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

CIO Business Intelligence

Despite these benefits, the core problems that data centralization so often fails to address are the pragmatic realities of many enterprise data ecosystems. These domain data leaders often cite the diminishing returns and significant effort of central data team engagement.

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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 Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. Creating a High-Quality Data Pipeline.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

But to augment its various businesses with ML and AI, Iyengar’s team first had to break down data silos within the organization and transform the company’s data operations. Digitizing was our first stake at the table in our data journey,” he says. That takes its own time. The company’s Findability.ai

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Business terms and data policies should be implemented through standardized and documented business rules.

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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

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The Rising Need for Data Governance in Healthcare

Alation

This data is also a lucrative target for cyber criminals. Healthcare leaders face a quandary: how to use data to support innovation in a way that’s secure and compliant? Data governance in healthcare has emerged as a solution to these challenges. Uncover intelligence from data. Protect data at the source.