3 Ways to Monetise Data and Analytics

Despite a state-of-the-art business insights platform, Dow Chemical had a data problem. The organisation had hundreds of dashboards and thousands of reports, but none of that information was generating better decisions. 

It is not uncommon for organisations to keep investing in systems even when they do not produce the benefits they had promised, but Dow Chemical’s D&A team decided to step back and re-examine what their platform could deliver, and how. 

Dow reviewed usage metrics and used that feedback to identify and solve unresolved user obstacles. The result? The platform’s consumption increased by 25% from 2015 through 2018, during which time the business value of Dow’s enterprise analytics and BI solutions grew 4.2 times. 

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It is not enough to simply have data. The value of data comes from the insights it creates, the processes it optimises and its ability to enable better decision making. The reality is, despite data and analytics hype and expectations, most organisations are not successfully monetising their data. 

“Data and analytics can be a valuable business asset that will improve business decisions, drive digital business transformation and generate new revenue for your organisation,” says Shelly Thackston, Sr. Specialist, Gartner. “But to do it right, you need to leave behind false assumptions about data monetisation and tackle the cultural, structural and procedural barriers that cause many organisations to fail.”

While many organisations are floundering to monetise data in an effective way, others like Dow Chemical have rethought their entire data strategy to demonstrate how to successfully monetise data. 

Use data to optimise the business

Organisations that realise the promise of analytics and BI platforms and act to optimise them across the business will find true value and recognise opportunities that were not previously apparent. 

Dow Chemical created value by optimising business processes. First, the company identified which teams were using which parts of the BI for what purposes. If that team was gaining a lot of value from an under-used solution, they were asked to share their wins and stories with other parts of the business. If there was a part of the business looking for a particular solution, the team guided them to the most effective option. The constant feedback loop and iterative solutions enabled substantial revenue growth. 

Use data to address business challenges

One of the biggest challenges with data is that it can exist in far-flung siloes and fragments. Different business groups have individualised set-ups and collect their own data for their goals, but companies often lack a cohesive overarching narrative. This makes it difficult to use the data for anything in the real world. 

This was exactly the issue at Turku City Data, a Nordic AI platform provider, which found itself unable to bridge the gap between data and real-world problem-solving. The organisation’s solution was a flexible graph analytics framework. This meant data from across the business was organised at a level of abstraction such that every data point represented a person, object, location or event. Turku City Data used this easy-to-understand frame as a common language to express and explore business problems in their contextual and structural richness.  

Use data to gather better data

A common mistake that organisations make when it comes to monetising data is looking only at readily available existing data for opportunities. It is an understandable mistake for organisations that have been led to believe that data itself is inherently valuable. However, global technology company ZF Group decided that a counterintuitive approach might make more sense. Instead of looking at data they already had, the organisation selected markets to target and took a close look at what type of data would create value for that market. 

Leaders realised that the data the organisation already had—and indeed that most organisations have—offered limited value, as it is often about common subjects and optimised for internal usage. Data monetisation requires unique data that organisations do not already possess.

According to the company, they typically have only 80% of the data they need to create a new product, and the challenge is where to find the remaining 20% that makes the product really valuable. For example, the organisation sells IoT-sensor-enabled ball joints that generate data that is used to train predictive maintenance algorithms. The organisation then sells consumer-friendly analytics and visualisations to enable predictive maintenance programmes. This means that the organisation is constantly looking for new opportunities to create data that may not even exist yet, which in turn makes that data valuable to others.