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Understanding Data Resource Management

Each organization has its own perception of the business world, and has the right to that perception and to build a data resource according to that perception.

The development of an organization’s data resource begins with how the organization perceives the business world according to its business goals, and how it choose to operate in that business world to achieve those goals That perception creates a business information demand that must be met for the organization to operate successfully in the business world. Failure to meet both the current and the future business information demand leads to a less than fully successful business. 

Each organization has its own perception of the business world, and has the right to that perception and to build a data resource according to that perception. Most organizations belong to a major subject area, such as health care, criminal justice, education, and so on, and need to recognize certain conventions within that subject area. An organization does not have to follow those conventions for developing its data resource, but it does need to translate their data according to those conventions for exchanging data between organizations. In other words, sharing common data does not mean having a common data resource. 

The organization’s perception can change in three ways. First, the business world changes which causes changes in an organization’s perception. Second, the organization adjusts it’s lines of business, adds new lines of business, or discards lines of business which causes changes in its perception. Third, and most important, the act of observing and documenting an organization’s perception changes both that organization and its perception. Collectively, these changes result in ongoing changes to the data resource that supports the organization. 

The business information demand is based on the organization’s perception and identifies the data needed to support the organization’s business activities. That business information demand starts the development of formal data schemas that lead to implementation, use, and improvement of the organization’s data resource. 

The strategic data schemas provide a generalized 30,000 foot view of the data for executives. The tactical data schemas provide a more specialized 10,000 foot view of the data for managers which is more detailed than the strategic data schemas. Collectively, these two data schema provide an overview of the data needed to support the business information demand. 

The business data schemas provide a detailed ground level view of the data for knowledge workers that are performing the business activities. They include all the documents, reports, screens, and so on, that the organization uses to conduct its business activities or to evaluate its business activities. 

The logical data schemas are developed from the tactical data schemas and the business data schemas, as modified by formal data normalization and formal data optimization. Data normalization assures that the data are properly structured and grouped according to how the organization perceives the business world. Data optimization assures that the data are not unnecessarily fragmented beyond that proper structuring and grouping. 

The physical data schemas are developed from the logical data schema according to formal data optimization and data denormalization techniques. Data deoptimization represents the distribution of the logical data schemas to different physical operating environments. Data denormalization represents the adjustment of the logical data schemas to the physical operating environment for optimum performance without compromising the logical data schemas. The physical data schemas are used to design and implement the physical data resource 

The formal sequence from the strategic and tactical data schemas, to the business data schemas, to the logical data schemas, to the physical data schemas, to implementation can be pre-empted in three ways. 

First, development might begin with the logical data schemas, shown in italics on the upper right of the diagram. Beginning with the logical data schemas eliminates the benefits of the organization’s perception of the business world, the strategic and tactical data schemas, and the business data schemas. 

The use of predefined data models to develop the logical data schemas imposes a predefined perception on the organization that may not match its perception of the business world. Developers of the predefined data models cannot impose their perception of the business world on an organization. Also, the predefined data models are often incomplete, having only a data structure, and seldom having formal data names, comprehensive data definitions, or precise data integrity rules. 

Similarly, data modelers cannot impose their perception of the business world on an organization, no matter how experienced they might be. They must seek to uncover and understand how the organization perceives the business world, and then develop the appropriate data models to portray that perception. 

Second, development might begin with brute-force development of the physical data schemas, shown in italics on the right side of the diagram. Beginning with the physical data schemas eliminates all the benefits of the logical data schemas and its predecessors, and severely compromises the ability of the data resource to support the business information demand. 

Third, development might begin with brute-force implementation of the physical databases, shown in italics on the bottom of the diagram. Brute-force physical implementation is absolutely the worst place to begin. It eliminates all the benefits of the organization’s perception, development of the logical data schemas, and development of the physical data schemas. It devastates the ability of the data resource to support the business information demand. 

After the data resource has been developed, it is used by a wide variety of applications to meet the business information demand. Although the actual use of the data resource appears small with only one symbol on a diagram, use of the data resource is by far the largest portion of the entire Data Resource Development Cycle. The use is where all of the operational, evolutional, and predictive processing occurs. 

Use of the data resource is limited only by people’s imagination. That imagination is maximized with the formal Data Resource Development Cycle that creates and maintains a data resource that is thoroughly understood. When that understanding is not readily available, the imagination is limited and the organization’s business activities suffer. 

Use of the data resource includes drawing existing data from the data resource and storing new data in the data resource. Those new data must be formally documented to ensure that they are as readily understood as the existing data. Otherwise, the data resource slowly deteriorates, becomes disparate, and does not fully support the organization’s business activities. 

The development and maintenance of the data resource is data resource management and use of the data resource to produce information for the business activities is information management. Data resource management produces and maintains a data resource that supports the business information demand. Information management produces information from that data resource to meet the business information demand. The quality of the information produces can be no higher than the quality of the data resource, and may be lower depending on the quality of the information management process. 

The Data Resource Data, shown in the center of the diagram, play a critical role in understanding and documenting the organization’s data resource. They are the heart of the Data Resource Development Cycle and contain the understanding necessary to fully utilize the data resource. They are the critical piece of the Data Resource Development Cycle. 

The traditional approach to integrating disparate data is performing data integration as the data are extracted from the data resource for use by applications, shown by the underlined Data Integration. However those integrated data are often stored for later use, without proper documentation, which further increases the data resource disparity. Even if those integrated data are not stored, the data integration process and the meaning of those integrated data are seldom documented, which puts the quality of the information produced in question. 

The preferred approach is to formally integrate the entire disparate data resource one time, shown by the underlined Data Resource Integration. The resulting comparted data resource is formally documented and the need for multiple undocumented traditional data integrations is eliminated. The result is a higher quality data resource that fully supports the business information demand. 

What has become clear over the years is that when the Data Resource Development Cycle begins with the organization’s perception of the business world and goes through the entire cycle, the data resource has high quality and low disparity, as shown by the bold italics in the upper center of the diagram. When the process begins later in the Data Resource Development Cycle, such with the logical data schemas, the physical data schemas, or direct implementation, the data resource has lower quality and higher disparity, as shown by the bold italics on the lower left of the diagram. 

Data management professionals must follow a formal data resource development sequence if they ever hope to develop a high quality data resource that supports the organization’s business activities. 

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