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AiThority Interview with Shaun McGirr, Field CDO at Dataiku

Shaun McGirr, Field CDO at Dataiku

Hi, Shaun.Welcome to our Interview Series. Please tell us a little bit about your journey in the AI tech space and how you began at Dataiku.

Hi Sudipto, thanks for having me. Before joining Dataiku I worked across many domains and countries, all around the data lifecycle from survey design, through data management and warehousing, business intelligence, and data science, machine learning and AI. From 2016-21 I was the first data scientist and then Head of Data Science & Business Intelligence at Cox Automotive UK. I spoke at quite a few Dataiku events, and they liked how I explained things, so they offered me a job as an AI Evangelist. I couldn’t turn down such a cool job title!

A lot of research is happening on AI and machine learning’s role in transforming the IT and Cloud industry. Could you tell us how Dataiku fits into this whole next-gen tech landscape?

The speed at which new tech is currently developed is often too fast for anyone but the likes of Google and Uber to take full advantage of everything the Cloud can offer, or make the most of ChatGPT. How will those opportunities reach the other 99.9% of organizations? Dataiku makes the data accessible, useful, and therefore valuable to all different kinds of people in an organization. We do the hard work of integrating with the next-gen technologies so that everyone can get to work applying them.

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We are curious about understanding the different AI capabilities that emerged in the last 5 years. What are the key trends that emerged in the last 2 years post-COVID?

A  trend that emerged during COVID and has continued is the enhanced need for collaboration. Once everyone was sent home and had to pivot to working on Zoom or Teams, the nature of collaboration entirely changed. This had a profound impact on how AI was built outside the lab. In the lab, teams of one can still do great things. However, in a 100-year-old enterprise – that does not fly. AI must be a team sport, so we have seen the collaboration features of Dataiku start to matter more to our customers.

A second big AI trend is the continued fall in the cost of renting computing resources from others, i.e. the Cloud. Before COVID, it was still common to find organizations insisting they would move to the Cloud slowly, but the cost and competitive pressures through and since COVID have made that untenable. And the final big trend all seemed to happen in 2022, from Dall-E and generative art through to ChatGPT, it is clear that some threshold has been reached in the capabilities of AI, and we now all need to work out how to make them truly valuable.

What are your core offerings? Please tell us more about your AI technology specifically implemented in the:

As we are a general purpose platform, we get to see customers in every industry do amazing things with Dataiku, sometimes those are more obvious, but often the impact AI is having comes from left-field.

  • Healthcare

Healthcare companies have used Dataiku to develop AI and machine learning solutions across a range of applications. For example, the platform enables healthcare organizations to use patient data to develop personalized treatment plans and monitor patient outcomes. With Dataiku, healthcare companies can improve operational efficiency by identifying inefficiencies and reducing operational costs. One example of how our healthcare customers use Dataiku is identifying high-risk patients who are likely to need costly interventions. Dataiku has helped healthcare companies to enhance their decision-making capabilities, optimize clinical workflows, and improve patient outcomes.

  • Banking and finance

In banking and finance our bread and butter continues to be freeing highly data-literate people from ways of working that have not changed much since the 1990s: Excel and Email. As regulations become more intricate, large majorities of those working in Banking in Finance actually need to become producers of data products, they need to become data scientists. Our customer Standard Chartered Bank has found almost unlimited opportunities to make their people more productive working with data, the most impressive being a 70-person team being able to hand off their work to 2 team members and Dataiku. After making these kinds of productivity they now have the time, data and ability to start applying machine learning to create new data products.

  • E-commerce

Dataiku has helped both retail giants, and smaller players, bring the latest AI developments to bear to improve customer experience. By now we can spot when automated recommendations are being served to us, but how often do they identify something we might truly need? A major clothing retailer worked with Dataiku and our partner Amazon Web Services to build such a capability for their website a few years ago, and it led to 42% e-commerce sales growth. One of the ways they got such great results was by including all their business stakeholders (marketing, in-store colleagues, planners) in the process of building that AI solution, which is a strength of Dataiku’s platform.

