The Business – Data Management Partnership

There is a major chasm at Row Three of the Zachman Framework; a major disconnect between the information that a business needs to operate successfully and the databases managed by the organization.
The growing disparity of the data resource in most public and private sector organizations is severely hampering the growth and success of those organizations. If this trend continues, these
organizations will continue to be less than fully successful, or could go out of business.

The author recently had three interesting discussions with industry experts regarding increasing data disparity. The first was with Dr. Peter Chen, founder of the logical data modeling concept that
supports business needs. He was quite concerned about what has been lost since his now famous 1977 article on data modeling and wanted to know what could be done to return to the basic principles
he laid down in his article. He emphasized that creating a logical data model that supports the business needs leaves a wide latitude for physical implementation.

The second was with Dr. Chris Date of database management fame. He was equally concerned about the apparent loss of basic principles for developing a normalized logical data model defining business
needs and the denormalization of that logical data model for specific physical implementation. He was concerned about a headlong rush to the creation and implementation of physical data models that
may not meet business needs, and wanted to know what could be done to return to the basic principles.

The third was with John Zachman, of the Zachman Framework fame, on a panel at the 2004 DAMA International Symposium in Los Angeles. He summarized his initial presentation with an all to familiar
statement that “We have met the enemy and he is us.” The data management people have created the data disparity crisis and are now complaining about the current situation. The data management
people, along with business concurrence of their actions, have created the data disparity problem and the Row Three Chasm.

What is needed to close this Row Three Chasm and build a high-quality data resource that meets the ever-changing information needs of the business? How can a high-quality data resource be built
that will continue to supply high-quality information to an intelligent learning organization? What needs to be done to refresh the basic principles about data resource management that have already
been established? These questions need to be answered to improve data resource quality.

The answers to these questions are provided in the following six steps. When these six steps are taken the growing data resource disparity will be substantially resolved and a high-quality data
resource that meets an organizations business information needs will be created.

First, organizations need to recognize data as the fourth critical resource equal to finances, real property, and the human resource. Data must be managed with the same intensity and formality as
the other three critical resources of the organization to improve data resource quality. Business information, which is derived from the data resource, can be of no better quality than the data
resource. Any improvement in information quality must begin with improvement in data resource quality.

Second, the already-known and established basic principles and techniques of data resource management must be refreshed and applied to management of the data resource. The basic principles of
normalized logical data for business needs, denormalized physical data for implementation, database management systems need to be refreshed. The concepts of frameworks, architectures, and models
must also be refreshed.

Third, the Zachman Framework, particularly Column One, should be used to focus the thinking about data resource management and the production of business information. The closing panel at the 2002
DAMA International Symposium put forth the principle that frameworks, architectures, rules, and partnerships are the route out of the current data resource dilemma.

Fourth, data professionals need to acquire more people skills. Traditionally, though not universally, data professionals are weak on people skills and writing skills. They have been excellent at
developing and maintaining database, but they have been relatively weak communicating needs and documenting those databases. This may be a difficult pill to swallow, but it is recognized as a
problem that needs to be resolved.

Fifth, data management professionals need to take the initiative to improve the data resource. They should not wait for business people to take the initiative and prove the case for professional
data management. The massive layoffs of data management people when the economy declined showed that data management is not perceived as adding value to the organization. Data management
substantially created the problem and they need to take the initiative to resolve the problem and prove the benefits of formal data resource management.

Sixth, data professionals need to form a partnership with the business professionals to improve data resource quality. The current data and business professionals have both inherited the current
situation. It does little good to lay blame for this current situation, because blame only polarizes people and prevents any progress toward resolution. Data professionals and business managers
need to get on the same track and interact in a cooperative manner to improve data resource quality.

The time is coming, and it is not too far in the future, when organizations will be held accountable for the quality of their data resource and the information produced from that data resource. The
trends in human resource management and, more recently, in financial management indicate that executives will be held accountable for the management of their critical resources. The sloppy past
practices of critical resource management, if continued, could result in future civil and criminal actions. This situation needs to be avoided by closing the Row Three Chasm.

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