AI readiness requires a unified data architecture

Data integration is a top priority for enterprise executives, with 82% of senior executives considering scaling AI as a top priority. However, this ambition is hindered by the longstanding practice of maintaining separate data architectures for batch and streaming use cases. Separate data architectures pose serious challenges such as expensive duplicate provisioning of infrastructure and engineering time, conflicting versions of truth, and redundancy in governance and security efforts. The emergence of governed data lakes combines the capabilities of data warehouses and data lakes, allowing a single data stack to support both batch and streaming use cases. A unified, automated data platform will be essential for supporting generative AI as new use cases emerge that leverage data from novel combinations of sources. Automation can sidestep the problem of mastering different engineering languages and paradigms for batch and streaming use cases by enabling teams to assemble a data architecture using easy-to-use, off-the-shelf tools.