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.

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“While it may feel counterintuitive to look backward in order to move forward in the face of significant economic events,” Payrix Chief Risk and Compliance Officer Billi Jo Wright told PYMNTS, “applying the proper data mining, data analytics tools and action plans to your business’ wealth of transactional data may provide a roadmap through adverse situations.” Learn how Wright mines data to power Payrix and its clients in Black Swan, a special report exclusively from PYMNTS.

The following is an excerpt from Black Swan, contributed by Payrix Chief Risk and Compliance Officer Billi Jo Wright.

How Learning From History and Digging Into Data Helps Steer Through Adverse Economic Environments

In the midst of a black swan event — and certainly in the early days of understanding what the United States and the world face amid COVID-19 — the downturn often feels sudden and unexpected. Looking surface-level at the defining black swan events of the past few decades – from acts of terrorism to the bursting of a major industry bubble to a global viral pandemic – you’d no doubt see few similarities in the events that came before.

But for businesses scrambling to reforecast and plan for potential economic developments (McKinsey is currently outlining three potential scenarios, ranging from a quick recovery to a global slowdown to a full recession), there are rich insights that lie within historical transactional data.

There are a number of key factors and fields that can be scrutinized and analyzed to enable a business to more successfully steer through uncertain times. A holistic view of payments data includes analysis at an industry and vertical level, taking into account geographic regions, company sizes, chargeback trends, and transactional clusters and patterns before, during and after an economic event. That information can then be used to proactively prepare or stabilize your portfolio.

Highlighted below are two key focuses for navigating and preparing for uncertainty.

Distilling Insights Within Historical Payment Data

To determine the potential business impact, look at past payment volumes to understand how an event could impact your business, monitor trends in chargebacks or refunds to advise merchants on what to expect, and consider isolating merchants and payment volume in affected areas. Margin-sensitive or event-contextual verticals will inform where to prioritize retention versus acquisition efforts and where to find resourcing during tight times.

Analyze Concentration Risk

The risk of amplified losses that may come from having a large portion of processing volume in a particular vertical is significant. A diversified portfolio requires ongoing and proactive analysis to minimize concentration risk in an economic shutdown. In the current environment, we can expect to see industries like nonprofit, travel (hotels, airlines and cruises), events and service (bars, restaurants and salons/spas) in despair.

While it may feel counterintuitive to look backward in order to move forward in the face of significant economic events, applying the proper data mining, data analytics tools and action plans to your business’ wealth of transactional data may provide a roadmap through adverse situations.

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The first question you need to consider is: what are the specific business drivers for your AI project?

In a broader context, the likelihood is that you are driven by a desire to gain or sustain a competitive advantage or to enter into new areas of business, stave off competitors, etc. Those are important drivers, but if everyone is getting into AI then they could arguably be table stakes. AI is a long-term investment and you need to think beyond the short term. Assume that your competitors and future competitors in adjacent markets are also at least considering the use of AI to keep parity. Reducing costs is a key driver – but just as important and just as overlooked is the fact that in the not too distant future both your suppliers and your customers will be expecting you to leverage AI. You need to think holistically about the short and long-term drivers, both for the internal and external factors that affect your business…

Unexpected Impact

The impact of AI on business processes gives us some cause for concern… Many seem to think this impact will come further down the line. In reality, even a small change in a process can have a big impact on a worker’s or department’s daily life. It depends on how you define a big impact. AI can have an immediate impact on the day-to-day work of individuals but it may take time for the results of all those small changes to have a big impact on the business as a whole. You need to plan for an immediate impact on your processes, as well as the greater impact AI will have in the mid and long term. Your processes will change, and hiccups and push backs not identified or mitigated against at the start could derail your project.

Finally, let’s consider your existing business operations; for example, your sales, HR, procurement, accounts, marketing processes, etc. To you these may seem to be straightforward and discreet activities. An AI system may not view these things as so clear cut; it may see more optimal ways of working, it may not respect the cultural silos that exist within your organization. Outside of very narrow, niche projects, the use of AI will likely have a knock-on impact, including potentially unexpected impacts, across your organization. Remember, when we said that you need to think holistically when planning the use of AI? Well, consider the chain effect of automating decision-making in one aspect of your business and how that may affect other areas of your business.

You must ask three essential questions:

1. What is the project? What value will it bring? What impact will it have?

2. What will the project involve in terms of changes to existing systems, processes and the organization?

3. What financial benefits will the project bring?

Costs to consider

Like IT projects, staff costs are a significant chunk of the overall costs. Because of a talent shortage in certain areas, the salaries for AI specialists can be higher than other IT roles. Also seemingly basic tasks like labeling of the training data can be a significant cost.

In AI projects, you also will have to budget for data and computing environment costs. Plus, if your teams need any external consulting support for opportunity identification, roadmap development, and working alongside the team, that cost also has to be considered. In contrast to normal Central Processing Units (CPU) in our computers and laptops, Graphical Processing Units (GPU) are special purpose hardware chips that are needed to optimize many machine learning applications. Another special purpose hardware chip is the Tensor Processing Unit (TPU) which is optimized for certain machine learning applications.

GPUs/ TPUs can make your machine learning applications run faster, but keep in mind that they can be more expensive compared to normal CPU-based environments…

The likelihood is your AI project is going to be processing a lot of data, fast. You may decide to use optimized on-premise servers or utilize cloud services such as Amazon, Microsoft or Google. Either way, you need to budget carefully. We suggest you do a five-year cost analysis as low monthly fees can stack up versus high initial costs for on premise hardware.

Finally, most AI projects will use some kind of external consulting and this can also be expensive. However, there can be great value in engaging experts that have AI project experience, saving you from reinventing the wheel.

This is the budget in the initial phases when you are about to begin your AI journey. After that, you will need to factor in solution deployment, solution management and solution retraining costs.

AI projects may require extra governance and specialist oversight teams. In fact, AI projects will be staffed differently to traditional IT projects and the vendor partnerships you will form will have deeper and different dimensions to them.

And, of course, AI projects will require a significant amount of change versus the status quo. Change may well be profound and any change, no matter how small, needs to be managed.

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