Analytics (data analysis) is crucial to all businesses today to gain meaningful insights into product and service quality, business profitability, and to measure value contributed. But data processes need to be examined regarding how data is collected, analyzed, and reported to determine and gain its current and potential value. Attention to data and its processes is crucial to insuring data is an asset, not a limitation. Begin by examining these seven ways data can hurt or help.
1. Data silos
Data silos are common in Workers’ Compensation. Individual data sets are used within organizations and by their vendors to document claim activity. Without interoperability (the ability of a system to work with other systems without special effort on the part of the user) or data integration, the silos naturally fragment the data, making it difficult to gain full understanding of the claim and its multiple issues. A comprehensive view of a claim includes all its associated data.
Manual data entry is tedious work and often results in skipped data fields and erroneous content. When users are unsure of what should be entered into a data field, they might make up the input or simply skip the task. Management has a responsibility to hold data entry people accountable for what they add to the system. It matters.
Errors and omissions can also occur when data is extracted by an OCR methodology. Optical Character Recognition is the recognition of printed or written text characters by a computer. Interpretation should be reviewed regularly for accuracy and to be sure the entire scope of content is being retrieved and added to the data set. Changing business needs may result in new data requirements.
People in the business units often have difficulty describing to IT what they need or want. When IT says the request will be difficult or time-consuming, the best response is to persist. It’s their job and they will usually protect it by exclaiming its complexity.