lifts in total sales and share penetration, and the company is now rolling out the initiative nationwide. Other companies have used lit- tle data successfully as well. In one case, a maker of industrial coating products had limited data on pricing broken down by customer and re- gion. As a result, it couldn’t build robust price elasticity models using classical regression analysis. By us- ing other analytical techniques, however, the company was able to identify specific areas in which it could improve pricing and service policies. It moved to a value-based pricing approach to ensure its most profitable customers were receiving the highest service levels. Implemen- tation in one business unit in one region alone yielded a 4 percent in- crease in sales. In another instance, a regional health insurance company trying to differentiate itself through outstand- ing customer experience realized that its call center was a potential source of data about customer pain points and potential solutions. The company took full transcripts of the calls—not just the summaries entered by service representatives— and applied available text-mining algorithms. From this data, the company was able to improve the format and language of its written communications, and streamline the call-center process. In addition, it uncovered an opportunity to in- troduce storefront locations in cer- tain neighborhoods in order to im- prove its customer interactions and increase customer retention rates. Even large companies are able to make use of little data techniques. The Chinese large-appliance giant Haier uses information gathered by service technicians to drive innova- tion. In the late 1990s, some techni- cians, for example, found that rural customers were using their washing machines to wash vegetables, lead- ing to clogs. Haier used this infor- mation to develop a new type of washer, which the company says is “mainly for washing clothes, sweet potatoes, and peanuts.” With the right mind-set, virtu- ally all sources of information can be exploited to improve products, the customer experience, or a com- pany’s profits. Little data techniques, therefore, can include just about any method that gives a company more insight into its customers without breaking the bank. As the examples above illustrate, mining little data doesn’t mean investing in expensive data acquisition, hardware, soft- ware, or technology infrastructure. Rather, companies need three things: • The commitment to become more fact-based in their decision making. This commitment is often spurred by a sense that competition is heating up or the company is fall- ing behind changing customer hab- its and preferences. But fact-based decision making can be an impor- tant source of competitive advantage for market-leading companies. • The willingness to learn by doing. Since little data applications are not commercially available via third parties, companies have to use trial and error. However, once a few priorities have surfaced, a series of pilot projects will give the company useful experience and, with a little luck, some early successes that can inspire the rest of the organization. • A bit of creativity. To generate richer data, companies need to get creative, in part by tapping into the customer interactions that take place naturally. For instance, retailers can intercept shoppers in store locations for quick iPad-assisted surveys. Any website with a registration form can add questions that reveal preferences beyond the basic data usually col- lected. Call-center conversations are another opportunity to gather data on a particular topic, and the text can be mined for greater insight into the customer. Some companies cre- ate advanced user panels of savvy customers to get input during the R&D process for new products. Others rely on their sales representa- tives to report trends in customer 2 leadingideas
  • 4. leadingideas 3 leadingideas 3 preferences and competitors’ activi- ties. The bottom line: Companies have to put in the extra effort re- quired to capture and interpret data that is already being generated. Companies often start the jour- ney by picking a product, a region, and a problem that needs attention and running one or more pilot proj- ects. This allows executives to dem- onstrate to themselves and the rest of the organization that the return on effort and cost is justified. Once companies start investing in analyt- ics, they almost never stop, because the things they learn drive improve- ments in the business that more than pay for the analysis. The activ- ity becomes self-funding. In some cases, companies that start with lit- tle data end up recognizing the value of the resulting insights and expand- ing their investment to incorporate larger data sets and more advanced analytics. For others, little data is all that’s needed. In either case, the benefits are clear: Executives get in- sight into what they can do to im- prove their competitive position, or—to put it in terms that a Marine Corps colonel might appreciate— identify what might be charging up the hill to surprise them. It’s hard to put a price tag on that. + Reprint No. 00250 David Meer david.meer@strategyand.pwc.com is a partner with Strategy&’s consumer and retail practice, and is based in New York. strategy+businessissue75
  • 5. Articles published in strategy+business do not necessarily represent the views of PwC Strategy& Inc. or any other member firm of the PwC network. Reviews and mentions of publications, products, or services do not constitute endorsement or recommendation for purchase. © 2014 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details. strategy+business magazine is published by PwC Strategy& Inc. To subscribe, visit strategy-business.com or call 1-855-869-4862. For more information about Strategy&, visit www.strategyand.pwc.com • strategy-business.com • facebook.com/strategybusiness • http://twitter.com/stratandbiz 101 Park Ave., 18th Floor, New York, NY 10178