In digitalized processes, large volumes of data can be analyzed using business intelligence methods. Exponentially increasing available data and exponentially increasing computing power make evaluations possible that were not possible before. A necessary prerequisite is both high data quality and meaningful data extraction, transformation and loading of data sets into a BI application (ETL process). Out-of-date data, data duplicates and incomplete data sets prevent “Big Data” from being turned into “Smart Data” through business intelligence.
This makes sense in processes in which large amounts of data are generated and correlations are to be recognized in relation to very specific issues that are not obvious without the use of analysis procedures. For this purpose, various pattern recognition methods are available that detect correlations and coherences. Here it can sometimes come to unexpected findings that contradict intuition. Such applications, which expand the imagination and/or decision-making ability of their users, belong to the category of “augmented intelligence”.
For example, you can use BI techniques to analyze your customers’ behavior and preferences and predict their future behavior. You can also infer from the behavior of your customers the behavior and preferences of potential customers who are not yet buying. This enables you to better plan your processes and market your services in a more targeted manner. Identifying contract terminators at an early stage by recognizing their typical behavior before they decide to cancel and reacting to it proactively is also a typical BI task. Trend forecasts can also be created with BI methods. Data mining methods are particularly suitable for such analyses.
BI algorithms are “trained” with sample data files in order to assess or group data in the desired sense in the future. In this way, patterns can be recognized in existing, unstructured data sets and decisions based on the insights gained can also be suggested (machine learning). Even natural language can be processed (natural language processing). But new problems cannot be solved with BI. Machine learning with BI, however, is limited to applied statistics. If you want to let machines solve complex or even novel problems, you cannot avoid artificial intelligence applications. Meanwhile, BI applications are embedded in standard software applications, for example in MS Excel. Microsoft has been talking about “BI for everyone” since the 2000s. Oracle has also been integrating embedded BI into their database systems for many years.
At the latest with the use of BI, compliance with the General Data Protection Regulation (DSGVO) becomes relevant. Set up your BI systems in such a way that data collection, processing, evaluation and results preparation preserve personal data rights. DSGVO-experienced lawyers can support you in this process.