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Do you know why you cannot and probably shouldn’t try to strongly define a Data Product? To answer that, we need to revise the simplest and core ideology of Product Management.
Product definition is the process by which a team describes what it is that they are building for their target customer(s).
There are many levels to product definition - a one-page summary or landing page is appropriate at the very beginning of the product development journey; as the team builds knowledge about the market, the customers and their needs and wants, more detail will come in making the product definition actionable by technical developers.
~MIT Orbit Knowledgebase
The reason why the community at large has struggled and debated over defining a data product is because you can never have one fixed definition or even an evolving one, for that matter.
Here are a few guardrails to define a product from the same source:
1. For[target customer]2. Who wants/needs[a compelling reason to buy]3. The[product name] is a[product category]4. That provides[these key benefits].5. Unlike[the main competitor],6. The[product name] [provides these key differentiation points].
Just like a broad range of products and their respective definitions that result from these parameters, data products, too, are diverse and cannot be simmered into one narrow definition. It changes shape and size depending on the market, the purpose, the demand, and many more such parameters.
The brutal truth is that this debate has stretched out for far too long since most of us in the data space haven’t really been product managers or have had specific experience on that front.
We have spoken about a product approach for data for almost half a decade with prospects, customers, our avid readers, and even students. What picked our curiosity the most was how practising or aspiring Product Managers in the data space raised the interesting questions and found the product approach most relevant to their objectives.
The Data Product speak resonates with the Product Management speak, which raises the importance of establishing the role of a Data Product Manager even more. And we are very glad to see those consistently popping up across different data-centric organisations and are quite enlightened to have an active dialogue with them.
Last week when we opened up our internal Data Product Strategy, we weren’t surprised to find most DMs and engagement from Product Managers. We use product frameworks such as the BCG matrix and focus extensively on enabling target metrics that move the needle for businesses.
Strategy is a very important part of product design, and it is no different for data product design. It helps us answer critical questions such as where to invest further, where to cap efforts, what can be decommissioned, where to be more consistent, and so on. Feel free to dive in for details, and watch this space for a sample use case soon!
For more context: The Data Product Strategy | Becoming Metrics-First
We have highlighted several amazing ideas and strategies from the community in the Data Product Strategy piece. So, we’ll use this section to highlight a few more strong cases and ideas!
Douglas B. Laney writes in CDO Magazine,
Creating a distinct, dedicated data product management role is vital, especially when business and data leaders agree on pursuing direct data monetization by generating revenue or other financial benefits from licensing or exchanging their data.
And we found a very interesting stat: Speaking of CDOs, Gartner’s most recent Chief Data Officer Survey finds that a CDO’s success is 3.5 times more likely when they have met data monetization objectives versus only 1.7 times more likely when they have demonstrated return on investment (ROI) from data and analytics investments, and 2.3 times more likely when they have successfully reduced time to market. All the more reason to hire a dedicated data product manager.
Douglas moves on to talk about some specifics such as the product management playbook for data monetisation and the right background suited for a data product manager.
Keith Schulze and Kunal Tiwary write about realising a return on investment from data.
Many data products fail because they are a solution in search of a problem – for example, ingesting a new dataset into the data platform because ‘someone’ will find it useful. Adding more data does not necessarily solve a customer’s problems – or provide them with value.
The Double-Diamond design process model from ThoughWorks is a clean way to streamline a product approach for data and helps to build the right product in the right way.
The authors also talk about rethinking our approach to data, redefining ownership and responsibilities, and some necessary change drivers.
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Event Date: September 13, 2023
Mode: In-Person
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As usual, here’s a light breather for you for sticking till the end!
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