How to Monetize Big Data

How to Monetize Big Data

Big data can be monetized in three fundamental ways. These are each discussed below.

1. Big data can be used to improve operations, thereby reducing costs, improving efficiencies, ramping up sales, and increasing profits.
2. It can also be sold, licensed or shared with other organizations as a product.
3. It can be used to build a “multi-faceted” business, or even to launch new businesses.

Improving Operations

The use of big data analtyics (BDA) is helping decision makers in dozens of industries. While it’s impractical to detail each one, the summaries below point out specific examples in key industries where BDA has taken a foothold and allowed businesses to extract monetary value by improving their operational efficiencies.

Manufacturers have used automation on the shop floor for decades. Today, companies are using big data produced by sensors built into manufacturing equipment to minimize outages through predictive maintenance. Such machine-produced data is expected to increase by more than 40 percent by 2020.

Healthcare operators can use BDA to map a patient’s data to the records of other patients, identifying patterns that can offer a more accurate diagnosis. BDA also helps prevent hospital readmissions that result from insufficient treatment on the initial admission, or from an incorrect initial diagnosis.

Commercial lending uses BDA to avoid risk by adding social media data to traditional risk assessment tools (credit reports, public data, etc.)

Energy companies have deployed smart meters in recent years and outfitted their distribution means (electrical transmission lines, pipelines, etc.) with sensors that give them near-instant awareness of problems. These advances reduce the cost of meter reading and of maintaining countless miles of distribution pumping stations and sub-stations.

The transportation industry has tapped big data from crowd-sourced data coming from mobile phone apps, and in some cases, from specially designed traffic monitors placed throughout a city.

While each industry uses big data differently, big data reveals more information, new information and new patterns of information that give insight that traditional warehoused data cannot. However, blending big data with traditional data stores adds context that allows better decision making. For example, the city of Boston maintains data on roads and highways that need repair. Citizens have long been able to report potholes and other obstructions via phone. The city decided its traditional phone reporting method could be enhanced with big data, so it created an Android app named “Street Bump.” The app uses the GPS and accelerometer in citizens’ phones to detect and report potholes and bumps that need attention. This crowd-sourced data is melded with existing operational data to facilitate road repairs, giving the city a richer and near real-time view of repairs needed on the city’s roads and highways. Combining existing data with big data adds the context that can impact budgeting, forecasting, and requirements for manpower and equipment.

Sell, License or Share Your Big Data

In choosing to monetize big data as a product you must give value to the buyer; it must be capable of revealing new information the buyer can can use to advance toward its business goals. It might help the buyer answer questions about risk, value of an asset or its future value. It might reveal insight into a market or customer behavior. But what gives a data product value? These are the essential ingredients:

• The data may be high velocity, which means it is real-time or near real-time. Uber, the ride sharing company, gives customers the ability to find an available ride within a given radius, and tells the consumer how soon the ride will arrive. The Uber app used by customers and drivers handles both supply and demand in real time. It uses and produces high velocity data. From another direction, retailers often provide Wi-Fi in their stores so they can track the movement of patrons through the store based upon their smartphone signals.
• The data product may offer greater precision than what is already available to the buyer. In today’s environment, large-scale digital systems such as mobile phone networks measure and record subscriber activity in great detail. Location, time of day, length of call and other facts become data as a subscriber moves through each day. Likewise, a web site visitor’s path through a site can be recorded in great detail. Similarly, a GPS tracking device like those often used by parents to monitor their novice teen drivers sends location, start/stop times, speed and other details to a server. Another example: Many web sites carry product reviews, however without appropriate safeguards, fake reviews can be posted to either bolster or disparage the product. Booking.com, for example, not only restricts reviews to people who are verifiable customers, it maintains quality control over each posting. As a welcome added feature, it also allows consumers to see reviews from their own demographic group: solo travelers, business travelers, families, etc. These are all examples of precise data.
• It may offer greater scale. For instance, a data product that includes all data in a given population may have value to a buyer who, until now, has only been able to accumulate sampling data. A cellular operator such as Sprint or AT&T collects data with every phone call. A researcher seeking information on subscribers no longer has to work with a sample population of “N” subscribers. With all data collected, “N” becomes the entire universe of subscribers. There will be no sampling error because there’s no need to sample. The confidence interval climbs to 100 percent.

Another component one can add to the creation of a marketable data product is known as data fusion. Merging proprietary, internal data with public data or data from social media or another firm can create new insights. Data posted on Facebook and other social sites provides rich detail on a person’s interests, activities and preferences. This can be married with other data sets to create powerful new insights.

For example, Choice Point Precision Marketing (now LexisNexis Risk Solutions) maintained more than 17 billion records of individuals and businesses that scored cohorts on factors such as home ownership, a “prosperity index,” bankruptcies, Spanish-speaking and many others. Combining such information with social data, for example, can sharpen an advertiser’s focus immeasurably.

Launch a New Business

Perhaps the company that has made launching new businesses part of its DNA more visibly than anyone else is Google. (Their formation of Alphabet, Inc. as its parent company attests to the diverse interests Google has pursued). This blog post announcing Google.org’s influenza tracking initiative shows how collecting massive amounts of big data can be used to solve real world problems.

While this free service is no longer operating, it could have become a standalone business selling data. Instead, Google became a major contributor to Calico Labs “whose mission is to harness advanced technologies to increase our understanding of the biology that controls lifespan.” Staffed by scientists, Calico intends to find interventions to slow aging and counteract age-related diseases.

Build a Multi-Faceted Business

Amazon, too, has collected mountains of big data on millions of book sales. With a view into the book selling business, they launched their Kindle reader product and encouraged writers around the world to self-publish their fiction and non-fiction in Kindle format. More recently, Amazon launched ACX.com. It is “a marketplace where professional authors, agents, publishers, and other rights holders can post fallow audio book rights. At ACX, those unused audio rights will be matched with narrators, engineers, recording studios, and other producers capable of producing a finished audio book, as well as with audio book publishers.”

Not missing a beat, Amazon bought Audible.com and encourages authors to publish their audio books through Audible to earn premium commissions and royalties. Along the way, Amazon also launched WriteOn, a site where aspiring writers can post their work and receive advice from peers. Amazon seems to have wrapped most of the writing, publishing and book selling business into these various business units. In the process it has become a model of the multi-faceted business. And all of this has been driven by creative thinking salted with big data.

For companies that have accumulated substantial volumes of big data, it may sometimes be possible to “flip” the relationships that exist to create a new data product.

Consider how the Amazon Prime Video service keeps track of what you watch, what devices you register (TV, phone, computer, etc.), and then makes recommendations on what else you might enjoy watching. Such “recommendation engines” are widely used at music streaming sites, at Netflix and other ecommerce sites. They help subscribers discover new products and encourage additional sales.

However, when viewing preferences are combined with subscriber information, and further combined with demographic and economic information gleaned from list management companies, social media and other sources, the big data can be “flipped” to reveal new, marketable information.

For example, the subscriber’s home address serves as a data point that speaks to his or her income level. A service like Amazon Prime Video could identify subscribers according to their income cohort, then segment each cohort by their preferences in movies. Changing the view of that big data, “flipping it,” so to speak, gives a new view—and a new data product—that may be of interest to movie studios, producers and screenwriters.

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