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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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Data-Driven Companies Leverage OCR for Optimal Data Quality

Smart Data Collective

OCR is the latest new technology that data-driven companies are leveraging to extract data more effectively. OCR and Other Data Extraction Tools Have Promising ROIs for Brands. Big data is changing the state of modern business. The benefits of big data cannot be overstated. How does OCR work?

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7 Major IT Infrastructure Challenges for Data-Driven Companies

Smart Data Collective

Big data technology has been a huge gamechanger for countless companies in every sector. Around 60% of companies rely heavily on data analytics technology to meet their goals. However, despite, the many benefits of big data technology, many companies still have difficulty implementing it properly.

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Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

In the ever-evolving world of finance and lending, the need for real-time, reliable, and centralized data has become paramount. Bluestone , a leading financial institution, embarked on a transformative journey to modernize its data infrastructure and transition to a data-driven organization.

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Using Data-Driven Lean Thinking to Optimize Business Processes

Smart Data Collective

Data-driven decision-making has become a major element of modern business. A growing number of businesses use big data technology to optimize efficiency. However, companies that have a formal data strategy are still in the minority. Furthermore, only 13% of companies are actually delivering on their data strategy.

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Is your data strategy ready for gen AI? LOB leaders may disagree

CIO Business Intelligence

Rapid advancements in artificial intelligence (AI), particularly generative AI are putting more pressure on analytics and IT leaders to get their houses in order when it comes to data strategy and data management. But the enthusiasm must be tempered by the need to put data management and data governance in place.

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Product lifecycle management for data-driven organizations 

IBM Big Data Hub

The key foundation of a strong PLM strategy is healthy and orderly product data, but data management is where enterprises struggle the most. To take advantage of new technologies such as AI for product innovation, it is crucial that enterprises have well-organized and managed data assets.