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Perspectives: Data Strategy Rules | ByCustomer
Niren Sirohi, Ph.D.
The Executive Journal by Peppers & Rogers Group
Perspectives Customer Experience Innovation
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Six Secrets to
Big Data Success
Time is the enemy of Big Data victory.
Here are six ways to improve your data
strategy right now.
Today, it is hard to escape all the chatter about Big Data.
As Google chief Eric Schmidt said, “While it took from the
dawn of civilization to 2003 to create five exabytes of information, we now create that same volume in just two days!”
Although Big Data is all around us, the reality is that
only a small fraction of CIOs are tackling this head on. Our
experiences working with large organizations on Big Data
projects suggest that there is frustration in organizations
trying to decide what the best course of action is in this
brave new world. There is a lack of vision and a fear of
making mistakes.
In order to make the transition easier, we believe
there are a few fundamental rules that should govern
Big Data plans:
1. Invest in the right skills before technology
More important than technology is having the right skills, of
which three are distinctly required:
• The ability to frame and ask the right business questions, with a clear line of sight as to how the insights will
be used. Big Data is noisy and plentiful. The ability to crystallize a business problem and not boil the ocean is critical
to being able to generate rapid and relevant insights.
• The ability to use disparate open source software to
integrate and analyze structured and unstructured data.
There is no single Big Data tool that does it all, meaning
one must bring together the best breed of tools to get the
job done. In addition, the landscape of tools and technologies is rapidly evolving; locking oneself into a proprietary
solution would be very risky. Open source software is the
recommended approach and the ability to understand a
diverse set of technologies is important.
• The ability to bring the right statistical tools to bear
on the data to perform predictive analytics and generate
forward-looking insights. The holy grail of Big Data is to
be able to predict the future with a high level of certainty.
Given data (big or small), the art of reliably predicting the
future requires a fundamental knowledge of disciplines
like statistics and machine learning. One has to be able
to parse the signal from the noise, even more so in the Big
Data world where noise is abundant.
These skills can be developed proactively both by
training and hiring. For example, find those in your organization who are good at skills 1 and 3, and have a penchant for 2. Give them the opportunity to play Big Data
steward. Hire individuals who have strong training in 2
and 3, and who show a penchant for business applications. It is also important to find senior leaders in the
organization who not only believe in the power of Big
Data, but are also willing to take risks and experiment.
These leaders can play a big role in driving rapid adoption and success of data applications.
2. Experiment with focused Big Data pilots
Many of the Big Data conversations today originate from
technology vendors, yet these conversations have little,
if anything, to do with the business case and ROI of Big
Data. Start by identifying the most critical business issues
and identifying how Big Data may contribute to finding
solutions. Bring various sources of data into a Big Data lab
where these pilots can be run before major investments
in technology are made. Big Data labs offer a collection
of various Big Data tools (e.g., text and speech analytics
software, Apache Hadoop, visualization software), and
expertise (predictive analytics, machine learning, vertical
knowledge) that allows businesses to run pilots and prove
value quickly without making significant investments in talent and IT. These efforts can be implemented at the grassroots level with minimal investments in technology.
18 Customer Strategist volume 5 • issue 2
Reprinted from Customer Strategist, Volume 5, Issue 2. ©2013 Peppers & Rogers Group. All rights protected and reserved.
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Data Strategy Rules | By Niren Sirohi, Ph.D.
Customer Strategist
The Executive Journal by Peppers & Rogers Group
3. Find the needle in the unstructured hay
Semi-structured and unstructured data is top of mind
among organizations. As Gartner highlights, enterprise
data will grow by 800 percent over the next five years
and 80 percent of this information will be unstructured.
There are three important principles to keep in mind with
unstructured data:
• Ensure you have the appropriate technology to store
and analyze unstructured data. Non-relational technologies (e.g., Mongo) that are schema-less and scale horizontally are needed. Also, ensure you have access to or
are licensing technology that can analyze unstructured
data (e.g., NLP engines for text, voice analytic technology,
speech to text transcribers, social graph analytic software,
and machine data analysis tools).
• Prioritize and focus on the unstructured data that
can be linked back to an individual and prioritize the
unstructured data that is rich in sentiment and informational value. This is the data that is most likely to yield the
richest insight. When it comes to text and speech (e.g.,
call center recordings, social conversations), the customer
sentiment embedded in those can be highly indicative and
predictive of future customer behavior, since it provides
insight about the “why” beyond just the “what.”
• Do not just analyze unstructured data. Extract relevant
signals from this insight and combine with structured data to
turbo-charge business insight and prediction. For instance,
knowing that a high-value, super engaged customer just
expressed the need to buy a new product to a representative over the phone demands a different action than knowing that a low-value, extremely price sensitive, and disloyal
customer expressed a need for the same product.
These three principles need to be the focus, NOT saving
and storing petabytes of unstructured information forever.
4. Data poor, insight rich is much better than
data rich, insight poor
The risk of data and analysis overload without commensurate actionable insight is at its peak. Many organizations
have never acted upon the information they already have,
even before the world of Big Data. Those who have generated insights have barely scratched the surface of being
able to implement and act on those insights at the frontline, where they really matter. The challenge has been that
those who are generating insights and knowledge in the
organization were far removed from those responsible for
operationalizing those insights at the frontline. Generating
meaningful insights and acting on them should be the first
order of business. It is important to think about Big Data
projects holistically, all the way from collecting, aggregating, and mining the data to operationalizing the insights.
5. Think operational analytic engines,
not just analytics
One of the potential benefits afforded by Big Data is the
ability to tailor experiences to customers based on their
most recent behavior, and therefore be more relevant. To
make Big Data a competitive advantage, companies can
no longer extract last month’s data, analyze it offline for
two months, and possibly act upon it three months later.
Take the case of a high-value, loyal customer who enters
a promotion code online at checkout and the discount is
not applied, leaving her dissatisfied and likely to attrite.
Being able to act on this insight within a few hours and get
back to the customer with an apology and a credit will go a
long way in retaining significant customer equity.
Businesses need to shift their mindset from doing traditional offline analytics to building technology-powered
analytic engines that enable near-time or real-time decision-making. We recommend that companies take a measured test and learn approach. Take 20 percent of your
decisions and enable them with technology-powered analytic engines. Measure success and slowly increase the
percentage of decisions enabled this way as the organization develops a greater level of comfort.
6. Adapt organizational processes to
take advantage of Big Data
The Big Data world enables one to act in near- or real-time.
However, many organizational processes are not prepared
for this shift. Taking advantage of Big Data is not just about
people and technology, but also the processes behind
data collection, insight generation, business decision making, and insight application.
These Big Data rules can enable competitive advantage by generating more comprehensive customer
insights faster and enabling real-time action based on
those insights. n
>Niren Sirohi, Ph.D. is Vice President, Predictive Analytics with
leading analytics firm, iKnowtion; [email protected].
volume 5 • issue 2 Customer Strategist 19
Reprinted from Customer Strategist, Volume 5, Issue 2. ©2013 Peppers & Rogers Group. All rights protected and reserved.
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