2. A DATA DRIVEN FUTURE
Big data is fast becoming the term keeping senior executives up at night. The promise of the op-
portunities it unlocks is sufficiently attractive, yet the sophistication required to extract the claimed
benefits is thinly spread and as such, the challenge is how to unlock the benefits of Big Data
before the competition. In our discussions with senior executives the key message we have picked
up is that the level of understanding is as high as it needs to be, but the capability to execute is far
below most executives’ expectations.
The Seymour Sloan view is that a small percentage of organisations have real strength in analyt-
ics, an elite group that deploys the correct people, tools, data and organisational focus. These are
the companies that are already using analytics insights to alter and improve the way they operate
or to improve their products and services. The difference is already visible. These companies are:
• Twice as likely to be in the top quartile of financial performance within their industries
• Three times more likely to execute decisions as intended
• Five times more likely to make decisions faster
Achieving excellence with Big Data is a three-part process that requires; defining the ambition, de-
veloping the analytics capability and shaping the company to maximise the opportunity. Here, we
will examine the second step—building up the analytics capability—to understand how leaders use
Big Data as a competitive advantage.
Data, tools, people and intent
Strong organisations build up their analytic capabilities through investing in four things: data-savvy
people, quality data, sophisticated tools, and processes and incentives that support analytical de-
cision making. We estimate, based on our high-level survey, a third of companies do not excel at
any of these elements, while many of the rest excel in only one or two areas. Building a high per-
forming analytics capability requires that you do all four well. Success in each capability depends
on strength in the others. It is a self-sustaining structure.
Data is the cornerstone of any analytical capability. Often, research suggests that between 50-70%
of organisations are unhappy with the quality of their data, extending well into the Forbes 500. This
is somewhat alarming, but also, a legacy of the relatively low status data was given in boom years.
That data strategy must align with the overall business view of how the data will generate real
insight and subsequently, value. We estimate that just over half of the companies lack the right
systems to capture the data they needed or were not collecting useful data, with two-thirds lacking
the right technology to store and access data. A good data policy identifies relevant data sources
3. builds a data view on the business to—and this is the critical part—differentiate your company’s
analytics capabilities and perspective from competitors. A critical aspect of good data policy is
to focus on identifying relevant sources of data. For example, capturing all queries made on the
company website or from customer support calls, emails or chat lines, regardless of their outcome,
may have significant value in identifying emerging trends; however, keeping detailed logs of re-
quests that were easily handled might be less valuable. An important additional element is the
ability to identify external sources of data that will further assist in providing a deeper view on the
customer. An example is how such consumer finance houses use browsing behaviour as an indi-
cator of credit risk.
In terms of analytical tools, we suggest that organisations ‘aim for the sky.’ In such a nascent peri-
od of analytics development, the key is to ensure you both acquire the most sophisticated analyt-
ics as well as being able to improve capabilities as more tools become available.
Advanced analytics and Big Data tools are developing so rapidly that they’re likely to help you get
to potential insights and statistical novelties in ways that were not possible even as recently as a
year ago. Tools and platforms like Hadoop, HPCC and NoSQL are rapidly emerging and evolving
to address analytics opportunities, as is the rich ecosystem of mature analytics, visualization and
data management. Today, these tools are available from a wide range of vendors and an even
larger community of open-source developers.
We find it surprising that less than a third of the executives we spoke with were aware of the possi-
bilities these tools could offer. This is time for a massive education exercise for executives around
the future of Big Data. They should be sufficiently knowledgeable in order to be the champion of
big data and to understand the impact of investing or not investing.
The biggest challenge facing organisations is the lack of people with the required skills to turn the
analytics into actionable information. The ability to turn data into opportunities will become more
complex as capabilities improve. The increase in technology must move in lock-step with the
increase in skill of your staff. Most organisations agree they are not up to the challenges of iden-
tifying and prioritizing what types of insights would be most relevant to the business. Successful
analytics teams build those capabilities by blending data, technical and business talent. Think of
a band as the model: a team with different but overlapping skills that knows how to effectively and
efficiently communicate and collaborate. Success with Big data requires:
• Data scientists, who provide expertise in statistics, correlations and quality
• Business analysts, who identify and prioritize the problems worth solving and the business
relevance of data anomalies and patterns identified by the data scientists
• Technical specialists, who help manage the hardware and software solutions needed to
collect, clean and process the data
Success with Big Data requires an acceptance by the organisation that, where possible, data must
drive business decisions. The increased analytical capability lies at the core of business strategy
with board-level supervision and support. The CEO and top leadership team need to shape the vi-
sion of how analytics will drive business improvement, whether by improving existing products and
services, optimizing internal processes, building new products or service offerings, or transforming
business models. Leading organisations excel at this, often building their organizations around
data and a commitment to make data-driven decisions.
4. Nest serves as an example of an organisation that can shape decisions with Big Data analytics.
Other companies provide an ability to remotely control their home thermostats using a Web in-
terface or smart phone. Nest goes further, crowdsourcing intelligence about when and how cus-
tomers adjust their thermostats to keep their homes comfortable. Nest gathers the information in
the cloud, and by correlating it with weather, location, type of home and when people adjust their
thermostats, the company can anticipate and control the settings to create a more comfortable
environment in their customers’ homes.
Committing to excellence in each area will require significant investment, commitment, and oc-
casionally a change in leadership. There is no value in focusing on one area without the other
elements. Tools won’t help if the data is of poor quality, and talent will walk if the company isn’t
committed to benefiting from the insights. Like an engine that must be firing on all pistons, all four
areas must be tuned for peak performance.
The opportunity, the urgency
There is a significant opportunity now to develop and deploy a sophisticated analytical capability.
This will place the early-adopters ahead of their rivals, but the window is closing on a daily basis
and as sophistication of tools improves, the opportunity for rivals to close the gap improves. The
time to act is now.
Industries such as, financial services, technology and healthcare are leading the market in rede-
fining the battlegrounds and business models, based on analytic capabilities and insight-driven
decisions. But opportunities exist in almost every industry.
An example is the mail-order pharmacy that analysed hundreds of thousands of customer service
logs and detected a spike in calls between Days 75 and 105 of some patients’ medication regi-
mens. Looking closer, analysts found that the calls correlated with refill dates, and they discovered
that some customers were calling for refills because their medications were taken with variable