A New Direction for Data Resource Management

What is the future for data resource management? Considering the past practices and the current data resource situation in most public and private sector organizations, is a new direction needed
for developing and managing the data resource? Is a new orientation toward improved data resource quality and increased business support needed?

Data administration has not been an effective way to manage an organization’s data resource.. People have tried to administer the data with an orientation toward the data, rather than towards
support of the business. Many organizations have been, and still are, oriented toward the technology aspect of information technology by trying every new technology that comes along hoping that it
will help them administer the data and provide better business support. They are looking for that elusive silver bullet and are sacrificing future business support for current needs.

A new direction is needed that focuses on managing data as a critical resource of the organization to directly support its business activities. The data resource must be managed with the same
intensity and formality that other critical resources are managed. Organizations must emphasize the information aspect of information technology, determine the data needed to support the business,
and then use appropriate technology to build and maintain a high-quality data resource that provides that support. In other words, organizations must manage data as a resource rather than
administer the data.

Business Information Demand

A new direction for formal data resource management begins with an understanding of an organization’s demand for information to support their business activities. The business information
demand
is an organization’s continuously increasing, constantly changing need for current, accurate, integrated information, often on short or very short notice, to support its business
activities. It is an extremely dynamic demand for information to support the business.

The result of past data resource management practices is rapidly increasing quantities of disparate data. Disparate data are data that are essentially not alike, or are distinctly different in
kind, quality, or character. They are unequal and cannot be readily integrated. They are low-quality, defective, discordant, ambiguous, heterogeneous data. The data resource is in a state of
disarray that does not, and cannot, adequately support and organization’s dynamic need for information.

Disparate data cause a dilemma for most organizations. The business needs integrated data to meet the business information demand, yet disparate data are being created faster than they have ever
been created before. There is no end in sight for a resolution to this dilemma with the current orientation. In fact, the current orientation will lead to increased quantities of disparate data and
decreased support for the business because the data resource naturally drifts toward disparity of it is not properly managed.

The sooner organizations make a conscious effort to alter the natural drift away from disparity, the easier it will be to achieve a high-quality data resource that supports the business information
demand. The surprising thing is that most people will not object to formal data resource management. Most people really want a higher quality data resource, are enthused about improving data
resource quality, and want to share data. The problem is that they just do not know how to go about those tasks without impacting business operations.

Data Resource Quality

Data resource quality is a measure of how well the organization’s data resource supports the current and the future business information demand of the organization. The data resource
cannot support just the current business information demand while sacrificing the future business information demand. It must support both the current and the future business information demand.
The ultimate data resource quality is stability across changing business needs and changing technology. This stability across change is the ideal that provides the foundation organizations
need to become an intelligent learning organization–an i-organization.

A high-quality data resource can only be achieved by developing a comparate data resource where the data are alike in kind, quality, and character, and are without defect. They are concordant,
homogeneous, nearly flawless, nearly perfect, high-quality data. The data are easily identified and thoroughly understood, readily accessed and shared, and utilized to their fullest potential.

Common Data Architecture

A comparate data resource must be developed within a single, organization-wide common data architecture. A data architecture is the science and method of designing and constructing a data
resource that is business driven, based on real-world objects and events as perceived by the organization, and implemented into appropriate operating environments. It is the overall structure of a
data resource that provides a consistent foundation across organizational boundaries to provide easily identifiable, readily available, high-quality data to support the business information demand.

The common data architecture is a formal, comprehensive data architecture that provides a common context within which all data at an organization’s disposal are understood and integrated.
It is subject oriented, meaning that it is built from data subjects that represent business objects and business events in the real world that are of interest to the organization and about which
data are captured and maintained.

The common data architecture contains concepts, principles, and techniques for developing and maintaining formal data names, comprehensive data definitions, proper data structures, precise data
integrity rules, and robust data documentation. Documentation about the data resource is often referred to as meta-data, which is commonly defined as data about the data. This term has been misused
and abused to the point that its real definition is unclear. An increased emphasis on meta-data only promotes the concept that they are something different from the business data that must be
designed and managed independent of the business data.

The term data resource data helps people understand the importance of thoroughly documenting the data resource. Data resource data are any data that document the data resource and
help people understand, manage, and use that data resource to support the business information demand. Data resource data are a major segment of the organization’s data resource that are designed,
developed, managed, stored, retrieved, and used the same as any other segment of the data resource. They support the business of managing the organization’s data resource just like human resource
data support the business of managing the organization’s human resource.

A New Direction

The traditional orientation to building an organization’s data resource is to develop data models independent of an organization-wide data architecture. In many organizations, only 10% to 15% of
their data resource has ever been modeled and portions of the data resource have been modeled multiple times with different tools and techniques. Many data models are oriented toward developing the
database rather than understanding the business. This from-below, brute force physical approach leads to increased data disparity.

A new data resource management direction emphasizes the development of an integrated data resource within one organization-wide, subject oriented common data architecture. A data model is developed
using a subset of the data resource data for a specific business activity for a specific audience. The concept is the same as any other segment of the data resource, such as affirmative action,
where specific data are extracted from the data resource and presented to the affirmative action audience in a form that is useful to them for performing their business activity.

The specific data model techniques and notations are simply an option for presenting a data model to a specific audience. Data model are not developed independent of the common data architecture.
There are no more conflicting data models or data model disparity because all data models are developed within the context of the common data architecture.

Conclusion

The future of data resource management depends on a change in direction from administering the data to managing data as critical resource of the organization. This new data resource management
direction emphasizes development of a high-quality, integrated, comparate data resource within a common data architecture that is stable across changing business needs and changing technology. It
emphasizes formal data resource management that focuses on the information aspect of information technology to support both the current and the future business information demand of an intelligent
learning organization.

The future of data resource management depends on development of an integrated comparate data resource within a common data architecture that improves data understanding and promotes data sharing.
It requires a self-perpetuating cycle where improved data resource quality increases data sharing, increased data sharing improves data resource quality, and so on. Organizations that change their
direction toward formal data resource management, establish a common data architecture, maintain robust data resource data, and manage data as a critical resource to support current and future
business information needs will be the organizations that survive. Those that don’t will fail to be fully successful due to information deprivation.

© Copyright 2001 – Michael H. Brackett

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About Michael Brackett

Michael Brackett has been in data management for over 50 years. During that time he has developed many innovative concepts, principles, and techniques for managing data. He has written ten books and numerous articles on data management. He is a prominent speaker at local, national, and international conferences and has become a legend in data resource management. He has been a member of DAMA International since 1985 and established the DAMA International Foundation in 2004. He received DAMA International's Lifetime Achievement Award in 2006 for his pioneering work in data resource management. He is semi-retired and lives in a log home that he built in the Olympic Mountains near Lilliwaup, Washington.

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