Anand Santhanam is the global principal delivery leader at Amazon Web Services. Read Anand Santhanam's full executive profile here.
A robust data strategy is paramount for harnessing generative AI , which is poised to significantly elevate global GDP byand enhance productivity within the next decade. In my interactions with numerous organizational leaders, I've observed a widespread recognition of GenAI's potential, yet often their ventures don't go beyond preliminary use case evaluation. Tackling the deeper challenges of data management is crucial.
Organizations often grapple with understanding the entirety of their data landscape, effectively connecting and leveraging their data and managing data accessibility. As the volume of data escalates, these challenges become increasingly daunting. An ineffective data utilization strategy can be a significant barrier to data-driven decision-making and continuous innovation.
An integrated data strategy should include a range of tools, resources and methodologies to cover all aspects of data management. This encompasses everything from data ingestion, storage and retrieval to analysis, as well as the employment of AI and machine learning technologies such as large language models and other foundational models to derive valuable data-centric insights for end users.
• The ability to establish governance of all your data to securely give data access when and where your users need itThe efficacy of data readiness hinges on the quality, quantity, diversity and intricacy of the data collected. Fundamental procedures such as data cleansing, validation, transformation and storage are critical to preparing data for effective use.
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