From Big Data to Business Value

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From Big Data to
Business Value
How to leverage new insights on any data for competitive advantage
By Gib Bassett
If you’re tired of the hype surrounding Big Data, you are not alone. Many business and
technology leaders no longer wish to talk about Big Data conceptually. Instead, they are
interested in understanding how analytics can improve their businesses and drive competitive
advantage.
This shift is borne out of frustration many report getting value from investments in new
technologies intended to unleash insights quickly, without the constraints of the past –
constraints such as the type of data employed in analyses and the forms of analytics which can
be applied.
Here in a single document is a compilation of my learnings and observations working with real
customers over the past couple of years. My thought in consolidating these posts from LinkedIn
was to provide an easy hyperlinked reference for leaders interested in breaking through the
clutter to learn ways to leverage data for competitive advantage into 2017 and beyond.
These are my thoughts alone and do not reflect the views or opinions of my employer or
anyone else. I won’t fool you though – I do believe that cloud computing has a lot to do with
simplifying access to Big Data capabilities such that you can focus more on business outcomes
and less time managing technology.
Thanks for looking! Connect with me on LinkedIn and follow me on Twitter.
Scroll down for hyperlinks to an outline of posts.

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Part 1: The Fundamentals
 Why just the potential for value won't cut it with big data analytics
 What exactly is a Big Data problem?
 Why Big Data is so hard…And what to do about it
 Anatomy of a successful Big Data project
 The Big Data Platform versus Point Solution Debate
 Has Your Big Data Train Left The Station?
Part 2: The Business View
 Executives Raise their Big Data Analytics IQs
 What a CEO should understand about their company’s approach to Big Data
 Overcoming the #1 Challenge to Realizing the Value of Big Data
Part 3: The Industry View
 Winning the Back to School Dollar with Big Data Analytics
 Making the Case for Better Retail and Consumer Goods Analytics
 Will CPG find the right balance between trade and consumer investments in 2016?
 Optimizing a Complex Marketing Mix in 2016
 Bringing Big Data into the Business
 The Human Side of Retail Digital Transformation
 Busting Big Data Myths
 Big Data at Walmart #LoveData
 Big Data at Starbucks #ThirdPlace
Part 4: Identifying and Prioritizing Use Cases
 Fishing for Big Data Success
 Do Something Different in 2016
 Prioritizing your Big Data Use Cases
 How to architect your talk with a businessperson about analytics
 The Art of the Doable
 3 ways to gain an edge with Big Data Cloud Services
Part 5: All About Skills and People
 Organizing for Analytics
 The Data Scientist is Here to Stay
 Smartsourcing your Marketing Analytics
 A good day for a Data Scientist is a good day for everyone
 Now Hiring: Retail Marketing Scientist

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Why just the potential for value won't cut it with big data analytics
Working in the Big Data market has been a very interesting experience. As a business-minded
person, I find the ability for data and analytics to inform decisions that help out-maneuver the
competition and keep in step with customers among the most compelling ways some
companies are addressing their utmost challenges. Despite many successes, I continue to see
companies fail to capitalize on this opportunity. Some thoughts recently came to mind about
why this is happening. Perhaps you have had similar experiences? Read on and let me know.
I think Big Data continues to perplex many companies and their executives
Big Data is still often viewed by many as just a bigger pile of data, to be used for some
unspecified end - and is therefore just noise. Maybe it’s cheaper storage, or a way of cutting
further costs out of your IT budget by moving a proprietary data warehouse to a Big Data
technology like Hadoop. People who think these ways haven’t been exposed to the strategies,
roadmaps, and successes of companies that adopt a “data driven mindset.” They don’t
understand how the data available today, plus new sources yet to be obtained or explored, can
help answer new questions about their businesses. It’s really not as muddy as figuring out
whether a sales report is representative of Big Data or not. It’s clearly about exploring factors
that influence your sales that you can’t see based on whatever data and analytics are available.
This is just an example.
The Illusive “Black Box”
The Big Data Analytics that reflect your business likely differ from your competition. Not
necessarily the measure itself, such as customer value, but the data sources and business
processes that provide data and are impacted by new analytics. Consider too that most
executives understandably view their analytic methods as trade secret. Executives who seek a
black box with pre-defined answers to questions they already know are not thinking about Big
Data in the right context. They may have operational reporting problems but that’s different.
Separate Skills / Same Foundation
At a Gartner presentation this week by Doug Laney, he highlighted the separation of SKILLs
related to operational reporting from those employed for advanced analytics. I capitalized
SKILLS, because he was speaking organizationally (similar to what I heard at this event earlier
this year). When it comes to the enabling technology that underpins the work of both groups,
there is a lot of value in considering an integrated environment where data and analytics are
easily shared, moved and improved across the two areas. The fact you organize resources
uniquely on one hand, yet they should share a common foundation, is another point where
people get confused. Laney mirrored this sentiment when he said that IT was slowly separating
into an “I” group focused on analytics and information, and a “T” group focused on technology.

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Big Data isn’t really an application with a targeted set of users
Big Data’s potential lies in the capabilities to support use cases. This is where Big Data is falling
down in some companies. Companies that have moved on “Big Data” in the name of lower
costs have placed their bets on a use case that will simply let down businesspeople. And
ultimately kill any potential benefit of Big Data.
Lowering costs is fantastic, but what’s happening is that execs in companies that pursue Big
Data to this end are starting to scratch their heads about the reported business value of Big
Data (like Tom Davenport talks about here). The head scratchers are people in the business
who are not seeing any benefits and these are the ones who are absolutely KEY to taking
advantage of Big Data. More data driven managers are needed, said Walmart Labs’ Esteban
Arcaute, not necessarily more data scientists.
The resolution to these questions is to start with a plan to develop a portfolio of use cases in
partnership with those in the business. So that Big Data represents more than just potential
value, it forms the centerpiece of a plan to drive business performance in very specific ways
using analytics. Laney said business leaders finally serious about Big Data would within 90 days
bring together analytic and business talent to work together to start outlining projects.
By bringing together IT, analytics and the business, specific analytic problems and opportunities
can be approached, operationalized, and socialized. Building wins and momentum is key.
Without it, the promise of Big Data is likely to go unfulfilled.
It’s being said in many reports that business culture is the biggest barrier to becoming more
data driven. Without an attack plan to fulfill the promise of Big Data Analytics with a business
use case oriented roadmap, I’m afraid a lot of companies are missing the value boat.
Return to the outline of contents

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What exactly is a Big Data problem?
In the iPhone world they say “there’s an app for this and an app for that.” In the business world,
when it comes to analytics, you could say the same. Whether it’s social media, marketing,
advertising, supply chain, and others, there are analytic applications (or services) available to
inform these discreet business areas.
So when it comes to Big Data Analytics, what’s the point versus an application? That’s a more
common and logical question than you might imagine.
Most applications are pre-defined around a particular problem or analysis. This accelerates
time to value and is especially appreciated by businesspeople hungry for new insights about
their social media campaigns, advertising effectiveness, supply chain efficiency and so on.
The challenge is that the packaged nature limits the extent to which different or new data
sources might improve upon what an application was designed to account for. That word
“might” becomes a central point of focus in addressing the question. You also have to think
about the way Big Data Analytics are unfolding successfully in companies large and small.
The idea behind Big Data Analytics is to leverage both new technology and new skills to uncover
new insights. In practice, companies are using their existing analyst teams, newly hired data
scientists, and partners to work with new technologies like Hadoop, Map Reduce, Spark, R and
others to tap into the value potential of examining data beyond what pre-defined applications
account for. Companies that do this well ensure close collaboration with business sponsors who
help shape the projects and put them into operation.
This would be a fool’s errand were it not for companies experiencing significant gains in
financial performance as a result.
With most business functions interrelated as it pertains to something important such as the
customer experience, it’s beneficial to look across the business and beyond to inform a variety
of decisions which could impact key moments in the customer journey. Check out Using Big
Data to Understand Small Moments of Truth for more about this idea.
This is challenging when most companies have already invested in a variety of applications and
services which align to different business areas. It begs for a means to collect, integrate and
analyze data across these different business domains, and extend that analysis when it’s
beneficial to add new data sources (such as a new social network that arises). It’s become
apparent that most companies’ legacy systems are ill suited to this new world.
This is where a business problem presents itself that begs for a Big Data solution. The next
challenge is to understand which decisions could be improved upon and are worth exploring
beyond whatever insights the business currently employs. It’s both a function of what can or
should be improved, and how much value is attainable.

