My career journey to Mindfuel has offered me a unique vantage point to observe the shift to AI & data product management. Even though I’ve always been in data, it’s been on the technology side, mainly involving cloud, big data, data federation, and AI. But my transition from an infrastructure-focused career to one that places a premium on business value from AI & data was inspired when I realized something: technology alone does not unlock the true potential of AI & data. It’s the combination of technology, processes, and behaviors that transforms it into a powerful tool for business innovation and growth.
Overcoming recurring challenges
Throughout my career, I've witnessed recurring challenges in the data space – challenges often self-inflicted by our technology-driven ambitions. We rush to adopt new technologies and jump on every trending topic without a clear understanding of why they’re needed in the first place.
As a technology-driven society, especially over the last few years or so, everybody immediately gets excited when a new technology emerges. And while trends like generative AI, large language models, etc., certainly have the potential to provide a lot of value, you've got to assess whether it's something that you actually need for your business. I think people often confuse what technology can do with what they need it for.
The shift to AI & data product management
This is what intrigued me about AI & data product management. It shifts the focus from a purely technology-driven mindset to a more holistic and managerial approach that starts with the 'why'—ensuring that every technological solution, in combination with implementation details, delivers real value.
Putting my sales hat on, I can draw a parallel with “dreamcasting”. The concept isn’t data product management-specific, but it's relevant. The traditional approach to forecasting, often hinges on gut feelings or partial data – tending to veer towards “dreamcasting” rather than informed strategy. The focus isn’t on the quality of the pipeline but rather the quantity. What I’ve found is people don’t connect metrics well.
This is the same as where business teams make requests of data teams without a fact-based approach to decision-making and where the focus isn’t on quality of the opportunities but quantity.
Leveraging AI & data product management principles transforms your forecasting from speculative guesswork to precise strategy to focus where there are actual opportunities. Moving away from dreamcasting to reality-based planning ensures that businesses are not led astray by the allure of potential but remain grounded in what is truly achievable and valuable.
Introducing the Data Product Manager
Another benefit of AI & data product management is level-setting between the business and the AI & data teams. I’ve seen it happen often where a business person has a great idea and they want the data team to implement it but the dialogue between the two isn’t clear.
Introducing a mediator between the data and business realms can foster the collaboration between the two extremes. Everyone knows what outcomes you want to achieve. The Data Product Manager can act in this role as the facilitator between the technical and business teams, ensuring that technology adoption is aligned with business needs and value creation.
The realization of value through AI & data product management
Even if the right people are following the right process, the right tools need to be in place. One of the most shocking discoveries I made was that some organizations with the most sophisticated data systems, the best machine learning models, and brightest minds still rely on traditional tools like Excel and PowerPoint for planning. I can’t imagine the weight on the shoulders of AI & data leaders who are having to prove what they’re actually doing each day and how they’re creating value.
This highlights the need for not only amore structured, value-oriented approach like AI & data product management, but also for a tool like our Delight software. It enables organizations to:
- Bridge the gap between business and data teams, fostering mutual understanding and collaboration.
- Attach tangible value to data, enhancing decision-making and strategic planning.
- Promote efficient collaboration, leveraging data for sustained business growth.
In this video, Mindfuel’s Co-Founder & Chief Product Officer, Max Könnings, shares more about Delight, and gives a sneak peek into the software.
The role of AI & data product management in business strategy
AI & data product management emerges as a key to unlocking the true potential of data, guiding businesses towards sustainable growth, innovation, and success in an ever-evolving digital world. As we look to the future, its role in shaping business strategies becomes increasingly evident. I’ve seen it resonating extremely well already in a lot of organizations I’ve worked with, helping them to not only be a lot more structured, but also more value oriented when they explore and develop data-driven initiatives.
If you want to dive further into the challenges and opportunities presented by this shift, reach out to us or see what Delight can do for you.
Watch my interview where I talk about the value of data product management:
