Optimizing data analytics pipelines with shift-left

In most organisations, data is cleaned, checked and reshaped by whichever team happens to need it, often long after it was created. The same work gets done several times over, in slightly different ways. When something changes at the source, the problems tend to surface downstream, sometimes in front of the business.

Each new use for that data, whether it’s a dashboard, an operational system or an AI application, tends to add another round of the same effort. Costs go up, inconsistencies creep in, and keeping everything current gets progressively harder.

Based on a survey of 400 data professionals, this report explores an alternative approach based on the ‘shift-left’ principle. The idea is to move processing and governance upstream, closer to where data originates, so that clean, well-governed data can be reused by many teams and applications. We look at what it takes to make it work, touching on the role of modern data streaming platforms, and how higher performing organisations are more likely to have adopted shift-left strategically.

If you’re reviewing your own data architecture or considering new requirements in areas such as real time operations, advanced analytics or generative AI, then this one’s for you.