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This is where businesspeople often get stuck in trying to divine the difference between an
application and Big Data Analytics. Some helpful ways to think about this include:
It’s about differentiation
Analytics are how industry leaders differentiate themselves and maintain their top dog
positions. They are less interested in the analytics their peers use, and more interested in the
analytics that pertain specifically to the unique characteristics of their business. The difficultly
many companies have is figuring out what decisions are important enough to approach from a
Big Data point of view.
It’s about business improvement
Just the other day I posted a piece to LinkedIn called “The Art of the Doable.” Check that out, it
describes how to go about identifying the high yield decisions to consider for Big Data Analytics.
It’s about valuing data
Beyond the rhetoric about valuing your data as an asset is the reality that companies who
embrace this idea at their core do better financially than their peers. It’s a cultural as well as
technical hurdle documented by analysts and other researchers who have studied Big Data
adoption across industries.
It’s about specificity
Once again, I refer to “The Art of the Doable” to read about some ways to be as specific as
possible when considering Big Data use cases. Honing in on those use cases that are both high
value and can be operationalized quickly is crucial to early success with Big Data.
The beauty of the fast paced Big Data technology industry is how quickly the software is
evolving to account for all the stages of the analytics process so that businesses can spend
more time thinking about what questions to ask and less time managing technology.
When you can in one fell swoop buy space in the cloud necessary to land whatever data you
want securely, then integrate, prepare and enhance it as well, you can greatly accelerate the
process of applying new forms of analytics to uncover those specific insights you are looking
for.

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Why Big Data is so hard…And what to do about it
If you’re like most managers, you’ve been approached by vendors proposing new ways to gain
more insights from your data. Unfortunately, this is easier said than done. Why? Besides having
a day job, you’ve come to rely on some form of internally produced Business Intelligence or
externally sourced analytic service. These more or less get the job done. How could you ever
consider doing something differently?
Just because something is possible doesn’t mean it’s worth doing. This is the essence of the
conundrum organizations face with the promise of Big Data – which is developing valuable new
insights leveraging data historically unaccounted for by your internal Business Intelligence or
external services provider. Critical is that these insights be worth the effort, in that they result
in a meaningful improvement in business performance. That’s where investments in Big Data
pay off.
Getting over this hump for all but the most sophisticated companies can be very challenging.
Big Data, just the concept, let alone the options available, is complex. Not many of us have the
time to understand what’s most important and how to get started. Here is what I have found
helpful.
Whether you are a business manager or work in information technology, recognize first that Big
Data is closely related to Data Science. I say related, because they are not one in the same but
rather leverage one another. Data Science is a relatively new discipline that brings forward the
roles of statisticians, analysts, data miners and programmers, into the world of analytics
possible when data can live in any format and at any scale. Big Data implies the use of data to
this end in a way that today’s Business Intelligence or your existing data warehouse simply
cannot account for.
Second, understand that Data Science is a “science,” which means experimentation is
fundamental to the job. Data experiments might be another way to describe use cases which
address questions such as how to attribute online and offline marketing investments to sales.
Businesses have little tolerance for technology projects that lack a clear ROI, so it’s a good idea
to think about how you might remove as little of the uncertainty around data science as
possible.
Data science skillsets may be scarce, but they are growing. If your company is waiting it out,
hoping for the hype to pass, know that in the U.S. the top 10 retailers have hired an average of
almost 100 data scientists. At the other end of the maturity curve, smaller companies are
making their first hires and augmenting these with consultants. This is no trend but the new
competitive battleground. I think the challenge many face today and will into the future, is how
to speed up the work of the Data Scientist so they can focus on more use cases, more quickly,
and thus increase the likelihood of surfacing meaningful insight.

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Thirdly, Big Data is not all about Data Science, but a whole lot of technologies that account for
what I call the “analytics process” – all the steps required to go from data to operationalized
insights. For those in information technology roles, I’m referring to your data management
platform, where you might have both relational databases and unstructured data lakes
managed apart or together. I’m also describing the products you leverage to integrate, enhance
and prepare the data for use by Business Intelligence users, analysts and data scientists. And
I’m referring to the burgeoning set of capabilities which help business managers and Data
Scientists perform discovery on Big Data, create and deploy descriptive, predictive or
prescriptive models, and share visualizations of the results.
This collection of technology broadly defines the architecture most companies possess today
and which comprise products and cloud services from dozens of companies.
What excites me about the Big Data space today is the momentum around bringing all the
required capabilities to support the Big Data Analytics process to the cloud. What this means in
practice is that you, as a manager or information technology professional, can start thinking
more about capabilities you need to execute a use case, versus products you need to buy,
implement and support for some discreet aspect of the analytics process. You can also start to
identify inefficiencies in your current process that hamper speedy execution of use cases and
which could be engineered out by leveraging cloud capabilities instead.
Where this makes a big difference and simplifies Big Data is in the ability to try out a variety of
use cases using a combination of cloud capabilities, either in tandem or apart from your current
technology architecture, much faster. This is especially valuable considering that companies
gaining the most value from Big Data are outsourcing less of their analytics, and instead
developing their own in pursuit of competitive differentiation. Cloud makes doing this simpler.
Remember too, your company today probably outsources a number of analytics to services
companies. From an information technology professional point of view, I would rather get in
front of my business users outsourcing analytics to help them in-source as much of this as
possible, add security and governance, while also helping my burgeoning Data Science team
start making a real difference to my company’s performance.

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Anatomy of a successful Big Data project
In my last post, I described how achieving the value of Big Data Analytics is so difficult for so
many companies, and how to think about overcoming the challenges. What often comes next is
a decision to proceed with a project. So I thought it useful to describe what a successful one
looks like.
The easy but incomplete answer is: a project that yields business value. This fails to illuminate
all the markers that line up and which lead to business value. So think about this.
First, very few Big Data Analytics projects succeed without a business sponsor. As an
information technology professional, it’s alluring to develop the technical capabilities necessary
to execute a project independent of a business sponsor. It’s easy to see why: the architecture
and its components can be deceptively easy to identify.
The issue here isn’t so much about architecture as it is about operationalization. How would an
insight be put to use? Most people won’t buy into a project which targets analytics related to
their job, without having been involved in the idea to begin with. It’s just human nature. Yet
this is the most commonly reported issue facing companies that fail to achieve the expected
value from their Big Data investments.
A wise former CIO for one of the most progressive consumer-centric CPG companies recently
told me the following in response to my question: What do you think are the most important
considerations for leveraging analytics in the cloud?
“I believe there are three considerations to adoption of cloud for analytics. First, what is
functionally of significant difference to what is already in place on premise or otherwise. The
second is whether there is the talent internal to the customer available to take advantage of the
capability. The third is a combination of ease of getting the relevant data sets and the
associated security established in the cloud.”
His comments illustrate first the importance of identifying something new. As I described in my
prior post, besides the challenge of identifying a business sponsor, the next issue to tackle is
how to accelerate the execution of a use case. Successful projects should be measured in part
by how quickly an insight is generated and evaluated. If you cannot benchmark this against a
current state, at the least target a realistically short timeframe that allows you to imagine
supporting many such use cases over time.
This is another barrier to success cited by many companies. It’s similar to trying to take your
craft beer business and scale it nationally. How do you scale the value of your investments in
Big Data human capital, not just the technology? This in turn relates to his second comment.

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Maximizing the value of your internal talent means streamlining the analytics process. Cloud
capabilities offer a path by providing access, on demand, to any element of that process – from
data management through modeling and deployment.
You may have holes in your architecture, use a wide variety of technologies, or spend too much
time and money managing technology as opposed to focusing on business sponsors and use
cases. These are all good justifications to explore Big Data cloud capabilities with a business
sponsor. This meets the “something new” hurdle and leads into determining the scope of skills
necessary to execute a use case.
The third consideration he mentions, access to the relevant data and security, are somewhat
obvious but you would be surprised how some companies consider use cases without first
documenting their Big Data assets. In fact, that’s a good exercise for almost any company to do
– produce an audit of the data available to your business, internal and external, regardless of
where it’s stored, the format or whether you believe it has business value or not. This greatly
accelerates the prioritization and scoping of an analytics project. Consider this an element of a
success as well.
Regarding security, it’s interesting how in reality enterprise cloud services have better
documented security than most on premise data centers. As companies gain more experience
taking a use case centric approach to bite sized projects, this no doubt become less of an issue.
Circling back to the first point, business value, it’s important to keep in mind the reason to
engage in this work is to move the needle on business performance. Successful Big Data
projects happen when an organization works collaboratively across IT, data science and the
business to leverage cloud capabilities that help accelerate the execution and
operationalization of new insights.

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The Big Data Platform versus Point Solution Debate
As I wrote recently, many marketers outsource analytics that could be more efficiently in-
sourced while providing greater value. Fact is, many companies outsource analytics for a variety
of business areas, or use “point solutions” -- pre-built applications or software tools focused on
specific tasks. All of this has happened over the past 10 to 20 years in the pre-cloud era, but I
think the world looks very different today. Especially in the Big Data space.
Fighting for mind- and wallet-share against services and point solutions are platforms which
enable multiple use cases, including many unaccounted for in those same services or points
solutions. The rationale for the platform instead has always been that it offers greater value
and more specificity to the business. For analytics, that kind of specificity is what drives real
value. Consider Netflix this week reporting their recommendation engine is worth about $1
billion dollars to their business. Competing on analytics is not just a catchy slogan.
The historical challenge with platforms has been the on premise model required a lot of
services and people to implement, operate and maintain. In the cloud era, much of this
necessary but not terribly valuable investment goes away.
In a Big Data context, what’s exciting is how formerly on premise technologies related to
database, data management, integration, discovery, analysis and visualization can be
collectively or individually provisioned on demand to address a variety of use cases that apply
specifically to a business’s challenges, opportunities and most relevantly, available data.
With these formerly disparate parts working together to speed up what is actually the analytical
process, companies have the opportunity to address some of the greatest pain points related to
getting value from Big Data. Namely, that data science investments are not paying off because
these very smart resources spend more time wrangling data than on focusing on a greater
variety of use cases in collaboration with business owners.
Without greater business participation in Big Data projects, it’s not realistic to expect
investments in Big Data to pay off. IT meanwhile is trying to lead with Big Data by leveraging the
core technology to reduce costs but offer no net-new analytical capabilities. Many executives
report frustration at their lack of value from Big Data, so you can see why for many businesses
the promise of Big Data is simply not happening.
With cloud, companies have the opportunity to course correct and adopt the agile, test and
learn culture of Big Data Analytics that is quickly becoming best practice for the Netflix’s of
every industry. In this realm, the tradeoff between leveraging a platform and a point solution
goes away, business owners get more engaged in analytics projects, and the value of Big Data
investments surfaces to executives anxious to generate new insights to truly improve the
business.

