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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 architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data Strategies for Getting Greater Business Value from Distributed Data

Data Virtualization

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

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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.

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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. Implement data privacy policies. Implement data quality by data type and source.

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The Secret Sauce of LeasePlan’s Award-Winning Logical Data Fabric

Data Virtualization

Reading Time: 3 minutes Last month, IDC announced that LeasePlan, a car-as-a-service company, was the winner of IDC’s European Data Strategy and Innovation awards, in the category of Data Management Excellence, for LeasePlan’s logical data fabric. This is a testament to the maturity of.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.