The process to generate revenue streams either directly or indirectly from data. An organization might use data or analytics to augment existing products or create new data or analytics products for sale.
These examples show that there are multiple ways to monetize data. Most data-driven organizations (let’s call them data suppliers) start small by harnessing data for external consumption. But as they gain experience building data-driven applications and see positive returns, they turn their sights outward to customers, suppliers, and the broader industry.
The first step in monetizing data is to recognize that there are three approaches: 1) Deliver data analytics internally to employees so they can make better decisions, optimize processes, and reduce costs. 2) Enrich existing products with data analytics, improving customer retention and preserving market share. 3) Sell data products and services to customers, generating new product lines and revenue.
To monetize data assets, organizations provide customers with business intelligence (BI) and analytics tools to view, interact with, modify, model, and visualize information. More specifically, BI delivers static and interactive reports and dashboards that enable customers to track business activity and measure performance. Analytics takes BI one step further--it enables data-savvy analysts to query, explore, and visualize data as well as create data models that expose patterns and relationships buried inside data sets. By giving customers powerful tools to understand trends, evaluate options, and make better decisions, data suppliers enrich their products, gain loyal customers, and generate new revenue streams.