In early-maturity companies, Data Product Managers are put into an organization that is as close to the product and customers as possible. Is that how it should stay?
To understand the best place for product management, you must understand the role's multiplicity. That's why there's so much confusion around this question. It'd be easier to answer if product managers only served one business function. You would align their organizational structure with the structure of the role.
But product managers typically have between three and four different aspects to their role. The first is customer or user-facing. They need to be as close as possible to the product users. That's where they get the best insights and the deepest understanding of the customer's needs,
The second part of the role faces technical development teams. Data Product Managers work directly with the people who create, implement, and support the product. They act as a bridge between other parts of the business, the people who will touch and use the product and those who create it in the first place.
The third part of the role faces monetization. Data Product Managers are the connection between technology and revenue or cost savings. For a business, the purpose of a product is to generate returns. The Data Product Manager must serve this function and explain why it results in some positive business outcome before anything's even developed.
Data Products Introduce New Challenges
Data products immediately bring an added layer of complexity since cross-functional teams develop data products. The data or model handles product functionality. Software or traditional digital products handle the interface between the data or model and customers’ behavior or how customers want to use the product.
There is an added layer of complexity for monetization. Data products are not monetized in the same way that digital products are. A single data set or model can be monetized across multiple products. That means there's a significant overlap between the development of an artifact and potentially numerous product lines.
Customers and internal users also need to be prepared to use a new type of product. I say this often. Models don't work. They function. Everything from requirements gathering to the final use and applications of a model are impacted.
Users and customers must be prepared in advance with data and model literacy training. Data Product Managers may need to plan for teaching a market segment about the need for the product. A lot of teaching is involved with delivering a novel product to market, and data products typically require more than most.
Here's the conundrum. Data Product Managers touch customers, marketing, product development teams, and revenue. As a result, there isn't a functional team alignment that covers every instance.
The Business Must Transform With Data Products. Who Owns That?
Data products also have a business transformation aspect to them. The existing business or operating models must transform for many products to support the larger product vision. This is one of the fundamental challenges of managing data products.
Product Managers’ traditional interactions with marketing or any other external team are to get that team to work along the lines of its core business function. However, now the transformation element throws that out of whack. Most business units don't see transformation as a core element or responsibility. When a Data Product Manager tries to get buy-in for transforming business units, they will encounter significant resistance.