1. Descriptive analyses
This process provides better insight into a company by dissecting it using historical data. Decision-makers can gain a holistic view of performance and trends upon which they can base their business strategy. These are basic analyses that can show, for example, the development of revenue and costs over a certain period. This form of analysis only displays the data as it comes from the data sources.
2. Diagnostic analyses
Diagnostic analysis is the process of using data to determine the causes of trends and correlations between variables. It can be seen as a logical next step after using descriptive analyses. It is meant to identify trends. For example, when selling prices increase and therefore revenue also increases, it may be interesting to analyze which part of the revenue increase is due to rising selling prices and which part is due to increasing/decreasing sales. It provides insight into the reason behind the revenue increase.
3. Predictive analyses
This is a form of analysis in which a prediction is made for the future using current and historical data. If a data quality issue arises, these analyses cannot be implemented. If errors are present in the data and a prediction is made on these errors (using regression algorithms), then the errors will be extrapolated. This creates errors on top of errors and the predictions become incorrect and inaccurate.
4. Prescriptive analyses
Prescriptive analysis is an analysis in which technology is used to help companies make better decisions by analyzing raw data. Prescriptive analyses take into account information about potential situations or scenarios, available resources, past performance, and current performance to recommend an action or strategy. This form of analysis is the opposite of descriptive analysis. Descriptive analysis uses historical data that has already occurred, while prescriptive analyses use historical data and predictive data to make the right decisions in the future.
