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

part of running its core operations to a company that is treating data as a 
source of innovation, new revenue, and strategic advantage.
Rethinking Data
The third domain of the digital transformation playbook is data. Growing 
a business in the digital age requires changing some fundamental assump-
tions about data’s meaning and importance (see table 4.1). In the past, 
although data played a role in every business, it was mainly used for mea-
suring and managing business processes and assisting in forecasting and 
long-term planning. Data was expensive to produce through structured 
research, surveys, and measurements. It was expensive to store in separate 
databases that mimicked silos of business operations. And it was used pri-
marily to optimize existing operations.
Today, the role and possibilities for data are seemingly limitless. Gen-
erating data is often the easiest part, with great quantities continuously 
created by sources outside the firm. The greater challenge is harnessing 
this data and turning it into useful insights. Traditional analytics based on 
spreadsheets have given way to big data, where unstructured information 
joins with powerful new computational tools. But for data to become a real 
source of value, businesses need to change the way they think about data. 
They need to treat it as a key strategic asset.
Table 4.1
Data: Changes in Strategic Assumptions from the Analog to the Digital Age
From
To
Data is expensive to generate in firm
Data is continuously generated everywhere
Challenge of data is storing and managing it
Challenge of data is turning it into valuable 
information
Firms make use only of structured data
Unstructured data is increasingly usable and 
valuable
Data is managed in operational silos
Value of data is in connecting it across silos
Data is a tool for optimizing processes
Data is a key intangible asset for value 
creation

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This chapter explores how the role of data is changing in business and 
what leadership challenges this poses. We will examine the value of data as 
an asset, the components of an effective data strategy, and the power and 
misconceptions of the big-data revolution. We will see where businesses 
are finding the data they need and how they are turning it into new sources 
of value. This chapter also presents a strategic ideation tool, the Data Value 
Generator. This tool allows businesses to use customer data to create new 
value in specific areas of their operations.
But, first, let’s look at what it means to manage and invest in data as an 
intangible business asset.
Data as Intangible Asset
For many of the digital titans of today’s business world, it seems clear that 
the data they capture regarding their customers is one of their most valu-
able assets. Much of Facebook’s market capitalization is rooted in the value 
of the rich data it collects on users and in its ability to harness that data 
with innovative tools for advertisers, helping them understand and reach 
precisely the right audience.
But other kinds of data can be valuable as well. In building its Maps ser-
vice, Google has invested heavily for years in developing a best-in-class set 
of cartographic data. This includes sending camera-equipped cars around 
the world to measure out every road and capture its photographic Street 
View (more recently, it has sent cameras by camelback to map the deserts of 
Arabia). The company is constantly updating and “hand-cleaning” its data 
with teams of human data wranglers. It tracks up to 400 data points per 
road segment (the stretch of asphalt between two intersections). Depending 
on the pace of economic development, that road data needs to be updated 
with daunting regularity.
3
On the other hand, we saw Apple’s failure to invest sufficiently in map-
ping data—which led to a famous competitive fumble in 2012. As part of its 
ongoing rivalry with search giant Google, Apple chose to remove Google 
Maps as the default mapping app on all iPhones. Instead, it gave iPhone 
customers its own new Maps app, running on data Apple had purchased 
from various third parties. True to form, the Cupertino company had 
designed a stunning user interface for its app. But it had underestimated the 
quality of Google’s data asset. Millions of iPhone users who were forced to 
use the new maps flooded Apple with complaints. Cities were misspelled or 