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Which industries are best suited to benefit from generative AI solutions? ChatGPT and Google’s Bard have taken the tech industry by storm. Could you let us know how generative AIs could transform various industries operating in the cloud?

At the moment I see the most traction in creative industries, and many people I know who design images or write text can really scale what they do by using generative AI. But it’s important not to get too distracted by only those applications because we see generative AI unlocking some very hard problems in other industries like manufacturing. We already know that manufacturing customers use Dataiku to rapidly prototype digital versions of potential new products, even to create thousands of new potential products based on detailed consumer preference data. Before generative AI this simply helped them conduct their search for the best ideas, starting in the smartest place to look. Now generative AI could actually build those products beyond just the idea stage. This could change the fundamental economics of many asset- and capital-intensive industries, not just those industries with more intangible inputs.

Your take on NFTs, Metaverse and AR VR technologies in AI-driven industries:

These are parallel trends that we monitor from time-to-time, but Dataiku is firmly focused on bringing the best AI technology into every company..

How can organizations quickly upskill their AI workforce?

The crucial factor in building effective AI is increasing diversity among contributors. To keep up with the rapidly evolving technology, businesses must involve a wider range of individuals in AI development, with diverse backgrounds and perspectives. This can be a little scary for some, because simply sending employees on a training course is not sufficient. The biggest question from many of our largest customers right now is how to develop a new kind of workforce for AI. Our advice is two-fold: first, avoid telling people they need to become data scientists and instead help them to develop skills that enhance their existing capabilities  (such as turning a supply chain analyst into an AI-enabled superpower version of themselves). Second,  use the training and upskilling budgets, programs and frameworks you already have, but adapt them for this specific task of upskilling the AI workforce. By following this advice and utilizing supporting technologies such as Dataiku, it’s possible to upskill hundreds of new AI workers each year.

Biggest challenges that could derail the AI race:

Personally I don’t worry too much about the still-far-off risk of AI overlords enslaving the human race. What does worry me is the day-to-day aspects of AI development. It is essential that we ensure every AI worker, whether an already-expert data scientist, or a recently-upskilled AI worker, is engaging their brain to think about what they are building, how it will be used, and its potential adverse effects. When reading about significant AI failures in the news there is often a point where an individual and perhaps their team invariably went off the road. So what could derail the AI race if the guardrails aren’t there in the first place? Every organization must manage these risks, because without guardrails, regulators will have no choice but to be much more restrictive in the applications of AI, resulting in significant loss of value.

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An event/ conference or podcast that you have subscribed to consume information about B2B technology industry: If invited, would you like to be part of a podcast episode on Ad tech/ martech, Automation / AI / NLP tech research?

One of my favorite podcasts is called “AI After Work”, from Stockholm. I love to hear the Nordic perspective on these developments because it’s slightly different to the rest of Europe (I’m in London). I was a guest recently (episode 89).

Very happy to be a guest on other podcasts too!.

Thank you, Shaun! That was fun and we hope to see you back on soon.

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Shaun McGirr is a data leader with experience across official statistics, academia, consulting, and data science in a large automotive services company. He recently achieved minor stardom in a documentary, “Data Science Pioneers,” coining the phrase “things that happen 35% of the time, happen ALL the time” to explain why quite likely outcomes are often dismissed out of hand. Shaun believes the toughest part of doing data well is finding the right questions and ensuring the answers will actually push a lever to change the world, a theme developed further in his podcast Half Stack Data Science. At Dataiku, he helps customers and colleagues identify and articulate the value of putting data science in the hands of everyone.

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Dataiku is the platform for Everyday AI, enabling data experts and domain experts to work together to build AI into their daily operations. Together, they design, develop and deploy new AI capabilities, at all scales and in all industries. Organizations that use Dataiku enable their people to be extraordinary, creating the AI that will power their company into the future. Founded in 2013, Dataiku has proven its ability to continue to develop its founding vision for Everyday AI, and to execute on its growth. With more than 500 customers and more than 1,000 employees, Dataiku is proud of its rapid growth and 95% retention of Forbes Global 2000 customers.

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