8/12/2019 CGT Straight Talk Big Data

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8/12/2019 CGT Straight Talk Big Data

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CONSUMERGOODS.COM | DEC EMB ER 2013 | CGT 2323 CGT | DEC EMB ER 2013 | CONSUMERGOODS.COM

EXPERTS DIS PROVE

BIG DATA MYT HS

JON VAN DUYNE

Senior Executive AdvisorBooz & Company

jon.vanduyne@booz.com

www.booz.com

Consumer products (CP) companies have al-

ways wrestled with numerous data sources.

Over the last several decades, the evolution

of quantitative-based marketing decision-mak-

ing combined with the increased availability of

external data and computing power has forev-

er transformed marketing

from a field rooted in gut

instinct to a discipline driv-

en by analytics related to

consumer insights. Today,

the average CP marketeris dealing with significant

amounts of data from IRI/

Nielsen, agencies, retailers,

media and internal sources.

The rapid arrival of big

data, however, is a chal-

lenge to existing process

and systems. The informa-

tion flowing in from exter-

nal sensor data (Google,

Facebook, Twitter, blogs) and other social set-

tings (online forums, web feeds, SharePoint)

is potentially rich but arrives with a host of

challenges not the least of which is its unstruc-

tured nature.

While most CP companies accept the po-

tential value of this data, big data initiatives

often focus on purchasing software platforms,

incorrectly assuming that a technology solu-tion is all thats required to solve the issue.

However, industry best practice suggests that

a more holistic approach should be incorpo-

rated by building data analytics capabilities

that address the following components:

Infrastructure: What kind of infrastruc-

ture should be created to scale and con-

solidate data?

Processes and Organi-

zation:What are the data

governance and organiza-

tional processes required

to manage the data and

analytics? Software and Data

Solutions: What is the

right technology solution

to implement?

Analytics: What analytic

capabilities (models, data,

skill sets) are required to

generate the insights?

Insights: What are the

new consumer and market

insights that can be generated to create the

highest value for the business?

A well-thought out, comprehensive ap-

proach to building each of these com-

ponents will result in a data ana-

lytics capability that effectively

harnesses big data, providing

deep insights in consumer be-

havior and trends, ultimate-ly yielding market-leading

consumer insights.

Big data initiatives

often focus on purchasing

software platforms,

incorrectly assuming that

a technology solution

is all thats required...

Consumers use of web, social media and mobile during the pathto purchase is creating potentially rich but unstructured sources for

information increasingly known as big data. The ability to process,

analyze, report and get insights from this data simply requires buying the

right software product then integrating that solution with legacy systems.

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8/12/2019 CGT Straight Talk Big Data

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24 CGT | DEC EMB ER 2013 | CONSUMERGOODS.COM

EXPERTS DIS PROVE

BIG DATA MYT HS

BHAVISH MADURAISenior Principal,Big Data & Analytics

CHARLES R. TROYERPractice Director,Consumer & Retail

For more information visitwww.csc.com

Big data made its debut in CPG in 1974 when

a pack of gum was first scanned in Ohio and

when 20+ years ago Wal-Mart created Retail

Link; to suggest big data is new in CPG is

disingenuous.

The analogy to oil is

also misleading. Big data

will not yield an economictransformation, like oil did

during industrialization.

To suggest otherwise is an

exaggeration at the least

and smacks of the hype

surrounding dot.coms.

Still, CPG companies

are immature in their use

of data. Largely defined

as downstream demand

data, it is managed in silos

across marketing, sales and category teams.

Most organizations use nascent approaches to

monetize the value of data by either going into

an endless strategizing mode or by jumping

on the technology bandwagon. Few pull it all

together to support wide-ranging decisions

that have significant economic impact.

A better analogy for big data is mountainscontaining rich veins of gold. Predictive ana-

lytics provides the extraction tools to uncover

the nuggets that CPG companies can mon-

etize. The missing link is the role played by the

prospector. The prospector quickly surveys a

territory to hone in on true veins of gold not the

fools variety. An enterprise intelligence strat-

egy plays the prospector

role, identifying key deci-

sions across the enterprisethat can be impacted by

big data.

The emphasis of the

strategy is to link analyt-

ics to the key levers of

business performance.

The enterprise intell i-

gence strategy enables

organizations to extract

value from structured, un-

structured and high vol-

ume data sets using an outcome driven and

data science-led approach.

Alchemists of yore tried in vain

to transmute lead into gold. The

hype surrounding big data today

would suggest a similar alche-

my. But, there is real gold in

big data hills. CPG shouldintelligently prospect which

are the right ones to mine.

An enterprise intelli-

gence strategy plays

the prospector role,

identifying key decisions

across the enterprise...

Big Data is the new Oil declared Clive Humby,

co-founder of dunnhumby in 2006. This is a rallying

cry for big data proponents. Big data is neither new

nor revolutionary in consumer packaged goods (CPG).

However, the potential for big data and more importantly

predictive analytics has not been realized fully in CPG.

