You’ve probably heard of “data democratization” and the need for putting AI in the hands of many, not the elite few. That is, enabling all people — regardless of their background, profile, or role — to gain insights from their data to work faster and smarter, together.
It surely sounds appealing, but how do we do it in practice? What does it actually entail? We’ve gained insights from those who are actually making it happen in the field, day in and day out, because they have kindly shared their stories for the Dataiku Frontrunner Awards.
The program — which is accepting submissions until July 15 — recognizes the data and AI builders who move the field forward with Dataiku. Head over to the submissions page to read stories and use cases from data practitioners across industries, and enter your own to showcase your achievements with the global community!
3 Cornerstones of AI Democratization in the Enterprise
In today’s enterprise, leveraging AI at a large enough scale to become an organizational asset requires data democratization — hence the eponymous category to highlight the pioneers who drive the field forward. Here are three cornerstones they’ve shared are part of their success:
1. Providing a Central Place to Access Data
As one of the U.K.'s largest online retailers, MandM Direct grew fast in 2020, which now means more customers and more data. Ben Powis, Head of Data Science, shared that a major challenge was to get all the available data out of silos and into a unified, analytics-ready environment. That way not only the core data team, but also analysts embedded across the business lines can leverage data to answer business questions, without necessarily involving code.
Teams can now easily push-down and offload computations for both data preparation and machine learning to GCP. Using Dataiku means this capability is accessible to all user profiles across the organization, without knowing the underlying technologies or complexity.”
-Ben Powis, Head of Data Science, MandM Direct
2. Collaboration to Leverage Cross-Functional Expertise
Giving more people access to the data means that more synergies can be created to better solve business needs. Modhar Khan, Head of People Analytics at SLB, explained that Dataiku enabled connectedness across multiple teams and drove efficiency in project decisions, as well as visibility on where support was needed.
In the past, we had many reviews to get stakeholders to understand what data was used and how engineering was applied, which went on for months. Today, they have instant visibility on the entire data pipeline.”
-Modhar Khan, Head of People Analytics, SLB
This increases operational efficiency, but also establishes a common ground for tackling the next big challenges in today’s changing business environment. Valerian Guillot, Nerve Center Data Science Architect at SLB, shares, “The data democratization has been successful in onboarding our existing population of data scientists, as well as technical experts, ranging from maintenance technicians, service quality engineers, well engineers, and more profiles who are now able to speak a common language.”
Graph showing the various profiles of the SLB employees leveraging Dataiku across the world.
3. Changing Not Only Processes, but Perspectives
A common theme in all of the award submissions is that democratizing AI eventually comes down to how people think about data and the opportunities they see.
We had an army of people copy and pasting data and, since we were now able to centralize all treatment within the platform in a lightning-fast manner, Dataiku allowed us to have different conversations about data.”
-Craig Turrell, Head of Digital Centre of Excellence P2P at Standard Chartered, when sharing insights from Building an Intelligent Data Operations for Financial Planning and Performance Management
And this can start early — Professor Perry Beaumont from Columbia University notes that, after training students on Dataiku in his finance class, “Their analytical skills increased markedly, though perhaps even more impressive was the greater comfort level they exhibited with regards to drawing connections between the mathematical results and the practical implications. The recommendations they made very much reflected a depth and breadth of understanding that went well beyond what would have been possible for them to achieve simply by reading a case study.”