Organizations have long struggled with the “eternal data problem” – that is, how to collect, store, and manage the massive amount of data their businesses generate. This problem will become more complex as organizations adopt new resource-intensive technologies like AI and generate even more data. By 2025, the IDC expects worldwide switzerland whatsapp number data data to reach 175 zettabytes, more than double our current threshold.
In all this noise, AI stands out as a viable option for automating pattern and trend identification. However, despite the hubbub surrounding innovative AI applications like ChatGPT and Bard, businesses must proceed cautiously. Data is not inherently decipherable, even by the most advanced AI algorithms, and hiccups can cause disaster. For example, imagine a bank using AI to match and merge names without deterministic rules outlined and audited – an error in this instance could have severe ramifications for the bank’s reputation and compliance status.
When organizations deploy AI at an enterprise scale, they take enterprise-level risks. As such, before taking drastic action, leaders must conduct due diligence on their systems to ensure their advanced algorithms will operate on the most current and high-quality information. The secret to doing so? A comprehensive data synthesis and management strategy, which can be realized through master data management (MDM).