CGT Straight Talk

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8/12/2019 CGT Straight Talk Big Data

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EXPERTS DIS PROVE

BIG DATA MYT HS

JANET DORENKOTT

Co-Founder & COORelational Solutions Inc.

janetd@relationalsolutions.com

www.relationalsolutions.com

At a recent conference, I heard a dozen

speakers interchangeably refer to big

data as e-commerce or digital marketing.

Those are components of big data, but the

terms should not be used interchangeably.

Unstructured big data refers to information in

the cloud, including e-com-

merce, digital marketing,

geo-spacial information,

RFID, mobile offers, video,

mobile wallets and pay-

ments, comments, tweets,

NFC (Near Field Communi-cation), blogs, likes, sche-

matics, photos, infograph-

ics, clicks, QR codes, online

searches and much more.

Structured big data

mostly refers to internal

data, including shipments,

manufacturing, orders, in-

ventory, CRM, promotions,

POS, forecasts, spread-

sheets, syndicated data, etc.

Characteristics of big data include volume,

variety and velocity. Relational Solutions ex-

tends that description to include complexity.

Thats because, unstructured big data has sig-

nificant value, but that value grows exponen-

tially when you are able to leverage it against

your internal, structured data. There is a lot of

complexity involved in that process.Some technology vendors claim there is

no longer a need for structured data. This is

untrue. Structured data exists in applications

from Oracle, SAP, IBM, Microsoft, etc. These

companies all offer solutions for unstruc-

tured data, but their structured solutions will

not be going away any time soon.

These two technologies can be merged

together leveraging a

sound infrastructure that

accommodates growth

and change.

What if you could: Use

comments to explain why

sales are down in certainstores rather than sending

out a rep? Localize senti-

ment and identify whats

impacting sales in cer-

tain regions? Proactively

push offers out to cus-

tomers when they arrive

at the store? Turn negative

commentators into advo-

cates? Determine what

clicks are leading to sales?

Integrate Amazon sales with internal sales?

Big data is about much more than just

e-commerce and digital marketing.

Its about leveraging all forms of

information to streamline pro-

ductivity, understand custom-

ers better, improve pu blic

perception, service retail-ers better and maximize

sales.

Unstructured big data

has significant value,

but that value grows

exponentially when you

are able to leverage it

against your internal,

structured data.

Big data is just about digital marketing.

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8/12/2019 CGT Straight Talk Big Data

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EXPERTS DIS PROVE

BIG DATA MYT HS

JUSTIN HONAMAN

Partner - Consumer Goods /Retail, Teradata

justin.honaman@teradata.com

www.teradata.com/

consumergoods

Whats most important to con-

sider when you think about the big

data concept is both the types of new data

sets and new data sources that are now

available for consumer goods (CG) and retail

enterprises. These are driving new analytics

and technologies, which in turn are driving the

business value of big data across an organiza-

tion to many users.

The entire organization can benefit

from big data analytics. The statistics from

McKinsey indicate that use of big data has the

potential of improving global productivity by 1

percent, which is a huge amount when youretalking about a global business.

In the CG space, big data enables retailer

collaboration efforts. For example, big data

allows CG manufacturers to merge consumer

shopping behavior and social network insights

with loyalty and transaction data to understand

what drives path to purchase across customer

segments. This insight can lead to increased

engagement, thus driving sales and loyalty op-

portunities for both the retailer and the CG firm.

For example, sending a promotion to a cus-

tomers mobile phone that has been triggered by

her scanning a products QR code in a store can

be a relatively simple automated process that

doesnt require extensive analysis. However,

lets say the promotion is not only addressed to

the shopper but is specifically designed for her,

based on factors including previous purchases,

online and mobile searches and even her po-tential to be a high-value shopper, based on her

demographic similarities to other shoppers.

Such highly personalized promotions

would require bringing together data from

customer loyalty and digital analytics solu-

tions, shaped by sophisticated predictive ana-

lytics applications, and all tied into in-store

and mobile promotional tools for delivery

while the shopper is at the point of decision.

This means a significant investment in not

only analytics but in good data management,

but it could also produce conversion rates

that would dwarf those of broader promo-

tional efforts.

Another example where speed is of the es-

sence is in the area of store-based fulfillment,which is becoming increasingly popular as

retailers realize the value of their existing store

network in getting products into customers

hands. However, operating such a fulfillment

network requires retailers to establish busi-

ness rules that analyze all the costs involved

in fulfillment and have a direct relationship

with CG suppliers to fulfill specific orders and

ensure on-time, in-full replenishment.

Transforming raw data into timely insight

is at the core of a good BI strategy, and doing it

quickly even with high volumes of data is the

mark of a good big data initiative, noted

Aberdeen Groups Nathaniel Rowe.

CGers that can master not just the

velocity but the volume and vari-

ety that define big data will be

well on their way to unlocking

its tremendous and to agreat extent still untapped

value.

Big data is an IT problemnot a business opportunity

CGT Straight Talk

2 6 CGT | DEC EMB ER 2013 | CONSUMERGOODS.COM