Seven Deadly Misconceptions about Information Quality
This is the second of a three-part series that describes fatal misconceptions about information quality. Falling victim to one or more of these will jeopardize an organization's information quality initiative. This month I will analyze the misconceptions 4 and 5.
Misconception 4: Information quality is data accuracy and its counterpoint information quality is "fitness for purpose."
Isn't it somewhat of a heresy to say that it is a misconception to equate data accuracy and information quality? After all, isn't one of the goals of information quality to improve the accuracy of information? The answer to that question is yes. But it is wrong to think that just because data is accurate it has quality. Quality exists only when a product is being used. Data in a database that is 100 percent accurate is not quality if it is not used to accomplish the work and mission of the enterprise.
Likewise, it is a misconception to equate information quality with fitness for purpose. Data is not a resource that supports only one process or purpose. A notable example is the insurance company that discovered 80 percent of its claims were paid for a diagnosis of "broken leg." No, they were not in a rough neighborhood. The claims processors were paid for how many claims they processed. So they let the system default to "broken leg" for the diagnosis code. The process to pay a claim did not require that the diagnosis code be correct only that it be a valid code. This data actually had "validity" the values were valid values. The data conformed to at least one of the business rules. The quality problem did not surface until the data was loaded into a data warehouse for the actuary to analyze risk. The data in this example was, in fact, "fit for a purpose" to pay a claim but it was totally worthless for the risk analysis process.
So what is information quality? Information quality is fitness for all purposes in the enterprise processes that require it. It is the phenomenon of fit for "my" purpose that is the curse of every enterprise-wide data warehouse project and every data conversion project.
The real tragedy of one business area creating data to meet only its needs, however, is that the data cannot be used by processes outside their business area. It forces downstream knowledge workers to have to re-acquire the same data or engage in information scrap and rework to clean up and correct the data problems caused by the original source processes.
Accuracy is only one characteristic of quality, just as validity and conformance to business rules are also characteristics of quality. These are some of inherent characteristics of information quality.
Fitness for purpose is the characteristic of usefulness of data for a specific requirement. Usefulness, timeliness and presentation clarity are characteristics of what is classified as pragmatic quality. Those are the characteristics that enable knowledge workers to do their jobs efficiently and effectively.
The truth is that data requires both accuracy and fitness for all purposes along with other required characteristics expected by its information customers to be considered quality information. This is data that "consistently meets knowledge workers and end-customers expectations."
Misconception 5: Information quality problems are caused by information producers and its corollary information quality is produced by an information quality group.
These related misconceptions misunderstand the root causes and solutions for information quality problems. The belief that information quality problems are caused by the people who create the data is based on a need to find "blame." If information quality problems exist, the processes are broken not the people who perform the processes. In the example of the "broken leg" problem cited earlier, the claims processors were doing exactly what they were trained and rewarded for paying claims as fast as possible. They were merely doing what they understood to be their job.
Information quality means analyzing the symptoms (the defective data) and the processes that produced the defective data to discover what caused the defects. Root cause analysis gets behind the immediate cause to discover the originating cause. Causes may be inadequate training, lack of understanding of downstream knowledge workers and the uses made of the data, or unclear or incomplete process procedures. In many cases, the cause is confusing "productivity" with speed of work and measuring the wrong things. In the "broken leg" problem, management had created incentives for how many claims a processor handled per day without considering the impact of nonquality information on any downstream processes. The real diagnosis for this was "broken process!"
Information quality improvement requires a blame-free and non-judgmental environment. "Fault finding" only creates fear and stymies creative change. It leads people to cover up "problems" and not be open to exploring process improvements.
Likewise, it is a misconception to think that the information quality team is the "savior" and "solver" of information quality problems. Information quality problems are the result of broken processes from the top of the enterprise to the bottom and from the front line processes to the shop floor processes to the back office processes. One information quality team or organization cannot physically "manage" all information quality. Rather, everyone in the enterprise must take responsibility for their processes and their information products.
Information quality cannot be delegated. Each and every person in the enterprise must assume accountability for their role with information whether they produce it, transcribe it or use it, design and define it, or build applications to capture or retrieve and present it.
So, what is the role of the information quality organization? First, it must sensitize the enterprise to the problems caused by nonquality information. Then it must define processes for measuring information product specification (data definition and information architecture) and data (content) quality. It must define processes for improving information product quality (cleansing and reengineering) and for information process quality. The information quality team must provide education and facilitation in the improvement of processes where information quality does not meet acceptable quality standards. Information producers generally know the problems with their processes. Give them a process to improve, empower them and provide a little facilitation. Then measure the cost savings and increased customer satisfaction that result from improved information quality.
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