Five Levels of Data Monetization Maturity

The future is data, said BI & analytics thought leader Wayne Eckerson during his keynote at our recent conference. New algorithm-based processing, the internet of things and data mining technology have combined to usher in an era of data and data-driven products.

In this new era, software applications capture essential information on the products and services they offer, for example:

  • Buyer/end user information like demographics, psychographics and personas
  • Product usage patterns
  • Service and support data
  • Sales and distribution data

Eckerson on Data Monetization in Applications

Savvy businesses will monetize the data they collect; for example, by using it to personalize their products or services, to create benchmarks, or to offer alert capabilities. In fact, according to Eckerson, a product without associated data ceases to be a product that you can monetize.

This is as true for software applications as it is for physical products like cars or hardware. In his talk, Eckerson outlined five levels of what he termed “data monetization maturity.”

What are they, and where does your organization’s software offering fit on the maturity spectrum? Here’s an excerpt from the keynote that breaks it down.


The future is data, not only for the car, but almost any device, any product that you use day in and day out. But especially for software applications. I know a lot of you are independent software vendors and creating the intelligence app is even easier for you – and more of an imperative, I would say.

So this brings us to the topic of data monetization. Data is what’s driving our products today, data is what’s driving our economy. So I’ve created this maturity model to help you gauge where you are in this whole data monetization trend.

Stage One: Distribute Analytics Internally

The first stage is what we’ve been doing for the last 20 years in business intelligence, which is to create warehouses, and create reports and dashboards and give them to our internal users, to make better decisions, to streamline processes, improve planning.

Stage Two: Distribute Analytics Externally

The next step, which a lot of companies have been doing for quite a while, is to actually distribute reports to customers to show them their activity on their accounts or the interaction with their products. This has typically been done in excel or pdf and then emailed out to customers, now it’s being done more on the web through pdfs.

Stage Three: Embed Analytics into Applications

The next stage is when things get interesting, and is probably why most of you are here today, is when we embed analytics into the application itself. So it’s no longer something that we send separately and we consume separately from the process that we are trying to optimize. It’s embedded right into the application.

Stage Four: Enrich Applications with Advanced Analytics

The next step I’ve already alluded to is to start to aggregate information across your customer base, which is easier to do if you are a web-based, cloud-based application vendor: mine that data and then spit it back to customers in the form of benchmarks, alerts and recommendations. Provide a huge valued-added service to customers, and make your product that much more valuable.

Stage Five: Sell Data or Analytics Products

And then finally the last is to actually sell data analytics. Either sell the data as a syndication service, sell the analytics as a separate capability or add on capability, or sell services – sell data analytics services.

These first two and a half stages are all inward-looking; focused on improving decisions, streamlining processes and getting cost efficiencies. These last two and a half stages are really about looking outward to your customers: improving satisfaction, loyalty and perhaps even generating new revenues.

The first stage, represented by legacy BI solutions, is to distribute analytics internally. Solutions that distribute analytics to external users, usually in the form of a spreadsheet or pdf, belong to stage two in this maturity model.

Stage three is embedding analytics directly into application workflows where users can consume them without leaving the application. In stage four, these analytics can be further enriched by mining the data for insights, alerts and benchmarks valuable to end users. The fifth stage of this progression is directly selling data or data-related capabilities and services.

Organizations typically embed BI because they can’t keep up with reporting requests or with their competition, but generating additional revenue streams also factors in the decision.

When they do embed, most companies face common pitfalls. In his keynote, Eckerson identified these as typical: cost, customization issues, integration issues, lack of functionality and lack of end user adoption.

How can software companies succeed when it comes to delivering analytics? Among Eckerson’s recommendations are: understanding your users and mapping analytics to their needs, as well as picking the right embedded BI product.

That’s where Izenda can help. If you are looking to monetize your application’s data, you need a licensing model that won’t penalize your success. Learn how our value-based pricing helps you scale – request a price quote.

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