For years, the latest and greatest technologies have been a differentiator in competitive, data-driven industries. Some of the most innovative and expensive tools and compute engines were out of reach for many.
Increasingly, that’s no longer the case. Companies across all industries more commonly have access to similar solutions and technologies, and most recently, generative AI has been made widely accessible.
So, when more people have access to the latest technology, what’s the new differentiator? What will set the innovators apart from the laggards?
The answer is people. Not the data scientists or IT experts who have traditionally been the primary users of technology, but domain experts and knowledge workers who are closest to the problems that need to be solved.
Breaking the data bottleneck
Many organizations make the mistake of adopting modern analytics tools without considering the range of non-technical employees who need to use them.
In other words, if your best-in-class data stack isn’t accessible to business users — the data, the technology, and the domain expertise can go unused. This also creates a system where the data team is a bottleneck to rapid insights — forcing business users to make requests when they need data insights to make better decisions.
“With the amazing BI and analytics platforms that are available now, it makes no sense to operate in this way,” said author Akshay Swaminathan on a recent Alter Everything podcast.
Swaminathan says data teams must assume a new role to help bridge this data skills gap.
Solutions to Bridging the Skills Gap
Putting analytics in the hands of domain experts sounds easier than it is in practice. Swaminathan says it begins with the ability of business users to learn data science enough to speak the language and ask good questions. He offers the following strategies to help domain experts get there.
- Adopt a Customer Mindset – Be a good data science customer and collaborator by learning data science enough to speak the language and ask good questions. This helps business users get the most value from the expertise of the data team. “You need to be familiar with the 20% of content that shows up 80% of the time,” says Swaminathan.”
- Close the Gap in Communication – Good dialog between data teams and business teams is one where people don’t use jargon. “If you’re talking with a data person who throws out words like cross validation or overfit, and you don’t know what that means, you need to stop them and say, Hey, I’m not a data scientist. What does that mean?”
- Host Upskilling Workshops – There are proven benefits to upskilling efforts, including improved innovation, productivity, business growth, and employee retention. Consider proposing weekly office hours where domain experts can learn how to become better users of data tools and level up their analytics skills. Swaminathan says companies will be surprised by the number of business insights that can come out of those efforts as excitement builds among the workforce about how data skills can benefit them and the value they bring to the business.
Organizations cannot rely on data scientists alone to generate business insights from the massive amount of data being created every day. Looking at it another way, it can be easier to teach an accountant the basics of data analytics than it is for a data scientist to learn the complexities of finance and accounting.
Businesses should instead focus on providing learning support and technology solutions to their existing workforce. Empowering knowledge workers with digital skills can deliver business value almost immediately while employees gain new skill sets that improve engagement, productivity, and retention.