Data Engineering
Data engineering is the discipline that designs and maintains the technical pipelines that collect, transform, and store a company's data in a usable format, before it's used for reporting, analysis, or training AI models. It's data infrastructure work, a prerequisite for any advanced use of that data.
This work becomes visible as soon as a generative AI or reporting project needs to rely on real data: an assistant that has to answer using an up-to-date product catalog, for instance, requires that catalog to be centralized, structured, and synced from the different systems that update it (ERP, e-commerce, CRM). Without this groundwork, every new AI feature has to start over from scattered, inconsistent data.
A common mistake is underestimating this work and trying to start a generative AI project directly on raw data scattered across systems that don't talk to each other. The result is a project that quickly stalls on incomplete or inconsistent data. A quick audit of the available data sources, before scoping the AI project, avoids this kind of roadblock down the line.
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