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Has Your Big Data Train Left The Station?
Despite evidence that success with Big Data starts at the top, the fact is few companies’
leadership have yet to embrace analytics in this way. Instead, from the grass roots and often
within IT, companies have embarked on uncertain Big Data journeys and the results have been
underwhelming in many cases.
The failure to demonstrate large scale value from the bottom up is simply evidence of the need
to get buy in from the very top of the organization. It’s become a self-fulfilling prophecy. Are
these companies doomed to fail?
The journeys are underway, investments have been made and continue, new hires are coming
into the organization, and many CEOs and top lieutenants are just now starting “to get it” that
data and analytics need to be woven into the fabric of the organization.
Having started in the wrong place, can these companies course correct? They simply don’t have
time to hit the pause button and unwind what’s already happened. Without the time or
resources to step back and re-evaluate, what can companies do to leverage what’s already
happened and get on the right track?
That’s the essence of many conversations I have with business and IT executives in retail and
consumer goods organizations. I suspect the same happens across many industries. The Big
Data train has left the station but the destination isn’t clear and the locomotive cannot be
stopped. If your company sounds like what I’m describing, here is what I recommend:
For IT
Almost every company has deployed an instance of the open source technology Hadoop and its
related projects - somewhere. The use cases vary, but tend to be focused on reducing the cost
of storage or a dependency on a pricey proprietary data warehouse. In the absence of such a
use case, the technology was adopted with an eye on a possible application, but beyond
creating a data lake, the use case wasn’t clear.
Often unrelated, perhaps your organization has a data scientist or a small team of advanced
analytics practitioners – maybe they use SAS or open source R to develop and deploy predictive
models focused on helping the business understand demand patterns or customer segments.
These folks may or may not know about or leverage your Hadoop investment. In many cases,
they do not – they may in fact have their own server right under their desk or rely on far fewer
data sources than are actually available.
Data scientists have reported preoccupation with tracking down and integrating data to
support projects. For them, productivity is very important – spending as much time on higher
value actions than lower value ones which can potentially be automated or abstracted, but
without restrictions.

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If you identify with this situation, it’s really incumbent on you, as a grass roots Big Data
evangelist if you will, to connect the dots between the current investments in Hadoop and your
Data Science team. The reasons are borne out of best practices from Big Data leaders. Namely:
 Think of the use case first and foremost – not that you just might be able to leverage
Hadoop to some unclear end. A use case may require scale access to very structured
data in a data warehouse, so maybe connecting the dots between the lake and
warehouse is necessary. Architecture is important but don’t get bogged down by it –
think instead about agile pilot projects leveraging cloud service options where possible.
 Work to strip out as much administration of the Hadoop and related open source
technologies as possible. You need to focus your scarce resources on serving and
understanding the business and use cases. Companies are struggling to manage up to
dozens of open source projects to facilitate a single use case which is not scalable.
 Inform the use of Hadoop with the needs and requirements of the Data Science team, or
the type of new role I wrote about last time being hired to act as business/analytics
liaisons between managers and IT. Focus on making their jobs easier too, so that they
may focus on collaboration with the business.
For the Business
If you work in the business and don’t care about IT, but leverage the “BI” or “Business
Intelligence” software provided by them, you may have no clue that the above is happening.
What you care about is your day job and spending as little time analyzing data as possible, but
also trying to be as data driven as the tools available allow.
Even without a mandate from the top, smart managers are right now working with their data
science or analytics teams and in partnership with IT to attack any number of powerful
questions simply unanswerable by Business Intelligence tools today. This happens because
those skilled technical and analytical resources have streamlined and scaled the underlying Big
Data capabilities such that they have time to collaborate.
In a consumer market context, deceptively simple questions such as, “What’s a satisfactory
shopping journey look like and what can I do to create more of them?” requires expertise,
technology and data of the kind available only when taking a Big Data Analytics approach to the
problem.
If IT and Data Science are organized as described here and proactively work with the business
to deliver an internal “insight as a service” capability, the potential to improve results drastically
increases.
Whether your CEO is on board or just getting acclimated to the potential of Big Data Analytics,
you have the power to elevate the visibility and value of Big Data within your company. For
your career and future job prospects, that’s a strong value proposition.

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Executives Raise their Big Data Analytics IQs
For non-technical executives, the thought of Big Data analytics can be either confusing or
nauseating, depending on how much exposure they’ve had to vendor pitches over the past few
years.
The hype, promised new benefits and urgency to move quickly, all have combined to create a
perfect storm to either perplex or frustrate executives hungry to improve their business with
better insights.
It’s a well-documented situation. On one hand, executives realize that their organizational
structure relative to the data which underpins the business is suboptimal. Operating off of “one
version of the truth,” is of course best practice.
It’s also become incredibly challenging to get your head around this idea, given the years of
silos building up in the business. At the same time, there are a whole slew of new data sources
available that might offer a path to some breakthrough insight. Certainly executives in the
industries I focus on – retail and consumer goods – are in a desperate search for new ideas to
address the challenges stunting their growth.
For vendors that provide Big Data technologies, the lens has shifted to a logical view of the data
available to inform business insights. There’s just no longer a single place, in a single form, for
data to live. It’s everywhere, internal and external, and in a variety of formats. The question is
now, how to manage all of this data in a way that is secure and supports decisions across the
enterprise?
Interestingly, the challenges executives face to taking action remain the same as in the past,
when there was only the monolithic relational data warehouse. How do you get started? It’s
debatable that a logical data warehouse or data lake, is still just a conceptual database in need
of compelling and actionable use cases to make it a worthwhile pursuit.
As a consequence of changes in the data landscape, there’s been a slow-down in demand for
technologies which support basic reporting on long-common data sources – I’m sure if you
work in this space, you know what I mean.
Instead, what’s resonating with executives, is exploring new forms of analytics on data they
have long held, in combination with new data sources, to develop new insights. The
technologies supporting such projects tend to be non-relational in nature and emphasize
discovery and hypothesis testing, over creating a highly structured reporting environment.
The challenge stemming from this situation is how to operationalize insights. There’s a building
perception that Big Data analytics projects are little more than science experiments that are
never woven into the business. It’s often not as simple as tying a new metric to a record in your
data warehouse. That’s what should happen anyway, but what executives want are analytics

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that get fed into the core of their business to drive better performance. That’s not solely a data
integration exercise.
Savvy executives are starting to understand what’s happening and approach Big Data Analytics
from a different perspective.
While recognizing the opportunity to manage their data as a logical unit available to support all
manner of use cases, executives are rightly demanding that this “end state” be approached in a
manner that creates and builds rapid value.
It often takes a change agent to lead the charge. With the end state in mind – managing and
leveraging data as an analytical asset – leaders need to be able to demonstrate value quickly
and socialize this with their peers. Absent this step, the ultimate goal is highly unlikely ever to
happen and the status quo will persist.
To get started, CIOs and analytics teams should approach big data with an eye on attacking key
processes, in collaboration with executives from other areas of the business. For consumer
goods and retail organizations, you can imagine analytics informing all the functions which
affect the consumer’s shopping journey. From communication, to fulfillment and the post
purchase experience, it’s the execution of activity across this process which separates a leading
organization from the pack. Better analytics, leveraging current and new forms of data, are the
engine for better execution of the activities which affect the shopping journey.
Vendors have to rise to this challenge by plotting out clear paths for embracing new analytic
methods that become living parts of the business. It’s about managing data such that it’s easily
sharable in an analytically-consumable way across the company, securely and reliably –
whether on premise, in the cloud or some combination.

