Return on Investment or Return on Data? Why Data Analytics Pays Off
Companies are pouring money into dashboards, data platforms, and AI models. But only those who can prove real financial impact secure long-term budget and support for their data analytics projects. That’s why one question needs to be addressed upfront: the ROI. Many ask themselves: How do I actually calculate it? Which KPIs matter? And when does data analytics really pay off? We sat down with Dr.-Ing. Michael Haub, data scientist and engineer at Device Insight, to talk about how ROI in data analytics works in practice.
Guest author
2025? 10? 8?
Technology
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Key takeaways at a glance:
When looking at the bigger picture, data analytics is less about ROI – and more about a true “Return on Data.”
Once deployed on the shopfloor, a positive ROI from data analytics is typically achievable within 12 to 24 months.
Between 15 and 30 percent scrap reduction and ROI in under 12 months – a real-world outcome from a data analytics project owned by Device Insight.
Technology should never be the starting point. Companies need to begin with a clear business problem and their most pressing pain points.
Facts build trust: every piece of value created through data analytics should be made visible.
What does ROI mean in the context of data analytics?
Michael Haub: “The ROI of data analytics is the measurable value created when data models and analytics are applied in business processes – minus the necessary investments in technology, talent, and implementation. In plain terms: it’s about saving costs. To get there, you need to bring transparency into processes that were previously guided by gut feeling or spot checks.
Dr. Ing. Michael Haub, Senior Data Science Consultant
To make the value measurable, goals must be clearly articulated from the start of a project, along with the KPIs that will be influenced. Unlike investments in physical assets that typically require long amortization periods, the ROI of data-driven improvements can often be realized faster and with greater flexibility.”
How is data analytics ROI different from more traditional investments, like a new machine or vehicle?
Michael Haub: “With a machine, ROI is usually straightforward: you invest a certain amount, it produces a predictable number of units, and the return can be calculated from the sales margin. Data analytics works differently. The value emerges not from physical output but from improved information that drives smarter decisions.
Another fundamental difference is scalability. Once developed, data models can often be applied to additional processes or departments with relatively little extra effort. Viewed over the lifecycle of a product, the total benefit of all data-driven decisions can far exceed the initial scope. In fact, you could speak of a ‘Return on Data’ – a concept that goes beyond financial ROI to include scalability and the reusability of data models for entirely new use cases.”
If you weigh the full impact of all data-driven decisions against a product’s lifecycle costs, data analytics is really about a 'Return on Data'.
Which KPIs matter for measuring ROI in data analytics?
Michael Haub: “In the short term, ROI in Data Analytics often reveals itself through very practical efficiency gains – and ultimately through cost savings. The key indicators here are familiar: less scrap on the shopfloor, higher machine availability, reduced downtime, and lower energy consumption. These metrics can usually be tracked quickly and precisely, making them excellent early markers of whether a project is on the right track.
Over the long term, the picture broadens. Incremental improvements accumulate into greater competitiveness, whether through higher degrees of automation, greater process transparency, or the company’s overall digital maturity. In that sense, ROI is not just about immediate savings but about building sustainable advantages – strengthening resilience, agility, and the ability to respond effectively to new challenges.”
Read the full blog post by our IoT specialist Device Insight to find out how long it usually takes for investments in data analytics to pay off and what options there are to speed up this process:
Alexandra Luchtai writes regularly about digital and data-driven innovation – covering topics around Data, Analytics & AI, Smart Products, and Smart Factory solutions. Her articles highlight the latest projects and insights across industries from Device Insight, the digitalization specialist within the KUKA Group.
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