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erased, tourist attractions were misplaced, famous buildings disappeared, 
and roads literally vanished into thin air. The errors were so bad that they 
compelled the first letter of apology by an Apple CEO to customers. In it, 
Tim Cook went so far as to advise customers to download and use competi-
tor apps from the App Store until Apple’s own maps improved.
Data is valuable not just for companies like Google and Facebook. For 
any business today, data—like intellectual property, patents, or a brand—is 
a key intangible asset. The relative importance of that asset will vary some-
what based on the nature of the business (just as brands have greater impor-
tance to a fashion company than an industrial manufacturer). But data is an 
important asset to every business today—and neglected at our peril.
One of the most common ways that businesses can build an asset out 
of customer data is through loyalty programs. For years, retailers and air-
lines have offered loyalty miles, points, rewards, or a tenth sandwich free 
in hopes of increasing customer retention and total spending over time. 
But, today, much of the value of loyalty programs is in the accumulated 
customer data that they generate. When I sign up for your loyalty program, 
I am explicitly asking you to track my shopping behavior in order to earn 
rewards. That gives your business much more than an address for direct 
mail; your data about me grows over time to help you better understand 
my unique behaviors and interests as a customer.
By designing new customer experiences with data in mind, compa-
nies can extend this model of providing customer benefits in return for 
customer data gained. Take Walt Disney Parks and Resorts and its new 
MagicBand wristbands. Promoted as a way to bring the convenience of 
smartphones in to the traditional theme park experience, these colorful 
rubber bracelets (outfitted with RFID tags) allow guests to enter the park, 
unlock their hotel room, purchase meals and merchandise, and skip the 
wait on up to three rides per day. The MagicBand is the heart of a $1 billion 
initiative to bring digital interactivity to Disney theme parks, and it aims 
to earn that money back by increasing the “share of wallet” that visitors 
spend at Disney. But it is also designed to provide Disney with previously 
inaccessible data on the behaviors of its guests: Where do they go when? 
Which rides are popular with which types of guests? Which foods might 
be better moved to different areas of the sprawling park? The MagicBands 
even allow guests to opt to be identifiable to Disney staff so that a child can 
be greeted by name by costumed characters or offered a birthday wish by 
a talking animatronic animal on a ride. These and other types of personal-
ized service experiences will become available as Disney builds more data 

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around its visitors on both the large scale and the individual level. The trick 
is in crafting the right experience so that, just as with a loyalty program, 
customers willingly exchange their data for added value from the business.
You don’t have to be a company as large as Disney or Google to start 
building your data asset. Even small businesses can now use Web-based 
customer relationship management tools to keep track of who opened 
which e-mails, tailor follow-up messages, analyze which offers are the best 
fit for which customers, and more. As we will see in our discussion of big 
data, the shift to cloud computing is putting ever more powerful data man-
agement tools into the hands of small and mid-sized businesses.
Every Business Needs a Data Strategy
Once you start to treat data as an asset, you need to develop a data strategy 
in your organization. That includes understanding what data you need as 
well as how you will apply it.
An explicit data strategy may seem obvious in industries like finan-
cial services and telecommunications, which are accustomed to copious 
amounts of customer data. But smaller firms and those in less data-rich 
industries must also develop forward-looking strategies for their data.
The following five principles should guide any organization in develop-
ing its data strategy.
r
 Gather diverse data types: Every business should look at its data asset 
holistically and include diverse types of data that serve different pur-
poses (see table 4.2). Business process data—such as data on your sup-
ply chain, internal billing, and human resources management—is used 
to manage and optimize business operations, reduce risk, and com-
ply with reporting requirements. Product or service data is data that 
is essential to the core value of your products or services. Examples 
include weather data for TWC, cartographic data for Google Maps, 
and the kind of business data that Bloomberg provides to business cus-
tomers. Customer data ranges widely—from transaction data, to cus-
tomer surveys, to reviews and comments in social media, to customer 
search behavior and browsing patterns on your website. Companies 
that do not sell directly to consumers (e.g., packaged goods compa-
nies) traditionally could gather customer data only through market 
research. As we will see later, even these businesses are discovering 

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new opportunities to piece together data to get a much clearer picture 
of their customers than was possible before.
r
 Use data as a predictive layer in decision making: The worst thing that 
companies can do with data is gather it and not apply it when mak-
ing decisions. You need to plan how your organization will utilize its 
data to make better-informed decisions in all aspects of its business. 
Operations data can be used in statistical modeling to plan for and 
optimize the use of your resources. Customer data can be used to 
predict which changes in your services or communications may yield 
improved results. With detailed data from its MagicBands, Disney can 
make better-informed decisions on which merchandise to feature near 
different rides and how to manage variable demand and foot traffic. 
Amazon uses your past browsing behavior to determine which prod-
ucts it should show you in your next visit.
r
 Apply data to new product innovation: Data can power your existing 
products or services, but it can also be used as a springboard for imag-
ining and testing new product innovations. TWC’s Hailzone mobile 
app is a perfect case of a company using its existing product data 
(for its TV shows and apps) to build a new service that added value 
for multiple customers (insurance companies and their insureds). It 
helped that TWC was able to step outside its normal perspective as a 
media company and think about different business models based on 
Table 4.2
Key Data Types for Business Strategy
Data type
Examples
Utility
Business process data
Inventory and supply chain
Sales
Billing
Human resources
Manage and optimize business 
operations, reduce risk, provide 
external reporting
Product or service data
Maps data (for Google)
Business data (for 
Bloomberg)
Weather data (for TWC)
Deliver the core value proposition of 
the business’s product or service
Customer data
Purchases
Behaviors and interactions
Comments and reviews
Demographics
Survey responses
Provide a complete picture of the 
customer and allow for more 
relevant and valuable interactions

