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TDWI Articles

Practical Data Science


Machine Learning and AI with Keith McCormick

From machine learning and transparency to unstructured data and career advice, data scientist Keith McCormick shares his insights on what’s worth paying attention to in the world of AI.

What Generative AI Means for Data Jobs

The technology-will-steal-your-job narrative has been around for at least 200 years. Some roles do disappear, but most simply evolve, adapt, and merge.

The Power of Spatial Analytics in Business Intelligence

The future of BI driven with spatial analytics promises to be transformative. We present a wide variety of today’s use cases.

The Unfortunate Decision Process That Is Leading to AI Deployment Failures

When implementing a new technology, you need a deep understanding of the problem you want it to solve.

Data Insights and the Role of AI with Philip Zelitchenko

Organizations can overcome barriers to timely insights. ZoomInfo’s Philip Zelitchenko explains how AI can help.

Four Tips to Modernize Legacy ETL Processes

Modernizing ETL, especially the underlying infrastructure that supports it, across all your lines of business is vital for enhancing downstream analytics and decision-making.

Building an AI Strategy with Caroline Carruthers

Before jumping into AI, it’s important to have a strategy. You need to know what problem you are trying to solve, where you are now, and where you want to go. The CEO of global data consultancy Carruthers and Jackson explains what’s involved in building this strategy and where AI -- not just generative AI -- is headed.

Q&A: Can Neuro-Symbolic AI Solve AI’s Weaknesses?

Neuro-symbolic AI integrates several technologies to let enterprises efficiently solve complex problems and queries demanding reasoning skills despite having limited data. Dr. Jans Aasman, CEO of Franz, Inc., explains the benefits, downsides, and use cases of neuro-symbolic AI as well as how to know it’s time to consider the technology for your enterprise.

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