Keys to Data Monetization

Data analytics professionals have toiled for years in relative obscurity in the back office of their organizations. They’ve created data warehouses and data marts, delivered reports and dashboards, and implemented self-service analytic environments to help business users make more informed decisions with data.

During the past five years, business executives have finally begun to see data as manna from heaven. Their organizations are awash with it. Thanks to the incessant drumbeat of big data evangelists, executives now see this plentiful resource as raw material for new products and services. Even executives in old-line manufacturing businesses—such as automobiles, lighting, and appliances—see information as the future.

Consequently, the stock of data analytics professionals has risen. If they are savvy and forward-looking, they can turn their cost center into a profit center and orchestrate a new, profitable career path.

Keys to Data Monetization

Monetizing data is not for the faint-hearted. It requires time, energy, investment, and an assortment of business and technical experts with complementary skills. Although an organization might have stellar internal data analytics capabilities, this doesn’t necessarily translate into profitable data products and services.

To succeed with data monetization, an organization needs the following:

• Vision– Executives who understand the potential for monetizing data and allocate their time, energy, and trusted lieutenants to execute the vision.
• Team- A close-knit team of product managers, data architects, analytics specialists, application developers, and sales and marketing professionals who turn data into dollars.
• Data- Voluminous data with lots of attributes that is clean, consistent, and timely. Product usage data and customer transaction and interaction data are good candidates.
• Analytics- Analytics that provides shape and meaning to the data through categorization, calculations, summarizations, benchmarks, and models. Data becomes more valuable the more
  it is processed and analyzed.
• Processes- A development process that tailors data and analytics to target customers and go-to-market processes that price, sell, market, service, and enhance the data product throughout its life-cycle.
• Delivery- A delivery system that distributes analytics to users. It can be as simple as a PDF document delivered by email, or as sophisticated as an embedded analytic service within a cloud application.

Data Analytics Platform.

A scalable, high-performance computing platform that supports a rich information supply chain that refines data for various use cases and a comprehensive set of reporting and analysis capabilities designed to meet a majority of business requirements. In short, organizations
that want to monetize data need to develop a comprehensive business plan that treats data like any other product. The plan needs to define the goals, the team, processes, and technology to design, sell, and support the data product over its entire lifecycle.

Three levels of Monetization

There are three levels of data monetization. Each delivers unique benefits and requires different mechanisms to implement. Not all levels put cold, hard cash in your organization’s pocket; some monetize data indirectly. I will explain the three levels in detail in the upcoming articles.

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