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3 Connecting the data dots Data is at the center of every industrial application, process, and business decision. While forward- thinking manufacturers use data to unlock insights that drive innovation, too often those efforts are confined to point solutions that don't scale across the enterprise. That's because industrial data systems are often sprawling, siloed, and complex, with diverse data sets spread out across data lakes, cloud databases, IoT devices, and on-premises systems. What's more, the machines, sensors, and devices found in industrial organizations all generate a massive, tangled trail of performance-tracking, real-time, and other unstructured data. Further, disparate IT and OT data sets bring not only physical challenges, but cultural and organizational challenges as well, making them difficult to unify. As a result, manufacturers struggle to know where all their data sits, how to connect and act on that data effectively, and how to manage data access. A comprehensive industrial data strategy connects, unifies, and enables accessibility to the massive quantities and types of data generated in an industrial setting to accelerate engineering efforts, optimize operations, reinvent supply chains, and so much more. It provides a governed, data-driven approach that can be cost effectively scaled across organizational use cases to achieve business outcomes. An industrial data strategy not only breaks down data silos, it also structures data and makes it more available, enabling manufacturers to capitalize on advanced, real-time, and predictive analytics or use artificial intelligence and machine learning (AI/ML) to determine the next best action to improve production. Amazon Web Services (AWS), the world's most comprehensive and broadly adopted cloud, has helped dozens of leading manufacturers transform their operations using the power of data to optimize productivity, quality, and sustainability. Read on to learn how an end-to-end industrial data strategy can help you create a standard way of managing asset data to drive results for your business. Challenge: Scaling from proof of concept solutions Without a comprehensive data strategy, most industrial customers start with a single use case, such as predictive maintenance in one of their plants. They typically do a proof of concept, and when that's successful, they decide to roll it out to other plants. But because those plants have different data structures, different standards, and different tooling, the solution that worked so well in the first case may not work in others—even if the problem is exactly the same. A better way to digitally transform is by first managing all the data across the business before launching a use case. Companies that take that approach position themselves to address multiple use cases, with a fraction of the time and effort and a much greater chance of success. What's more, driving data management at scale enables greater innovation and agility organization-wide.