Getting and Keeping Your Data Right

As our world has transitioned from an industrial age to an information age, data has become a very valuable resource since it is the raw material used to produce information. In this perspective,
data should be as important to a business organization as any traditional asset, probably even more. In spite of this obvious fact, as well as all the recent hype regarding enterprise data asset
management, most organizations continue to be data challenged as their data grows further out of control. This begs the question, if data is such an important enterprise asset, why is it not
managed as one? Do business organizations really understand data; after all, you cannot manage what you do not understand.

Data is simply a representation of the real world business organization. A representation “stands in for” whatever it represents; it depicts or is a likeness of something. It serves to
capture the essence and account for the known or inferred properties of the real world thing. Everything in a typical business organization today is based on this data representation, from
recording and operations to predicting, planning and reporting. Fundamental to all these activities is decision making. Rarely in today’s world are business decisions made on actual
observations, but rather using this data representation. The ability to make accurate, timely decisions is essential to the survival of any organization and requires the truth: integrated, clean,
reliable, accurate, managed data. This gives an organization the strategic advantage to compete and win; it can actually make or break an organization. Getting the data right and keeping it right
is what guarantees the truth. Enterprise data asset management is all about getting and keeping the data right.

Data as an Enterprise Asset

An asset is defined as any item of economic value owned by an entity that can be measured and tracked. Assets can be physical such as cash, inventory, or property; or intangible such as patents,
copyrights or trademarks. Data clearly has an economic value to an organization because if it were destroyed, lost or stolen, there would be a significant cost. In most business organizations
today, data has become as critical in the creation of products and services as equipment and materials; data can sometimes be a product itself.

Data assets differ from the typical business assets in several ways. Data is not depleted when used; it can actually grow as it moves through an organization. The physical location of the data
asset is not as important because technology provides a virtual environment. Regulations, laws, and taxes typically assigned to other types of assets present much more of a challenge to assign to
data assets. This is mainly due to the elusive nature of the value of data, as its value depends on situations, content, human competencies, and time. Downsizing in regards to physical assets is
often seen as a good cost-cutting method, but that is rarely the case for data assets. In spite of these differences, data assets still need to be tracked, measured and accounted for in much the
same manner as the traditional physical assets. What is not tracked and measured cannot be managed.

The majority of what’s written today about how to treat data as an enterprise asset gets twisted into a tools or technology sales pitch. Many of these methods start with the existing broken
data, overlooking the most essential step – getting the data right in the first place. If the data was never right in the first place, then all the asset management methodologies in the world
cannot make it right. Since data is used as a representation of the business organization in order to track, manage, and predict it, an accurate representation is absolutely necessary in order to
begin to treat data as an important enterprise asset. The entire organization is dependent on a correct representation, as every single function within the organization, including enterprise data
asset management, hinges on this.

The Foundation – The Enterprise Data Blueprint

If it is critical to get the data “right” in order to manage it as a valuable enterprise asset, then the first step in any enterprise data asset management initiative is to have a
clear, correct understanding or picture of the business organization – a data blueprint representing the real world organization. Without this data blueprint, how would you ever know if your
data is right and by what criteria would you judge? It is very similar to the architectural blueprint used to build and maintain any physical structure or building, where everything is evaluated
against this plan. The data blueprint or map of the organization needs to represent one single version of the truth, not any one specific view or functional usage of data. It must represent the
business organization as a whole, its things and events. Most technology projects focus on a specific business functional use of the data. The data blueprint, unlike a logical data model that
typically represents a specific business usage of the data, represents the business organization, and these are very different concepts. Once this data blueprint, also known as an enterprise
data model
is complete, then everything else needed to manage data as an enterprise asset can be accomplished successfully.

While many organizations recognize the need for their data to be right, they attempt to “clean,” or fix it without this data blueprint. How would they ever know if the fix correctly
represents their real world organization? Unfortunately, there is an unconscious consensus that if the data “looks” right, then it must be right. That is one of the biggest data
fallacies today and underlies many of our data challenges. Data quality statistics account for only the data that is obviously or proven incorrect. As long as the data does not appear to be off, it
is assumed correct. Think about it, if you already know the correct answers, you would not need to ask the question. So when questions are asked of the data, most assume the answers are correct, as
long as they do not look obviously incorrect; blindly, many believe their data merely on an assumption of it being correct, not ever really knowing the truth. Therefore, within what is assumed as
good quality data, there is likelihood of error. Attempting data repair without a data blueprint often moves the obviously incorrect data to this assumed correct state. This data is then used to
plan, operate, predict and steer our business organizations.

