Home > News & Studies > News > What do low-code tools mean for enterprise Data Literacy requirements?

What do low-code tools mean for enterprise Data Literacy requirements?

Dr. Paul Barth, Global Head of Data Literacy, Qlik


The pandemic has forced businesses to find a way to design and deliver digital services. This only revealed the deficiency of technical know-how available as employers search high and low for staff with the right skills to help them compete in a permanently hybrid world.

If businesses around the world needed a reason to step up competition for digital and data skills, the pandemic stretching into its third year is a more than sufficient catalyst. It has forced the global population to shift their lives online and engage more with digital services, while businesses have had to find a way to design and deliver those services.

This has only revealed the deficiency of technical know-how available as employers search high and low for staff with the right skills to help them compete in a permanently hybrid world. In the latest Global Digital Skills Study by AWS, two out of three workers said they are not confident they are gaining digital skills fast enough to meet future career needs. There isn’t one discipline that seems to have avoided this growing gap between supply and demand, from software developers and cybersecurity specialists to data experts.

While policymakers and educators attempt to plug these gaps, advances in the tools used to build digital products and systems are also helping to alleviate the pressure on those using them – whether IT teams or employees from the rest of the business. Could better technology be the answer to plugging the talent gap and if so, what does this mean for how we upskill the workforce in digital and data literacy?

Levelling the playing field

A trend that is taking off, partly in response to these challenges, is the increasing adoption of low-code (or no-code) tools, often in conjunction with coding and programming. In contrast to traditional programming that sees developers manually write code, these tools include features such as drag-and-drop editors to help users create products and features without having to actually write code themselves. It would be a stretch to say that non-technical workers can suddenly develop complex applications, but due to the automated and assistive nature of low-code platforms, non-expert programmers or those with more basic digital skills can realistically contribute to simple technical tasks. For example, the evolution of digital tools that are more ‘consumable’ could have a drastic impact on how efficiently employees interact with data.

They can empower people who don’t necessarily have or think they need data skills– whether in sales, marketing or HR – lower the barrier to working with technology. This increases their productivity at the same time by making it easier for employees to use more analytics tools that produce more specific or detailed insights. By using low-code tools developed by them or their teams, they don’t have to constantly rely on specific data or IT teams to gather the information and analysis they need. It puts the analysis into their own hands. To date, the time, effort and skill required to prepare and analyze data has been a significant barrier to data democratization – which the low-code methodology goes some way to allaying.

So are low-code tools the answer for enterprises trying to upskill their employees in data literacy? Although undoubtedly useful to help workers discover insights more efficiently and remove the fear factor from engaging with data in the first place, enterprises should not disregard the broader skillset required to be genuinely data literate. Employees must understand not only how to interpret data but how to argue and tell stories with it – taking into account contextual factors such as bias and provenance. While automation can support one stage of the journey to data understanding, there are others that require a human touch.

Rise of the citizen data scientist

A term now in widespread circulation due to the accelerated adoption of low-code tools is the ‘citizen developer.’ In fact, research from Gartner suggests that by 2023, citizen developers will outnumber professional developers at large enterprises. Given the right framework and oversight, low-code tools could enable employees to write their own software and support stretched IT teams. It also allows those with basic programming skills to evolve and become more efficient.

It’s hard to find an equivalent term for data skills. Although the term ‘citizen data scientist’ implies the democratization of deeper technical expertise in the same ways as ‘citizen developer’, understanding data is already a much broader ‘citizen’ challenge compared with app development, experienced in both every day and professional life. But it is a challenge we aren’t yet rising to; our recent report, Data Literacy: The Upskilling Evolution, found that just 11% of employees are fully confident in their data literacy skills. Therefore, plugging the data skills gap is as much about solving a lack of confidence in basic data literacy than it is about expertise in in advanced data science principles – particularly given the potential of low-code tools to bridge the gap between basic and advanced analytics .

Enterprises supporting employees’ data skills can take inspiration from the low-code movement and the rise of the citizen developer. Although it can certainly help, it’s not a fix-all for the digital skills shortage. Data has a much broader role in the enterprise, applicable to everyone from the finance department to human resources, meaning everyone can directly apply such skills in their day-to-day roles. Relying on automated, ‘low-code’ tools to become data literate is like trying to become fluent in a language using Google Translate – you might get the right answer on paper, but what you do with the words will always be superficial. Using these tools as a stepping stone to show people the power of data is what’s needed, to start people on the wider journey to data literacy.