Take A Product Management Approach To Data Monetization

 

 

Central to treating data as an asset, data monetization should align with familiar research and development (R&D) and product management/marketing approaches. Not to oversimplify the many challenges and activities involved in monetizing data, certain basic concepts will reap significant rewards if executed well. 

Evolve from Data Project Management to Data Product Management

Although you may already have a data leader such as a chief data officer (CDO), or an analytics leader, the first step toward data monetization is to designate a team tasked with identifying and pursuing opportunities for and generating demonstrable economic benefits from available data assets. They may report to a data and analytics executive, into the enterprise architecture group, a chief digital officer, or perhaps even a business unit head. 

Creating a distinct, dedicated data product management role is vital especially when business and data leaders agree on pursuing direct data monetization by generating revenue or other financial benefits from licensing or exchanging their data. Typically, companies already have a defined approach for managing and marketing products. Analogously, if you are considering licensing data in any form, you need someone whose job is to define and develop the market for the data asset and to productize it.

Finding qualified talent for this kind of role can be difficult. Traditional product managers may have an advantage over other candidates, even without significant knowledge of data and analytics. But why not consider hiring individuals with experience at a data broker such as Experian, Equifax, D&B, IRI, LexisNexis, Nielsen or J.D. Power? 

Ideally, the data product manager reports to the CDO (itself an emerging role for data-savvy organizations) or into a new data product line of business head. This chain of command, askew to the IT organization, underscores that data is a business asset, not an IT asset. Moreover, a data product manager provides a counterweight to data scientists, especially, who can get seduced and obsessed by intriguing problems that may be tangential to the business objectives.

 Continue reading >>>