As data science proceeds to evolve and become even more blended with operation systems, the role of data science product manager is growing significantly. But, often the same work that is going into one data science product (within an enterprise) may also have another use case for a separate business department.
There is a space for both big and smaller players like startups to satisfy this demand for data-focused products. The startup boom has also fuelled this, and we have witnessed many startups coming up with data science products. As most of the enterprises are shifting to the cloud, it generates a humongous volume of data, which can be used to optimise processes and bring value for businesses, be it large or small. Among this situation and growth of data science products, we can see that the demand for product management will increase. But, what does product management entail when it comes to data science?
If we look at the role of a data science product manager, this job role demands innovation, comprehending and leading business needs that can be addressed with AI/ML, which is the quintessential function of a product management team. Product managers working along with the data science teams would want to uncover innovative applications based on the insights they have inferred from the data.
The job role would require managers to communicate their decisions, and so would need to be technical, creative and enterprise-oriented to communicate to everyone from engineers to designers.
Planning Product Roadmap & Features For Data Science Products
The role of product managers entails coordination between various teams, particularly software and data science. Data science product managers have to work extensively heavily with data scientists to extract insights for products’ features, recommendations, etc. Also, you need to be able to have conversations with your data scientists and engineers around their day-to-day work. As a data product 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. Projects’ scopes should always begin delivering MVP without the requirement of too many involvements. Scoping projects need examining questions of data assets, measurement, organisations’ structure, and assessing the potential impact of projects.
Obviously, the specifics will depend on the type of industry and the data itself, among other factors including the API functions, opportunities to enrich the data, the various formats to support, and target use cases. For example, some AI efforts which are based on the development of intelligent devices and vehicles, may include concurrent development streams of software, hardware, and continually developing machine learning patterns.
Bridging Data Science Interaction With Business Stakeholders
While the role of data science product managers is not much distinct from regular product managers, they are still required to showcase release plans, generate business cases for data products, and serve as an interface for the data science team and internal and external business stakeholders. Data science product managers shouldn’t be just basically focusing on data, and they should be fully tapped into business stakeholders and be able to understand and explain their needs to solve customer problems and figure out product features and delivery challenges.
Products managers working in data science should also have knowledge with machine learning concepts product lifecycle when it comes to model development. But product managers, depending on organisations, do not always even need to have a deep understanding of the domain. That is the expertise needed to manage a project does not directly depend on the understanding of core data science, but more on how to leverage data science to solve problems.
A wide range of data science products exists in the market from a plethora of vendors. Such data science and machine learning product solution companies are estimated to expand their market force and achieve the growth in the forthcoming years, both in India and around the world. Because of many inefficient processes within businesses, it is expected that we may see the need to optimise those processes using customised data science services or plug-and-play data science and analytics products.
Dealing With Data Science Complexity
Unlike traditional software which does not need to be re-trained, data science products may usually differ from desired performance over time. A designated person with relevant expertise should step in and manage the full product lifecycle, and this is where the product manager comes into the picture.
An efficient product manager can lead a timeline to produce a series of small data science solutions first before the broader market rollout. A good product manager knows these numerous competing demands, prioritises product advancement on the most critical needs, and adjusts the product with the overall business strategy.
A product manager needs to understand what progress looks like for a product or a feature, working with data scientists who extract evaluation metrics that determine the outcome of an experiment. Both product managers and data scientists then must be able to demonstrate their decisions to business stakeholders on other teams unquestionably.
Balancing Agility With Data Science Product Development Complexity
The other aspect of product management is managing the development of a software product within a specified time. For accomplishing fast delivery of products, scrum or similar methods are used by software product managers. But for AI/ML, not all stages of the machine learning cycle work on tight schedules in fixed times.
For example, in various stages of product development and research, a significant amount of experimentation is required. This demands product managers be less confining in their agile methodology. Also, data science is science, so it’s very open-ended and exploratory, some experiments are successes, but some are failures. Data science teams require an entrepreneurial sort of faith in your team that a few successes can pay for the experiments that go nowhere.