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We recently had the opportunity to work with the data team of one of the world’s most admired heavy equipment businesses. They’ve been featured in many strategic business cases in the last decade for everything from their supply chain strategy to their content marketing strategy. This time around, they are building a world-class data strategy. I’m sure we’ll hear a lot more about it in the future.
The initiative started when their COO asked their CMO to drive more targeted, local marketing campaigns. These campaigns would would have a direct effect on sales, supply chain and thus, both their own and their distributors’ working capital. To achieve this goal, they needed a strong data strategy.
To achieve the goals set out for them, the company started adding microcomputers and sensors to their heavy equipment. This initiative gave them a new data feed that can map out soil properties in real-time across the US. This map helped them construct the best mix of equipment and complementary goods for each geographical location.
After this initial success, their data team now plans to use this project as a foundation for future change. There are many initiatives they can start given the data strategy they’ve established. Here are a couple of examples that I believe would make a strong business case:
What if we disrupt our consumer experience by providing our industrial customers an overview of all the machines they own, leveraging real time geolocation and sensor data for predictive maintenance? No need to go to the local mechanic based on data that you manually maintain (e.g. when you bought each machine, its usage and maintenance schedule, and so on). As a result, the manufacturer can now actually provide maintenance as a service in a cheaper, more efficient way, opening new recurring revenue streams.
What if we can integrate these new collections of data into our daily business operations? By doing so, we could anticipate customer churn by building thresholds and triggers around our customers’ use of our equipment. These thresholds could generate a message to cross-functional teams, like product management, quality assurance, and the sales account manager.
What if we can develop a machine learning algorithm that analyzes an incoming data stream of machine defects and correlates that to our production traceability data? By doing so, we could automatically understand that the machine defects that occurred last week all use parts that originated from the same batch. This might automatically predict other issues and trigger a recall before we lose any reputational damage.
The future of commerce has endless possibilities for creating new customer experiences and associated revenue streams, new ways of working together (see the digital workplace) and new ways of automating business processes for increased agility.
Every business is becoming a data business. How you are expanding your data initiatives to stay ahead of the curve?
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