Product management is the process of managing a product from its inception to its end-of-life. Product managers are responsible for the product lifecycle, which includes defining the product strategy, managing the product roadmap, and coordinating with stakeholders to ensure that all aspects of a project come together. 

Data scientists on the other hand are responsible for analyzing data to find insights that can be used to improve products or create new ones.

While product management and development can be data-driven, it is not always so. In the Tesseract Academy we believe that the future of product management and development lies in data, and this is one reason we are offering a workshop on data-driven product development. However, we wanted to check whether other experts share our opinion. That’s why we conducted a survey on the role that data science and AI will play in the world of product management and product development.

We asked 27 product experts, and here are their opinions.

The importance of data science/AI in product management

The first two questions were the following:

  1. From 1 to 10, how important do you think data analytics are right now in product management?
  2. Do you think in the next few years, data science will become more or less important in product management?
Forms response chart. Question title: From 1 to 10, how important do you think data analytics are right now in product management?. Number of responses: 27 responses.
Forms response chart. Question title: Do you think in the next few years, data science will become more or less important in product management?. Number of responses: 27 responses.

It’s clear from the responses that most product managers believe that data science has an important role to play in product management, and they believe that this role will become more and more important over the next few years.

Data science technical skills for product management

The second set of questions concerned the necessity for cross-skilling between data scientists and product managers. Do data scientists need to know product management and do product managers need to know data science?

An interesting observation was that there was no participant saying that data scientists should not understand product management. About 60% actually believe that data scientists should understand aspects of product management. In Tesseract Academy we believe this clearly demonstrates that data science acts like a support function in many organisations. High technical sophistication is less important (in many cases), compares to using analytics with a high degree of focus to make the right improvements in product, operations or some other part of the business.

Forms response chart. Question title: Do you think it's essential for data scientists to understand product management?. Number of responses: 27 responses.

This story is corroborated by the mirror question. It looks like that all survey participants feel that some understanding of data analytics is useful for product management, whether it is basic data analytics, statistical modelling or SQL. 

Forms response chart. Question title: What level of data proficiency do you feel a product manager should have?. Number of responses: 27 responses.

Why is data science useful in product management and development?

When asked why data science would be useful in product management there were three answers that ended up being the most popular:

  1. Coming up with new products
  2. Testing new features
  3. Improving existing products through data-driven insights
Forms response chart. Question title: If you were going to use data science in product management, where do you think it would be the most useful?. Number of responses: 26 responses.

Mikey de Mello says:

“Making data-driven decisions. I benefit the most where they provide me with the metrics I need to measure the success of a release. From here I can iterate on features to make them even more impactful for our users.”

Another one of our expert says:

“I believe that data is the most important for a great execution of the product. On the ideation, market research and strategy, any public data available is key to understand and evaluate the idea however having a good data scientist within the team is key after you build the product and understand your customer’s experience within your product. Most common areas we use data science in our products are:

– Onboarding Experience

– Retention and LTV Prediction

– Monetization (Pricing)”

Teddy Favre-Gilly says:

It’s useful in understand at scale and improving existing products: uncovering bugs, issues, consumer preferences, down-stream-impact, categorising customers etc. In new product development there often is not much data in-place to analyse.”

Summary: product management and data science/AI

Data is fundamentally the way decisions are being made at all organisational levels. Data-driven decision making is the key for success in business, and it has also started affecting the world of product management and development.

The results of our survey indicate that data science is clearly part of the future of product management. This will lead product managers acquiring skills in basic data analytics and statistics, whereas data scientists will be expected to have a more in-depth understanding of product management and development. Organisations are looking into a future of cross-functional teams, with individuals picking up skills in related areas, instead of just overspecialising in a single domain.

The Tesseract Academy is proud to be supporting product managers in this journey. Make sure to check out our courses, and our workshop on data-driven product development.

Tesseract Academy

 

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