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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 Lakes: What Are They and Who Needs Them?

Jet Global

To address the flood of data and the needs of enterprise businesses to store, sort, and analyze that data, a new storage solution has evolved: the data lake. What’s in a Data Lake? Data warehouses do a great job of standardizing data from disparate sources for analysis. Taking a Dip.

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Analyzing the business-case approach Perdue Farms takes to derive value from data

CIO Business Intelligence

On the agribusiness side we source, purchase, and process agricultural commodities and offer a diverse portfolio of products including grains, soybean meal, blended feed ingredients, and top-quality oils for the food industry to add value to the commodities our customers desire. The data can also help us enrich our commodity products.

Data Lake 125
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A comparative assessment of digital transformation in Italy

CIO Business Intelligence

Finally, the flow of AMA reports and activities generates a lot of data for the SAP system, and to be more effective, we’ll start managing it with data and business intelligence.” The goal is to correlate all types of data that affect assets and bring it all into the digital twin to take timely action,” says D’Accolti.

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Informatica’s new data management clouds target health, finance services

CIO Business Intelligence

In order to help maintain data privacy while validating and standardizing data for use, the IDMC platform offers a Data Quality Accelerator for Crisis Response. Cloud Computing, Data Management, Financial Services Industry, Healthcare Industry

Finance 137
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What is a Data Mesh?

DataKitchen

First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Second-generation – gigantic, complex data lake maintained by a specialized team drowning in technical debt.

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Avoid generative AI malaise to innovate and build business value

CIO Business Intelligence

GenAI requires high-quality data. 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.

Data Lake 131