There is a strong relationship between data science and product management¹ in the sense that both strive to offer solutions and achieve actionable outcomes. Data scientists tinker with raw data to derive insights while product managers explore ways of improving customer experiences. See the connection? Both focus on outcomes which companies need to become competitive and address customer pain points.
However, there are arguments that product managers and data scientists² operate in different spheres which do not connect in the real world. But as the current digital environment has shown, product managers need the support of data scientists to make sense of their roles and become successful in creating products that customers love.
You do not need to be a data scientist to be a product manager. You do not need to have any serious data analysis experience to start your product management path. Nevertheless, the best products are data-driven products³, which means you need to get comfortable with handling data on a day-to-day basis.
In Product Management, the most important things are to be able to work with your team, understand whom you are building for and why. How do you get to know your customers? In addition, how do you develop the ability to communicate with everyone? The key is building up your skillset as much as possible.
In this article, I will explore the intersection of #datascience and product management and what the landscape looks like in 2020.
Both product managers and data scientists use data to make decisions and specific metrics to measure the outcome of those decisions. A product manager needs to know what success looks like for a product or a feature, while a #datascientist chooses evaluation metrics⁴ that define the outcome of an experiment.
Both product managers and data scientists then need to be able to explain their decisions to stakeholders on other teams clearly. They need to be technical, business-oriented, and creative enough to communicate with everyone from engineers to designers.
The role of product managers entails coordination between various teams, particularly #software and data science. Data scientists have to work extensively heavily with data scientists to extract insights for products’ features, recommendations, etc.
In addition, you need to be able to have conversations with your data scientists and engineers about their day-to-day work. As a data product⁵ manager, you can anticipate influencing what that data offering will look like, how it is priced, and how you take the product to market.
Product managers work with the engineering teams to administer products’ roadmaps. Product managers define the overall path of products, their development stages, and align products with the companies’ goals.
Project scopes should always begin delivering MVP without the requirement of too many involvements. Scoping projects need examining questions of data assets, measurement, organizations’ structure, and assessing the potential impact of projects.
Let us explore how data science can support product managers in their roles:
1. Collaboration with Data Science Teams
Teams work cross-functionally, so knowing data and being able to talk about it with your data scientists is a great advantage. It is like learning their language. You will be able understand them and better communicate your questions and ideas to them.
There are other data-centric roles⁶ within big businesses, and if you want to move up the career ladder, you need to be able to work with them.
Some people are naturally more data-centric than others are. If you move to a new company you may find that, for example, your new Product Marketing Manager loves data. If you understand and can talk about #data, your collaboration with them will be stronger.
2. Customer Education and Understanding
To be a good #productmanager you need to know what your customers want and which problems, habits and preferences they have. To figure this out, you first define the customer profiles.
Putting together the profiles is hard enough. However, you need to understand them as well. This is where you need data knowledge⁷. Your customers’ behavior affects your decision-making, as customer profiles allow you to make good decisions based on user experiences and desires.
Not only do you need to understand your customers before building your product, but also you need to understand how they respond to it after launch. You will start receiving information about where the pain points are for your users, and where they are dropping off. If you understand data, you will understand exactly where those are and be able to prioritize which ones to fix first.
It will also help you when it comes to gathering feedback.
Feedback helps you find out what that group of customers think about your product. Product data⁸ provides a larger scale of answers to who exactly your customers are, what they use your product for and what they think about it. Having at least a basic understand of NPS scores can help a new product manager our immeasurably.
3. Teams Management
I bet you have heard this phrase: “Without data, you are just another person with an opinion”. When it comes to influencing team members and getting them aligned on one product vision, data is key. Data helps you to back up your opinions because, after all, data does not lie.
Not only will this help with team members, but with stakeholders. If you need to convince someone important why the feature they requested will not be shipped in V1, show them the numbers that prove it to be redundant. Data will also help you give useful updates for more #datadriven stakeholders who want more details.
4. Product Strategic Decisions
Learning data helps #productmanagers make better product decisions. One of Product Managers’ tasks is to find out whether the product is actually successful and how different changes affect the product. To do this, you need to combine feedback and data. Knowing how to interpret the data will allow you to refine your product.
As you work on refining your product, a steady stream of data will help you make decisions based on fact, rather than on instinct. This is especially important for building brand new products for product managers who are working in a new industry. When venturing into the unknown, you need as many facts behind you as possible.
5. Better Time Management Structures
Knowing data will help optimize your time by differentiating urgent things from other urgent things. This will require trade-offs. You will have to be aware that if you work on one specific thing now, it might affect another thing negatively somewhere else.
However, data should give you a good idea what that change might be and what effect it will have. Every time you choose to work on something, it also means not working on something else. Make sure you choose the right one.
Time for Data Scientists and PMs to Collaborate
Here is the truth: An understanding of data science should now be part of every product manager’s general education, not so, they can get into the details of how, but rather, they can understand what could be. This understanding gives product managers a sense of questions data science can answer, so they begin to think more creatively about solutions that would benefit their biggest stakeholders-customers.
¹Product Management, ²Data Scientists, ³Data-Driven Products, ⁴Evaluation Metrics, ⁵Data Product, ⁶Data-Centric Roles, ⁷Data Knowledge, ⁸Product data