A Product Management Approach To Data Monetization

To consider data as an asset, data monetization should be connected with established research and development (R&D) and product management/marketing processes. To avoid oversimplifying the myriad challenges and tasks involved in data monetization, several essential concepts that are effectively executed will reap huge rewards.

Making The Switch From Data Project Management To Data Product Management

Even if you already have a data leader in place, such as a chief data officer (CDO) or an analytics leader, the first step toward data modernization services is to form a team tasked with identifying, researching, and providing demonstrated economic benefits from accessible data assets. A data and analytics executive, the enterprise architecture group, the chief digital officer, or even the head of a business unit could be reporting to them.

Establishing a discrete, dedicated data product management function is crucial when business and data executives opt to pursue direct data monetization by generating cash or other financial benefits from licensing or sharing their data. Most companies already have a plan in place for maintaining and promoting their products. If you want to license data in any form, you'll need someone to define and build a market for it, as well as productize it.

Finding qualified individuals for this post can be difficult. Traditional product managers may have an advantage over other candidates even if they lack comprehensive knowledge of data analytics services. Why not hire personnel who have previously worked for a data broker such as Experian, Equifax, or D&B?

In the best-case scenario, the data product manager should report to the CDO (an emerging function for data-savvy businesses) or a new data product line of business intelligence services. Data is a business asset, not an IT asset, according to this command structure, which is inverted from the IT organization. A data product manager also serves as a check on data scientists, who can become distracted by interesting topics that are unrelated to the company's objectives.

According to the latest Gartner Chief Data Officer Survey, a CDO's success is 3.5 times more likely when they achieve data monetization goals, versus only 1.7 times more likely when they demonstrate return on investment (ROI) from data & analytics investments, and 2.3 times more likely when they successfully reduce time to market. That's all the more incentive to hire a full-time data product manager.

Take A Page From Traditional Product Management's Book

The data product manager can and should borrow heavily from established product management practices for the following purposes:

• New data modernization solutions are being developed and planned.
• Identifying or establishing information marketplaces in collaboration with partners and others, as well as
• Coordination with IT, marketing, finance, legal, and other product management departments is required to fulfil information productization goals.

CEO of Pythian Pythian leaders highlighted their experiences with adopting a more product-driven strategy, according to Paul Vallée, a former CEO and current board member. They realized that a committee approach was ineffective and that the company required a single owner to lead the charge:

"We needed someone who knew the business inside and out." We needed someone who had been with the organization for a long time and had helped establish our practices. That's exactly what we needed to do to overcome inertia and reduce the committee's role in day-to-day decisions. Although a variety of stakeholders should be consulted throughout the project, at the end of the day, only one person is necessary.

Similarly, Samir Desai, Chief Digital and Technology Officer at Abercrombie & Fitch, remarked that selecting the right individual for the role is crucial: "Not everyone is cut out to be an inventor." I believe you should choose someone who is both business and technology savvy, as well as possessing the appropriate attitude for the job."

It's Possible You're A Data Product Manager Already 

Many data and analytics gurus feel they've been managing data products without realizing it for years. "The title may or may not matter depending on the organization," said Steve Prokopiou, Data Product & Proposition Lead at First Central. "It's about communicating with business intelligence solutions and offering what they're seeking for by acting as a translator and asking logical, organized questions regarding data consumption and benefit." And maybe utilizing product management language in the process as well." Prokopiou also believes that having the official title will encourage people to get part in the requirements definition process sooner rather than later, rather than waiting for incomplete or difficult-to-translate requirements to appear on their desk.

"A data product manager must be entrepreneurial, but not necessarily have product management experience," says Lillian Pierson, who works as a data product manager for Data Mania, a company that specializes in educational content. Approaching almost everything you make as if it were a finished work, she believes, forces you to be more disciplined. As a result, Pierson suggests that a data product manager has a broad range of skills, including:

• Data science and analytics knowledge, as well as data strategy knowledge
• Knowledge of how systems and processes operate
• Capable of anticipating which technologies will work well together.
• Understanding how to design features and functionalities is essential.
• It's a benefit if you've done market or stakeholder research before.
• And, of course, a soft spot for others.

To Fine-Tune The Data Product Vision, Work Backwards

Legendary golfer Greg Norman claims to mentally play each hole backwards. "My thoughts turn to the green when I take my first step onto the tee. Before determining which club to hit or how to play my tee shot, I want to know the exact position of the flag; once I know that, I mentally play the hole backward."

Starting with a vision of what you want to achieve is equally important as a data product manager. This is exactly the approach used by companies like Amazon.

Working backward begins with "trying to work backward from the consumer, rather than starting with a product idea and trying to attach customers onto it," according to Ian McAllister, former director of Amazon Day. For each new effort, a product manager writes an internal press release announcing the final product. "Internal press releases focus on the customer problem, how current (internal or external) solutions fail, and how the new product will exceed previous solutions," noted McAllister. "If the advertised benefits don't sound interesting or exciting to customers, they probably aren't, and they shouldn't be built." If this isn't the case, the product manager should keep revising the press release until something better emerges.

It may appear to be a lot of work for an idea that will almost certainly never come to fruition. "Iterating on a press release is a lot less expensive than iterating on the product itself...and iterating on a press release is a lot faster!" explains McAllister.