Companies are always looking at ways to improve the value and quality of their assets. Whether it is a capital reinvestment in hardware, upgrading disk storage systems, purchasing new database systems or applicationware, upgrading software packages, and particularly employee training, a large amount of money is invested in ensuring the high value of corporate assets. Yet, there is one corporate asset that is not only allowed to gradually degenerate but, as a rule, is almost completely ignored: information.
Walk into any CEO's office and ask, "How many customers does your company have?" and see how many can give the right answer. The focus on infrastructure (hardware, databases, delivery, etc.) instead of content (business-driven data modeling, information integration, business intelligence, information compliance, data quality) hampers the business side from being able to exploit an organization's information asset. The information asset is a huge source of hidden advantage, and the key to exposing this hidden advantage lies in properly managing and exploiting the value of data. This value of data can be viewed as having two opposing parts: a positive component that accounts for recognizing benefit and a negative component that accounts for increased costs or lost opportunities.
Making use of a business intelligence application to discover new cross-sale opportunities is an example of the positive value of data. On the other hand, having a 40 percent duplication rate in your customer database not only indicates an extra overhead cost for storage and maintenance, but also prevents the business side from properly understanding how to interact with your customer.
Unused or ignored data is just a liability; but when that data can be transformed into an information asset, there can be significant added value, which is supported by a number of aspects, including:
Validity, which refers to the "truth-value" of the data. Information that is not valid subtracts from the value of data, while valid data adds value. Within any business context, there may be a number of rules or assertions that drive our expectations of what we want our data to look like or what values the data may have. A success factor to information exploitation is working with business-side stakeholders who can articulate their expectations for data validity such that those expectations can be turned into a form that can be used to monitor and measure validity.
Timeliness, which refers to taking advantage of information within the right time frame. The value of a piece of data often decreases as time moves on. For example, a hot stock tip is of great value when it is first divulged; but as more people learn the tip and the information is integrated into the price of the stock, the value of that stock tip dissipates. The same can be said for any piece of knowledge that describes or predicts certain kinds of behavior. If the information is known with enough time, the more value that information holds.
Believability, which refers not to the validity of the data but rather the perception of its validity. When a system is designed and built in the absence of an information compliance policy, the resulting system may have credibility problems. Any time a businessperson makes use of information that is actually not correct, the potential for disaster looms. Even if significant improvements are made in the quality of the information as time goes on, the stigma of poor data quality can cripple an application's "popularity," which can lead to project failure.
Leverage, which refers to the merging of pieces of information to discover new information that could not have been derived independently from any of the pieces. For example, consider these pieces of information:
An individual recently bought a house at a particular address.
The current mortgage rate for a 30-year fixed loan is 7 percent.
The average price for a home in that neighborhood is $250,000.
The average yearly taxes for that area are $3,000.
The general rule of thumb is that a lender will allow a person's monthly mortgage and tax payment not to exceed 40 percent of his or her monthly gross income.
These are all valuable pieces of information; but if we assume that the individual had a down payment of 20 percent of the price of the house, then the borrowed amount would be $200,000. The monthly mortgage payment would be $1,330 and the monthly tax would be $250, for a total monthly payment of $1,580. Because this cannot exceed 40 percent of monthly income, the purchaser's maximum monthly income is not less than $3,950. By combining all these pieces, we can infer the purchaser's yearly income of not less than $47,400 a piece of information we did not know before!
The hidden advantage of the information asset can be exposed rapidly if we take into account the aspects of information value. This month we started looking at some of the factors that add or subtract value from information. In the coming months, we will look at how to incorporate these factors into our system design.
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