The most sought-after digital skills ranking over the last five years have been Software Engineering, Data Analytics, Artificial Intelligence (AI) and Machine Learning (ML), Blockchain and Cryptocurrency, Augmented Reality (AR) and Virtual Reality (VR), and Cloud Computing. However, certain skills are doing well than others, with some on the verge of falling off the chart.
Per data by Cornerstone OnDemand, a cloud-based development software provider and learning tech company, in its 2024 Global State of the Skills Economy report, the demand for Data Analytics skills has consistently dominated the market and showed a significant 52% growth between 2019 and 2024, while accounting for 8% of global job postings in 2024 alone.
Meanwhile, AI and ML skills, though contributing a bit over 2% to global job postings, are making a gradual but consistent upward trajectory with a strong 65% growth within the same period.
Conversely, demand for emerging technologies like Blockchain and Cryptocurrency, which performed well between 2020 and 2022, has been on a downward slope in the last two years.
If there was a next-rated award for global digital skills, AI would have won, given the attention and growth it has achieved over the past year. However, Data Analytics’ place remains undisputed and might be for a long time for obvious reasons.
Why Data Analytics?
The world is drowning in data, both mined and untapped; it is now accepted as an essential resource for powering any form of technological advancement. Big data is critical for any business’ competitive advantage and survival.
One of the most significant strengths of data analytics is its role in decision making. With organisations facing the pressure to make faster and smarter decisions on growth, revenue, cost optimisation, and adapting to market shifts, data analytics helps in uncovering patterns, identifying weaknesses, and offering insights at a time when the market is constantly changing and the workplace is rapidly evolving.
Take business intelligence, for instance, it heavily relies on data analysis to track performance, monitor market trends, and draft strategies. At a time when the global market is volatile post-pandemic, there’s a need for companies to stay agile.
The same can be said about sales and revenue. A McKinsey report reveals that 64% of B2B companies plan to increase their investments in predictive analytics due to its proven impact, including boosting EBITDA from 15% to 25%. Industry benchmarks also suggest that using analytics effectively can enhance revenue by up to 15%.
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Another compelling use case is user experience and personalisation helping platforms to drive user retention and engagement. For instance, Wrapped, one of Spotify’s most exciting and viral features.
It uses data visualisation to offer users insights into their listening habits, such as their favourite songs and artists over the past year. It even gives them an idea of how they rank when it comes to their favourite artistes. This year-in-review feature has inspired not only streaming platforms like YouTube Music, but also savings platforms like Piggyvest, education platforms like Duolingo, and communities like Reddit. The same principle is also applicable to recommendation engines, which have become common with most digital platforms.
Data analytics also complements AI and ML, acting as a critical enabler. Well-structured big data powers AI models, positioning analytics as the driver of innovation in automation, robotics, and intelligent systems.
It’s worth noting that, according to the Cornerstone report, the global hubs for expertise in data analytics include India, the United States, and the United Kingdom.
AI is the contender for a reason
AI and ML exist on the side of applying data to solve real-life problems. However, their most popular impact is currently seen in the creative landscape than other aspects of the economy. Generative AI tools like ChatGPT, Claude, Gemini, Sora, and HeyGen are impacting content creation, enabling the generation of text, code, art, videos, and even music.
However, this would not be the same for long. Per Gartner’s 2019 AI business value forecast, decision support/augmentation will dominate AI applications by 2030, contributing 44% of global AI-derived business value. This will outpace decision automation (19%), smart products (13%), and AI-powered agents (24%).
AI and ML boast potential across various sectors. In healthcare, ML drives advancements in predictive and personalised medicine for better patient outcomes. In finance, AI plays a critical role in fraud detection and risk management. Meanwhile, the hospitality industry leverages AI for personalised guest experiences, and the broader economy increasingly integrates AI tools into logistics, customer service, and beyond.
Human skills are still in demand, by the way
Despite the rapid rise of digital skills, human capabilities remain indispensable. According to the Cornerstone report, human skills consistently rank higher than digital skills across all regions. For example, in the Middle East and Africa, the demand for human skills is 2.2 times greater than that for digital skills, supporting the idea that technology is only as effective as the people wielding it.
Skills such as leadership, emotional intelligence, critical thinking, creativity, and teamwork are essential for navigating the complexities of a data-driven, AI-augmented world. These abilities complement technical skills and are vital for adaptability in an evolving workplace.
Hence, continuous learning cannot be ignored. Predictions suggest that by 2026, 40% of employees will need to acquire new skills due to advancements in AI and automation. Additionally, 66% of business leaders indicate they would no longer hire candidates lacking AI proficiency.
This creates a unique challenge and opportunity for Millennials and Gen Z. As younger generations with more years left in the workforce, they must embrace flexibility and a mindset of lifelong learning to stay ahead, especially as older generations near retirement.