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Norbert Kytka, Headquarters PlattlingContact
Consider social media activities, shopping through digital touchpoints, the proliferating networks of end devices in the Internet of Things. One consequence of all these things is that the quantity of data generated in today’s world is simply vast. There are lots of Big Data application areas exploiting these behemoth datasets. They determine various factors based on past experiences and the current state. By contrast, predictive analytics – as its name suggests – is concerned with prediction and the likelihood of specific events or human behavior. A mathematical model is derived from the historical data, and then applied to current data to generate predictions.
The idea behind this has been in use for many years. For example, market researchers have long been making hefty use of it to predict e.g. customer behavior. However, in the past it was always necessary to collect data at huge costs. This has changed with Big Data, Machine Learning and the Internet of Things. Nowadays, it is simpler than ever to collect enormous volumes of data and use them to generate precise predictions. As the digital revolution rolls on, predictive analytics is taking on ever increasing importance.
One use of predictive analytics is to predict customer behavior. This allows us to answer questions such as “What products will be purchased in what quantities, and what is the maximum the customer will be willing to pay for them?”. The information can then be used to derive requirements and price plans. As customer services become more ubiquitous, it is easier to identify unsatisfied and thus potentially migrating customers. Migration countermeasures can then be put in place, e.g. personalized offers and price reductions.
Predictive analysis technology is also highly relevant to production. Predictive maintenance is about using historical machine and process data to determine optimal timing for machine maintenance, thereby avoiding maintenance downtimes or carrying out maintenance before it is needed. This saves on time and costs. In fact, predictive analytics has added value to offer every field where there is sufficient data available.
SAP offers one of the leading solutions for predictive analytics: SAP Predictive Analytics. The software can be used for rapid, easy development of the desired forecasting models. These can then be used to predict business results. With SAP, it is straightforward to integrate predictive analytics technology and automated analysis processes into your business processes and departments.