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The data practitioner for the AI era

Emerging generative AI applications are pressuring data and analytics engineering teams to ship trusted data faster—how are data practitioners responding?

The rise of generative AI, coupled with the rapid adoption and democratization of AI across industries this decade, has emphasized the singular importance of data. Managing data effectively has become critical to this era of business—making data practitioners, including data engineers, analytics engineers, and ML engineers, key figures in the data and AI revolution.

Organizations that fail to use their own data will fall behind competitors that do and miss out on opportunities to uncover new value for themselves and their customers. As the quantity and complexity of data grows, so do its challenges, forcing organizations to adopt new data tools and infrastructure which, in turn, change the roles and mandate of the technology workforce.

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Data practitioners are among those whose roles are experiencing the most significant change, as organizations expand their responsibilities. Rather than working in a siloed data team, data engineers are now developing platforms and tools whose design improves data visibility and transparency for employees across the organization, including analytics engineers, data scientists, data analysts, machine learning engineers, and business stakeholders.

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This report explores, through a series of interviews with expert data practitioners, key shifts in data engineering, the evolving skill set required of data practitioners, options for data infrastructure and tooling to support AI, and data challenges and opportunities emerging in parallel with generative AI. The report’s key findings include the following:

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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