The Digital Transformation Playbook: Rethink Your Business for the Digital Age

Embedding Data Skill Sets
The first challenge in the transition to a more data-driven organization is 
finding people with the right skill sets.
This starts with data scientists—the folks who can do the technical 
work of data analysis, be it hand-cleaning the raw data, programming algo-
rithms to apply real-time data in an automated fashion, or designing and 
running rigorous data experiments. Depending on the organization, it may 
be using an outside partner for analytics, hiring a single analyst, or building 
an entire team. Good data scientists have strong statistical and program-
ming skills and often come from an academic or scientific background. 
They also serve as truth-tellers within the organization. These are the folks 
who know that data can lie very easily, and they will keep a company honest 
about things like sample size, significance testing, and data quality (the old 
“garbage in/garbage out” rule).
But the data experts cannot be the only people in an organization 
who understand or think about data. In order to truly build data into 
a strategic asset, everyone in the business has to adopt a mindset that 
includes using data, and the questions they pose to it, as a part of their 
daily process. Part of this is educating the workforce about the ways data 

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can be applied in their business. Another part must be developing a com-
pany culture that embraces data and analytical thinking. For a consumer 
goods company like Coke or Frito-Lay, that involves a shift from thinking 
about marketing as an art to thinking about it as a discipline that includes 
both art and science.
Lastly, the company may need someone who can bridge two worlds: 
the world of quantitative analysts and that of business decision makers. 
This person will be the one who can connect the work of data science with 
that of the senior managers or the creative types in the marketing depart-
ment. Think of Somaya, the former art history major who learned to speak 
the language of both the data scientists at TWC and the advertisers and 
brand managers who were his clients.
Bridging Silos
Sometimes the biggest challenges to sharing data are within the organiza-
tion. At Coca-Cola, Weaver found that website analytics data was sitting 
in one database while data on consumer purchase behavior from loyalty 
programs was being kept somewhere else entirely. In order to create a com-
plete picture of the customer, he first had to bring all the data together in 
a unified way.
In many organizations, these divisions are reinforced by departmental 
silos and each department’s desire for “ownership” of its data (sales data 
vs. marketing data, etc.). In a research study that I coauthored with my 
colleague Don Sexton, we spoke with hundreds of senior marketers at busi-
nesses across a wide range of B2B and B2C industries. The most commonly 
cited obstacle to using data effectively was internal sharing, with 51 percent 
of respondents reporting that “the lack of sharing data across our organiza-
tion is an obstacle to measuring the ROI of our marketing.”
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In large organizations operating in different locations, another 
important question is whether or not to centralize data analytics. This is