To get executive buy-in for essential but unglamorous data initiatives like governance, technical leaders cannot rely on jargon. They must act like marketers, framing the projects in terms of business value—such as increased revenue or cost savings—to convince stakeholders and build trust in the data.
Randomly testing new ideas under the 'fail fast' mantra is inefficient and costly. Instead, experimentation should be guided by a high-level business goal or question. This 'compass' focuses efforts, ensuring that even failures produce valuable, relevant learnings rather than just being expensive dead ends.
True data advantage arises from discovering patterns between seemingly unrelated domains, like operations and finance. Merely organizing data within its own silo is insufficient; the real value lies in analyzing the interconnected whole to uncover correlations that drive strategic decisions.
The problem with AI has evolved beyond 'garbage in, garbage out.' Today's systems can rapidly ingest misinformation from public sources and present it as fact, creating a feedback loop. This means bad information is not only used for poor decisions but is actively amplified and distributed faster than ever before.