There are many studies that have researched into the average tenure of a data scientist. The number ranges from 11 months to about two years. Regardless of which number we feel is most realistic, there is a consensus that data scientists are difficult to find and even more difficult to retain. They are hard to find because there’s so few of them on the market, but why is it so hard to keep them happy?
A few years ago, it was declared that the data scientist is the sexiest job of the 21st century. Imagine the excitement of those aspiring data scientists who spent years studying statistics and computer science and then landing a “data scientist” position, where their skills and experience would finally be applied, well, that is, until reality hits. Unfortunately, many organisations use data scientists rather as a data request centre. Instead of building machine learning models and innovation as their primary role, they become bogged down with data pulls, making small adjustments to reports and other ad hoc requests. One only needs to wear a data scientist’s hat to experience their frustration and disappointment.
This raises many questions. Why are these valuable resources, who are driven by curiosity and innovation, being used in ways that force them into mundane work? Why are administrative requests not being handled by other business roles or executed in ways that make business sense?
The answers, most of the time, are because many businesses don’t have the correct platforms to support and enable their human resources to autonomously understand the answers. They don’t have self-service augmented analytics.
What is augmented analytics?
Before we dive deeper into how augmented analytics will make data scientists happy in their places of work, allow me to clarify what is meant by augmented analytics.
Gartner defines it as the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation, transforming how people develop, consume and share data and analytics on a platform. Augmented analytics can not only increase the speed to insight, but also provide more accurate answers.
The demand for augmented analytics is certainly growing. Organisations that want to grow a data culture and increase general data literacy understand that the business side of the organisation needs a better methodology for people to get the answers they require.
So instead of sending endless admin requests to a data scientist, rather empower resources to pull their own data, interrogate it and dive deeper until they are 100% comfortable that they have the answers they need, leaving no ‘what ifs’ unanswered.
The solution: the entire data analytics process, augmented and guard railed by AI, shared and done seamlessly on a single platform.
The result: data scientists can fulfil their ‘sexy’ positions, enabling them to fuel their curiosity and focus on what keeps them excited, while unlocking a whole new world of data analytics for everyone.
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Pyramid Analytics
Pyramid Analytics’ vision is to automate the decision-making process to empower anyone to make faster, more intelligent decisions with any data, for any person and any analytics need. Decision Intelligence is what’s next in analytics.