(US and Canada) Ranjan Sinha, IBM Fellow, VP and Chief Technology Officer, IBM Global Chief Data Officer, speaks with Virendra Dafane, Technology Leader, about the need for data governance and the steps to modernize data architecture to make it AI-ready.

Sinha says data, traditionally, has been created, managed, and used in silos. Sharing the data across the enterprise facilitates insights and provides a 360-degree view of the business and its operations. This helps optimize the decision-making process. 

He suggests the enterprise data strategy must encompass certain aspects. First, connect, catalog, analyze and understand enterprise data where it resides and measure the data-related KPIs. Second, modernize toward a hybrid cloud data architecture with efficient and trusted data movement. Third, streamline data pipelines, using data ops principles to provide quality data with the appropriate lineage tracking. The final aspect is data discovery and semantics search, which enables knowledge workers to find data using intuitive business language.

Why do organizations need a strong data foundation for successful AI implementation? Sinha notes there is no AI without IA — information architecture. A unified information architecture accelerates the AI journey, modernizing the data architecture to make it AI-ready.

He describes the AI journey as a five-step ladder. Step one is to modernize all data assets in a multi-cloud environment. Step 2 is to collect to get easy access to the data. The third step is to organize to create a trusted and governed analytics foundation. The fourth is to analyze, to drive insights at scale. And the fifth step is to infuse to create a trusted and governed analytics foundation.