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What a CEO should understand about their company’s approach to
Big Data
Becoming a more data driven organization is at the heart of the Big Data movement. Most
industries have been disrupted, or are in the process of being disrupted, by accelerating
changes in their markets. Shifting consumer behaviors, enabled in part by digital technologies
and the Internet of Things, are the prime movers of the headwinds numerous industries are
facing. Enormous amounts of new data are being created and coming online every day, that
presents the opportunity to do something differently and move the needle on business
performance.
Another force of change, however, are the companies leading the way in Big Data. These
companies are illustrating how analytics and new insights can not only differentiate you from
the competition, but in some cases literally crush the competition (as pointed out in this recent
article about Amazon’s dominance). For everyone else, this march of progress is creating
urgency to act now.
What companies in many industries are experiencing is a gap between their investments in the
underlying technologies of Big Data, and the anticipated benefits. That gap looks a bit different
between companies at the bleeding edge of adoption and those just beginning their journeys.
Yet there is one thing in common between the two: wide-ranging views on what Big Data
represents in terms of value creation and how it fits within the organization.
CEOs have enough to worry about and focus on, without getting into the weeds of Big Data
technologies. But I do think they have a mandate to understand at the level I describe in this
post how their companies view Big Data through the lens of strategic, competitive
differentiation.
Considering your data as an asset
Gartner’s Doug Laney has described how companies can value data as a financial asset or
instrument, with data being accounted for on a company’s balance sheet. Even if you do not
literally approach your data in this manner, consider this: When retailer Radio Shack filed for
bankruptcy recently, initial reports around the sale of its assets suggested Amazon highly prized
their store locations to facilitate large scale local pickup and return service. In the end, the
bankruptcy court found Radio Shack’s most valued asset to be its customer data.
The moral of this story is this: had Radio Shack used its decades of consumer insight to
understand changing consumer buying preferences and adapt their business model accordingly,
could they have avoided bankruptcy in the first place?

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Your Big Data leaders
Some companies are creating new roles, such as Chief Digital Officer, or Chief Analytics Officer,
to own Big Data strategy. Many have yet to appoint such an executive, and are instead
assembling a proxy for the role: some combination of the CIO, CMO, CFO or COO, an Analytics
Leader and line of business leaders. Whichever model your company employs, it’s important to
know that Big Data is a multi-dimensional value driver.
What I mean here, is that Big Data is not a functional application, or a single enabling
technology for one silo of your business. The difference with Big Data is that it represents ways
for individual areas of your business to improve with new insight, based on an understanding of
all the possible data available to the company – across silos, and even extending outside the
company to the dozens, if not hundreds, of vendors working on behalf of line of business
managers.
The role of line of business managers
The implication of the above directly reflects the experiences of progressive adopters of Big
Data analytics as well as those less mature. Companies at the bleeding edge are finding the #1
inhibitor to Big Data value is having more line of business managers engage with them in
projects.
We all know data science and advanced analytics talent is in high demand, with a scarce supply,
and the accompanying challenges of retaining these people. Irrespective of the hiring and
retention challenges, however, the lesson is that we must remember: most managers, in most
companies, still make most decisions by gut feel based on inaccurate or incomplete information
– and we have conditioned them in this way. That’s the data driven gap that companies are
desperately trying to close with Big Data, no matter their maturity.
Therefore, you must also remember that Big Data value is as much a function of culture and
mindset, as technology. You want managers interested and engaged in making data driven
decisions. So how do you approach this problem?
Throwing technology at a business problem
Many companies report struggles with very fundamental problems such as daily business
performance reports. Your IT organization is likely the provider of this internal service, and
likewise struggles meeting business manager needs, let alone appreciating the decisions on
which the information depends. This constant push and pull is the rallying cry behind more IT
collaboration with the business. It’s also why many business managers have become
conditioned to rely more on their intuition for making decisions, and less on data driven insight.
Many companies see Big Data innovations as a way of leapfrogging these problems by giving
even more technology to business users. There is absolutely innovation happening here, with
new ways for an average business person to tap into new insights offered by many data
sources.

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This leads to a debatable question: do business users want better reports, interactive visuals
and new metrics, based on a lot of new data? I’m not so sure, especially if an IT organization
alone is behind the effort. Would not the same issues persist around the push and pull I
mentioned? To me, it sounds like throwing technology at a business problem and hoping for a
better outcome.
Start with the end in mind
Instead, I would step back and start with the end in mind. Your ideal state is one in which your
business managers leverage data to make better decisions, your organization is realizing the
value of data and analytics as differentiators, and functions collaborate with one another based
upon the new insights offered when you can leverage all available data sources to fuel a data
driven business. That sums up the strategies being pitched by technology and consulting
providers alike – a roadmap, journey, or vision.
Today, some of these activities happen in your company, some do not. For many companies,
there are dozens of technology and consulting providers involved. So how, as an executive, do
you even consider a roadmap when the existing environment is so complex? You don’t have 3
years to implement a strategy; you want to act in 90 days or less.
The changing role of IT
At a Gartner conference recently, a large roster of companies succeeding with Big Data was
cited. Led by the aforementioned Doug Laney, the session also mentioned what Gartner was
seeing among these companies: a bifurcation of IT, into an “I” organization for information
analytics and a “T” group to manage technology. The rationale? A singular group could not
easily balance the needs to operate technology at the lowest cost, while simultaneously helping
the business unleash the value of Big Data.
Even if this is a very nascent idea, I think it’s super predictive of what we will see happening
within the next 36 months in many industries. It’s reflective also of the moves many companies
have made to develop new departments outside of IT for Data Science and Advanced Analytics.
Organizational versus technology considerations
However, Gartner also found that the same division would not occur with the underlying
technologies supporting both “I” and “T.” This is not a fine point, but a very important one.
A single “thing” (platform or technology) should ideally account for the needs of both your day-
to-day business performance reporting that IT manages, as well as the needs of analytics
professionals who need a productive environment to explore, model and deploy new insights
back into the business to execute and measure. The underlying data is therefore available to
both, and a single approach also offers the most efficient and cost effective solution versus
maintaining separate and loosely coupled technologies.
These ideas differ substantially from how companies on the bleeding edge have adopted
numerous open source products outside the core technologies used for business performance

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analysis and reporting. This is the where the battle among technology and consulting
companies resides in my opinion. Whoever helps you set this table and makes you successful
most quickly, will disproportionately benefit from the budget you allocate to these efforts.
Paint the vision, pursue the project
The way to think about all of this is to adopt a vision for your company – such as the end state I
mention above, and proceed with specific actions, or projects, that demonstrate steps to
achieving the value. Your teams have the opportunity right now to rapidly spin up projects
leveraging cloud technologies to start delivering value today.
Why now and why the reference to a technology delivery approach such as cloud computing?
Because to help managers believe in the value of data driven decision making, they must see
the value first and you must deliver results quickly – which is what cloud computing represents.
People’s attention spans and personal agendas simply will not accommodate the typical
timelines associated with traditional technology projects aimed at addressing core business
culture and old school decision making methods.
Managers must see for themselves the difference analytics makes to their peers, and how it
contributes to their professional success. Doing this well with an initial set of internal
champions creates positive chatter and momentum for others to engage in the process. This is
how you overcome the challenge that progressive adopters of Big Data analytics are
experiencing with a deficit of data driven managers.
As a CEO, you should expect the leaders in your company carrying the Big Data torch to proceed
along these lines – not getting bogged down by how Big Data can save money or power
innovative new insights leading to significant increases in sales, because ultimately Big Data
done right offers both! You want your leaders to drive to an integrated vision for data and
analytics, which leads to more managers adopting and championing data driven decision
making that materially improves the performance of your business.

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Overcoming the #1 Challenge to Realizing the Value of Big Data
Some Retailers and Consumer Goods companies are in the early stages of adopting Big Data to
explore new ways of doing business, improving existing operations and differentiating from
competitors. Many more have just dipped their toes into the Big Data waters by focusing on
cost savings around data storage and alternatives to proprietary data and analytic technologies.
I think the same could be said of many industries.
A select few have moved beyond the honeymoon stage, and have gone “all in” on Big Data as a
distinct competency outside of IT (e.g. Data Science or Advanced Analytics). Generally speaking,
these are the large market leaders who set the agenda for everyone else.
What’s emerging in all of these cases is a common thread – the importance of having business
managers engaged to realize the value potential of Big Data Analytics. That’s what this post is
all about.
Big Data is a complex idea to get your head around. Just a few years ago, you were an expert if
you understood Big Data in terms of new forms and higher volumes of data, which old
technologies were not designed to accommodate. That was the hype phase, but those days are
long gone.
Too many companies have shown and continue to show the value of exploring new insights on
all manner of data types. Think about pure internet companies literally born from data, such as
LinkedIn, a company with a market capitalization of nearly $30 Billion and not the consumer
darling that is Facebook. Consider too, that Amazon has a valuation of nearly $300 Billion.
That’s why many of the largest companies in both Retail and Consumer Goods sectors are well
along on their Big Data journeys – yet, if you look at their performance and that of the
industries’ overall, sales, margin and consumer loyalty remains challenged. What’s wrong?
The blocker that many market leaders now face is too few “data driven managers” interested
in helping shape the way Big Data analytics are applied and put into practice.
For the many less progressive companies first adopting cost-centric Big Data use cases, the
problem is similar: they have created the perception that Big Data is about saving money, not
improving business performance. To change course at this point requires a change in mindset
by the business – no easy task. Analytic skills remain scarce, but alone are insufficient to scale
the business value of Big Data.
Few leaders in IT, insights and analytics roles have formally approached business leaders to
bridge this gap. The reason is, how do you have this conversation? What would be the focus?
Certainly businesspeople don’t care about the technicalities of data or analytics -- they just
want capabilities. Their primary job is around managing a process, people and their careers.

