An unparalleled amount of data and information forms the basis – and at the same time, the product – of the digital transformation. Intelligently and efficiently using these data and information flows promises to be one of the most disruptive waves of transformation in recent history.
The purpose of predictive analytics is to support decision making in companies. Data is no longer seen as a by-product, but rather as a key competitive advantage for driving and tracking the company’s strategy. Modern methods and powerful software solutions enable statistical analyses of increasingly large and complex datasets. The focus is moving away from decades-old descriptive, diagnostic data analysis towards future-oriented action recommendations – also integrating external information sources. The question is no longer “Why and how did something happen?”, but rather “What is going to happen and what is our course of action?”.
Especially when it comes to planning and forecasting, regularly previewing and assessing the development of the business is critically important. Predictive analytics provides in the following scenarios, in particular:
- Reducing complexity: what are the main drivers and levers in our business and our company and how can they be quantified?
- Accuracy of planning: which methods, data and information is needed to improve annual planning and intra-year control?
- Scalability and automation: how can planning and forecasting be made more flexible and at the same time less resource-intensive?
- Objectivisation/debiasing: how can controlling counteract the subjective perceptions of management and promote objective information processing?
- Risk awareness: which factors have the greatest impact on profits and what materiality do they have?