From reporting to predictive analytics

Data creates transparency. But while reporting only makes past developments visible, competitive advantages emerge when data is actively used for decision-making, forecasting, and automation. This article shows how companies can successfully move from reporting to predictive analytics. 

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We often observe that even when data is available and usable, companies still tend to rely primarily on retrospective analysis rather than using it for operational steering and AI-driven, forward-looking decision-making. Dashboards, KPI systems, and automated reports may visualize trends and improve access to information, but they do not show what will become relevant next.

In practice, this also leads to different interpretations of the same data. For example, when sales, operations, and finance work with different data states and forecasts need to be consolidated manually, decisions are delayed not because data is missing, but because there is no shared, end-to-end data foundation. The solution therefore lies in a structured, integrated approach to data across system and process boundaries.

Companies that successfully use data for steering, planning, and automation typically develop their data capabilities in several stages:

Descriptive Analytics – What happened?

Data is visualized and analyzed, for example through dashboards, reports, or KPI systems. Companies gain transparency into past developments and current performance indicators. A retail company, for instance, can use dashboards to identify which products sold particularly well over the past weeks or which locations are experiencing declining sales.

Diagnostic Analytics – Why did it happen?

Relationships and root causes are analyzed, and patterns, anomalies, and influencing factors within processes and data are identified. An insurance company might analyze why processing times for certain claims are significantly higher than average. By linking process and case data, patterns can be uncovered – such as specific case types, process breaks, or missing information that cause delays.

Predictive Analytics – What is likely to happen?

Historical and current data are used to forecast future developments. A manufacturing company, for example, can plan maintenance windows early before machines fail. In retail, demand trends can be predicted more accurately to better manage inventory and supply chains.

AI & intelligent automation – What action makes sense?

Systems actively support decision-making, automate processes, and respond to defined events or predictions in real time. For example, a logistics company can detect delivery delays early and automatically suggest alternative routes or time slots. At the same time, affected customers are informed and internal processes are adjusted before operational issues occur.

In the final two stages in particular, a noticeable impact emerges in day-to-day operations: faster decisions, improved planning reliability, and processes that can be actively controlled and scaled.