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Among the most progressive companies, they haven’t been able to do this enough. Their
challenges tend to be a bit different. Instead, they are busy doing the work of analytics and not
the business side of analytics. They need more engagement with the business.
Here’s how I’ve heard IT and businesspeople describe their typical interaction:
 IT – “give us your requirements.”
 Business – “tell us what’s possible.”
 Result: Gridlock!
A great way to frame this conversation instead, is to first address a few questions. Such as:
What exactly is Big Data? What does it mean to your company? How can Big Data make a
difference to how business managers perform their jobs today?
On a functional or departmental basis, it makes sense to hold collaborative meetings with
business managers to outline these answers and engage them in a formal process around
exploring use cases focused on very specific business outcomes.
I think there’s a three-legged stool emerging that is necessary here – IT, analytic talent and the
business – who must unite behind this effort. Even if you don’t have a Big Data strategy or it’s in
development, it’s critical to have these conversations now, not later. The reason?
It’s become fact that done right, you can move the needle on business performance with Big
Data Analytics, at a significant return on your investment. The challenge is getting it “right.”
Regarding the question: “What is Big Data, in general or to your company in particular?”
Businesspeople use data or have access to it every day, to some extent. As I’ve said before,
most managers still use gut feel to make decisions, even when they have access to data. This is
a well-documented problem that flies in the face of efforts to give ever more powerful software
tools to business users. A certain population of businesspeople desires this but they are not the
majority.
Personally speaking, I think the value of analytics isn’t an “either / or” question as to a
particular type of employee with a particular set of skills. Right now, it’s about collaboration
given the specialization of talent necessary to do analytics right.
Today IT provides reports and dashboards, as well as access to data that many people
interrogate in Excel. This is operational analytics, which has been used for decades. This is not
Big Data Analytics. The key word here is “Analytics.” Big Data is just the identification of data
sources, some of which could form part of a report a sales manager sees every day. They don’t
care how that metric is pushed to their dashboard, just that it’s there.
So, if you are staging messy sales data in a Hadoop file system somewhere and generating a
query against that data every day or week that feeds someone’s dashboard, that’s a great use
case for the underlying technologies of Big Data management and storage – but it’s not a form
of advanced analytics.

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Gartner mirrors this sentiment, saying that their clients in many industries taking the lead on
big data are carving out resources, specialized skills and budgets to attack advanced analytics
problems apart from IT. In fact, they envision IT splitting into two groups, one aligned to
operational IT and cost containment, and another on information analytics. If this comes to
pass as true, it will be a major shift in the technology market.
You should clearly explain to business managers that Big Data is about leveraging new forms of
data, along with data that you have already, to answer questions about your business that you
simply cannot today. Or, discover questions of your business that you never thought to even
ask. This leads right into the next question.
Regarding the question “How can it make a difference to my job today?”
This is where the rubber meets the road and the problem companies are struggling with today.
Once business managers understand Big Data, the next step is to have them engage in shaping
projects. And doing so quickly, as in 90 days or less, not 6 months. Gartner is reporting this as
best practice.
Business managers don’t care about technology, but they must actively participate in projects
to ensure that new insights are operationalized into the business, measured, and improved.
Remember, asking business managers to engage in this process is not the same as asking them
to care about technology -- it’s asking them to collaborate to leverage new forms of analytics to
move the needle on business performance.
A great way to frame the conversation is around how the different functions in your company
serves your consumer throughout the purchase journey. Every Retailer and Consumer Goods
company professes a focus on their consumer, yet silos define the way many go to market. That
creates blind spots for sales, marketing, service, supply chain, and operations that Big Data
Analytics can illuminate to improve outcomes with consumers.
Lead with Business Outcomes Focused Use Cases
I prescribe a focus on business-outcome focused use cases, to appeal to the needs of line of
business managers and which requires the type of collaboration I talk about in this post.

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Winning the Back to School Dollar with Big Data Analytics
There are a few seasonal events that retailers count on to contribute the lion’s share of sales
every year. The upcoming back-to-school shopping season is one such example. eMarketer
recently reported the July through August period should generate $56 billion in e-commerce
sales. This represents 16.5 percent of full-year online sales for the industry.
With so much at stake, you expect retailers to leverage all of the data and analytics at their
disposal to capture wallet share amid a hyper-competitive marketplace. Clearly there is a need:
another recent eMarketer article reports just 25 percent of marketers are confident in their
marketing mix.
More often than not, the challenge retailers face is identifying where to focus their analytic
energies – those very specific use cases that differentiate and win many thousands of moments
of truth that lead to sales. These moments happen over a fragmented time horizon according to
eMarketer’s research. Their conclusion: retailers need to be always-on when the shopper is
ready to shop, or you risk missing that customer.
To execute a winning seasonal marketing strategy, retailers have the opportunity to optimize
the mix of media tactics, content, and channels on an individual customer basis. This extends to
the planning and analytics of programmatic advertising.
Cloud and analytic technologies are now available that greatly speed the ingestion, analysis and
operationalization of insights. Regardless of season, marketing leaders should partner with their
analytics and IT teams to identify new ways of engaging customers in more relevant and
effective ways. See this checklist to direct your planning.

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Making the Case for Better Retail and Consumer Goods Analytics
For several years, Retail and Consumer Goods industries have been converging on the same
target – the consumer. Specifically, developing an understanding of consumers to inform
marketing, product design and assortment, commerce channels, supply, service, and cross
industry collaboration. Many have made moves in the right direction, but challenges remain.
Both industries continue to face price oriented buyers and declining loyalty. For branded goods
suppliers, the bar is that much higher for creating compelling products that justify higher
margins. Trends have some looking for growth instead via mergers and acquisitions, the latest
example of this being Heinz and Kraft. It’s arguable that this is a sustainable strategy for brands
increasingly falling out of favor with consumers (even if international markets have proven
growth engines for certain categories).
For retailers trying to transform into more attractive experiential businesses, the hurdle is just
as great for consideration over lower cost channels. News this week that many retailers are
dialing back discounts and promotions – sensing a recovering economy – suggests consumers
accustomed to lower prices can be convinced to spend more. Absent substantial changes in the
shopping experience, experts rightly question the approach, some calling it a gamble.
As shoppers, we are nearing the point of anticipating and preferring a personalized purchase
path. The implications for retailers and suppliers are many. These include breaking through a
crowded digital marketing landscape featuring many businesses targeting the same consumers
and households. It extends to an almost individualized assortment and fulfillment capability
needed to capture sales at a moving moment of truth.
There’s a case to be made for stepping back from what’s not working today, and consider new
forms of analytics. Technologies and data sources exist that can illuminate ways to better serve
consumers throughout the purchase journey. Industry trends suggest current approaches are
struggling.
Sales Growth
Blame economic conditions or consumer price sensitivity, but sales have not been improving
much in either Retail or Consumer Goods industries. According to this article:
Dollar sales of foods and beverages edged up a modest 2.1% to $470bn in the US retail market
in 2014, but unit sales (an indication of volumes) were pretty flat (up just 0.9%).”
While pure online retail growth continues to outpace traditional peers, omni-channel shopping
behavior is having an impact (such as browsing online and buying in-store). Consider this:

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Online retail spending grew by 9 percent in the year to January to $16.6 billion, almost double
the growth rate of traditional bricks-and-mortar retailers. But the days of double-digit annual
growth rates for online appear to be numbered.”
Retail industry growth has been trending down, as shown by McKinsey, which also notes that
prospects are brighter for retailers able to target segments that promise to outspend others
(Baby boomers, Hispanic consumers and Millennials).
Products and Assortments
Category-creating new products are growth drivers for the Consumer Goods industry, but the
difficulty of doing this well and the cost of failure remains staggering.
According to McKinsey, SKU proliferation has led to structural problems in how traditional
retailers determine assortments. Keeping up with Amazon, for example, places many retailers
at a huge cost disadvantage.
Amazon and other online pure plays exploit this -- taking sales away from bricks and clicks
retailers amid a market of price sensitive shoppers.
Marketing
Marketing effectiveness across both industries has declined as promotion and mark downs
reflect an almost systemic reliance on discounting. As a result, marketing costs for most exceed
returns.
Value-based marketing is more difficult than ever when it depends on understanding cross
channel shopping behaviors. Consumer use of technology is continually evolving. Leading retail
marketers say keeping a pulse on this is their greatest challenge.
Collaboration
There’s no doubt that collaboration between suppliers and retailers has made great strides, but
there’s a lot more to be done. Slow growth and continued pressure on better demand and
supply synchronization demonstrate opportunities for improvement. On shelf availability
remains a costly problem for both industries (in terms of sales and shopper satisfaction). For
example, Walmart reports that in 2014 it lost $3B in sales due to out of stocks.
Of note, when technology and analytic competencies are out of balance between supplier and
retailer, effectiveness suffers. The more dependent party pays for this disparity with greater
concessions in the relationship.
Gartner finds it necessary for suppliers to accelerate Demand Signal analytic capabilities that
extend beyond transactional, order and shipping data, to include new, prescriptive indicators
like social sentiment, loyalty, trading partner promotional plans, and others. It’s essential that

