What is Predictive Analytics? – A data-driven glimpse into the future
Predicting the future from data! That sounds like science fiction, but it is becoming more and more a reality. Thanks to predictive analytics. Here we explain what this is and what data science and data-based forecasting models can do for companies.
Most companies today are able to store large amounts of data across platforms and pools. However, the real challenge remains to derive business-relevant findings and decisions from bulging databases. This is often not possible without predictive analytics.
Why Predictive Analytics? Crystal ball was yesterday
Predictive analytics originates from data mining and comprises an analysis method based on data and statistics. Its central task is to identify trends, developments and problems of the future on the basis of data patterns. Thus its analysis techniques go beyond pure data mining methods such as clustering, which segments and classifies data. Artificial intelligence technologies – especially machine learning – are used to help decision-makers finding answers to the central question: “What does the future hold?”
How does Predictive Analytics work? Application and workflow
A forward-looking analysis always begins with a concrete starting point and a defined goal: For example, data should be used to reduce a company’s maintenance costs. In the first step, the existing data from different sources is collected, missing data is added and outliers are removed. Followed by developing, testing and finally applying a prediction model based on the collected data.
What methods of Predictive Analytics are there?
Description, diagnosis, prognosis and recommendation
The IT market researcher Gartner (https://www.gartner.com/en) distinguishes four basic methods at the beginning of each data analysis. The different analytical methods are partly based on each other and cannot always be clearly separated from each other:
Descriptive Analytics is concerned with the past and serves primarily to understand relationships between customers and products. This form of analysis is used for the subsequent observation of processes. For regional market analyses, for example, different data categories (here: product, price, sales region) can be related to each other to find intersections, such as all products sold in a region for a certain price.
Diagnostic Analytics clarifies causes, effects and interactions of conditions. It is strongly based on Descriptive Analytics. With the crucial difference that it tries to find reasons for the results achieved. Foremost the question of “why”.
Predictive Analytics deals with the future and allows the probability of a future event occurring to be calculated. To this end, historical and current transaction data is used and compared using algorithms to identify relationships and data patterns and transfer them into the future.
Prescriptive Analytics is based on predictive analytics, but goes one step further. It explains why a future event will occur and makes recommendations on how to respond to it.
What happens next with Predictive Analytics?
First conclusion: decision-makers’ benefits increase
However, the biggest challenge of predictive analytics is to transfer the results successfully into your own business. According to BARC (https://barc.de/advanced-predictive-analytics-survey), the greatest obstacles to projects are the insufficient agility of the infrastructure and the often lack of resources in the IT departments. It is advisable for companies to proceed predictively here as well – in other words, to create the appropriate basis with wise foresight and professional support.