Finance departments are realizing significant efficiencies through data consolidation and automation. Also, organizations that have invested heavily in data are seeing great rewards. Plus, three ways ML is helping health plans connect members with the healthcare they need, improving both outcomes and plan performance. Lastly, what companies need to deliver on their goals as we enter the “golden age” of AI and ML.
By Mary Shacklett, contributing writer for Techrepublic.com
When I was a CIO, no department demanded more data than finance. Finance had a team of financial analysts who manipulated data into myriad spreadsheets and reports—and a demanding CFO who would always want more data.
By Chris Ehrlich, contributing writer for Datamation.com
A new report shows that organizations with “mature data practices” release twice as many products and increase employee productivity at double the rate of organizations with less mature data practices.
By Craig Wigginton, contributing writer for Hitconsultant.net
For years machine learning (ML) has been touted as a way for health plans to leverage the mounds of data they collect on their members, but practical use cases remain relatively uncommon. For some, the perception may be that ML is a futuristic and somewhat impersonal way to operate, but the exact opposite is true — ML is already being used and is here to stay, and when deployed correctly it actually creates a more personalized experience for members.
By Joe McKendrick, contributing writer for Zdnet.com
Can businesses trust decisions that artificial intelligence and machine learning are churning out in increasingly larger numbers? Those decisions need more checks and balances — IT leaders and professionals have to ensure that AI is as fair, unbiased, and as accurate as possible. This means more training and greater investments in data platforms. A new survey of IT executives conducted by ZDNet found that companies need more data engineers, data scientists, and developers to deliver on these goals.
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