Business Intelligence vs. Predictive Analytics

Business intelligence or predictive analytics: both are available as application packages for analysing data. But what is the difference? While a Business Intelligence (BI) solution takes care of implementing the representation and exploration of the data, predictive analytics software makes a model of likely future events based on the analysis of the data.

The term BI has been on everyone's lips for some time now. The attractive aim is to visualise data on dashboards in such a way that they quickly and easily provide the information that is being searched for. This allows users to recognise simple patterns, trends or relationships as "intelligent". An important tool for managers and directors who have to make decisions based on such analyses.

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Predictive Analytics: The Glass Ball Business Intelligence

And this is where the function of predictive analytics really comes into play. Instead of "What happened?", the question is now "What could happen?". The analytical technique draws on the patterns that emerge in the data records and maps what will happen if these patterns remain or what could happen if these patterns change.

Benefits for companies

This possibility of analysing the future can be of real benefit to companies, as it makes customer behaviour, product and risk management, but also profit forecasts etc. visible and, above all, foreseeable. Predictive analytics software is often used as an extension to the BI in ERP system developed and offered. However, until now, the competent maintenance of the data and a certain know-how about the software is necessary to make the advantages of the application usable for the company. In the future, however, solutions will be simplified for non-specialist users.

However, even then, the advantages of predictive analytics software can only be used by companies that have a healthy data management system. Because only reliable data provide reliable results. The Completeness of the dataA sensible system to avoid redundancy and errors is necessary. Here a contribution on how to optimise your data master.

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