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TDWI Articles

Case Study: Tracking Human Mobility with Mobile Data Amid COVID-19 Lockdown

Did Parisians change their behavior when social distancing regulations were implemented? Here's how one organization conducted detailed research using data on human mobility to check the accuracy of anecdotal news media reports.

Executive Perspective: DataOps in Volatile Times

Chris Bergh describes himself as "CEO and Head Chef" of DataKitchen, a company that enables analytics teams to iterate and innovate with their DataOps platform. He shares his enthusiasm for DataOps in this interview, explaining what makes it so popular and what's driving its adoption, as well as how to get the most from AI.

Speeding Time to Value: How a Common Data Model Allows You to Do More with Less

Extracting insight from the masses of data inundating organizations is easier with a common data model. Here's an overview of its benefits.

CEO Perspective: Future Trends in BI and Analytics

What are the big trends enterprises face in BI and analytics? Satyen Sangani, CEO and cofounder of enterprise data catalog vendor Alation, explains why he thinks machine learning, data literacy, data governance, and data catalogs are so important right now.

Data Digest: Centralizing and Modernizing Data Management, Data Ethics

Today’s data architecture considerations, data management trends, and data ethics requirements.

The Benefits of Balancing Self-Service and Enterprise Data Systems

If organizations use enterprise systems to solve common challenges, users can focus on resolving business questions and improving performance rather than on how to query and access data.

Self-Service Ingestion: The Key to Creating a Unified, Scalable, Cloud Data Lake

Self-service ingestion helps enterprises capture, enrich, and process data smoothly and free up data and IT pros to focus on higher-value tasks.

5 Data Management Lessons Learned from the Front Lines of COVID-19

Across industries, COVID-19 has exposed common data and data management challenges. These five key lessons are shaping post-COVID-19 expectations of data management and analytics.

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