We usually cannot scope the extent of data quality problems without an assessment project. This is needed to really nail the impact on the organization and identify the areas of potential return. The numbers showing the potential savings are not lying around in a convenient account. They have to be dug out through a concerted effort involving several organizational entities. Some areas in which costs are created and opportunities lost through poor data quality are
transaction rework costs
costs incurred in implementing new systems
delays in delivering data to decision makers
lost customers through poor service
lost production through supply chain problems
Examples of some of these, discussed in the sections that follow, will demonstrate the power of data quality problems to eat away at the financial health of an organization.
Many organizations have entire departments that handle customer complaints on mishandled orders and shipments. When the wrong items are shipped and then returned, a specific, measurable cost occurs. There are many data errors that can occur in this area: wrong part numbers, wrong amounts, and incorrect shipping addresses, to name a few. Poorly designed order entry procedures and screens are generally the cause of this problem.
One of the major problems in implementing data warehouses, consolidating databases, migrating to new systems, and integrating multiple systems is the presence of data errors and issues that block successful implementation. Issues with the quality of data can, and more than half the time do, increase the time and cost to implement data reuse projects by staggering amounts.
A recent report published by the Standish Group shows that 37% of such projects get cancelled, with another 50% completed but with at least a 20% cost and time overrun and often with incomplete or unsatisfactory results. This means that only 13% of projects are completed within a reasonable time and cost of their plans with acceptable outcomes. This is a terrible track record for implementing major projects. Failures are not isolated to a small group of companies or to specific industries. This poor record is found in almost all companies.
Many times you see organizations running reports at the end of time periods and then reworking the results based on their knowledge of wrong or suspicious values. When the data sources are plagued by quality problems, it generally requires manual massaging of information before it can be released for decision-making consumption. The wasted time of people doing this rework can be measured. The poor quality of decisions made cannot be measured. If it takes effort to clean up data before use, you can never be sure if the data is entirely correct after cleanup.
This is another category that can easily be spotted. Customers that are being lost because they consistently get orders shipped incorrectly, get their invoices wrong, get their payments entered incorrectly, or other aspects of poor service represent a large cost to the corporation.
Whenever the supply chain system delivers the wrong parts or the wrong quantity of parts to the production line, there is either a stoppage of work or an oversupply that needs to be stored somewhere. In either case, money is lost to the company.
The general nature of all of these examples is that data quality issues have caused people to spend time and energy dealing with the problems associated with them. The cost in people and time can be considerable. However, over time corrective processes have become routine, and everyone has come to accept this as a normal cost of business. It is generally not visible to higher levels of management and not called out on accounting reports. As a result, an assessment team should be able to identify a great deal of cost in a short period of time.