AI, machine learning, and predictive analytics are used synonymously by even the most data-intensive organizations, but there are subtle, yet important, differences between them. Machine learning is a type of AI that enables machines to process data and learn on their own, without constant human supervision. Predictive analytics uses collected data to predict future outcomes based on historical data.
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The stakes for expert data management continue to escalate. Data volumes are expanding rapidly and it is well-understood today that companies that harness the value of this precious commodity will be the winners. With a growing appreciation for data's value, spending on data management and analytics products and services is also on the rise. IDC forecasts revenues for big data and business analytics solutions will reach $189.1 billion in 2019 with double-digit annual growth projected through 2022, according to its "Worldwide Semiannual Big Data and Analytics Spending Guide."
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IT executives and their business counterparts understand the importance of a strong data strategy and its value to their businesses, and most are starting to get the key pieces in place to drive transformation through next-generation technologies and processes. One in four enterprises now regards real-time data as critical to their ongoing operations—and another one in four is actively preparing to introduce real-time data capabilities into their infrastructures, according to a new Unisphere Research survey.
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Quantum computing will bring unprecedented advances in medicine, science, and mathematics—knowledge currently out of reach. Many secrets of the universe are on the verge of discovery. But, are we ready for everything to be unlocked? Are we prepared to manage what comes with quantum computing's limitless architecture?
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