SLOANREVIE W.MI T.EDU SPRING 2017 MIT SLOAN MANAGEMENT REVIEW 19
3. The Expected Future
Value of Data Although the
phrases “digital assets” or “data
assets” are commonly used,
there is no generally accepted
definition of how these assets
should be counted on balance
sheets. In fact, if data assets are
tracked and accounted for at
all — a big “if” — they are typically commingled with other
intangible assets, such as trademarks, patents, copyrights, and
goodwill. There are a number
of approaches to valuing intangible assets. For example,
intangible assets can be valued
on the basis of observable
involving similar assets; on the
income they produce or cash
flow they generate through
savings; or on the cost incurred
to develop or replace them.
No matter which path a company chooses to embed data
valuation into company-wide
strategies, our research uncovered three practical steps that
all companies can take.
1. Make valuation policies
explicit and sharable across
the company. It is critical to develop company-wide policies in
this area. For example, is your
company creating a data catalog so that all data assets are
known? Are you tracking the
usage of data assets, much like
a company tracks the mileage
on the cars or trucks it owns?
Making implicit data policies
explicit, codified, and sharable
across the company is a first
step in prioritizing data value.
A few companies in our
sample were beginning to
manually classify selected data
sets by value. In one case, the
triggering event was an internal security audit to assess
data risk. In another, the triggering event was a desire to
assess where in the organization the volume of data was
growing rapidly and to examine closely the costs and value
of that growth.
The strongest business case
we found for data valuation
was in the acquisition, sale, or
divestiture of business units
with significant data assets.
We anticipate that in the future, some of the evolving
responsibilities of chief data
officers may include valuing
company data for these purposes. But that role is too new
for us to discern any aggregate
trends at this time.
2. Build in-house data valuation expertise. Our study
found that several companies
were exploring ways to monetize data assets for sale or
licensing to third parties.
However, having data to sell is
not the same thing as knowing
how to sell it. Several of the
companies relied on outside
experts, rather than in-house
expertise, to value their data.
We anticipate this will change.
Companies seeking to mone-
tize their data assets will first
need to address how to acquire
and develop valuation exper-
tise in their own organizations.
3. Decide whether top-down or bottom-up valuation
processes are the most effective within the company. In
the top-down approach to
valuing data, companies identify their critical applications
and assign a value to the data
used in those applications,
whether they are a mainframe
transaction system, a customer
relationship management system, or a product development
system. Key steps include
defining the main system linkages — that is, the systems that
feed other systems — associating the data accessed by all
linked systems, and measuring
the data activity within the
linked systems. This approach
has the benefit of prioritizing
where internal partnerships
between IT and business units
need to be built, if they are not
already in place.
A second approach is to define data value heuristically —
in effect, working up from a
map of data usage across the
core data sets in the company.
Key steps in this approach include assessing data flows and
linkages across data and applications, and producing a detailed
analysis of data usage patterns.
Companies may already have
much of the required
information in data storage de-
vices and distributed systems.
is taken, the first step is to
identify the business and technology events that trigger the
business’s need for valuation. A
needs-based approach will help
senior management prioritize
and drive valuation strategies,
moving the company forward
in monetizing the current and
future value of its digital assets.
James E. Short is a lead
scientist at the San Diego
Supercomputer Center at
the University of California,
San Diego, in La Jolla, California. Steve Todd is fellow and
vice president of strategy
and innovation at Dell EMC,
a part of Dell Technologies.
Comment on this article at
x/58331, or contact the authors
The authors wish to acknowledge
financial and research support
from Dell EMC, Intel Corp., and
Seagate Technology Inc. for this
study; in addition, Cisco Systems
Inc., IBM Corp., and NetApp Inc.
provided financial and research
support for earlier stages of the
research. Several individuals
made important contributions:
Barry Rudolph of VelociData Inc.,
Douglas Laney at Gartner Inc.,
Barbara Latulippe and Bill
Schmarzo at Dell EMC, and
Terry Yoshii at Intel Corp.
Reprint 58331. For ordering information, see page 4. Copyright ©
Massachusetts Institute of Technology,
2017. All rights reserved.
Making implicit data policies explicit, codified,
and sharable across the company is a first step
in prioritizing data value.