Upside


Can the New Thing Replace the Old Thing?

This is an important question to ask about many new IT systems and tools. The answer depends on many factors -- including forming the question properly.

Executive Perspective: Text, Voice, and Facial Recognition

Natural language processing is helping enterprises analyze their data, but its complexities extend beyond understanding inputs and outputs. Behavioral Signals’ CEO, Rana Gujral, explains NLP’s strengths and weaknesses and compares text, voice, and facial recognition technologies.

Data Digest: Data Governance and Using AI for Security and Privacy

How to communicate the importance of data governance, use machine learning for cybersecurity, and protect data privacy with AI.

Data Stories: Shifting to the Long-Term View

As the COVID-19 pandemic wears on, many are adjusting to the idea that nothing is going back to normal soon. These visualizations show the path to a vaccine, possible repercussions from the pandemic, and higher education paths for the class of 2021.

Data Digest: Data Science and BI, Biases, and Data Prep

How data scientists can work alongside BI and avoid biases, plus new information about time spent on data preparation.

The 7 Self-Service BI Essentials

This list of essential self-service BI capabilities goes beyond report creation tools to include the means of supplying and distributing premade assets.

A New Approach to Streaming Analytics That Can Track a Million Data Sources

Combining streaming analytics and in-memory computing can provide a potent tool for tackling new problems.

Benefits and Best Practices for Data Virtualization in the Real World

Ralph Aloe, director of enterprise information management at Prudential Financial, Inc., explains how his enterprise put data virtualization to use, including how the technology fits in with their data fabric, benefits they enjoyed, and lessons they learned.

Data Digest: Data Science Trouble, ML Flaws, Data Science Culture

How to manage problem areas for data science, compensate for machine learning’s weaknesses, and improve your data culture.