Are big data and machine learning methods enough?
Seeing Through Data
David J. Hand, author of Dark Data, talks about the art and challenge of modern data science.
“Every day we read about how powerful data are,” says Hand. “We read that with data, data science, statistics, machine learning and so on, we can do amazing things. We can improve understanding, we can get better decisions in areas like government policy, improving sporting performance, more effective medical treatments, better customer understanding, scientific breakthroughs.
“They make it sound terribly easy, as if there’s nothing to it. But the truth is, however, that the data might not be what you think they are.”
Watch to learn more about:
- Why it’s not enough to have a huge data set and powerful statistical, machine learning and AI tools.
- Tensions in data science (for instance, timeliness and accuracy or accessibility and security).
- A taxonomy of the types of dark data you may encounter, using examples from the COVID-19 pandemic.
Industry experts on what you need to know to apply advanced analytics
Many organizations want to use machine learning, artificial intelligence and other advanced analytics methods because they hold great promise. But large data sets and powerful tools may give us a false sense of security. It’s important to know the pitfalls of missing data, how various algorithms work and when to use them, and how to document your processes properly so that your science and engineering projects are reproducible.
Panelists with experience in manufacturing, pharmaceuticals, government and product R&D talk about the paths they’ve taken to become adept at using advanced statistics and what’s holding organizations from making the most of their data.
You’ll hear from:
- Sam Gardner, Wildstat Consulting
- Cameron Willden, W. L. Gore
- Jason Wiggins, SAS