Data Governance Needs Data Strategy.
There are several good reasons to develop an organizational data strategy that include:
• improving an organization’s data. Pareto’s law is supported by the premise that the
majority of an organization’s data is redundant (has been duplicated in multiple data
bases and files), obsolete (was needed for an application when developed, but is no
longer used) or trivial (added for an anticipated need but never used).
• using data to motivate, measure and manage change by teaching personnel to
perceive data as a corporate asset.
• improving data quality and training personnel that will lead to having data assets
that are optimally utilized to support both data and organizational strategies.
Attempting to manage the unique characteristics of data assets without guidance has
proven to be less than effective. A data strategy is therefore needed to provide said
guidance to organizational data governance programs. Not having a strategy will result in
your data management program not having a clear direction and will most likely not
support your organization’s business strategy.
Is Data Really the New Oil?
We sure hear and read this a lot. It’s true in the context of helping people grasp the
concept of data being an organizational asset. However, data is more valuable than
oil…..by far! Oil is depleted when used and must be replaced – we have to extract more
from the ground. Data, on the other hand, is durable and can be reused as much as
people want. Therefore, we feel that data should be viewed as an asset vs. an expense.
When you compare the lifespan, the reusability and uniqueness of data assets to other
traditional assets, it is easier to see why data needs its own strategy in every organization.
Motivations for Data Strategy Development
Motivation-1 – Improving Your Organization’s Data
A data strategy provides the “what” and organizational data governance provides the “how”
for organizations to achieve data goals. Motivation to develop a data strategy might
• Improving data assets. Sadly, many or our clients report that:
o They do not know what data they have
o They do not know where some of their data is located
o They do not know what their personnel are doing with data assets
o They do not know the quality of some of their data
Data Points to the Locations of Valuable Things
You probably know where your personal assets are located. Your car is in the garage,
your jewelry is in a drawer, and your money is in the bank. But, can you say the same
about your organization’s data assets? Some of our clients struggle to determine:
• which data collections house specific data items.
• the identity of specific individuals who are using data assets to make specific decisions.
• the purpose for which the data is collected and accessed.
• the transformations being applied to which data collections.
The answer to solving these issues is metadata management. Improving metadata
management capabilities in concert with supporting technologies will permit an
organization to gain confidence in the amount of data assets that can be processed and
Data Has Intrinsic Value by Itself
In the past, organizations used data processing / information technology to save money.
Today, organizations use data to make money. And some feel that data should be valued
and included on the company’s Balance Sheet with other intangible assets like intellectual
property, trademarks, etc. In 2012, AT&T established a value of $2.7 Billion dollars for
their data such as customer lists and relationships and recorded it on their Balance Sheet.
In the UK, more than 20% of companies have assigned financial value to their data and
recorded it on their Balance Sheets. That said, and despite growing consensus that data
is an asset, there is no uniform or common method accepted by the various accounting
bodies to assert the value of an organization’s data.
Data Has Inherent Combinatorial value
So, data has intrinsic or inherent value, but it also has combinatorial value when integrated
with other data. Many of our clients are combining external data (weather, GPS, IoTS)
with internal data to enable significant data monetization opportunities.
Motivation-2 – Improving How Your Personnel Use Data
One of the inhibitors to this improvement is the perception on the part of many that data is
a by-product of I.T. systems; people define data in terms of software and systems
Perceiving the Value of Data
A few years ago, Capgemini interviewed senior business leaders from 1,000 companies.
61% of them stated that data was a driver of revenue in its own right. More companies are
realizing that their data, especially when combined with external data, is the path to
Using Data to Measure Change
More organizations are using data analytics to improve competitive positions and gain
marketplace efficiencies via the implementation of dashboards, scorecards, Key
Performance Indicators (KPI’s) and business intelligence.
Using Data to Manage Change
Big data, ML and AI technologies are allowing companies to learn more about their
customers, their buying habits and preferences. This data informs the judgement of
management in tweaking and tuning sales and marketing activities. Combining external
data with an organization’s data allows companies to have an ever deeper view of their
customers and motivates changes to products and services as a result.
Using Data to Motivate Change
We see some of our clients who have used external and internal data to justify the startup
of entirely new lines of business. Another used data to drive the development of a new
product versus the older way of using focus groups and surveys of customers. A large
grocery chain has monetized customer data by selling aggregate data to its suppliers, who
are happy to pay for it in order to facilitate the tailoring of products and marketing efforts to
Motivation-3 – Improving Data to Support Organizational Strategy
Some of our clients err in perceiving the development of a data strategy as a project
versus a program. We disagree with this perception. Implementation of a data strategy
must be accomplished at the program level. This mindset helps ensure success.
Before an organization can fully leverage its data and benefit from its full value, that
organization must be confident in the quality of their data. To attain these goals,
organizations must address data management across the dimensions of people,
processes and then technology. An understanding and acceptance that all three areas
must mature in concert and that this process can literally take decades because on-going
improvements should be perceived as never-ending. Business and I.T. must integrate at a
granular level in order to interoperate with the most efficiency and productivity.
Creating a Competitive Advantage with Data
Digital transformations have changed the competitive landscape and thus created an
enhanced awareness of a new, less tangible asset: data. When organizations accept that
data is inherently valuable, they can begin the process of developing returns on their data
management efforts by developing a comprehensive data strategy. An effective strategy
will provide direction for growth and development, as well as performance metrics to gauge
If you would like to learn more on this topic, we encourage you to download our free PDF
file of Chapter-2 from “Data Strategy and the Enterprise Data Executive – Ensuring That
Business and IT are in Synch in the Post-Big Data Era” by Peter Aiken and Todd Harbour.