Why It’s Time to Revisit Your Data Strategy for Generative AI

As organizations embrace Generative AI, many are moving fast—experimenting with new tools, pulling data into isolated pipelines, and building solutions tailored to specific teams or use cases.

Add in the growing complexity of unstructured and multimodal data—text, audio, video, images—and things can start to move in many directions at once.

It’s worth pausing to reflect:  

Are we setting ourselves up for scalable success, or building toward future fragmentation?

Before the wires go up……

In the early days of telephony, around the early 1900s, cities became overwhelmed by a chaotic web of telephone wires—each provider installing their own infrastructure, often without coordination. It worked temporarily, but eventually, the clutter had to be rethought for long-term scalability and efficiency.

We may be at a similar crossroads with GenAI today.

For data leaders, this is a timely opportunity to step back and align:

– Does the Data Strategy support GenAI initiatives?

– Are we prepared to handle unstructured and multimodal data at scale?

– Can we enable innovation while maintaining structure, governance, and interoperability?

The takeaway:

Generative AI holds immense potential—but to realize it fully, a thoughtful enterprise data strategy is essential.

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