Organizations seem to be more interested than ever about the economics of information — that is, in exploiting the information that they have for financial or strategic gain. However, based on my experience working with clients, I believe that most organizations are not ready to reap the benefits of this field — at least not yet.
This is because the economics of information — and specifically, the monetization of data — is actually the outcome of a long, sustained process. An organization has to commit the time, resources and data analytics effort before it can see significant ROI, although it may experience incremental increases in its data maturity and operational benefits along the way.
As the chart below illustrates, the costs and ROI related to the economics of information have an interesting relationship to each other. In the early stages of the process, it’s almost all investment in capabilities around data, with little if any measurable return. It’s only after an organization has addressed some of its fundamental data issues — roughly around Stages 3 and 4 in the chart — that the organization can begin to realize more strategic advantages, such as risk reduction, cost controls, and improving business inefficiencies.
Functional improvements along the way
One aspect of the efficiency gain is simple and straightforward: reducing data redundancy. In a typical organization, there are as many as 1,000 separate business data elements (BDEs) that that describe specific areas of information, such as Social Security Number, birthdate, and so on. In the typical organization, these data elements exist in multiple instances, sometimes under multiple naming schemes in spreadsheets, databases, flat files, and so on across the organization.
A data quality initiative can help resolve these redundancies, by building and curating a data inventory that provides a clearer and more up-to-date view of each data element in all its instances. When this effect is realized across multiple data elements, data quality improves across the board. This in turn gives management a more accurate and timely view of data about the organization’s operations, finances and more. It’s during this phase that costs begin to drop, just as ROI begins to take a dramatic turn upward.
Also in the final two stages, an organization matures to the point that it has a better and more efficient data supply chain — a process that stretches from creating or acquiring data to when the data is modified and eventually consumed. It’s at this stage that an organization can begin to analyze its data, and then package it in such a way that buyers can search for and purchase the data they need. Meanwhile, by structuring a commerce platform with the corporate data buyer in mind, you can gain insights into the specific types of data your buyers are most interested in, and optimize your processes to generate more of the data that is in greatest demand.
Weighing costs and benefits
The big takeaway is that the economics of information, and data monetization in particular, are real things, and can definitely impact your bottom line. However, it’s important to understand that getting there requires a sustained effort at data quality, metadata and data governance operations — steps that each come with their own demands on time and resources.