Rebecca Nolan

Artificial intelligence is no longer a buzzword — it’s embedded into the DNA of modern business operations. From automated analytics to generative tools, AI is changing how we store, move, and extract value from data. And in 2025, if you’re a data engineer, being AI-aware isn’t enough. You need to be AI-ready.

By 2030, data engineers will surpass data scientists, ML engineers, analysts — even AI specialists — in total compensation. The evidence is clear, and it’s driven by one unstoppable force: AI at scale. AI is automating analytic tasks such as EDA, model selection, and in some cases even code generation. But it cannot eliminate the need for robust, scalable data systems.

INTRODUCTION Evidence and gap maps (EGMs) first emerged in 2003 and have seen significant growth over recent years [1]. They are interactive web-based tools that provide a visual overview of the existing evidence and gaps in a domain-specific area of research or policy, existing within the evidence synthesis ecosystem [1-3].