In today’s data-driven economy, AI and ML are pivotal for innovation and business growth, especially in the context of the accelerated digital transformation prompted by the COVID-19 pandemic.
The success of AI relies on effective data management. AI models need access to a diverse range of high-quality data, and data management becomes crucial in ensuring the accuracy and reliability of AI predictions. Concerns include data usage, privacy, transparency, and bias that need to be addressed through intelligent data management.
We need to pay close attention to the reciprocal relationship between AI and data management. While AI relies on quality data for training and scaling, AI and ML technologies play a critical role in scaling data management practices, automating tasks, improving data understanding, and identifying anomalies.
Traditional approaches to data management are deemed inefficient, with limited automation, expensive processing, and challenges in governance and privacy compliance. More accelerated and automated approach to data management is needed.
AI is positioned as a solution to enhance data management efficiency. CLAIRE engine, a metadata-driven AI capability by Informatica, as an example, automates routine data management tasks across the entire data environment, offering improved scalability and efficiency.
The future state requires comprehensive, automated, and metadata-driven approach to address the challenges posed by the exponential growth of data in the modern business landscape.
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