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Reinventing with the cloud PAID PROGRAM Reinventing Your Business With Data Why modern organizations are investing in getting better value from their data. By Rahul Pathak, VP Analytics, AWS Creating a data strategy has become a top priority for most leading organizations. That's because with any major business initiative, from creating new customer experiences to building new revenue streams, leaders must be able to quickly gather insights and get to the truth. They want to know the truth about how effective their operational processes are, what customers think about their products and services, and how they compare to competitive alternatives. To do this, they need to build a data-driven organization. The data-driven organization Data-driven organizations seek truth by treating data like an organizational asset, no longer the property of individual departments. They set up processes to collect and store valuable data. Their data is democratized, meaning it's available to the right people and systems that need it. And their data is used to build new and innovative products that use data and machine learning (ML) to deliver new customer experiences. When opened up across the organization, data can help organizations make better decisions, respond better to the unexpected, enhance the customer experience, uncover new opportunities, and improve efficiency. Data-driven organizations in action A great example of a data-driven organization is The BMW Group. When it needed to innovate faster to keep up with consumer demands while also providing employees with more data to make decisions, BMW migrated from an on- premises data lake to its Cloud Data Hub on Amazon Web Services (AWS). The Cloud Data Hub processes terabytes of telemetry data from millions of vehicles daily and makes it easily accessible to internal teams. With its Cloud Data Hub, BMW employees can gain insights from several petabytes of data coming from BMW vehicles around the world. For example, they can monitor vehicle errors to identify potential issues across vehicle lines or apply ML to better forecast the demand for its range of vehicle models and equipment options.