By Elliot King
But data quality improvement does not have to be high cost. Data quality rests on people and processes as well as technology and by focusing attention on the first two, companies can make significant progress in improving data quality inexpensively.
Perhaps the easiest first step is to make somebody responsible for data quality--appoint a data steward charged with monitoring data quality or at least trying to determine how data quality could be monitored. Depending on the size of the organization or the department within an organization, this does not have to be a fulltime job; nor does the person initially have to be an expert. In the beginning, data stewards can educate themselves about the quality issues.
The next easiest step is to have the data steward poll employees responsible for entering data about where mistakes happen. Front line personnel represent a deep repository of knowledge about which they are seldom asked. If a process or data screen is broken leading to data entry errors, they will know.
Then, companies should focus their efforts on safeguarding the quality of their most important data. Organizations do not have to do everything at once. Just knowing what is most important is a critical step forward.
These ideas are not completely cost-free but they certainly are not expensive. The key is starting somewhere, even if the first couple of steps are very small.
In many organizations, when one side of the house starts talking about improving data quality, the other side of the house starts hearing one thing and one thing only--costs. They assume that initiating a data quality program is going to be a heavy lift financially, requiring consultants, investments in technology, training and more. And even if those
responsible for containing costs agree that improving data quality is important and can have a real impact on the bottom line, they often wonder if the impact will be big enough to justify the investment. Investing in data quality is seen as a choice and there may be more effective ways to invest limited resources.But data quality improvement does not have to be high cost. Data quality rests on people and processes as well as technology and by focusing attention on the first two, companies can make significant progress in improving data quality inexpensively.
Perhaps the easiest first step is to make somebody responsible for data quality--appoint a data steward charged with monitoring data quality or at least trying to determine how data quality could be monitored. Depending on the size of the organization or the department within an organization, this does not have to be a fulltime job; nor does the person initially have to be an expert. In the beginning, data stewards can educate themselves about the quality issues.
The next easiest step is to have the data steward poll employees responsible for entering data about where mistakes happen. Front line personnel represent a deep repository of knowledge about which they are seldom asked. If a process or data screen is broken leading to data entry errors, they will know.
Then, companies should focus their efforts on safeguarding the quality of their most important data. Organizations do not have to do everything at once. Just knowing what is most important is a critical step forward.
These ideas are not completely cost-free but they certainly are not expensive. The key is starting somewhere, even if the first couple of steps are very small.
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