Big Data, Big Data and Analytics, Career Advice, Digital Strategy, Innovation
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.
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 Analytics. The 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.
Professor Walker provides keynote talks, seminars presentations, executive training programs, and executive briefings.
Recent talk topics enjoyed by clients have included:
“From Big Data to Big Profits: Getting the Most from Your Data and Analytics”
“Leveraging Artificial Intelligence and Automation at Work”
“Winner Take All – Digital Strategy: From Data to Dominance”
“Success with an Inter-Generational Workforce: From Boomers to Millennials”
“FinTech, Payments, and Economic Trends and Outlooks in Consumer Lending”
“The World in 2050: Risks and Opportunities Ahead”
Exceptional executive training programs have included:
“Digital Disruption, Automation, Analytics, Data Science, the IoT, and the Big Data Wave”
“Master Course on Operational Risk: Measurement, Management, Leadership”
“Complete Course in Risk Management: Credit, Market, Operational, and Enterprise Risk”
“Cyber-security Training: Prevention, Preparation, and Post-Analysis”
“Managing Your Brand and Reputation in a Crisis.”
“Strategic Data-Driven Marketing”
“Enterprise Risk Management and the CRO”
About Russell Walker, Ph.D.
Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University.
His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.
Follow me at @RussWalker1492 and russellwalkerphd.com
Algorithm, Analytics, Asset Surveillance, Big Data, Big Data Analytics, Big Data to Big Profits, Data Analytics, Data Capture, Data Monetization, Data Science, featured, LinkedIn, LinkedIn Data, Microsoft, Netflix, Northwestern University, Social Media Risks, Stanford University, Tripadvisor, Wired, Yelp
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