The process of monetizing Big Data is really an exploration, driven by experimentation and identification of value. Here are a few points to keep in mind:
1. Look for Patterns in the Data – before trying to find the needle in the haystack, look for the patterns that describe customers, markets and operations that are directly linked to your decisions and investments. Look for the data to support big decisions, first! Such patterns can lead directly to wide-scale improvements and increase firm performance. It is about big hits. Before addressing finer points in complexity, look for answers to fundamental business questions: when, why, how, and through whom do your customers buy your products. For financial service firms this has been the vein of gold that has led to risk-based pricing and now on-line coupons. For eBay, the analysis is about popular products, successful sellers, and tracking customer preferences.
2. Allow and Encourage Your Data Science Team to Experiment – opportunities in data will not be self- evident. This will require exploration, time, and investment. Just like a stock analyst is expected to spend a great deal of time in research to find a few winners, expect your data science team to explore and to experiment to find the nuggets of value, too! With data scientists being in such high demand, this extra use of data scientists might be hard to justify. Liberating them of the coding and mundane analytical work (in part) needed by the firm for tactical work will be challenging. However, it will be important to do so if you want to achieve strategic goals of monetizing data.
3. Examine what Your Data Says about Assets – Just like Zillow has amassed a large data set about home prices, attracting the interest of outside users, including homeowners, home buyers, realtors and tax assessors (among others), consider who else might be interested in your data and who you can attract. If the data is not directly actionable to you, would others find it valuable? If so, consider strategies for selling the data or making it available to other parties via data products.
4. Form Data Products – Internally and Externally – Only very sophisticated teams are able and willing to buy raw data. They are also unlikely to pay high prices because they will expect to spend the time extracting the value or bringing the data to market. When leveraging data for value, it needs to be transformed into a data product that answers questions about the business. Internally, this is often about a resource allocation – where to market more? What to sell? What to make? For an external user of data, the questions are often about market performance and how they did relative to a competitor. eBay, for instance, has data products on how sellers perform and what products are selling where and at what prices. This creating of data products has many similarities to creating physical products. The product must be conceived, developed, marketed, sold, managed, and updated to create value for the user (and buyer). Zillow, Netflix, Google, eBay, and LinkedIn have all successfully created data products that help users. These data products are evolving in the market and change based on customer needs. Monetizing data will not be a one-stop operation but rather an on-going process.
Recognizing that these steps must in place to monetize data is important. These steps suggest functions and requirements that are not often part of an analytical or data science team. Firms that broaden their vision and use of the data will lead to more value.
These important steps 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.
Russell Walker helps companies develop strategies to manage Risk and harness value through Analytics and Big Data. He is Clinical Associate Professor at the Kellogg School of Management of Northwestern University where he teaches courses on Big Data and Analytics, Strategic Data-Driven Marketing, Enterprise Risk, Operational Risk, and Global Leadership.
This article appeared in Forbes Business on September 9, 2015.
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”
Analytics, Asymmetric Information, Big Data, Big Data Analytics, Big Data to Big Profits, Data Analytics, Data Monetization, Data Products, Data Science, Digital Platforms, Economic Science, Kellogg S, Leadership, Location Based Services, Mobile, Oxford University Press