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TDWI Articles

4 Reasons to Use Graphs to Optimize Machine Learning Data Engineering

Semantic knowledge graphs accelerate data engineering for machine learning, helping you maximize results.

Emerging Practices in Location Data Management and Analytics

Traditional geographic data combined with new geocoding is giving business operations and analytics greater precision and innovation.

How to Avoid the Hazards of Big Data Projects

A new TDWI report looks at six things your enterprise can do to avoid pitfalls and maximize the benefits of big data integration and analytics projects.

Data Digest: Graph Database Advances and Applying IoT

A graph database trying to connect everything, a standard language for querying graph databases, and a case study for IoT and predictive maintenance.

Data Governance: Benefits and Best Practices

What can data governance do for your enterprise, and how can you improve your data governance program? Semarchy's Michael Hiskey offers some perspective.

Minimizing the Complexities of Machine Learning with Data Virtualization

How the features and benefits of data virtualization can make working with data easier and more efficient.

Data Requirements for Machine Learning

Machine learning can enable new forms of predictive analytics and embed algorithm-driven intelligence into many software applications. However, none of that is possible without the right data, captured and processed the right way.

Data Digest: Security Flaws, Increased Attacks, Governance Tips

Learn about the basic security many enterprises lack, the increased cyberattacks faced by industrial IoT, and how to keep data lakes properly governed.

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