There are tons of articles and books on big data, but sometimes a video format resonates best. Whether big data concepts feels too esoteric or just plain hard to wrap your head around, sometimes the easiest way to grasp an idea is to have someone explain it to you.
If you just want to know what the big data hubbub is all about, want to hear a couple TED talks about data in our lives, or dive into a documentary, then this list of videos can help. Here are some of the most useful YouTube videos about big data for anyone who wants to learn more.
Quick big data primers and introductions
A very short video that explains the gist of big data, how it exists in the world, and how it affects people and privacy. If you’re looking for a very quick introduction to big data as a topic, this video acts as a primer for complete novices.
In this video, EMC explains exactly why big data is considered “big” and how large it can actually get. Big data doesn’t just refer to the sheer size of the data, but also what it consists of and the complexity (or lack) of data structures. The speed and sources generating data contribute to the “big” claim as well. As datasets get larger, they starts to push against the constraints and limits of available technology. As big data growth outpaces the technology needed to wrangle and analyze it, new challenges arise for analysts.
This is a follow-up to the above video, “How Big is Big Data.” Big data challenges systems and forces technology to change. It provides new information about the world as we know it, giving us insights, and helping us to make choices. In this video, EMC discusses why big data matters, the challenges involved, how it is changing our lives, and how it affects people everyday.
In this TED Talk, Kenneth Cukier, economist journalist and co-author of “Big Data: A Revolution That Will Transform How We Live, Work, and Think,” discusses how data is more useful when we have more of it. Having more data puts more information into our hands and allows us to sift through to discover trends and patterns. Patterns aren’t always clear in smaller sample sizes. Smaller data limits our worldview and what conclusions we can draw. As we gather more data, we can begin to make more informed decisions. Cukier also looks to the future of data collection and how technology and machine learning will transform what we know.
Cathy O’Neil, a mathematician, data scientist, and author of “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy,” talks about the pitfalls of big data and how even the best intentions can cause misused or misinterpreted data. More and more decisions are made by algorithms that affect actual people and O'Neil tells a cautious story. She talks about how we’re being scored based on confusing and hidden metrics, using real examples to show how data can sometimes get things wrong. Algorithms and machine learning protocols are only as good as the people who designed them. O’Neil warns that it goes beyond the basic math of misinterpreted statistics and leaks into political and societal effects. The warning is clear: techno-optimism is no excuse for not being critical of data or ignoring the importance of informed human analysts and data literacy.
In this video, Joel Selanikio talks about how data has started to revolutionize global healthcare in struggling and underdeveloped areas. He points out how information was spotty at best almost 20 years ago, when researchers relied on workers with paper forms to collect data from the people in the area and enter data by hand. Then, a simple Palm Pilot revolutionized data collection. Better data collection led to data consulting programs, which then led to global cloud-based software program. Access to technology accelerated growth, which has led to better information, better healthcare decisions, and better healthcare worldwide.
This video offers an in-depth look at machine learning and its relationship to large datasets. As any analyst will tell you, data is useless without a way to parse or analyze it—and machine learning provides extra resources for parsing and analyzing. Deep learning is a subset of machine learning that employs algorithms in neural networks to develop powerful and flexible learning systems. What might sound like sci-fi, is a developing machine intelligence field. In this video, and panelist of industry veterans talk about how intelligence is shaping data and the possible applications of new technologies.