There's a lot of talk among both technology vendor communities and high-paid consultants about the need to make your business “data-driven.” While it's easy to talk about being data-driven, there are some key questions you might want to ask to get clarity from a practical perspective:

• What does it really mean to be data-driven?
• How does the concept translate into pragmatic guidance that businesses can apply?
• Can a conventional organization transform into a data-driven business?

I believe that there are different ways organizations can be driven by data. Specifically, there are those that are completely data-driven, others that use data to drive a more conventional business, and still others that use data to enhance or optimize their business. In this post, I’ll examine these different classifications. In a later post, we'll consider what a conventional business can learn from these different classes of data-driven businesses to increase their competitiveness in our information-rich world.

Fully data-driven businesses

An organization that is completely data-driven competes solely on the basis of transforming information into a monetizable asset. These businesses effectively create revenue streams through their platforms to adapt information sharing into a means for value exchange. Consequently they benefit by extracting some kind of fee layered on top of the basic costs of a transaction.

A prime example is Airbnb, which is a two-sided marketplace for customers (individuals looking for temporary housing) and goods/services providers (people with space available for short-term rentals). The company basically partners with the providers, collects their information and makes that information available to the customer pool. A customer selects a provider, executes the transaction, and then a negotiated commission is transmitted back to the company. The company, however, does not own any of the assets; the assets are owned and managed by the providers. Uber and ebay are similar companies. Other information companies – like Craigslist and Angie’s list – either charge providers to post a listing about their products/services, or charge a subscription fee to customers. And of course, companies like Facebook and Linkedin use data to drive advertising revenue.

Data-infused businesses

A different class of data-driven business, which I call a data-infused business, manages and sells from an inventory of products (and possibly services) and manages the end-to-end sales process, but uses information to drive marketing to increase sales. An obvious example of a company like this is Amazon. Amazon has developed a website driven by recommendation engines that make product recommendations, and its massive supply chains are driven by predictive analytics. It relies on the sharing economy (like Uber) by outsourcing delivery to independent drivers who use a similar marketplace application. Another example is Netflix, which transacts with content providers (like movie studios) for material that can be streamed – and it charges a monthly fee to customers to generate revenue. While Netflix doesn't own the content, it uses its platform to broker content delivery.

Data-informed businesses

The third class of data-driven business, which I call a “data-informed” business, includes more conventional companies that are adapting data technologies to fit their existing business models. One example is John Deere, an equipment manufacturer that's embracing Internet of Things (IoT) devices and embedding them into its newest models of equipment. We can presume that adding IoT devices to this equipment helps generate data that can be used to monitor equipment performance, help the purchaser maintain the equipment, identify opportunities for improving designs, and find the best ways to market products to customers.

There are clear differences between the ways businesses can be driven by data:

• Completely data-driven businesses create revenue out of data.
• Data-infused businesses disrupt their markets as they use data to significantly boost efficiency and gain advantages over competitors in the industry.
• Data-informed businesses recognize that there's a need for improved data management so they can continue to be competitive.

In my next blog post, I'll examine the implications of adopting these different models as well as what companies that aspire to be data-driven can learn from the models.