Upside


Data Stories: Smoke and Fire

Using visualizations to understand air quality, smoke in the atmosphere, and the aftermath of fire.

Data Digest: AI Innovations, Risks, and Governance

Groundbreaking examples of artificial intelligence, protecting your enterprise from the risks of generative AI, and possible guardrails for generative AI.

The AI Problem We’re Not Taking Seriously Enough

The key to employee acceptance of AI is an aggressive management commitment to focus on using AI to enhance -- not replace -- a user’s job. Also key: loud and clear communication of that goal.

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Determining when data can’t solve a problem, identifying the dangers of generative AI, and removing outdated or inappropriate training data from machine learning systems.