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. The survey confirmed that AI and ML initiatives are front and center at most enterprises.

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. The survey confirmed that AI and ML initiatives are front and center at most enterprises.

Artificial intelligence (AI) is actually a group of linked technology. It is complex and, to many people, a bit frightening. It is also huge and expanding rapidly. That size is evident in both value projections and the expansive range of industries using the technologies. In February, Markets and Markets valued the 2017 AI market at $16.06 billion.

So, began Kimberly Nevala, Director of the SAS Business Strategies, Best Practices and Advisory, and Business Solutions who was in Australia for a flying visit. We caught up following an interview in April this year at the SAS Global Forum. Nevala is a well-published expert on most things "Big Data" and she is not afraid to broach the big issues like Big Data for good or evil. Today we mainly chatted about Big Data and machine learning to get her unique perspective on what is a reasonably controversial subject – big data, machine learning, and the insights and patterns relentless machines can uncover. The interview took the form of a chat, so it is all paraphrased and attributed to Nevala.