August 04, 2021
August 04, 2021
Contributor: Kasey Panetta
How three organizations learned to think differently and successfully monetized their data.
Despite a state-of-the-art business insights platform, Dow Chemical had a data problem. The organization had hundreds of dashboards and thousands of reports, but none of that information was generating better decisions.
It’s not uncommon for organizations to keep investing in systems even when they don’t produce the benefits they had promised, but Dow Chemical’s D&A team decided to step back and reexamine 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’s not enough to simply have data. The value of data comes from the insights it creates, the processes it optimizes and its ability to enable better decision making. The reality is, despite data and analytics hype and expectations, most organizations are not successfully monetizing 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 organization,” says Shelly Thackston, Sr. Specialist, Gartner. “But to do it right, you need to leave behind false assumptions about data monetization and tackle the cultural, structural and procedural barriers that cause many organizations to fail.”
While many organizations are floundering to monetize data in an effective way, others like Dow Chemical have rethought their entire data strategy to demonstrate how to successfully monetize data.
Organizations that realize the promise of analytics and BI platforms and act to optimize them across the business will find true value and recognize opportunities that were not previously apparent.
Dow Chemical created value by optimizing 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 underutilized 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.
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One of the biggest challenges with data is that it can exist in far-flung siloes and fragments. Different business groups have individualized 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 organization’s solution was a flexible graph analytics framework. This meant data from across the business was organized 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.
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A common mistake that organizations make when it comes to monetizing data is looking only at readily available existing data for opportunities. It’s an understandable mistake for organizations 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 organization selected markets to target and took a close look at what type of data would create value for that market.
Leaders realized that the data the organization already had — and indeed that most organizations have ― offered limited value, as it is often about common subjects and optimized for internal usage. Data monetization requires unique data that organizations don’t 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 organization sells IoT-sensor-enabled ball joints that generate data that is used to train predictive maintenance algorithms. The organization then sells consumer-friendly analytics and visualizations to enable predictive maintenance programs. This means that the organization is constantly looking for new opportunities to create data that may not even exist yet, which in turn makes that data valuable to others.
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Recommended resources for Gartner clients*:
3 Ways to Monetize Data and Analytics
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