Turning Data into Product

With today’s technology not only can nearly everything be gathered, counted and measured, but the information can be stored and then processed at record speeds. The result is analysis that goes beyond sums, averages and basic statistics to aggregates, benchmarks, recommendations and predictions. So what does one do with all of this game-changing data and analysis? Create a data product. They’re all around us and they’re changing the way we as consumers interact with companies and the way businesses interact with each other. Tom Davenport, of Competing on Analytics fame says, “It is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.” Analytics 3.0 by Tom Davenport.


Defining a Data Product

In the simplest terms, data products turn the data assets a company already owns or can collect into a product designed to help a user solve a specific problem. Still unsure? Here’s an example. Think of car shopping fifteen years ago. You relied on write-ups from industry magazines or ratings in publications like Consumer Reports to help gauge performance and reliability, then drove to the handful of dealerships in your area to price shop and hoped for the best. Today, you use a data product. You access reports and dashboards that tell you precisely how much people across the country paid for the exact same car, how often that make and model goes in for repairs, estimates for how much you’ll spend on gas and tune-ups, what you can expect the trade-in value to be in five years and nearly any other piece of information you will need to make an informed decision. You’ve also probably shopped for hotels using data products, harnessed sites like zillow.com to research current and future estimated housing values and allowed devices like your DVR to suggest other shows you may like based on your previous history.

Finding Value

Companies benefit from data products in two ways:

Direct revenue:  Charging consumers for access to the data and analysis.

Indirect revenue:  Augmenting existing products or services, driving customer loyalty, generating cost savings or creating revenue through alternate channels. For example, you may be able to use the data product to research your car purchase for free, but the company that provides the analysis sells advertising on its site. Or, an organization may stay loyal to a particular staffing and training firm because of the data products they offer that generate analysis on retention, hiring costs and benchmarking information about other similar companies in their industry.

Both online and traditional companies are getting in on the game. In healthcare alone there is an estimated $300B to $450B in reduced healthcare spending achievable through data applications. For example,  Blue Shield of California is parterning with NantHealth to develop an integrated technology system that will allow doctors, hospitals, and health plans to deliver evidence-based care that is more coordinated and personalized. This will help improve performance in a number of areas, including prevention and care coordination.1 Data products like these are beginning to revolutionize the information we collect, moving it from a state of rest to a state of interaction.

Rules of Engagement

So what makes a good data product? While it may be tempting to think that any organization of a large set of data can be productized, that really isn’t the case. Here are some guidelines. Data products:

  • Go beyond simply putting data in pretty packaging. They contain unique connections to data such as benchmarking results; mashups of disparate, possibly public, data sources; calculated and/or composite metrics.

  • Generate insights and guide users to decisions rather than just supply data points.

  • Are designed with the audience in mind – not just how they will view the information, but how they will interact with it

  • Use data exhaust – consumer generated data that is “left behind” during their product interactions.  You can use this to improve interactions and product usage.

  • Are part of an evolution of how we want our audiences to consume information.


Intrigued? We’ve barely scratched the surface on the data products and what they can do. Here’s a couple of items to add to your back to school reading list to really get your wheels turning.

  1. Data Jujitsu by DJ Patil.  An e-book, so its not a quick lunchtime read, but easy enough to digest in an evening.  Provides a really good roadmap to organize your thoughts and priorities on data products. You may find yourself re-reading it often like we do.  It’s on Amazon too and his SlideShare is pretty awesome too.

  2. The evolution of data products by Mike Loukides.  Some great examples and validation of how the data itself isn’t the product, but a means of “enabling their users to do whatever they want, which most often has little to do with data.”

After you’ve added these articles to your Flipboard or Zite, register at a local ProductCamp. There are a few coming up this fall, and they’re a great place to do some mingling with Product Managers, get tips on product thinking or brainstorm your data product ideas. Juice will be at the one in Atlanta, registration here,  in October –so if you’re in the area, drop by and say "Hi". Otherwise check out some other ProductCamps coming up this Fall all over North America.


1 The big-data revolution in US health care: Accelerating value and innovation, April 2013, McKinsey & Company

Photo credit: Dave Bleasdale