The Myth of the "Citizen Data Scientist"

With advances in the branch of AI that is using AI to create bespoke machine learning solutions, sometimes known as AutoML there is a lot of talk of the “Citizen Data Scientist”. Which means any person suddenly made data scientist by using AI tools and services such as our platform Kortical. Some of our competitors have run ads touting cucumber farmers as great citizen data scientists. While AutoML tools do empower non data scientists to realise the benefits of AI, you still need some data science capabilities to realise the full benefits. This article aims to debunk some of the hype and tell you how to get the most out of AI and ML no matter your skill level.

The people who tend to know what their data means to the business inside and out, are often not the data scientists but more business aligned folks. Getting the power to experiment with ML models and trying new approaches themselves is hugely valuable to any business and can rapidly accelerate value extraction. With AutoML platforms like Kortical, this has never been easier and can be a huge boon. The challenge for these users is that there are a few simple and not so simple common mistakes, such as “leaking a class variable”.

The fix is actually pretty simple, a few minutes with an experienced data scientist is often enough to course correct and allow business users to harness the power of machine learning. For smaller businesses, this might mean booking in a slot with our team to review your models before you put them live or use them for critical decision making. For enterprise, it more likely means putting a governance framework in place with a centralized team of data science capability that teams can draw upon and a review process before models are put into production or used for decision making. With time and experience business users can learn to become proficient in ML model creation.

So while cucumber farmers and shopkeepers are unlikely to see much benefit from driving AutoML tools, there are many people in most organisations who are well placed to pick up these technologies and start delivering value with AI and machine learning. What we see work best is training those with adjacent skills in data insights or coding. And while they should be able to be largely independent, they will need a small amount of support and guidance and this should be a key consideration in the purchasing decision, when adopting these technologies. When getting customer references, ask them about their experiences of support, how they helped through the adoption curve to get real value from AI and ML.