How AI-Powered Upskilling is Benefitting Data Analytics
A global skills gap is creating significant challenges for the tech industry.
With demand for skilled workers outstripping available talent — particularly in areas like AI — challenges in hiring, innovation and business growth are emerging as a result.
Data analytics is another area where the skills gap is becoming a critical challenge as businesses increasingly rely on data-driven decision-making.
Many employees lack the expertise to analyse complex datasets, interpret trends and generate actionable insights.
However, Amazon Q in QuickSight is one tool addressing this gap.
What is Amazon Q in QuickSight?
An Amazon Web Services (AWS) solution, Amazon Q in QuickSight is a generative AI tool designed to democratise data analytics.
This platform enables employees — regardless of their technical background — to perform advanced data analysis using natural language queries.
By simplifying complex tasks like forecasting trends or optimising operations, Amazon Q empowers every employee to become their own 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, VP 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 new scenarios capability of Amazon Q in QuickSight uses an advanced AI agent, which Amazon says can “empower all employees to engage via natural language to perform data analysis without any specialised skills or expertise”.
Upskilling in action with Amazon Q in QuickSight
By democratising data analysis, Amazon is empowering workers while reducing reliance on specialised analysts, which in turn accelerates decision-making — helping organisations unlock new opportunities.
The generative AI tool eliminates the need for specialised expertise, allowing users to explore trends, model scenarios and make data-driven decisions in minutes.
BMW is one of many companies leveraging this tool to enhance decision-making and reduce reliance on overburdened analysts.
As a leading manufacturer of premium automobiles and motorcycles, BMW uses Amazon Q in QuickSight to efficiently manage inventory across thousands of vehicles.
Before adopting this Amazon solution, BMW was investigating complex issues like supply chain bottlenecks and identifying factors contributing to aging vehicle stock manually.
With sifting through dashboards and spreadsheets a time-intensive process, Amazon Q in QuickSight has revolutionised this process with its scenarios capability.
Now, these tasks can be completed in minutes using natural language queries to model new scenarios swiftly — significantly streamlining data analysis and enabling faster, data-driven decisions that enhance operational efficiency.
“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.
Encouraged by early success, BMW Group is exploring the expansion of this advanced capability across multiple business units to further optimise processes and improve decision-making company-wide.
AI’s role in data analytics upskilling
As well as automating tasks, identifying patterns and providing personalised learning experiences, AI helps upskilling in data analytics by enabling those coming to grips with data analytics to apply theoretical knowledge in real-world scenarios.
DataCamp’s The State of Data & AI Literacy Report 2024 finds that 62% of leaders believe AI literacy is now important for their teams’ day-to-day tasks. As well as this, four of the top seven fastest-growing skills were data and AI skills.
“As the rate of AI adoption increases, humans are essential to guiding the technology’s implementation and usage of these technologies,” Igor Tulchinsky, Founder, Chairman and CEO of WorldQuant said at the World Economic Forum annual meeting in Davos.
“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.
“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.
“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.
“We need to rethink our approach to reskilling to achieve this goal while ensuring the skills of our workforce match the pace of technological advancement.”
Igor suggests three main ways of bridging the skills gap to harness the power of AI:
- Invest in human capital
- Integrate reskilling as a change management initiative
- Leverage new technology to support education
He says: “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.”
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