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supply chain capabilities become a competitive differentiator for Consumer Goods makers,
which will not happen via incremental improvements.
X-Commerce
In search of growth opportunities, Consumer Goods companies are exploring direct sales to
consumers. As these efforts ramp up, there will be pressure to rationalize direct versus retail
partner channels.
Retailers also have a rationalization challenge. The pressure is on to create a seamless shopping
experience regardless of channel, making inventory and supply chain integration for
brick/mortar and online sales essential. Some suggest right now this is table stakes for
competitive parity in retail, with the lens shifting next to how to effectively message and
connect with consumers most relevantly around their omni-channel shopping journey.
Analytics Illuminate the Path
According to research reported in the October / November 2014 issue of RIS News, more than
20 percent of retailers and CPG brands will invest at least 10 percent of their IT budgets in
analytics. With so much at stake, making the most of these investments is critical.
To get started, CIOs and analytics teams should approach their challenges with an eye on
attacking key processes, in collaboration with executives from other areas of the business. For
retail and consumer goods organizations, you can imagine analytics informing all the functions
which affect the consumer’s shopping journey.
From communication, to fulfillment and the post purchase experience, it’s the execution of
activity across this process which separates a leading organization from the pack. Better
analytics, leveraging current and new forms of data, can be the engine for solving many
challenges in the Retail and Consumer Goods industries.

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Will CPG find the right balance between trade and consumer
investments in 2016?
It’s been well established that manufacturer trade promotion budgets are considerable,
reportedly the second greatest expense after cost of goods sold. These dollars target store and
shopper interaction to drive sales and volume using incentives such as price reduction and
coupons.
Retailers have come to depend on trade promotion for their bottom lines and that isn’t likely to
change. Their margins are even slimmer than manufacturers’. If anything, trade funds
requirements will continue to grow as branded goods face pricing pressure from
macroeconomic conditions and growing competition from retail private labels.
If you haven’t noticed, private labels are beginning to innovate and market themselves just like
big brands — in the process taking share from manufacturer brands — while these same
retailers continue to demand margin-eating trade funds and own the relationship with the
consumer.
That latter point - the consumer relationship - has many consumer goods brands struggling to
balance trade promotion with direct consumer marketing investments. Will this continue into
2016?
Budget Right
I think the situation begs for diverting a percentage of trade and marketing funds to innovative
data and analytics projects that attack many of the underlying problems branded CPG
companies face. I often pose the hypothetical question, “If you were to start a CPG company
today, you would probably start with an emphasis on consumers, their needs, wants, and
desires – not your factory – right? You would make the requisite investment in data and
analytics necessary to develop innovative, differentiated and high demand products, before
sinking untold millions into systems to manage manufacturing processes.”
I think the concrete answer to this question is one that marries the goldmine of consumer and
shopper insights from every source available within and outside the company, with ERP/supply
chain and detailed sales data from retailers and syndicated sources. Such a vision is one that
companies in many industries are adopting, but in the CPG space starting with such critical
business processes as trade and consumer relationships makes a lot of sense.
This integrated foundation then underpins and powers a real-time consumer facing business
agile enough to counter trends hurting growth in the CPG industry. Insights are not just offered
monthly or weekly, but daily, by the hour, by the minute if necessary.

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I’m not describing a siloed consumer database, demand signal repository (DSR) or ERP reporting
system, but rather all of these in a single living record which ties insights together in ways not
otherwise possible.
Justification
What if less than 1 percent of the 20 percent of revenue that comprise the typical trade funds
budget were allocated to detailed, cross-company data acquisition, integration, and analytics?
To avoid a continual decline in share as store brands begin to innovate, while at the same time
taking margin hits due to escalating trade promotion, the time is now to consider doing
something differently in 2016.

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Optimizing a Complex Marketing Mix in 2016
The television spot remains a mainstay in many b-to-c companies’ never ending quest to reach
millions of eyeballs, differentiate their brands, and ultimately increase sales. Meanwhile, all
things digital have swept over most industries because that’s where consumers are spending
more time at the expense of television.
In parallel to mass media, agencies acting on behalf of brand marketers execute all manner of
digital marketing campaigns, but typically without an eye on capturing, integrating and
measuring these interactions. Amid these dual approaches to influencing consumer behavior,
brand marketers struggle to apply highly organized, integrated and proven-effective Data
Driven Marketing techniques. For many, it feels unrealistic, and seems to hamper the creativity
at the heart of the most successful brands.
Progressive brand marketers recognize the opportunity to marry the brand storytelling
exemplified in mass media with direct response marketing principles to develop measurable 1:1
connections with their consumers. This intersection of marketing disciplines is where Data
Driven Marketing provides brand marketers and their agencies with a measurable storytelling
platform.
When in place:
 The quality of marketing content in addressable channels can be gauged by indicators of
brand affinity. For example, how many consumers communicate with your brand
through multiple channels versus one, how many consumers that you have direct
relationships with express affinity in social channels and also participate in your loyalty
program? How much content is shared, in either emails or social channels, and what is
the reach afforded?
 Responses to digital channels like web and email are recorded, evaluated and targeted
for improvement over time. When enabled at scale, “test and learn” serves to make the
most of your marketing budget by applying dollars to the programs that promise the
highest response. Permission-based digital channels are addressable – meaning you
have the opportunity to communicate with consumers on a one-to-one basis, measure
response, and accrue knowledge about consumers that informs brand creative,
segmentation and targeting.
 The value of agency creative can be evaluated objectively for either addressable or mass
media. Indicators of quality and response for addressable channels allow brand
marketers to quantify the value of their agency investments. In the case of mass media,
brand marketers are increasingly tying it to complementary digital activities such that
they work in unison to yield an overall better result.

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Engagement and Sales
A primary benefit of Data Driven Marketing is that the dials on many of these codified
consumer interactions can be tuned in-market to increase the likelihood of positively impacting
sales. If you have engaged with your consumers correctly, sales should correlate. If sales remain
flat or down, it indicates that something is amiss – you haven’t connected with the right
consumers, your strategy is flawed, or your agency creative and execution is off. The key is to
be able to turn these dials quickly to improve affinity with the right audience, which in turn
should meaningfully impact sales figures.
The stakes for engaging well are high. A CMO.com article titled “20 Years After Peppers And
Rogers, A Wake-Up Call For Brands” reported:
“According to a Janrain/Blue Research study from earlier this year, 98 percent of people receive
information and offers from brands that are simply not relevant. And the impact is beginning to
be felt, with consumers taking action on their annoyance. Nearly half of them report
abandoning brands after just two mistargeted communications. They stop using products,
unsubscribe from email lists, and abandon Web sites. This is obviously a big problem--
companies are potentially losing nearly half of their customers due to frequent mistargeting. It’s
a wake-up call to fix this problem or risk losing customers, prospects, and market share.”
Thinking about the value of Data Driven Marketing in this context sets the stage for embracing
consumer engagement as an ongoing brand mission that can sit at the table among other
measurable business processes like retail sales and supply chain.
Digital Marketing is Dead?
In September of 2013, Procter & Gamble Chief Marketing Officer Marc Pritchard said:
“Let’s celebrate the end of digital marketing. Let’s focus on creating the great ideas that move
people and build great brands. And let’s leverage the tools, platforms and technology to make
them bigger and engage with people like never before. Our brand building teams, our agencies
and most of the people who see our stuff and buy our products will thank us for it.”
He further said that P&G’s marketing teams no longer think of digital in terms of “the tools, the
platforms, the apps, the QR codes, augmented reality, holograms or whatever is coming next”
or as a “mysterious medium with its own set of metrics,” but for what it is: “a tool to build out
brands by reaching people with fresh, creative, campaigns.”
Prichard reported that P&G campaigns were now formulated by starting with digital, then other
channels and approaches added to the mix – or “digital back.” In this way, marketing teams are
able to think creatively and not focus on the latest digital “shiny object.” One of Prichard’s
colleagues, Roisin Donnelly, corporate marketing director and head of marketing at Procter &
Gamble UK and Ireland, echoed this sentiment when she said:

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“As the consumer is spending more time on digital media, it will continue to be a growing part
of our marketing activity. However, it is complementing traditional media. In the UK consumers
are watching more TV on more devices than before and we need to be present where and when
they are most receptive there. Online newspapers and magazines are complementing offline
print. Digital radio brings more availability of more stations to more people.”
The result of a “digital back” approach ensures addressable and mass media channels leverage
one another and are measurable in near real-time. A campaign for Braun shavers was cited by
Pritchard as an example that employed digital and mass media channels in unison to exceed
sales targets eight times over. In this instance, mass media advertising was added to the mix
right after awareness was created among the target consumers through digital channels.
Every marketer wants to be more effective, and data and insights are the ways to achieve it. At
the same time, brand marketers know that killer creative, done well, spurs consumers to action.
Thinking about the Data Driven Marketing opportunity as a blend of time-worn brand
marketing principles and direct response techniques is a good step to understanding how to
succeed amid these seemingly conflicting disciplines. Will we see examples of this in 2016?

