Informatica, the enterprise cloud data management leader, introduced new intelligence and automation capabilities to the industry’s first Cloud Native Data Management solution. Powered by Informatica’s AI-powered CLAIRE™ engine, these capabilities will enable organizations to see even faster return on investment from cloud data warehouse, data lake and lakehouse investments.
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Informatica will showcase the Cloud Native Data Management solution at its Intelligent Data Summit for Cloud Data Warehouses, Data Lakes & Lakehouses, the second event in its free, virtual CLAIREview series. The event will kick off with a keynote at 10:00 a.m. PDT with Jitesh Ghai, Senior Vice President, Data Management, Informatica; Mark Beyer, Distinguished VP Analyst, Gartner; Rahul Pathak, General Manager of Analytics, AWS; Sumeet Agrawal, Senior Director, Product Management, Informatica; James Newsom Jr., Managing Director of Data Services, Home Point Financial; and Christopher Eldredge, Senior Director of Business Intelligence, Paycor.
“It’s more critical than ever to deliver rapid business impact from digital transformation initiatives,” said Ghai. “But companies often struggle to see ROI from their cloud data warehouse and data lake investments. Informatica Cloud Native Data Management provides the foundation critical to successfully delivering on businesses’ top priority transformations, while solving common challenges with automation and intelligence to shorten the time to value from cloud data warehouse and data lake investments.”
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Most organizations point to a lack of sufficient data integration, data quality, and metadata management as the chief barriers to succeeding with their cloud data warehouses and data lakes. According to Ghai, there are three common reasons organizations fail to maximize value from cloud analytics.
“Using hand coding to address data integration, data quality, and metadata management issues is one of the biggest reasons we see organizations struggle,” said Ghai. “This approach is costly and time-intensive, hampering the enterprise’s ability to innovate swiftly and putting the project’s long-term success at risk. We also see organizations depending on disjointed point products to achieve end-to-end data management, which can lead to inconsistent data governance and quality, and relying on limited solutions from cloud vendors that only offer basic data integration or ingestion, which doesn’t suffice. It’s critical that modern enterprises pursue end-to-end cloud-native data management – including the three pillars of data: metadata management, data integration, and data quality – that supports a multi-cloud strategy and deployment model by design.”
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