Instead of creating a data blueprint, often an organization chooses to “purchase” an enterprise blueprint such as an enterprise package or a generic industry data model. These can
represent an organization theoretically, but it is still essential to develop a data blueprint of the actual business organization. How would you ever know if the purchased picture is a correct
representation of the actual business organization? If you had the actual data blueprint of the organization, then it would not matter if an enterprise package forces a black box or generic
implementation because the differences could be identified, understood, documented, mapped and managed. This includes all of the deviations from the truth that the package solution presents. The
same thing holds true for a generic industry data model. The organization’s data blueprint can be measured or compared to an industry view in order to understand and manage the differences.
Just like people, organizations are all uniquely different, and it is this difference that can give a competitive edge. A data blueprint of the real world business organization is absolutely
essential in order to begin to manage data as a critical enterprise asset.

Enterprise Data Asset Management

If a business organization actually viewed their data assets as they did their traditional assets, there would be little doubt that the data would get managed properly. For example, a typical
delivery service business understands their delivery vehicles as critical assets. They know the importance of having each correctly configured and appropriately maintained. It is essential that
they uniquely identity each vehicle and keep an accurate inventory at all times. If their fleet were initially configured inaccurately or tracked and maintained improperly, then the business
operations would be compromised and inevitably fail. For example, a business specializing in appliance delivery with vehicles not tall enough to accommodate refrigerators or improperly maintained
will result in missed deliveries and angry customers. These same principles hold true for the data assets.

Data Inventory
Organizations that treat data as they would a physical asset can maximize its value by maintaining their data to a high level of consistency and correctness. Most physical assets are managed
through a process involving identification, inventory, classification, ownership, valuation, depreciation and maintenance – all of which can be applied to the data assets. The data needs to
be uniquely identified and inventoried similar to the way physical assets are tagged and cataloged in order to easily account for them. An inventory of the data can then be compared and analyzed
against the ideal state described within the enterprise data blueprint. Data can also be classified so it can be grouped in meaningful ways, adding to its manageability and usability, similar to
the way physical assets are classified for management. Things like prioritization and dependencies can then be established. The point is to understand the data so it can be managed properly.

The preference would be to design, capture and store the data according to the enterprise data blueprint, but that is rarely considered for organizations with a large, well established legacy
data infrastructure. Most feel they cannot afford to do this; but in many cases, it actually may be less expensive in long run due to the high cost of continual data repair, as well as the cost
of distorted data itself. Every discrepancy of the organization’s actual data from the enterprise data blueprint needs to be accounted for and managed. There are always a number of
solutions depending on the circumstances, on a case-by-case basis, but most require many hours of human effort to research, analyze, and design a solution. An entire book could be written on the
details of how each type of data discrepancy is best resolved.

Data Ownership and Governance
During the creation of the enterprise blueprint, as well as the inventory of the actual data, it is essential that both be documented in detail. In concert with this, a data ownership and
stewardship program needs to be established. This includes an enterprise data issue resolution process and guidelines; getting to “one version of the truth” resolves multiple opinions
and perceptions. There is much written on the subject of data ownership, but the most important aspect to data ownership and issue resolution is the establishment of an overall data governance.
There is nothing worse than “spinning” and wasting time trying to figure out which “right” solution to use, as there are always many right ways to spin the data. There
needs to be a “deal breaker” process and authority that can quickly make the calls. Probably the most important understanding is the difference between the one version of the truth
and the multiple views and usage of data. There will always be many views, but only one truth. An experienced enterprise data architect will be able to quickly understand the difference. Data
ownership and issue resolution are a vital part of the design and construction of data systems, as well as the ongoing enterprise data management.

Data Valuation
Data about the real world business organization is rarely captured and stored just because it exists. It needs to be of value or have a purpose for something, as there is a cost to recording
reality. Information is expensive in terms of time, money and resources because data is costly to collect, store, and use. Understanding the value of data is not only important to justify its
expense, but also to assure the proper level of management and care. Cost always implies value, and data is a valuable asset that should be given the care and consideration much like any other
physical asset. Establishing the business value of the data assets helps in understanding its importance, determining what level of care must be given. All data does not have the same value. This
is very important in managing risk and assuring the reliability of data, just as it is for physical assets. The valuation of data also plays a critical role in assigning responsibility or
ownership. Lack of ownership often results in data falling “between the cracks” among business departments as well as between the business and technology.

