How to value your data? Is data currently you have is really an asset or not?

1. Putting a $ value on Data Assets: A Brief Introduction
Data has evolved to become a key building block for the modern Enterprise in recent years. Most
functions of an organisation are in some way dependent upon data to perform optimally.
Organisations are using bigger and ever-growing datasets to build a decisive competitive advantage
by enabling better decision making, measuring & optimising performance and obtaining a better
understanding of their customers. It is not surprising that an increasing number of enterprises refer
to their datasets as “data assets”.
The term data asset is interesting considering that in contrast to an enterprise’s traditional assets,
data is intangible and may have multiple consumers utilising it. Another interesting distinction
between a traditional asset and a data asset is that utilising a data asset often leads to creation of
more data. Similarly, unlike other organisational assets, data assets can be shared infinitely while
retaining their value.
This raises a fundamental question around data valuation i.e. if organisations choose to label
datasets and collections as data assets then what is the best way to assign these assets a value.
Could it be treated like traditional intangible assets like goodwill or a more nuanced approach needs
to be considered which addresses the intrinsic complexity of viewing data as an asset.
The Why of Data Valuation
Why measure the value of data in the first place? Especially since data is the new “oil” and the more
one has the better. While the analogy may ring true to many, like any commodity or asset, data still
needs to have a fair nominal value before it can be monetised.
Consider a Tech start-up: It would need to put a value on their data assets as one of the ways to
attract investment and funding by demonstrating what future opportunities and growth those
specific datasets can drive in the future. As an example, Facebook acquired WhatsApp in 2014 for
$19 billion to utilise its data assets for an immediate enhancement in the social network’s user
footprint and the possible future synergies that it could leverage in the user engagement space.
Similarly, for a traditional enterprise undergoing digital transformation understanding the value of
their data assets provides the opportunity to:
• Assess the potential revenue that can be generated from its data assets
• Assess what competitors might pay for the data and how they might use it for building a
competitive advantage
• Understand the cost of securing and replacing data
• Understand the cost of increasing data quality
• Understand the costs associated with obtaining and storing data
Data valuation therefore may be used by organisations to gauge the economic potential of their data
assets but also to use it to drive adoption of formal data management framework.
Key Challenges to Evaluating Data Assets
As there is no established standard to conduct data asset valuation the main challenge arises from
using a traditional asset valuation approach. The traditional asset valuation approach tends to fail
when working with data assets, as it relies upon cost of an asset to predict the potential revenue it
can generate. So, for e.g. if a business purchases a delivery truck for X Dollars then this cost will be
used as a basis for calculating the potential revenue that the truck will generate over its lifecycle.

2. Data Assets on the other hand have no direct correlation between the cost and the potential
revenue it can drive.
The potential revenue that a data asset can generate is highly contextual i.e. different organisations
may be able to leverage the same data asset differently and with different revenue results for e.g.
Customer demographic data in the hands of tech giant like Apple, Facebook or Google is utilised to
create a highly tailored experiences and rich services while same data in the hands of a small retail
store is much less likely to create compelling outcomes.
The other aspect is the temporal nature of data assets i.e. data that was valuable yesterday may not
be valuable today or the future for e.g. Customer data that a company holds may consist of
deprecated elements such as Fax numbers or purchase data for legacy/discontinued products which
may have had some value at a point in time but may not necessarily be valuable anymore. Other
factors such as legal requirements and industry regulations may also impact the value of data over a
time period for e.g. a user’s right to be forgotten or a requirement to purge records of inactive
customers after a predefined time period in a certain industry.
Data Valuation Methodologies
Organisations can choose between two methods based on their intended data valuation objectives.
For a data driven enterprise or a tech start-up, the foundational approach maybe well-suited which
advocates calculating the intrinsic, business and performance value of Data Assets:
A more traditional or large enterprise embarking on improving and optimising the information
management function may adopt the financial valuation approach which focuses on calculating the
economic potential of data using forecasting different economic viewpoints:
Method Primary Outcome Typical Measures Example
Data Validity/Accuracy
(To what extent is data
representative of what is
being measured)
For e.g First Name field in a dataset
contains valid name data
Performance Value of Data
What improvements to
business outcomes
could the data set
enable?
Existing business KPIs pre and
post
For e.g. enable an increase in market
share for an enterprise
For e.g. a dataset which provides
detailed voting preferences of a
country’s population
Data Scarcity
(To what extent is the
dataset hard to obtain)
Intrinsic value of data
How correct and
complete is the data
set?
Business Value of data
Whether the data set is
useful for achieving a
business outcome?
Data Completeness
(Is the dataset
comprehensive?)
For e.g. A dataset containing no null
entries for fields defined as
mandatory
Data Relevance
(To what extent will the
dataset help achieve a
business objective)
Data Timeliness
(Will the dataset be available
when expected and
needed)
For e.g a ride share app utilising a
customer’s past usage activity to
offer future discounts
For e.g. A company using sentiment
analysis to create real time offers

3. Conclusion
The above-mentioned valuation methods are certainly not exhaustive however they do provide a
basic approach which organisations can tailor according to their unique needs and objectives.
Regardless of the chosen methodology, data valuation can be viewed as an activity which can
provide organisations with clarity on how their data assets can drive future growth, optimise
existing business processes and serve as a means to assess the need for establishing data
management framework.
Method Primary Outcome Typical Measures Example
Income Opportunities Assessment
What value in terms of
revenue or bottom-line
enhancements can
the dataset provide?
Forecasted impact on
Revenue
For e.g. a dataset may enable a
unique product design driving higher
revenue for an enterprise
Market Value Assessment
What would someone
pay to utilise or
purchase this dataset?
Use Case based valuation
(e.g: Data/Insights as a
product)
For e.g. a credit bureau selling credit
scores to financial service providers
Cost of Maintaining Data
For e.g. An enterprise's cost of data
storage on a public cloud platform in
a readily accessible state
Cost of Data Replacement
For e.g. Cost incurred by an
organisation for rebuilding or
acquiring a dataset after a data loss
incident
Cost of Regulatory
Compliance
For e.g. an industry regulator
penalising an enterprise for not
purging inactive customer data after
a predefined period
System Remediation Costs
For e.g. An enterprise incurring cost
on redesigning an application to
incorporate changing data privacy
laws
Risk Quantum Assessment
What would it cost the
organisation if the
dataset was lost?