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things like utility and risk management rather than just viewer eye-
balls and advertising. Netflix uses its vast amounts of data on viewer 
preferences—for genres, actors, directors, and more—to help it craft 
new television series like House of Cards. This practice lets Netflix cir-
cumvent the traditional network TV practice of investing in pilots for 
numerous new shows in hopes that one or more will pan out. That’s 
using data to innovate more quickly and cheaply.
r
 Watch what customers do, not what they say: Behavioral data is any-
thing that directly measures actions of your customers. It can include 
things like transactions, online searches (a powerful measure of your 
customers’ intentions), clickstream data (which pages they visited, 
where they clicked, and what they left in their shopping carts), and 
direct measures of engagement data (which articles in your newsletter 
they clicked to read). Behavioral data is always the best customer 
data—it is much more valuable than reported opinions or anything 
customers tell a market researcher in a survey. That is not just because 
people lie in surveys but also because, as humans, we are extremely 
fallible at remembering our behavior, predicting our future actions, 
or considering our motivations. This is why Netflix shifted its recom-
mendation system from customers’ own rankings to behavioral data 
as soon as it moved customers from DVDs to streaming video, which 
made it possible to measure what we actually watch rather than the 
unopened red envelopes on our dresser. Netflix knows that there are 
big differences between the movies that we give a five-star ranking and 
those that we actually wind up watching while doing the dishes on a 
Wednesday night.
r
 Combine data across silos: Traditionally, businesses have allowed their 
data to be generated and reside in separate divisions or departments. 
One of the most important aspects of data strategy is to look for ways to 
combine your previously separate sets of data and see how they relate 
to each other. A memorable example of the benefits of combining data 
sets comes from municipal government here in New York City. Scott 
Stringer, the city’s comptroller (CFO), was seeking to reduce the costs 
of lawsuits against the city. He launched an initiative to compare the 
data on lawsuits and damages paid with other city data sets, including 
the budgets of different departments over time. A surprising correla-
tion was discovered: after the city’s parks budget had been slashed a 
few years earlier and its seasonal tree pruning reduced, legal claims 
from citizens injured by falling tree limbs skyrocketed. The cost to the 

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city from a single lawsuit was greater than the entire tree pruning bud-
get for three years! Once this was discovered and the budget funding 
was restored, lawsuits dropped dramatically.
4
As your business envi-
ronment becomes increasingly complex, your ability to find, combine, 
and learn from diverse sources of data will become more important 
than ever.
In putting together a data strategy, it is also important to understand 
that many of today’s data sets are very different from the spreadsheets and 
relational databases that drove the best practices of data-intensive indus-
tries in the pre-digital era. The entire nature of available data, and how it 
can be applied and used by business, has undergone a revolution in recent 
years. That revolution is commonly termed big data.
The Impact of Big Data
The term big data first appeared in the mid-1990s, introduced in tech circles 
by John Mashey, chief scientist of Silicon Graphics, around the time of the 
birth of the World Wide Web.
5
But the phrase entered the broader busi-
ness conversation around 2010 as businesses of all kinds began to grapple 
with the vast supply of data generated by digital technologies. At first, the 
term seemed a bit faddish, a marketing ploy used by data storage firms 
to get IT departments to increase their spending on data servers. But the 
real changes at work have been much more profound than the size of hard 
drives or server farms.
Make no mistake: the size of data sets is increasing rapidly. Every graph 
representing the amount of digital data stored worldwide each year shows 
the skyward leap of an exponential curve. These curves all recede expo-
nentially into the past as well. The sheer amount of recorded data, in other 
words, has been growing for a long time—likely since the origin of comput-
ers, maybe since the origin of writing.
So what is new about big data if not the rapidly growing “bigness” of it?
The phenomenon of big data is best understood in terms of two inter-
related trends: the rapid growth of new types of unstructured data and the 
rapid development of new capabilities for managing and making sense of 
this kind of data for the first time. The impact of these two is shaped by a 
third trend: the rise of cloud computing infrastructure, which makes the 
potential of big data increasingly accessible to more and more businesses.

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