Data Maintenance
The idea of depreciation typically associated with physical assets can be applied similarly to data because data also ages and changes with time as its
value changes over time. This not only helps with the proper level of care, but also identifies maintenance points as well as the end of its useful life. Much like physical assets, data assets
need to be maintained over their useful life due to the law of entropy – everything in the universe heads toward chaos without energy to keep it in order. It is a daunting enough task to
get data in order, but it is even more challenging to keep it that way. Data can be viewed as organic, a living thing within the organization as it moves through the data pipeline. Proper data
maintenance can ensure or extend its useful life. Audits for accuracy, consistency, and overall correctness, as well as an ongoing data quality program should be carried out in a way similar to
physical asset management practices.

One of the biggest issues facing enterprise data asset management is maintaining the quality of the data asset. A data quality program for the ongoing maintenance of the data asset needs to be
established. Fundamental to a data quality program is the knowledge of both the nature of data and its quality challenges. This is where the enterprise data blueprint plays a vital roll, in both
understanding and prioritizing the data for quality management. Ownership can provide the governance as well as the accountability for the data quality. Data quality expectations, measurements,
methods and audits need to be established. Tied to data quality is data integration. Here again, the enterprise data blueprint provides the fundamental understanding of how the data is integrated,
as it is the one version of the truth and will assist in the resolution of redundant and disparate data challenges.

The simplest of all management techniques is prevention. Today, more than ever, organizations need to address enterprise data asset management from a point of prevention, eliminating anything that
absorbs system and staff resources while providing little long term value. This should include all of the resources that continually band-aid the data quality symptoms, like giving an aspirin to a
person with a brain tumor. The typical approach to data used in many organizations today is very reactionary, chasing the symptoms because most organizations feel they can not afford the time and
resources to get their data right in the first place, as urgency always trumps strategic. All data problems severely handicap business plans by draining productivity and capital as well as
inhibiting agility and growth. The irony is that organizations in today’s world simply cannot NOT afford to get their data right because of the very critical and strategic nature of this

In Closing

The first challenge many organizations face in regard to managing data as a valuable enterprise asset is to first recognize that their data problems severely hinder their organization. Nearly all
organizations admit their data is riddled with issues, but believe it is nothing they can’t handle. All they have to do is just fix whatever does not look right with the magic tools. Simply
do whatever it takes to make it look right. Most do not see that there is something seriously wrong with this approach. These organizations do not realize just how poor and out of control their
data really is because they hold tight to the false belief that they can make the data “right” by simply making it look right.

Bad quality data is but a symptom of the problem and not the problem itself. This scenario is similar to the typical alcoholic who does not think he has a problem because he still has a job or owns
a home, and thus thinks he is in control. It is interesting that the inability to recognize an alcoholic problem is a big part of the problem in itself, exactly like our data problems. We could say
many organizations are dataholics. Add to that the typical codependent behavior of the individuals responsible for the data – how would it make them look to recognize that there are serious
data problems? It could actually be a career-ending move to call the baby ugly.

Once there is recognition of the severity of the data problems, a business-driven holistic approach, mandated from the executive level, is needed to establish data as an important enterprise asset.
This often requires a cultural transformation because typically the information technology arm of the organization drives the data strategy, as most organizations assume data to be a technology
discipline. Data is clearly a strategic business asset and needs to be owned and managed by the business, not technology. When data is driven by technology, much of the organization’s
critical data ends up in departmental silos, with each group using different technology approaches, resulting in the lack of a holistic view of the data. It requires bold executive leadership and
financial support to establish data as a critical enterprise asset. This also takes experienced data professionals that understand the nature of data and have the expertise to properly manage it.
An enterprise data asset management initiative cannot be successful if it is driven from the bottom up, chasing the data symptoms with tools or technology.

Enterprise data asset management is a strategic business initiative. As with most strategic initiatives, it is often the first to be cut in the wake of economic risks or challenges because it is
considered non-critical activity. It is very difficult to account for the return on investment that fails to directly link to the bottom-line contributions of the business organization. It has been
a challenge to understand the nature of data enough to establish and attach actual value as very few organizations have successfully incorporated this understanding. The lack of a strategic
approach to enterprise data asset management is also fueled by the short-term thinking within most organizations today as they compete in the new world economy. Ironically, managing data
strategically as a valuable enterprise asset will assure the survival of a business organization in the future. The organizations that figure this out first will be leap years ahead of the rest.

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Noreen Kendle

Noreen Kendle

Noreen Kendle is an accomplished data leader with 30 years in corporate data leadership positions. She has also held positions as a data industry advisor at Gartner, Burton, and TechVision Research. From her wealth of experience and knowledge, Noreen developed an insightful business-centric approach to data strategy, architecture, management, and analytics. She is a well-respected author and speaker covering many core data topics. Noreen may be reached at Linked-In at: or

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