The Evolution of the End State Architecture

The story of business value across the end-state architecture begins with an understanding of the evolution of the architecture across the history of the organization. The following describes the typical evolution of the architecture. (NOTE: the depiction is for a hypothetical “typical” corporation. There is absolutely nothing to say that the evolution of the end-state architecture will be different in some organizations.)

The typical evolution is shown in Fig. 16.1.1.

The first part of the end-state architecture to be built is the applications. After the applications have matured and the siloed effect of the applications is noticed, a corporate data warehouse is built. The corporate data warehouse integrates the application data and provides a place for historical data to gather. After the data warehouse is built, the different data marts begin to spring up.

Data start to accumulate in the data warehouse. Then after a while, the data lake/big data environment is built. The data lake/big data environment is also used to collect data from external and extraneous sources.

Finally, textual data are gathered from raw text and are incorporated into the infrastructure.

In such a fashion, the corporation gathers data and organizes it according to the end-state architecture.

What is Meant by “Business Value”

In order to understand how the end-state architecture relates to business value, it is worthwhile to examine what is meant by “having business value.”

There are actually many interpretations of having business value. The interpretation used here is a classic interpretation of business value. Enhancing business value means the following:

• Growing the revenue of the corporation
• Growing the customer base of the organization
• Increasing the profitability of the corporation
• Increasing the product and packaging mix of the corporation

All of these factors lead to the enhancement of the business health of the corporation.

Fig. 16.1.2 shows this interpretation of business value.

Tactical Business Value/Strategic Business Value

In general, there are two approaches to address business value. Business value can be addressed at the tactical level, and business value can be addressed at the strategic level. Successful businesses address business value at both of these levels.

As an example of the tactical level of business value, the business considers the activity of the prospect/customer at the point of purchase. Typical tactical considerations are such things as follows:

• Can the purchase be made conveniently?
• Can the purchase be made quickly?
• Is the process of making a purchase simple?
• What does the customer need in order to make the actual selection?

These are but a few of the tactical considerations.

Strategic decisions are far-reaching, long-term decisions.

Typical of strategic decisions are as follows:

• Should the corporation open up business in a new territory?
• Should the corporation acquire another company?
• Should the corporation start to develop a new product line?

These are the kinds of decisions that are typical of strategic thinking.

Fig. 16.1.3 depicts the difference between tactical and strategic decision-making.

Volume of Data Versus Business Value

There is a relevant interesting graph that depicts something very unintuitive. The graph shows that as the volume of data increases, the business value of data across the corporation decreases. Stated differently, when the corporation is first gathering data and using the computer, the business value achieved is quite high. But as time passes and the volume of data in the corporation grows, the business value of the data in the corporation decreases.

Fig. 16.1.4 shows this relationship.

The “Million in One” Syndrome

There are several reasons for the phenomenon shown in Fig. 16.1.4. The first of these reasons can be called the “million in one” syndrome.

Consider the diagram shown in Fig. 16.1.5.

Fig. 16.1.5 shows that there is a lot of data. And only one occurrence out of all the data is of interest. That one unit of data is shown in a different color. The red unit becomes lost in all the other data.

In order to illustrate the million in one syndrome, consider this. A record is made of every phone call in the United States every day. In a day's time, there will be hundreds of millions of phone calls for which a record is made. Every time a dial tone is made, a new record is recorded.

Now, suppose an analyst is looking for phone calls made by a terrorist. In a day's time out of hundreds of millions of records, the analyst may find two or three calls. The probability of a phone call being of interest approximates 1/100,000,000. The odds are infinitesimal that any phone call may have a legitimate business interest. And finding that one or two phone calls out of all the phone calls is a really complex and expensive thing to do.

There is a tremendous amount of data that is stored in order to find a paucity of business value

That is one reason for the equations shown in Fig. 16.1.4.

Where Business Value Occurs

But there is another reason for the relationship shown in Fig. 16.1.4. That reason is that the preponderance of business value occurs where there is the least data. Fig. 16.1.6 shows this phenomenon.

In Fig. 16.1.6, it is seen that different environments have different amounts of business value. The scale goes from dark to light, where the darker the color, the greater the business value.

The darkest color is in the application environment. There is a great deal of business value there. There also is a great deal of business value in text. There is still a lot of business value in the data warehouse and in the data marts. And there is only a scant amount of business value in the big data environment.

Yet proportionately, there are much more data in the big data/data lake environment. This is another reason for the relationship shown in Fig. 16.1.4.

Data Relevancy Over Time

There is yet another reason for the relationship seen in Fig. 16.1.4. That reason is that as data age, over time, the data lose its relevancy.

Fig. 16.1.7 shows this phenomenon.

In Fig. 16.1.7, it is seen that as data age, the data lose relevancy. Stated differently, the fresher the data are, the greater the chance that the data are relevant to today's world.

Over time, business conditions, technical conditions, market conditions, product conditions, and government conditions all change so much that looking at data from an earlier day and age is simply meaningless. The data grow so old that any conclusions are simply not relevant to today's world.

Yet, that older data are held within the system—usually in the data lake environment.

There are then a whole host of reasons for the truth shown in the Fig. 16.1.4.

Where Tactical Decisions Are Made

When considering business value, it is worthwhile noting that there is a symbiotic relationship between tactical decisions and strategic decisions. Fig. 16.1.8 depicts this relationship.

Fig. 16.1.8 shows that—for the most part—tactical activities and transactions occur in the application environment. And strategic activities and decisions occur in the data warehouse and the data mart environment. A cycle of processing occurs between the two.

Transactions are run in the application environment. Data are produced as a by-product of those transactions. The data find its way into the data warehouse. The data in the data warehouse and the data mart are studied, and new decisions are made. The new decisions have an impact on the transactions that are run. In turn, a new set of transactions are run, and their data are stored in the data warehouse.

This cycle of data has a profound impact on the business of the corporation.