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

include:

• 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

strategically employed.

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

development.

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

competitive advantages.

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

consumer preferences.

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

success.

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.