Big Data Alchemy: Turn Info Into Money

Data markets could become a new market category. Here, companies buy, sell or trade data for mutual benefit.

John Lucker, Principal, Deloitte Consulting LLP

May 13, 2013

5 Min Read

5 Big Wishes For Big Data Deployments

5 Big Wishes For Big Data Deployments

5 Big Wishes For Big Data Deployments(click image for larger view and for slideshow)

There's an old aphorism that anything you're not using is costing you money. That's true of data, especially in light of the fact that collecting and storing it isn't cheap. It would make sense, then, for companies to try to wring more value out of the data resident in their systems.

IT pundits tout data as a strategic asset, but the value of data is often seen as unquantifiable, somewhat like goodwill, intellectual property, patents and business methodologies. Even when executives understand the value of their company's data, they don't always make it a priority to monetize it.

In quantifying the value of data, it's critical to separate the gold from the pyrite. One method is to determine its material value to the business. For instance, by applying analytics in real time to data from revenue-generating initiatives, companies can adjust rapidly to changing market conditions. That data thus becomes a tangible, valuable asset.

[ Find out how retailers harness big data. Read Big Data Helps Retailers Target Mobile Customers. ]

It's widely understood that social media companies seek to monetize the data they capture from personal and professional interactions. They rely almost exclusively on advertising revenue, thus their data is valuable to ad buyers who use that information for brand building or more effective customer segmentation and marketing. Those social media customers reach their own customers through both online and mobile platforms, via a variety of tools for cross-selling, up-selling, next-best offers, customer attraction and attrition management.

Online merchants analyze customer transactions at the point of sale or via online product searches, then tailor their offers by aligning a customer's purchasing patterns with similar shopping behavior information from other customers. The more data these merchants collect on each consumer, the better the targeting becomes.

There are newer, innovative approaches to data monetization. Consider data markets, a model that has the potential to create a new market category. Here, companies buy, sell or trade data for mutual benefit.

For example, a cloud service provider that has installed a large-scale technical infrastructure can analyze the performance and reliability of the installed hardware. It can then provide insights to the hardware vendor -- via a data market -- on warranty issues and improvements to incorporate into future versions of the hardware. In return, the hardware vendor can provide the cloud service provider with complementary product or component performance data. The hardware vendor could also supply the cloud provider with product enhancements or service-level customizations in exchange for the product feedback.

Similarly, a maker of snacks might decide to share its proprietary product nutrition information with a data market in exchange for consumer feedback on the product by specific locations. The data market, in turn, might sell or exchange the snack company's nutritional profile data to a developer of a smartphone app that compares the calories and fat content of various confections and offers product recommendations.

As you can see, significant revenue-enhancing synergies are possible. The cloud service provider delivers a more reliable service; hardware and software providers gain product usage information to improve their goods and services; the snack company gets field data from a variety of data sources not previously available to it; and the software company has the accurate data it needs to build its application.

With all this data sharing, there are issues to resolve. Privacy is a critical concern, especially when companies are looking to market potentially revealing customer data. This is where terms-of-service agreements and privacy policies play a critical role. If companies keep their customers well informed, and they include appropriate opt-in or opt-out provisions, they can mitigate privacy concerns.

They should make such notifications and agreements easy to understand and, when preparing data for sale or trade, ensure they scrupulously comply with all privacy regulations. Failure on this front can be incendiary. Companies also need to ensure that their valuable data doesn't land in the hands of competitors.

IT can assist by scrubbing the data for the purposes of sharing it to minimize competitive risk. C-level execs must also begin to understand that the potential risks can bear even greater rewards. It's a new world out there, and there's money to be found in data for those intrepid enough to look for it.

John Lucker is a leader of Deloitte Consulting's Global Advanced Analytics & Modeling practice. Ajit Prabhu is a principal in Deloitte Consulting's Strategy and Operations practice.

E2 is the only event of its kind, bringing together business and technology leaders across IT, marketing, and other lines of business looking for new ways to evolve their enterprise applications strategy and transform their organizations to achieve business value. Join us June 17-19 for three days of 40+ conference sessions and workshops across eight tracks and discover the latest insights in enterprise social software, big data and analytics, mobility, cloud, SaaS and APIs, UI/UX and more. Register for E2 Conference Boston today and save $200 off Full Event Passes, $100 off Conference, or get a FREE Keynote + Expo Pass!

About the Author(s)

John Lucker

Principal, Deloitte Consulting LLP

John Lucker is Deloitte’s global advanced analytics and modeling market leader and a leader for Deloitte Analytics. He provides clients with end-to-end strategy, business, operational, and technical consulting services in the areas of advanced business analytics and analytic business solution approaches. His clients are in many industries, including insurance, banking and financial services, retail, consumer products, telecom, healthcare, life sciences, media, and hospitality. He has developed unique advanced analytic business solutions and methods as well as the operational, change management, and technical implementation tools to realize analytics' latent value. He often speaks about these topics for industry and professional organizations, and he writes for a variety of publications. He is a co-inventor of four predictive modeling patents and three pending patents. He holds a BA and an MBA from the University of Rochester.

Never Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.

You May Also Like

More Insights