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Is SaaS dead? Not quite, but it’s evolving rapidly

CIO Business Intelligence

This shift is partly driven by economic uncertainty and the need for businesses to justify every expense. This can not only reduce costs but also simplify your IT landscape and improve data integration. While value-based pricing is appealing in theory, it can be extremely difficult to measure and implement in practice.

ROI 116
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Key Data Trends And Forecasts In The Energy Sector

Smart Data Collective

With the Coronavirus pandemic, the world has been thrown into complete uncertainty. According to a new study called Global Big Data Analytics in the Energy Sector Market, provides a comprehensive look at the industry. The uncertainty comes with a major market shift, the dimensions of data software cannot be ignored.

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Turning the page

Cloudera

Datacoral powers fast and easy data transformations for any type of data via a robust multi-tenant SaaS architecture that runs in AWS. Said simply, Datacoral offers a fully-managed service for worry-free data integrations.

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CIO insights: What’s next for AI in the enterprise?

CIO Business Intelligence

“Our biggest blocker to unleashing the power of AI is uncertainty over the integrity of the dataset it’s working from,” Dan Cohen, CIO and director of operations at The Amenity Collective, says in the report.

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Is an integrated ERP suite or a composable ERP strategy right for you?

CIO Business Intelligence

Today’s business climate is rife with economic uncertainty that is causing IT leaders to do more with less while still innovating to support the business. At Rimini Street, we believe that all businesses can benefit from a composable ERP strategy thanks to the abundance of SaaS platforms and data integration solutions.

Strategy 105
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Avoid generative AI malaise to innovate and build business value

CIO Business Intelligence

However such fear, uncertainty, and doubt (FUD) can make it harder for IT to secure the necessary budget and resources to build services. Ensure that data is cleansed, consistent, and centrally stored, ideally in a data lake. Data preparation, including anonymizing, labeling, and normalizing data across sources, is key.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream.

Testing 169