A data gap analysis determines existing gaps in any one of a number of metrics that denote how a business is performing in a specific area. This type of analysis is often conducted for the purpose of not only reviewing gaps, but also removing them through improving data collection. Data gaps, also referred to as perception gaps, may span any area of business performance in production or in services provided to clients. In a data gap analysis, managers or consultants seek to improve current performance by closing gaps in how data is collected. Determining what gaps to measure is often challenging, as business metrics are generally intertwined and interrelated.
Statistical and performance data covering a wide range of commercial activities is often collected by managers. This data may be used to quantify business performance in a particular area or areas. Managers use the information from a data gap analysis to make changes in production or provision of services to achieve greater efficiencies.
The main focus in data gap analysis is on devising procedures to capture data in a particular area going forward, not on reviewing historical data. Essentially, the operating principle generally at work is that what has not been measured may be the ideal place to trim waste and increase productivity. Until a data gap analysis occurs, the actual level of efficiency remains unknown.
Gaps in an organization's data collection reduce the feedback managers would typically use to measure performance in a particular area. For example, managers may want to know how many customers return with a complaint about a particular product within a certain window of time. If no one has been tracking this data, the business may not know the actual level of customer satisfaction. In addition, problems with a particular product may be more numerous than a business realizes because that data is not being reported to those in a position to address the reason for the deficiency.
Reviews of the collected data are typically conducted to find the gaps — the areas where data is lacking. The next step is usually to determine what metrics should be captured in order to close those gaps. This step often involves asking exploratory questions, then taking the answers and instituting a series of actions to capture that data. The process of discovering a gap in data may be challenging, as it is often hard for people to imagine which questions are not being asked. This is why most data gap analysis begins by first determining what predictive capabilities should ideally be introduced into a particular operation.
