What Leaders Must Know About Data for Machine Learning

Machine learning is taking predictive analytics to the next level to drive tangible business value for a wide array of industries. Algorithms allow credit card companies to detect fraud in real time and help retailers direct offers to the customers most likely to respond. In health care, tools powered by machine learning help doctors transcribe notes more easily so they can focus on patient care. Manufacturers can take in data from sensors on plant equipment and recommend maintenance before malfunctions cause production delays.

But machine learning models are only as good as the data they ingest. “If data is not clean, if it’s not accessible, if it isn’t stitched together to form a strong foundation, the machine learning and artificial intelligence capabilities built on top of it will have problems,” warns Ashok Srivastava, senior vice president and chief data officer at financial software provider Intuit. This can lead to difficulties such as inaccurate insights or inherent bias — factors that can hamper intelligent business decision-making.

Fortunately, businesses can avoid these perils by designing a data management strategy that develops new capabilities, initiatives, and roles around machine learning. This guide aims to share lessons from business leaders and industry experts on how, with the right policies and frameworks in place, data can serve as a strategic corporate asset.

 Digital
AWS

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