Data-driven approaches are increasingly valuable as our ability to store massive amounts of it, the computational power to crunch through it, and the advanced analytics to make sense of it have come to maturity. These opportunities have led to the development of major facilities for aggregating, analyzing, and monetizing data from industrial sources. But the promise of Big Data, machine learning, and data analytics is predicated on access to data. This article delves into four distinct but somewhat overlapping challenges at play in terms of access to data: ownership of data, data nationalism, cybersecurity, and data privacy.
Decision support for distribution automation: data analytics for automated fault diagnosis and prognosis