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Bringing Big Data into the Business
This week at the National Retail Federation Big Show in New York, the theme that emerged was
very clear: how to deliver a differentiated experience across the shopping journey, no matter
the channel.
With retailers focused on building out e-commerce channels for the past few years to address
the Amazon challenge, what’s happened is the in-store experience has suffered. That’s where
most brick and mortar stores make most of their money. Recent news and financial results
reflect the struggle – brick and mortar retailers are trying to find that secret sauce to win with
connected consumers who buy in store, but are heavily influenced by digital.
So omni-channel is the new rallying cry to improve retail performance.
Walmart Moves
In case you didn’t notice, Walmart is in the midst of making organizational moves that I think
reflect this trend -- integrating today’s separately operated data science function embedded
within its e-commerce business, with the “on the ground” and “day to day” operational
analytics used by store operations every day (“Walmart Merging Arkansas, Silicon Valley Teams
To Speed Up New Tech”).
Stated another way from a technology perspective: marry Big Data analytics focused on
generating new (actionable) insights from online behaviors, with the foundational operational
analytics that business managers use every day to understand how their division, store or staff
is performing.
Another theme from NRF was how sensors and the Internet of Things were now capable of
instrumenting the store much like a website – so the opportunity for data analysis across the
online and physical shopping experiences was now possible. This must be part of the
motivation for Walmart to bring the smarts of data science closer to execution at the store
level.
Implications
Throughout 2015, I’ve written about the need to operationalize Big Data insights. From a purely
architectural standpoint, what’s necessary is an approach which blends Big Data with the day to
day operational data management and analytics used to measure and power the business.
Gartner has said as much based on their work with their clients.
Retailers face many challenges around this problem. Having placed bets on various
technologies to embrace unstructured data and advanced analytics, as well as measure their
core operations, retailers need to decide how to marry these capabilities both organizationally
as well as technically.

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The probability of chaos resulting from these attempts is high. Consider that many data science
organizations are based on open source technologies and require as much administration as
analytic talent to extract new insights. That contrasts with the commercial grade and legacy
systems that today power the daily reports that managers rely upon, as well as serve many
operational systems.
Retailers require a simplified approach to bringing two worlds together to unleash the value of
Big Data insights. Developing and operationalizing analytics must achieve a level of commercial
scale to ultimately make a meaningful difference to retail performance.
Consider too that business executives possess OpEx budgets to fund technology projects, but
these must be “as a service” or cloud based. So expect to see progressive IT and analytics
groups leveraging cloud capabilities to work with their business counterparts to fund and
execute projects focused on business-outcomes focused use cases.
Operationalizing Big Data Analytics has been a rallying cry for a couple of years now, but in
2016 it has to scale quickly lest retailers as a whole continue to struggle. I think Walmart’s
moves reflect what we will see among many retailers seeking to extract more value from
investments in Big Data capabilities.

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The Human Side of Retail Digital Transformation
I recently made purchases from two different well-known mass merchants. Both experiences
seemed to highlight the main challenge and opportunity facing brick and mortar retail today.
One, a more upscale retailer, I visited to purchase a pair of shoes. In the shoe department were
several clearly identifiable store associates who ignored me while I browsed the selection. I
eventually found a shoe style I liked and asked one of the associates for my size. I wasn’t
offered to have my foot measured and was handed the box, then left alone. The price was
about $150.
My other visit, to a discount retailer, was to buy a few pairs of pants. In the men’s section were
no associates to be found. I eventually located the style and size I needed. Total price was
about $200.
In both cases I purchased. But was the experience memorable? Did my experience do anything
to make me interested in returning again in the future? Not really. Since I made the purchases
anyway, does it matter?
I think so. Know also that I really don’t enjoy shopping. I have the mobile applications for these
retailers installed on my phone, but did not use them for these purchases. I didn’t do any
research online beforehand either. Since both retailers were located close to my home and I
wanted to shop as quickly as possible, I was satisfied with myself that I was able to get the job
done quickly. I was happy with myself, but not feeling anything for either retail experience.
Sales growth is pretty flat for most retailers. My experience, multiplied by millions of
consumers, would explain the problem.
The drive to serve customers well regardless of channel is leading many retailers to embrace
digital transformation. Yet my personal experience highlights a piece of digital transformation
that should not be forgotten and which is decidedly human – the role of the store associate.
Retail is a service business but the “service” part of the equation seems to be lacking in many
retail shopping experiences. Arming associates with tablets, applications and analytics to
engage customers and be more helpful is part of the solution. The other though requires a
cultural or organizational shift that considers store associates as ambassadors of the retail
brand. How do you affordably hire, train and retain such employees for what many consider a
low wage, low skill position?
To solve this dimension of retail transformation requires thinking about retail as a “business” in
a different way. The Apple Store may not be a model that any retailer can embrace from an
employment perspective but it’s a good start. It’s not an easy challenge, but I also think placing
blame on tax laws and regulation is just another example of focusing on a symptom and not the
core problem.

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Just as most retailers begin to ramp up investments in e-commerce, Amazon is doing the
reverse. The reason? While e-commerce growth has greatly exceeded in store sales for many
years, the growth rate is slowing -- and Amazon is all about growth. Most of the money spent in
retail happens in physical stores and that’s where Amazon is chasing customer
walletshare next.
In case you didn’t notice, Amazon has been expanding beyond e-commerce into brick and
mortar book and grocery categories. They are also exploring the creation of their own shipping
and delivery service.
You may not know about Amazon’s business model whereby they happily fund new businesses
at a loss with profits from other businesses. This helps them dominate markets by scaling fast
and crushing competition. If Amazon could make it easier and more helpful for a shopper like
me, would I choose to shop an Amazon brick and mortar store versus other options? I probably
would.
What helps Amazon succeed is their vast amount of customer data and the analytics they apply
to that data. Other retailers have a similar opportunity, but arguably have an advantage in
possessing the chance to engage customers face to face in the last mile of the shopping
journey. Amazon’s not there -- yet.

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Busting Big Data Myths
Earlier today I sat on a panel discussion at the Retail and Consumer Goods Analytics Summit
here in Chicago. Thematically, the event emphasized the need to experiment, fail fast, and
approach analytics from a test and learn point of view.
That is one way of separating traditional reporting and analytics, from Big Data Analytics (a
challenge cited during the conference last year when content revolved around staffing for
analytics). This is also counter-intuitive for many people who find it challenging to consider a
technology project without a confident ROI plan.
Our panel moderator, David Weinand, Vice President & General Manager, Technology Brands,
Stagnito + Edgell, posed questions about analytic myths within the retail and consumer goods
industries (and others too I expect).
I thought the questions were provocative and the conversation productive. Following are my
thoughts on the topics we discussed if you were not able to be there.
Myth 1: Predictive Analytics is at the top of the analytics maturity ladder -- but no companies
can achieve this maturity without millions in analytics budget.
Whether it’s predictive -- where you are looking to anticipate what might happen -- or
prescriptive -- where you are seeking a recommended action to take to affect an outcome --
both are relatively advanced concepts. Many companies continue to struggle with operational
reporting problems in terms of data currency and accessibility. This unfortunately oftentimes
blocks thoughtful attempts to sketch out a strategy for Big Data. Companies in every industry
now have the mandate to explore more advanced analytic methods simply because their
competition is and is gaining an advantage as a result. Fortunately, it doesn’t take millions to
get started and achieve the benefits of Big Data insights. The key is to focus on bite-sized
business centric questions and leverage cloud wherever possible to speed up analytic projects.
Myth 2: To utilize analytics effectively, a large staff of data scientists and analysts are
required.
If you attended the Summit last year, you heard from several speakers about the different
analytical roles in their organizations, from analysts to data scientists. The skills you need
depend largely on the business problems you want to address. While it’s true a data scientist
can go off on his or her own to identify a problem or opportunity, that’s the exception to the
rule. Today most successful analytics projects involve business people in the shaping and
execution of the projects. That’s how insights get put into operation and value is rendered. Last
year one speaker said their larger challenge to the data science skill glut was identifying data
driven managers. I expect this to be the case for many retailers and consumer goods
companies. This trend is reflected in stories I hear about analytics projects failing when led by IT
without much or any involvement from the business. One of the wrinkles here is the emergence

