The Future of AI for Enterprises: A Q&A with Sri Raghavan:

Sri Raghavan, senior global product marketing manager at Teradata, answered a few questions on algorithms, bias detection and the maturity of enterprises using AI.

As data analytics progresses, do you think there will be significant progress in the sophistication of algorithms?

It’s not so much that one algorithm is going to make a difference in your life. Gone are the days when there was a silver bullet you could used to be able to address questions.

To get better analytics, we are doing a couple things. We are, of course using new algorithms and theoretical practices to be able to develop algorithms, but we are also combining algorithms in a multi-genre way. That’s Teradata’s way — being able to mix and match intelligence. Yes, new models and algorithms are always developed, but we are always finding ways to mix and match the techniques, and we are getting better results over and over again.

How can enterprises be aware of any biases appearing in their analytics?

There’s no universal panacea for this, and that’s an unsatisfactory answer. Everyone is biased. Once you have a frame of reference in your mind, a bias always occurs, unless it’s a tabula rasa, which no one past 6 months of life ever has. It’s impossible not to remove biases out of the picture.

I’m not concerned about removing biases in a numerical notion. What I’m concerned more about is if you are aware of your biases. Then an honest person will at least acknowledge their biases and provide the recommendations that work around them.

I don’t think biases will ever go away. But I want people to understand that there are biases that they bring to the discipline of data collection. And as long as they are able to detail it out in as honest of a way as possible, that’s the best thing you can do.

Do you think organizations are properly structured right now to deal with the level of agility they need to have to be data-driven in the manner described in “The Sentient Enterprise” book?

It’s a hard question to answer, because many organizations don’t experiment with organizational structure to figure out sentience. The notion of sentience is something we are — no pun intended — becoming conscious of, but it involves many parts of the organizational hierarchy. Teradata recognizes that a business needs to center around the customer. How are you impacting your customers, and how are you working for them? It affects other activities in the organization.

Where are we in the continuum of AI use in business?

I don’t believe that AI even has come to the point where people are focused on ROI. I think there is still an enormous amount of confusion about what AI is. Everyone talks about AI and machine learning, deep learning, neural networks, expert systems as if all these terms can be interchangeably used. We have a huge taxonomic confusion. People don’t understand the genealogy of how these disciplines evolve, and they don’t quite understand how they can be applied to address customer problems. The industry is getting better at education, but many companies have a long way to go before they significantly lower the barrier of AI use.

If companies aren’t focused on ROI yet, how can they get there?

One of the big things is not to focus on AI as monolithic. There are many intractable problems today that can be addressed through perfectly well-established techniques, not that AI isn’t well established — it’s been around forever. But what’s most important, and what Teradata has been focused on for quite some time, is it’s not the technology or the underpinning analytics. It’s about what ails you. Is your problem customer attrition? Is your problem fraud? Is your problems patients are not satisfied? Is your problem the need to provide quality services?

Once you put a frame around that and define the use case, then that automatically allows data scientists and analytics professionals to pick and choose the various analytical techniques which then inform the use cases.

I think that’s a far better approach than a frame of reference that says, “I’m going go to do AI.” It’s not fitting the use case of the problem to the analytic; it’s fitting the analytic to the problem. The problem needs to comes first.

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