Augmenting products and services with analytics can help to monetize your data.
Data analytics continues to evolve, from finding insights in data and making predictions to providing self-service capabilities embedded in everyday applications. The next evolution, however, is turning analytics into a new revenue stream.
Data monetization is considered the next wave of business intelligence, moving analytics from a cost centre to a profit centre. And with embedded analytics, you can turn your data into dollars by offering it as an add-on to an existing or new product, service or application.
The evolution of data analytics
Historically, we’ve used analytics to create reports and dashboards, and to create models based on historical data to make predictions about the future. With this next evolution of business intelligence, however, organizations can augment existing products or services with analytics or build standalone analytics solutions—creating entirely new revenue streams.
Executives are starting to realize they’re sitting on a gold mine, says BI thought leader Wayne Eckerson, founder and principal consultant of Eckerson Group, in a recent webinar. And once they view their data as an asset, they can start to understand how to monetize it. For example, other companies might be willing to pay for that data, or customers may sign up for a fee-based subscription of an aggregated view of their personalized data.
These applications are increasingly moving to the cloud, which makes it easier to embed data and analytics.
“BI as we know it is about using data to make our processes more efficient to save money, cut costs and make better decisions, which could impact the top line, but oftentimes that’s hard to calculate,” says Eckerson. “The next level is enriching products, which involves wrapping information around an existing product, whether it’s tangible or intangible, and providing information to users about their usage of that product.”
Building a foundation for monetizing data
From there, organizations can start to turn their data into dollars through data-driven products and services. According to Eckerson Group, there are three basic strategies for data monetization:
- Sell the raw data that you’ve collected.
- Build analytics on top of that raw data and sell a data analytics solution.
- Upcharge customers for data analytics within an existing product or service.
At the most basic level, it’s possible to monetize your data through syndication, but many organizations are looking to add value by enriching that data—segmenting, aggregating and filtering—so it’s easier for users to analyze (and get value out of) that data. After all, the more valuable the data, the more you can charge for it. Another option is providing professional consulting services around your data, or even outsourcing your data analytics infrastructure.
The building blocks of monetizing data
To get started down this path, however, Eckerson says you need executives with vision and a cross-functional team of specialists, including developers, product managers and analytics professionals.
And, of course, you need good data, along with good processes for developing a data analytics solution, which means you need people who understand your user requirements and can translate those requirements into data products and services.
You also need a BI platform that’s capable of ingesting, aggregating, refining, securing and storing data, while giving customers a means to get value out of it—such as embedding analytics into existing applications. Ideally, you want to provide users with access to self-service reporting so they don’t need to call up the IT department for help.
“You need a strong team and a platform to support this solution. That platform has to be fairly robust, especially if you’re pulling in data over the Internet of Things [and sending it to a cloud repository],” says Eckerson in the webinar.
Creating data-driven products and services
One way to monetize data is to enrich a product or service with analytics via a dashboard or application that allows users to view data, drill down into it and interact with it or even personalize it.
Consider fitness trackers, which provide value to the user through data (via a highly personalized dashboard on a wearable device). Not only can users view activity logs, but the application makes proactive recommendations based on the user’s activities to help further their progress or reach their fitness goals.
After all, it’s one thing to deliver insights; it’s another to take action on those insights, and that’s where organizations can add value—and charge for it. And there are many ways to enrich an application with ‘actions,’ such as generating alerts based on metrics and predefined thresholds, increasing stickiness.
Build or buy?
But to succeed, should you build or buy? You can either build these capabilities in-house or buy them from a third-party provider. Consider your needs: How complex are your reports? Do you require deep integration? What are your in-house capabilities?
If you don’t have in-house capabilities, you could buy a third-party solution with best-of-breed functionality. But if you’re buying a third-party solution, look for a data analytics vendor that will partner with you on a white-labelled solution.
“Companies need to white label the analytic GUI [graphical user interface] so it looks and feels like their core product or application. They may also want to add functionality not available in the tool or build bidirectional data flows between their application and the data analytics product. Both cases require time and money,” according to Eckerson Group’s A Guide to Monetizing Data.
Eckerson recommends looking for a platform with a flexible data architecture that’s designed for the web, for the cloud, for mobile and for APIs. For example, Dundas BI is designed for white-labelled, embedded BI, so you can enhance existing products and services with embedded analytics or create new standalone data products. White-labelled data products can be deployed on-premises or hosted and sold in a SaaS model.
For instance, IT Weapons—a company that provides secure cloud solutions and managed IT services—integrated Dundas BI into their platform to build a branded client analytics portal. Their clients now have the ability to run self-service analysis on their data to meet their daily needs. By improving the user experience and increasing their accessibility to data, IT Weapons has been able to open new revenue streams.
Next steps
Build it (or buy it) and they will come? Not necessarily. It’s not enough to build a new analytics capability; you need to ‘operationalize’ it—whether it’s a standalone product or an add-on to an existing product.
Eckerson recommends asking some key questions: How are you going to sell it? How are you going to market it? How are you going to support customers? Do you need to retrain your sales people? Are there legal issues related to privacy if you’re using customer data?
You’ll also have to figure out pricing for your data analytics solution. One of the more popular methods is to create a tiered model: basic reports for free, with self-service analytics for a small fee and additional functionality for a higher fee.
Ultimately, it comes down to knowing your users so you can provide value they’re willing to pay for.
“Just throwing some data and some dashboards and reports up there is not going to get you any adherence—it’s probably not going to succeed at all,” says Eckerson. “What you need to do is figure out what your customers really want. And that’s not easy. It requires you to roll up your sleeves and do some requirements gathering, understand what they’re trying to do and how they use information, and where their pain points are.”
For organizations sitting on a gold mine of data, the possibilities are endless—but understanding your users, creating a business case, getting executive buy-in, developing a cross-functional team and finding the right platform are all key steps to successfully monetizing your data.
About the Author
Vawn Himmelsbach is a writer and editor specializing in enterprise IT, writing for national newspapers and technology trade magazines on everything from AI to zero-day threats. She also spent three years working abroad as an Asian correspondent, covering all things tech.
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