How Poor Quality Data Impacts Businesses

In cases where the analysis is done on reasons for project failures and a decision is made to invest resources into mitigating the factors that affected the project performance it is very important to know the reasons why the failures happened because it is important to justify the corrective measures. In case where inadequate data quality has affected the performance it becomes very important to know how it has affected the project performance.

In other words the impact of the investments in the tools used for improvements must fall in line with the business expectations of the data quality. The business expectations are present not only before the project actually takes place but also after the improvement paths have been adopted.

Categories of impacts

Poor data quality in practice has four different types of impacts on the business and they are financial, confidence, productivity and risk and compliance based impacts.

The description of these impacts is as follows:

  1. Financial impacts – Effects like increased operating costs, decreased revenues, missed opportunities, reduction or delays in cashflows or increase in fees and penalties show up in the results
  2. Confidence and satisfaction – The areas of impact include customer, employee or supplier satisfaction, reduction in trust in organization, low trust in forecasting, mis-match between operational and management reporting and delays and/or errors in decisions.
  3. Productivity impacts – These include increase in processing time, reduction in quality of goods manufactured, more inputs for the same output produced which in other words means increased workload and lower throughput.
  4. Risk and compliance impacts – The areas affected include credit risk, investment risk, competitive risk, fraud and leakage, compliance with government regulations, industry expectations or self-imposed policies such as privacy policies. It also includes capital investment and/or development.

These categories help to identify areas where the data quality has effects on the business and differentiate those which are very active in their effects as well as those that are benign in nature. In other words it helps in identifying those causes that have very serious business ramifications. The observations made by the organizations must also be verifiable by the external auditors. In most cases the motive is to maximize the value of information available for use by the organizations.

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