AI: The view from the Chief Data Science Office Posted on : Sep 21 - 2018

It's challenging to get data scientists where you need them. And if you're managing an AI project, better be prepared for handling moving targets. These are some of the results of a survey of chief data scientists and analytics officers that we recently concluded.

During a briefing with Kimberly Nevala, director of business strategies for SAS this afternoon, we facetiously posed the question of why they were wasting their time engaging with clients about artificial intelligence (AI). The topic of her talk last week at Strata on rationalizing risk with AI and ML struck a chord with us. Navala's message was that understanding what your models can and cannot do is key to the getting AI to succeed in your business, with her presentation outlining how to quantify your confidence levels in AI and ML models.

One of our most-read posts was the one a few months ago about the importance of not forgetting people and process when running AI projects. Over the past few months, we had the chance to speak at depth with over a dozen senior analytics and data science executives to get a better handle on managing the people and process side of AI projects. In full disclosure, this was a survey cosponsored by Ovum and Dataiku.

We targeted early adopter organizations that were well ahead of the curve with dozens to hundreds of projects in production. Given that each organization had dozens of data scientists or more on their staffs, the insights we received reflected over a century of staff years of experience. By far, the brunt of their AI projects was in machine learning.

We wanted to know, what was the impetus for AI, what was the criteria for utilizing AI, how do you staff and manage projects. But probably the most interesting question was how AI projects differed from more traditional data science.

As we've noted, you can't do AI projects without the data science. Not all data science projects require AI. For instance, if a customer segmentation model for a highly stable market, such as home heating oil deliveries, probably doesn't require a lot of machine learning if you have a neighborhood with a stable housing stock and demographics. But if you are trying to stay a step ahead of cyber attacks, machine learning or deep learning models may be necessary because of the constantly morphing threat.

Another core assumption with AI is the central role, not only of models, but data. And because AI models are extremely hungry for data, errors in data set selection or data quality can readily snowball. If getting the data right is important for analytics, it's even righter for AI models.

So should the impetus for AI start from the top down, or is it more effective for ideas to percolate up from the trenches? Given the makeup of the survey group, it wasn't surprising that in most cases, the inspiration for AI came from the C suite. But that doesn't mean that CEO mandates are the only way to go. View More