Valuable Data Products: Answers to Career Questions and More

Collecting high quality data from various resources and turning it into data products is one of the ways to monetize data in today’s digital economy. Lets take a deeper look into it.

Wired Universities to Companies Graph

Turning Data into Valuable Data Products

The acquisition of LinkedIn by Microsoft raises many interesting and valuable business possibilities. LinkedIn is the first and largest repository of data on career paths for millions of people. It has deep and valuable information on firms, the path of careers, and even the best majors for specific careers. LinkedIn’s data can also answer (in part) questions such as, which college is better for a specific major? And where do the alumni work? Universities should be nervous. Someone else can answer those questions. LinkedIn is a great example of a firm that has turned data into data products, specially on questions about career decisions.

WIRED published an infographic that showed the top feeder schools for big technology companies. It used information available on LinkedIn to see where recruits to companies like Apple and Twitter went to college. It meant that the merits and benefits of a college (and presumably all colleges) could be measured.

colleges where big tech companies get recruits

While the results were interesting, the part that intrigued me was this novel use of existing “data products” to answer interesting new questions. LinkedIn is essentially free to most individual users, but the data generated through the normal use of the network is an incredibly rich source of insight – so rich that it generated over $500 million in revenue in 2015. Advertisers, search firms, and premium users are pleased to pay for LinkedIn’s valuable data products.

For example, if I choose to get a premium account, I can find out exactly who has seen my profile. If I’m a professional headhunter, I can pay to get even more specific information, such as all the engineers who graduated from Stanford University or Northwestern University in any year. With some minor error, due to the fact that not quite everyone is on LinkedIn (yet), I can learn where these engineers work and also good estimates of what they earn.  And if I’m a university administrator, college advisor, or student loan underwriter, I can look for connections between specific schools or degrees and actual careers, to the point of calculating the potential ROI for any given degree.

The evolution from data to data product is important for any company that wants to create more value from its information assets. Of course, there are a number of factors to consider: Who owns the data? What about privacy? How can – or should – I use the data? In part these questions are open for society to answers.

How we make decisions, as individuals and firms is changing. Novel data products are becoming more accessible in guiding out decisions from where to eat (Yelp) to where to stay (TripAdvisor) and even where to go to college (LinkedIn). I do think it’s interesting to look at how today’s big data technology is enabling the creation and monetization of new data products. The value of these data products is also becoming more recognizable. Just as Angie’s List once charged and LinkedIn charges for specific views of the data, we can expect users to pay up for valuable data products. Companies are finding ways to get people to pay who wouldn’t normally be expected to pay, or to form new markets that wouldn’t be expected to form. It’s changing how business is conducted and how we make decisions in a digital economy.

These important implications in monetizing Big Data in the digital economy and more are developed in my recent book, From Big Data to Big Profits: Success with Data and AnalyticsThe book examines the evolving nature of Big Data and how businesses can leverage it to create new monetization opportunities. Using case studies on Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leading-edge users of Big Data, the book also explores how digital platforms, including mobile apps and social networks, are changing customer interactions and expectations, as well as the way Big Data is created and managed by companies. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which assesses companies for Big Data readiness and provides direction on the steps necessary to get the most from Big Data.

Original post. Reposted with permission.

Bio: Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Associate Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University..