Published on the 19/10/2022 | Written by Heather Wright

Capitalise on data with new products, and a new role…

If you’re planning on capitalising on your data with some new data products, it might be time to add a new role to your company lineup: The data product manager. 

“The key is to manage data just as you would a consumer product.”

Data products are rapidly gaining acceptance as a way for companies to monetise their data and insights, creating reusable datasets, often packaged with AI or machine learning and analytics, to develop offerings to help end users achieve specific business or operational outcomes. 

McKinsey has dubbed it a way to unlock the full value of money.

“A data product delivers a high-quality, ready-to-use set of data that people across an organisation can easily access and apply to different business challenges,” the global management and consulting company says.

It offers the example of a data product providing a 360-degree view of an important entity, such as customers, employees, product lines, or branches or delivering a given data capability, such as a digital twin that replicates the operation of real-world assets.

Some software companies, such as Starburst, are already embracing on the trend, adding data products modules to their software to enable companies to more easily build and share data products.

According to HBR data products can be a powerful offering for companies, generating impressive returns. It cites the example of small business design and marketing company Vista, for whom HBR says data products have added an incremental US$90 million in profits – much of it recurring annually. 

An earlier report, also from HBR, referenced the example of the customer data product at one large bank, which has nearly 60 use cases, with those applications generating US$60 million in incremental revenue and eliminate US$40 million in losses annually.

But HBR says powerful though data product may be, and despite their lack of newness – in reality they’ve been in use for at least a decade for digital-native companies – many are struggling to implement the idea both for customers and internally.

So how do you successfully create data products?

“The key is to manage data just as you would a consumer product,” McKinsey says.

“We find that when companies manage data like a consumer product – be it digital or physical – they can realise near-term value from their data investments and pave the way for quickly getting more value tomorrow.”

It says success in product development requires an operating model that ensures dedicated management – including a dedicated data product team with a product manager – and funding, the establishment of standards and best practices, performance tracking and quality assurance.

“Success with data products is no different,” McKinsey notes.

For HBR, that includes having data product managers, something they say could help legacy companies create and deliver data products.

Just as a product manager is responsible for product success, identifying customer needs and developing a strategy and roadmap for product development, the data product manager is a cross functional role product development and deployment and, unlike data scientists or CDOs, they don’t have to have all the technical and analytical expertise required to actually create the model or engineer data for it.

“What they do need to have is the ability to manage a cross-functional product development and deployment process, and a team of people with diverse skills to perform the needed tasks,” HBR says. 

“They must also be able to communicate effectively with the business leaders whose operations are going to be changed by the model and the programming surrounding it.”

They’re the ones responsible for that minimum viable product and the creation of a scalable product and its ongoing use and value.

If you’re thinking its a familiar sounding role, you’re not wrong. Software product managers hold a similar role and HBR notes both need to understand software development.

Where the data product manager differs, according to HBR is the need to know how to capture, extract, clean and integrate data and an understanding of statistics along with analytics and AI, though not to the degree of a data scientist.

In fact, HBR says, don’t look to data scientists to hold the position of data product manager – their focus on optimising the fit of models to their data means they’re not an ideal fit for the job.

“As we create more specialised technical roles like data scientist and data engineer, we need people with broad business skills who can work across the different roles, combine them into effective teams, and bring them home to deliver value to enterprises,” HBR says.