Data at Work

In my line of work, many organizations ask me how they can sell data in a secure and scalable way. This questioning is to be expected, since data monetization - and the data economy as a whole - is still a relatively new and growing area of development.

Having been involved in hundreds of these conversations, I've realized that there are two basic questions that that need to be addressed when starting out:

1. “What is the process for monetizing data?”
2. “How do you create valuable data products for external users?”

To answer these questions in a fun way, I've produce the following infographic (and companion guide) that breaks down each step in the process. 

Like any commercial agreement, data product sales are bound by terms and conditions between a data vendor and data buyer. These T&Cs include service level agreements for uptime and response time, authorizations for use and resale, and technical support for bug fixing and API integration. If a vendor is new to the data economy, these T&Cs may be a daunting task and require significant institutional change.

The following post if part of a five part series detailing how to successfully bring a data product to market. To learn more, download the free “Visionary Leader’s Guide to Data Monetization.”

Direct Sales vs. Chanel Approach:

Direct:

If the primary consumers of a data product are easy to define, segment and access, then vendors may find it useful to employ a direct sales force. This is especially true if data sales are a primary component of the vendor’s business model.

The following post is part of a five part series detailing how to successfully bring a data product to market. To learn more, download the free “Visionary Leader’s Guide to Data Monetization.”

Economic Trade-offs:

Based upon decisions in the previous step (Go-To-Market Guidebook #2: Built for Buyers), a data vendor should now hopefully be able to assess the cost to make data available for sale. This effectively serves as the price floor for the data product. From there, the task of pricing data is a balance of art and science.

Once earmarked for commercialization, a data product must be structured correctly in order to be credible and sellable to promote potential data sales

In order to appropriately construct a product, it is important for organizations to address the following four considerations.