How AWS’s AI Tool is Bridging the Data Analytics Skills Gap

The technology industry has been facing the challenge of widening skills gaps for some time, with demand for qualified workers exceeding the available talent pool across multiple sectors.

This shortage creates barriers to hiring, innovation, business growth and particularly AI development and implementation.

It is well known that data analytics are a critical area where this skills shortage manifests as organisations increasingly depend on data-driven decision-making processes – yet many employees lack necessary expertise to examine complex datasets, identify patterns and produce actionable business intelligence.

Now, Amazon Web Services (AWS), has developed Amazon Q in QuickSight as a targeted solution to address this growing capability gap.

This is a Gen AI platform that enables workers without technical backgrounds to conduct sophisticated data analysis through natural language queries rather than requiring specialised programming knowledge.

What is Amazon Q?

Amazon Q democratises access to analytical capabilities by allowing users to perform tasks such as forecasting trends or optimising operations by asking questions in ordinary language, effectively transforming any employee into a functional data analyst.

“We are at the beginning of a workplace transformation driven by agents and Amazon QuickSight is pioneering how this technology can break down the technical barriers between employees and their data,” says Dilip Kumar, Vice President of Amazon Q Business at AWS.

“With the new scenarios capability, everyone becomes their own data analyst who can dive deep into their company data, helping them unlock insights, make better decisions and explore countless possibilities faster than ever before.”

The scenarios capability within Amazon Q in QuickSight utilises an advanced AI agent that AWS claims can “empower all employees to engage via natural language to perform data analysis without any specialised skills or expertise,” Dilip adds.

This means that by making data analysis accessible to a broader workforce, Amazon enables organisations to accelerate decision-making processes while reducing dependencies on specialised analysts who often represent bottlenecks in information flow.

BMW has already implemented Amazon Q in QuickSight to manage inventory across thousands of vehicles more efficiently.

Previously, the company investigated supply chain bottlenecks and identified factors contributing to aging vehicle stock through manual analysis of dashboards and spreadsheets.

As a result, Amazon Q in QuickSight has transformed this workflow through its scenarios capability, enabling BMW to complete these analytical tasks in minutes using natural language queries rather than technical database commands.

“This is why we integrate Amazon QuickSight into our data portal, the Cloud Data Hub — to get more transparency about what's happening there and share the insights,” says Ruben Simon, Head of Product Management, Cloud Data Hub at BMW.

Following early implementation success, BMW is exploring expansion of this capability across additional business units to optimise processes and enhance decision-making throughout the organisation.

The role of AI in data analytics upskilling 

The role of AI in upskilling extends beyond Amazon Q, to automation of routine tasks, identification of patterns in large datasets and delivery of personalised learning experiences.

AI systems enable employees new to data analytics to apply theoretical knowledge in practical situations.

Research from DataCamp's The State of Data & AI Literacy Report 2024 indicates that 62% of leaders consider AI literacy important for their teams' daily responsibilities.

The report also notes that four of the seven fastest-growing professional skills relate directly to data and AI capabilities.

Igor Tulchinsky, Founder, Chairman and Chief Executive Officer of WorldQuant, a quantitative investment management firm, addressed the skills gap challenge at the World Economic Forum annual meeting in Davos: “As the rate of AI adoption increases, humans are essential to guiding the technology's implementation and usage of these technologies,” he says.

“Before we can fully embrace this revolution, we have an obligation to reskill our talent to use these technologies effectively, equipping them to succeed in today's environment.”

The scale of this workforce transformation appears substantial, with Igor noting that “executives estimate that up to 40% of their workforce may need to reskill as a result of implementing AI or automation over the next three years.”

Rather than viewing this challenge negatively, he frames it as an opportunity for workforce development: “While this percentage seems daunting, the demands on today's workforce present a unique and exciting opportunity to empower a new group of people to enter desired, skills-oriented jobs in the digital economy.”

Igor proposes a three-pronged approach to address the skills gap:

• Investing in human capital
• Integrating reskilling as a change management initiative
• Leveraging new technology to support education efforts

“Empowering employees with opportunities to reskill will help them better leverage AI tools, helping to drive business value, improve efficiency and shape the future of success,” he adds.

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