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Moving Toward a Citizen Data Science Model

Use Cases & Projects, Scaling AI Marie Merveilleux du Vignaux

When picturing a data scientist, many people might imagine a highly skilled individual working alone. Indeed, data scientists are often very specialized and it is therefore challenging for individuals to replicate their capabilities in order to increase the benefits businesses receive from such skills.

Organizations like Unilever and Capgemini tackle this challenge notably by embracing the rising trend of citizen data science. In a recent Egg On Air Episode, Jason Hardman, Head of PDC Lab at Unilever, and Jon Howells, Lead Data Scientist at Capgemini, discussed the rise of citizen data scientists and what this means for more specialized roles such as data scientists. In this blog, we'll unpack some of their key insights.

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Jason Hardman and Jon Howells on an Egg On Air Episode

Building Tools to Increase Access to Data

To the wider business, new machine learning and AI developments seem quite inaccessible and opaque. The first step to solving this issue and making the most of data scientists’ skills is that of building tools. For example, Capgemini builds tools that enable non-technical profiles to engage in data analysis and exploration through citizen data science. This involves working with platforms like Dataiku to build plugins and different tools that analysts can use.

Such efforts make data more accessible and lower the barrier to entry to data analysis. Analysts can directly get insights to the hands of businesses, enabling them to make real decisions and essentially deliver better service to customers.

I’m not a data scientist by training, but I can do data science now as we’ve got plugins at Dataiku to let me do it — which is fantastic!”

Jason Hardman

How Will This Impact the Role of Specialists?

While it is fascinating that analysts can now use such specialized skills to incorporate AI and machine learning into their analyses directly within Dataiku and via Dataiku plugins, it is important to note the continuing necessity of specialists like data scientists. There is always going to be a role for specialists as organizations need them to make sure processes are robust and that insights are well tested and true. For example, teams need specialists to build the plugin in the first place and then to teach people how to use it.

team using DataikuUnilever's People Data Center (PDC) university program involves training performed by specialists to teach analysts how to use plugins. The training covers everything from interpreting statistical outputs to understanding complex data flows. The PDC university always keeps specialists on hand to make sure people understand what they are working with and don’t misuse outputs or perform other errors. Analysts need to be correctly trained to ensure they know what kind of story they can or cannot tell based on their data. Therefore, data scientists can relax — their role is not diminishing, but simply evolving.

To Conclude

Data analytics is becoming increasingly democratized and more companies are starting to consider how citizen data scientists can help them reduce costs and risks. While this new concept brings along certain challenges, including determining how such analytic roles can most efficiently work with specialized data scientists, Unilever and Capgemini’s joint efforts prove favorable for the ongoing rise of citizen data scientists.

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