Unlocking Real Business Value from Data: Why We Invested in Mindfuel

by Leopold Lerach

Data is the new oil, the new gold, the new sand, and many more things. For many companies, however, data seems to be foremost one thing: hard to get value out of.

Many smart people believe strongly that data, and data-driven companies, will have a bright future. For example, PWC estimates that until 2030 the total economic impact of AI will be a whopping $15.7 trillion and McKinsey predicts productivity gains of up to $4.4 trillion through gen AI alone. Unsurprisingly, companies across the world are investing heavily into data and AI capabilities, making it a top strategic priority. At the same time, failure rates of data projects are astonishingly high with estimates ranging up to 80%, implying investments of more than $120 billion annually are lost. Even the data industry itself regularly challenges the justification of its own existence and the ROI of data investments. I cannot think of any other professional communities that have conducted a similar soul-searching exercise in public.1

How to create value from data?

To address these problems, the data industry has obsessively reinvented technologies, which are getting marketed as a holy grail of the data world, as in “if you only buy my technology, you will be able to generate business value from your data investments”. And while modern, cloud-native platforms have brought tremendous improvements in performance, scalability and usability when building data stuff, the fundamental issue of value creation has not been solved yet. It has not been solved through technology, because the underlying problem is a business problem. All these new technologies have been focused on making building data stuff easier. Still, many companies struggle to create value due to a lack of effectiveness: they do not know what their data teams should focus on to generate business value and lack alignment of data teams and company strategy. 

When we initially met the Mindfuel team last year, it was immediately clear that we shared a common understanding of the pain points of the industry, which the founders had experienced first-hand. After years of delivering data initiatives a top-level executive of a DAX company asked them: “Why do we have more pilots than Lufthansa? We are spending so much money on data, when will we see an actual ROI?” The team had a strong hunch that the answer would lie in applying product management to data, so they decided to quit their jobs and dig deeper. 

Enter Mindfuel, pioneer in Data Product Management

Since 2020 the team around Nadiem and Max has been pioneering the field of Data Product Management (DPM), which is their solution to the problem. The idea is simple yet effective: let’s take well-established best practices of product management and translate them into the data domain, introducing a new and customer-/user-centric perspective to data strategy. After having bootstrapped the business initially, the team has shifted its focus to Delight, Mindfuel’s SaaS offering. Delight is a strategy and execution platform for data teams, which has been built on the company’s deep roots in DPM. It allows customers to systematically quantify the business value of their data products, so data leaders can manage resources efficiently and demonstrate their teams’ value contributions transparently to stakeholders. I am convinced that now is the time to turn data and AI teams from cost to revenue centers and Delight and DPM will play a crucial role in this transformation process. 

We couldn’t be more excited to partner with Nadiem, Max and team on this journey, because we fundamentally believe in the transformative power of data and AI products, which Mindfuel will help unlock. So trust me, if you really want to get value out of your data teams: go buy Mindfuel Delight and all of your problems will be solved! 

  1. Now, you might object that I am portraying an overly pessimistic picture, because the data industry is still young. So let’s make a comparison to the IT industry: failure rates of software projects ten years ago were half the failure rates of data projects today. It seems fair to say that something about the data industry is off indeed. ↩︎