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As organizations continue to evolve their information strategy and find innovation opportunities, many seek to generate value through data monetization. Today, an increasing number of companies are actively seeking ways to monetize their data both internally and externally. Although still nascent in some industries, data monetization is already prevalent in certain sectors, including utility and energy, financial services, and high-tech. In fact, according to the March 2017 Forrester blog post, Insights Services Drive Data Commercialization, by Principal Analyst Jennifer Belissent, Ph.D., “the technology industry is at the vanguard of data commercialization.”
While data monetization at its core is tied to a tangible financial value, it does not always translate to a commercial product. In our experience, companies monetize their data in one of four ways:
- Creating Data Products or Services: There are many examples in this area, including creating subscription-based analytics products with the data collected from IoT devices to provide consumers with trends, patterns, and insights about their usage. This strategy creates a subscription-based revenue stream, while the physical good itself is often a one-time purchase.
Other examples in this category include enriching upstream manufacturing data for sales into downstream part distributors or building an information services and consulting business in data intensive sectors, such as healthcare, accounting and tax services, or marketing services.
- Establish New Partnerships by Sharing Data: With this strategy, monetization is not a direct outcome, but a means to an end: a way to build partnerships and expand market share. Examples include banking services that share anonymized customer segment analysis with their retail partners, or supply chain operators sharing data with retailers to optimize shipping logistics and improve customer satisfaction.
- Optimize Business Processes to Save Costs: Using data can immensely impact the way an organization runs its business processes. For example, by knowing the types of customers that respond well to a certain product, companies can improve their marketing spend and sales strategy and reduce the time and resources needed to win new customers.
In the internet of “industrial” things, monitoring device data has replaced reactive services giving organizations the ability to be preventive as opposed to reactionary. For instance, many are using real-time product performance data to predict failures and create preventative maintenance strategies that avoid down-time and are often cheaper than dealing with issues after the fact.
- Enable Better Decision Making that Uplifts Revenue: Contrary to the previous point where data monetization is aimed at cost savings, this strategy is about leveraging insights from customer usage data to enable product enhancements. For software or sensor-based systems, Machine Learning can glean insights from customer interactions to help identify usability improvements as well as new ideas for innovation and product development.
Other examples in this category include using data to better understand customer propensity to buy or create product recommendations to increase share of wallet.
While creating a data product can be the most lucrative option of the four, it is important to realize that a new data product, just like any other product, requires a full spectrum of support and services. These include continuous innovation and development, customer service, licensing, entitlements, marketing, sales, operations, resources, and staffing.
Companies who have successfully created data products are those that have done this methodically and in a phased approach. Perhaps McKinsey’s recommendation sums it up best when they advise organizations to:
“Focus on yourself first,” stating that it is “nearly impossible for a company to succeed at creating externally focused data-based businesses while still struggling to get clean, consistent data that are shared internally across the organization. Before companies start down the path of monetization, they should take the time to shore up their data foundations, which will help them build the business case and technical platform they need to monetize data effectively.”
This is an intuitive, but important, first step for those seeking to capitalize on data monetization opportunities. However, the real challenge is turning raw data into information. This is because data, whether from clients’ system of records, internal, external or public sources, is often disparate, comes in different formats, and is frequently complex and inconsistent. Unfortunately, conventional techniques, such as manual manipulation through Excel or SQL are usually time-consuming, error-prone, and often require additional headcount to get the job done. Nevertheless, it’s clear that the quicker companies automate the collection and validation of data, the sooner they can get to revenue.
As shown in the case studies collected in the e-book How 5 Companies Monetize Their Data Assets Using Self-Service Data Prep, businesses are using Self-Service Data Prep to accelerate their manual data prep processes, maximize the productivity of their internal resources, and enable the monetization of their data assets.
In fact, Self-Service Data Prep technology allows data-to-information process to take place in the line of business where most of the knowledge about data, its context and understanding resides. This enables organizations to turn data into monetizable information assets – rapidly and seamlessly.