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TDWI Upside - Where Data Means Business

Achieving Success with Modern Analytics (Part 2 of 2)

Teresa Letlow, SVP Global Cloud Alliances at StreamSets, and Jason Yeung, VP NA Center of Excellence, BTP at SAP, share their thoughts and insights about achieving success with modern analytics.

Data Digest: Hiring Trends, Switching Tracks, Specialization

News about hiring for tech jobs, guidance for job-seeking Ph.D. grads, and advice for improving your data science career.

Achieving Success with Modern Analytics (Part 1 of 2)

Fern Halper, TDWI’s vice president and senior director of research for advanced analytics, discusses her recent TDWI Best Practices Report on achieving success with modern analytics -- including exciting use cases and common challenges organizations need to overcome.

Generative AI and Its Implications for Data and Analytics

Generative artificial intelligence has captured the imaginations of people around the world. Though not entirely new, generative AI is evolving so rapidly that technology professionals need to track its maturation closely to effectively evaluate the associated opportunities and risks. Here are the basics you need to know.

Data Stories: Stylish, Effective Data Visualizations

Making a data visualization elegant, conveying insights clearly, and choosing a visualization tool.

Data Digest: Data Difficulties, AI Risks, Adjusting ML

Determining when data can’t solve a problem, identifying the dangers of generative AI, and removing outdated or inappropriate training data from machine learning systems.

Evaluating Open Source: Green Flags to Look For

Why are open source technologies still so popular? What questions should you ask when evaluating an open source solution, and where is open source headed? Jozef de Vries, chief product engineering officer at EDB, shares his perspective.

Succeeding with Data Governance and Data Quality

Deanne Larson, Ph.D., president of Larson & Associates, discusses data governance and data quality -- including success factors for data governance and which roles are responsible for data quality within an organization.

Data Digest: Improving and Shrinking Machine Learning

What you should know about MLOps, boosting ML models, and putting ML on smartphones.

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