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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 governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

The data mesh debate This is not to say that there is a consensus that data mesh is a universal solution. Stakeholders are currently waging an open debate across the industry of centralization versus federated data strategies. Data Center, Data Management, Digital Transformation To learn more, visit us here.

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3 Steps to Faster Insights in Data Analytics

Data Virtualization

In recent years, we have seen wide adoption of data analytics. Some issues that have been most often cited for this include: Poor data quality: While preparing. However, most organizations continue to find it challenging to quickly yield actionable insights.

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How to leverage data to create a more intelligent organization

CIO Business Intelligence

Mason, highly skilled in using data to inform transformational changes in a business, will share insights about leading data projects as well as field questions in a live discussion with attendees. Travelers Senior Vice President and Chief Data and Analytics Officer Mano Mannoochahr will discuss creating a data-first culture.

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Data architecture strategy for data quality

IBM Big Data Hub

Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving. Meaning, data architecture is a foundational element of your business strategy for higher data quality.

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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 2: How Data & Analytics Can Help in a Downturn

bridgei2i

And so that process with curation or identifying which data potentially is a leading indicator and then test those leading indicators. It takes a lot of data science, a lot of data curation, a lot of data integration that many companies are not prepared to shift to as quickly as the current crisis demands.