There is exciting news in the world of artificial intelligence (AI). Some of the largest corporations on the planet have recently discovered that AI needs good data to work properly. CU Today brought this subject to us in their article, “Bank Of America CEO Says AI’s Biggest Challenge Isn’t Technology—It’s Data Quality.“
We’ll give everyone a moment to absorb this groundbreaking revelation.
After billions of dollars invested in AI initiatives, ambitious transformation strategies and enough enthusiastic presentations to exhaust every available synonym for innovation, organizations are confronting an inconvenient truth: AI cannot magically fix bad data.
Poorly structured, inconsistent and disconnected information does not become brilliant simply because an AI model can access it. It just becomes bad data delivered faster and, occasionally, with impressive confidence.
For data professionals, this is not exactly breaking news. Organizations like Access Innovations have spent decades emphasizing the importance of data quality, metadata, taxonomies and semantic enrichment. Long before generative AI became the newest corporate obsession, information professionals understood that technology needs context and structure to deliver reliable results.
AI has simply made the consequences harder to ignore. When organizations implement AI without preparing their data, results can include inaccurate answers, incomplete retrieval and unreliable recommendations. Suddenly, data governance and semantic structure are no longer quiet conversations happening somewhere in the information management department. They are business priorities.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
|
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
