By Elliot King
The root causes of data quality problems are deeply intertwined with daily business operations coupled with the simple truth that businesses are not static. Things change. Time decay is perhaps the most obvious root cause of data quality issues. People move; they die; they get divorced; they no longer have a need for your specific product, and so on. Updating data records to reflect those changes in a timely fashion is difficult since you may not even be aware that the change has occurred. Over time, data that was once right becomes wrong.
Time decay is a serious ongoing problem. But more dramatic quality issues often emerge when organizations grow, change the application infrastructure, or must respond to external demands. Too frequently, for example, when a company purchases another company, there is not enough time to fully merge the information infrastructure. Instead, the IT organization looks for a workaround or stopgap measure to insure the consolidation does not impede ongoing operations. These workarounds bring with them both known and unknown risks to data quality.
A workaround is often the solution of choice in a variety of other situations as well. A company may choose to eliminate certain applications or face a new regulatory requirement. Branch office or remote operation personnel may feel that the central IT staff cannot respond to their needs fast enough. In response to the pressure to "keep things online," an organization may opt for a workaround.
A third root cause of data quality issues is that the company simply has not established a sufficient data quality program. Since every piece of data cannot be verified, the data quality system itself may be flawed. Alternatively, data entry screens may be poorly designed and generate too many errors. And too few companies have standard data dictionaries.
As long as business is a fluid process, data quality issues will always arise. The key is to recognize their source and have programs in place to minimize the number of problems and reduce their impact on business.
Safeguarding data quality is a continual and ongoing process. Sadly, no matter how diligent companies are, data quality problems will be created. But why is that?
The root causes of data quality problems are deeply intertwined with daily business operations coupled with the simple truth that businesses are not static. Things change. Time decay is perhaps the most obvious root cause of data quality issues. People move; they die; they get divorced; they no longer have a need for your specific product, and so on. Updating data records to reflect those changes in a timely fashion is difficult since you may not even be aware that the change has occurred. Over time, data that was once right becomes wrong.
Time decay is a serious ongoing problem. But more dramatic quality issues often emerge when organizations grow, change the application infrastructure, or must respond to external demands. Too frequently, for example, when a company purchases another company, there is not enough time to fully merge the information infrastructure. Instead, the IT organization looks for a workaround or stopgap measure to insure the consolidation does not impede ongoing operations. These workarounds bring with them both known and unknown risks to data quality.
A workaround is often the solution of choice in a variety of other situations as well. A company may choose to eliminate certain applications or face a new regulatory requirement. Branch office or remote operation personnel may feel that the central IT staff cannot respond to their needs fast enough. In response to the pressure to "keep things online," an organization may opt for a workaround.
A third root cause of data quality issues is that the company simply has not established a sufficient data quality program. Since every piece of data cannot be verified, the data quality system itself may be flawed. Alternatively, data entry screens may be poorly designed and generate too many errors. And too few companies have standard data dictionaries.
As long as business is a fluid process, data quality issues will always arise. The key is to recognize their source and have programs in place to minimize the number of problems and reduce their impact on business.
Leave a comment