Today, it is important to differentiate relevant and impactful data from noisy data. Relevant and impactful data, along with its context and relevance, can be used to train artificial intelligence (AI) algorithms. According to Amit Walia, president, products and marketing at Informatica, the ability to separate relevant and impactful data from noisy data will help enterprises leverage the decision-making capabilities offered by AI.
This is where intelligent data—that makes its own context and relevance clear to users through metadata— comes in. This metadata enhances holistic data management for AI and machine learning and thereby unlocks data’s value. Furthermore, enterprises can leverage technical, business, operational and usage metadata to deliver comprehensive, relevant and accurate data to implement AI.
When AI and machine learning are applied to the collection of metadata, it will help identify and recommend relevant data. Consequently, this data can be processed and made suitable for use in enterprise AI projects. This creates a cycle of good data feeding AI, thereby creating a launch pad for enterprise digital transformation.
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