Different Definitions of Data Products

The conversation with my next guest was going so deep and so well…it became a two part episode! Today I’m chatting with Nadiem von Heydebrand, CEO of Mindfuel. Nadiem’s career journey led him from data science to data product management, and in this first, we will focus on the skills of data product management (DPM), including design. In part 2, we jump more into Nadiem’s take on the role of the DPM. Nadiem gives actionable insights into the realities of data product management, from the challenges of actually being able to talk to your end users, to focusing on the problems and unarticulated needs of your users rather than solutions. Nadiem and I also discuss how data product managers oversee a portfolio of initiatives, and why it’s important to view that portfolio as a series of investments. Nadiem also emphasizes the value of having designers on a data team, and why he hopes we see more designers in the industry. 

Highlights/ Skip to:

• Brian introduces Nadiem and his background going from data science to data product management (00:36)
• Nadiem gives not only his definition of a data product, but also his related definitions of ‘data as product,’ ‘data as information,’ and ‘data as a model’ products (02:19)
• Nadiem outlines the skill set and activities he finds most valuable in a data product manager (05:15)
• How a data organization typically functions and the challenges a data team faces to prove their value (11:20)
• Brian and Nadiem discuss the challenges and realities of being able to do discovery with the end users of data products (17:42)
• Nadiem outlines how a portfolio of data initiatives has a certain investment attached to it and why it’s important to generate a good result from those investments (21:30)
• Why Nadiem wants to see more designers in the data product space and the problems designers solve for data teams (25:37)
• Nadiem shares a story about a time when he wished he had a designer to convert the expressed needs of the  business into the true need of the customer (30:10)
• The value of solving for the unarticulated needs of your product users, and Nadiem shares how focusing on problems rather than solutions helped him (32:32)
• Nadiem shares how you can connect with him and find out more about his company, Mindfuel (36:07)

• “The product mindset already says it quite well. When you look into classical product management, you have something called the viability, the desirability, the feasibility—so these are three very classic dimensions of product management—and the fourth dimension, we at Mindfuel define for ourselves and for applications are, is the datability.” — Nadiem von Heydebrand (06:51)
  
  
• “We can only prove our [data team’s] value if we unlock business opportunities in their [clients’] lines of businesses. So, our value contribution is indirect. And measuring indirect value contribution is very difficult in organizations.” — Nadiem von Heydebrand (11:57)
  
  
• “Whenever we think about data and analytics, we put a lot of investment and efforts in the delivery piece. I saw a study once where it said 3% of investments go into discovery and 90% of investments go into delivery and the rest is operations and a little bit overhead and all around. So, we have to balance and we have to do proper discovery to understand what problem do we want to solve.” — Nadiem von Heydebrand (13:59)
  
  
• “The best initiatives I delivered in my career, and also now within Mindfuel, are the ones where we try to build an end responsibility from the lines of businesses, among the product managers, to PO, the product owner, and then the delivery team.” – Nadiem von Heydebrand (17:00)
  
  
• “As a consultant, I typically think in solutions. And when we founded Mindfuel, my co-founder forced me to avoid talking about the solution for an entire ten months. So, in whatever meeting we were sitting, I was not allowed to talk about the solution, but only about the problem space.”  – Nadiem von Heydebrand (34:12)
  
  
• “In scaled organizations, data product managers, they typically run a portfolio of data products, and each single product can be seen a little bit like from an investment point of view, this is where we putting our money in, so that’s the reason why we also have to prioritize the right use cases or product initiatives because typically we have limited resources, either it is investment money, people, resources or our time.” – Nadiem von Heydebrand (24:02)
  
  
• “Unfortunately, we don’t see enough designers in data organizations yet. So, I would love to have more design people around me in the data organizations, not only from a delivery perspective, having people building amazing dashboards, but also, like, truly helping me in this kind of discovery space.” – Nadiem von Heydebrand (26:28)

• Mindfuel: https://mindfuel.ai/
• Personal LinkedIn: https://www.linkedin.com/in/nadiemvh/
• Mindfuel LinkedIn: https://www.linkedin.com/company/mindfuelai/