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of the Citizen Data Scientist who is enabled to become an active participant in data analysis.
New software tools are emerging designed to help businesspeople engage in the analytics
process beyond traditional dashboards and business intelligence.
Myth 3: For retailers, leveraging customer analytics to be able to market to customers based
on behavior is an invasion of privacy.
This is a hot topic of conversation. I think there are several dimensions to this. On one hand,
retailers and brands need to make clear to their customers how data is used. This renders itself
in the form of a privacy policy or terms the customer accepts when opting into email
communications, registering on your website or downloading your mobile app. Secondly, when
marketed to at a segment level based on aggregate attributes, the “creepy” factor is less in play
than when very specific recommendations are made based on attributes that could only be
associated with a particular customer (like a pregnancy if you recall that now infamous case).
Regardless, the idea is to be helpful, respectful and transparent as you can be. So no matter
how the analytics are derived, it’s in the application of that insight which determines how it’s
perceived by the customer.
Myth 4: Consumer goods business executives believe that their analytics tools are their
biggest tactical inhibitor, even more than resources or data quality.
I’m not sure business executives know what their inhibitor is, but it’s true that many consumer
goods executives, as well as those in retailers, are frustrated by the lack of value from their
investments in analytics. Last year’s moderator cited research saying as much. Many still
struggle with the fundamentals of operational reporting. What many companies lack is a data
management and analytics strategy closely tied to business objectives and rendered in terms of
specific business use cases.
Myth 5: Leveraging Big Data in the consumer goods industry is just 3-5 years out.
I’d say most consumer goods companies are trying to figure out how to take advantage of Big
Data Analytics to address their core issues around sales growth, collaboration, supply chain
effectiveness and product innovation. Many are at different stages of maturity and progress,
but all are making some moves in this direction. I’d say it’s a bit of a race for many to catch up
with companies already well down the maturity curve.
Myth 6: The #1 analytics area that retailers will focus on this year is social media analytics.
Analytics related to Customer Experience and the factors behind it are a much higher priority.
Consider the trend toward omnichannel business where customers are served equally well
regardless of channel. There are numerous marketing, sales and supply chain issues that take
precedence over analytics focused on social channels alone. Social channel data however is a
helpful part of these broader analytical problems. From a Big Data perspective, I think you need
to consider how social channel data could yield a new insight when looked at in combination
with data from other areas of the business. That is a bit different than focusing on metrics
related to social brand sentiment alone.

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Myth 7: Getting to advanced analytics requires huge investments, which most mid-sized firms
can’t afford.
With the advent of cloud computing, powerful Big Data Analytics capabilities are now available
to companies both large and small. The requirement for skilled analytical professionals doesn’t
go away though, and it’s important to start with a clear use case roadmap to make sure your
dollars are well spent. The beauty of advancements in analytical software is that many of the
pain points of projects are getting engineered out of the process so that teams can focus on
asking questions more so than worrying about data wrangling and managing the overall
environment.
Myth 8: The data-sharing collaboration between retail and consumer goods manufacturers
has been steadily improving.
Honestly this seems to be at a standstill but innovators on either side are looking for ways to
improve. On the consumer goods side I have heard of sales teams seeking analytics to inform
their efforts to become whole store, cross category advisors to their retail partners. That
necessitates closer data collaboration. On the retail side, the opportunities to pull supplier
partners into the omnichannel fold through more targeted in store mobile marketing via
Beacons also implies the need for greater data sharing and collaboration. Supplier brands often
have decent consumer insights, better than their retail partners, and these can help drive
higher performing marketing in the store. E-commerce and online advertising are two other
areas that represent data collaboration opportunities.
Myth 9: Connected devices (IoT) is creating more data than is logically usable. It’s too early to
make investments here.
The data from products with sensors and devices in the supply chain, as well as from in store
beacons engaging shoppers or monitoring product shelf placement, is completely useable to
inform a variety of business decisions right now. The key is to identify the right use case first
that may necessitate sourcing data from sensors and smart devices. The challenge many
companies face is getting too fixated on IoT as a shiny new object and skipping the critical
business use case.

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Big Data at Walmart #LoveData
At a conference recently, an attendee asked me what was so special about Big Data.
Specifically, he asked what was different about Big Data from the types of analyses companies
have always performed with their data warehouses and business intelligence software for
years. Nothing answers such a question better than real examples, some of which I described to
him.
Those of you in the LinkedIn ecosystem probably often ask the same question. It’s a good time
to review examples, given recent news reports about how elusive Big Data success can be. So I
thought I would compile public sources of insights into exactly what market leaders are doing
with Big Data to stay on top of their games and beat the competition.
Examples from leaders also illustrate lessons that any company less progressive can take away
and learn from. This first post covers Walmart – others to follow.
Walmart is about as complex a company as you can find, so it’s not surprising to learn the
company uses Big Data to many ends. Honing in on exactly what, why and how though, brings
to light a very strategic and forward looking approach to taking advantage of new data and new
forms of analytics. Consider this:
Walmart collects 2.5 petabytes of unstructured data from 1 million customers every hour.
Lesson: Even if you feel much less data intense at your company, understand there is likely an
opportunity to collect much more data about your customers than you do today. It’s why many
companies have gone down the path to build a data lake based on Hadoop to store data in any
format and any volume over any number of years. The key question then is: what do you do
with it?
Walmart’s main objective of leveraging big data is to optimize the shopping experience of
customers when they are in a store, browsing the Walmart website or browsing through
mobile devices when they are in motion.
Lesson: Have an over-arching strategy for how Big Data applies to your business, from which
multiple business-focused use cases follow. In a retail or consumer market context, there is no
better framework than the purchase journey.
Walmart employs a portfolio of use cases. Some of these include market basket analytics,
product launches, product recommendations, shipping or fulfillment optimization and supply
chain efficiency.
Lesson: Note that these are not sexy use cases. For Walmart and others, the idea is to pursue
new insights without the constraints of the past – no constraints on data and no constraints on
the analytics possible. At the same time, demonstrating value quickly is important at the onset

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of your big data journey. Developing new and improved versions of existing analytics are good
places to start and don’t usually carry complex operationalization challenges. The question then
becomes, how do you do this most productively since the human element (i.e. data science)
becomes as big a part of the solution as the data and technology?
Walmart also employs very sexy use cases.
Lesson: The beauty of developing Data Science as a core competency is that it sets the stage for
developing “data products” with which your company can differentiate in super valuable ways.
Walmart’s creation of a “Social Genome” is one example: The Social Genome product combines
public data from the web, social media data and proprietary data like contact information,
email address and customer purchasing data. This data helps Walmart better analyze the
context of customer journeys.
Walmart is well past the experimental phase of Big Data and today expects these investments
to pay off in big ways. Walmart observed a significant 10% to 15% increase in online sales for
$1 billion in incremental revenue.
Lesson: Operationalizing insights is where the payoffs occur. So scaling the winning use cases to
impact business performance is how leaders create a self-sustaining big data value system. It’s
that scale piece which less progressive companies can learn from. For example, how do you
scale the utilization of your data science team to surface more winning insights, more
frequently? Resolving this question needs to be part of your strategy lest you get bogged down
in too few use cases and become discouraged.
Walmart is having a tough time finding professionals with experience in cutting edge
analytics applications and working knowledge of data science programming languages like
Python and R to build machine learning models. Walmart used the hashtag #lovedata for its
recruitment campaign to raise its profile amongst the growing data science community in
Bentonville and Arkansas.
Lesson: Walmart is able to do this because of their scale and reach, but less progressive
companies would be challenged to crowdsource talent like this. It’s worth trying, but it’s equally
important to think about how to take the scarce talent you do have, and make them as
productive as possible. What’s become apparent, and is behind recent news reports, is that Big
Data projects are complex. So anything you can do to reduce complexity while maximizing focus
on a business outcome is worth your consideration.
The following was the single source of information for this post. Check it out for full details and
stay tuned for more posts about the inner workings of Big Data leaders.
May 23, 2015 - How Big Data Analysis helped increase Walmart’s Sales turnover?

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Big Data at Starbucks #ThirdPlace
In the first post of this series, I described how Big Data was unfolding at Walmart according to
public sources. For Walmart, a large part of their work centers on executing against the
consumer’s shopping journey.
In this second post, I cover what can be gleaned about Starbucks’ Big Data efforts from the
interwebs. Like Walmart, Starbucks holds its cards close to the vest when it comes to details.
What you can discern from public information about Big Data at Starbucks relates to their
loyalty program, new site selection and store execution. All three reinforce the company’s
longtime efforts to be the third place in consumers’ lives – at home and work being the first
two.
Loyalty Program
I am a very loyal Starbucks customer, usually buying a cup or two every day. For some reason, I
don’t get any offers pushed my way. I only receive “stars” based on how much I spend, which at
some point can be redeemed for a free drink. What gives?
Well, Starbucks isn’t afraid of losing my business because I am so consistent in my behavior – so
why waste time and money on driving business from me?
Instead, Starbucks has laser focus on cases where a once loyal or infrequent customer is not
buying as much as they could, and attempts to isolate their efforts on these customers.
This data is used to deliver targeted advertising and discounts directly to the mobile devices
of its customers. However, do not expect as a loyal customer that you will receive such a
discount; Starbucks is not afraid of losing them so they are not willing to give them a
discount. Instead, the discounts are sent towards customers whose buying behavior shows
that they may not be returning soon.
Lesson: A best practice use case I’ve seen emerge for direct digital marketing like this, as well as
advertising, answers the question: “How do I avoid spending marketing dollars on consumers
who will buy no matter what or will never buy no matter what I do?” That is a tough question
to answer because you have to also understand what motivates an inactive customer to buy.
It’s a great customer segmentation and personalization Big Data Analytics use case.
Store Site Selection
Figuring out the optimal places to open stores is an analytical question by definition. Starbucks
has apparently made a science out of answering this question using Big Data.

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