15 Must-Watch Data Analytics TED Talks

This article is part of a series. To discover more inspiring TED Talks covering a variety of industries and technologies, be sure to check out our round ups for Coding, Cybersecurity, or FinTech.

Today, data forms the foundation of our stories, and data analysts are the storytellers. Consider that by 2025, according to the World Economic Forum, the world will generate 463 exabytes of data per day. An exabyte is 1 quintillion bytes, or a 1 with 18 zeroes behind it.

We are swimming and swirling in a sea of data, but how do we make sense of it all? That’s what data analysts help us do. Data analysts collect and collate a variety of data points, turning them into stories that can produce actionable results. They impact every government, regulatory, commercial, and educational organization and help leaders make better decisions.

Listening to data analysts discuss their jobs, their dreams, and their concerns offers a unique window into how we can better use data in our lives and careers. A good forum for this is TED, a nonprofit organization that enlists experts around the world to share stories and insights. TED Talks are thought-provoking and short (usually under 15 minutes each).

To learn more about data and data analysis, check out these 15 must-watch TED Talks.

1. A Data Analyst’s Journey

Anna Leach, a Ph.D. student at the University of Arizona, says data analysis is something everyone should embrace — even if they don’t study it. Data analysis covers more than inputting numbers into an Excel spreadsheet and collecting the results. It requires investing time with people and the process of analyzing data, asking questions, and understanding that data can have biases.

“Data analytics is as much an art as it is a science,” Leach says. “Anyone with any background can be curious and investigate information to tell a complete story.”

2. Demystifying Data Science

Data science is a big, scary field that most people don’t understand — or at least, that’s the perception. But Asitang Mishra, a scientific application scientist at the NASA Jet Propulsion Laboratory, seeks to explain this fast-growing, often opaque field in easily relatable terms. For example, explaining his work to an Uber driver can be a challenge, and in this TED Talk, Mishra establishes a thoughtful baseline for data science and data analytics.

“We convert a human problem into a computer problem,” he says. “We solve human problems using principles of mathematics. But it’s important we communicate our ideas in a language that is not necessarily mathematics, that is more human.”

3. We’re All Data Analysts

Don’t believe we’re all data analysts? Listen to Rebecca Nugent, who explains the concept with some relatable, funny examples. Nugent, a professor of statistics and data science at Carnegie Mellon University, illustrates how we are all data analysts by performing simple daily activities like crossing the street.

Nugent also untangles the curious way in which some people are embarrassed to be called “illiterate” but proudly identify as “innumerate.” For example, she presents a 1992 Barbie doll ad that says, “Math class is tough,” then posits, “Can you imagine Mattel creating a Barbie that giggled as she said, ‘I can’t read’?”

4. Why Everyone Should Be Data Literate

You’ve probably heard the phrase, “data is the new oil,” but what does it mean? Jordan Morrow, who delivers frequent talks advocating for data literacy, explains: “Data is this valuable asset, but just like oil, it has to go through people and refinement to get value.”

In this TED Talk, Morrow covers how data literacy is important by using two everyday examples: deciphering truth in social media and buying a refrigerator. And, while not everyone is a data scientist, he thinks everyone should be comfortable with data.

5. Data and Your Health

For your next doctor’s visit, come prepared with data. That’s the recommendation from Talithia Williams, Ph.D. and Associate Professor of Mathematics at Harvey Mudd College. Williams supports this recommendation with a story about her pregnancy, during which she used data she compiled to challenge a suggestion from her doctor.

“By taking ownership of your data … just by taking these daily measurements about yourself, you become the expert on your body,” Williams says. “You become the authority. It’s not hard to do.”

6. What to Do With All This Data?

Counting things is easy. Understanding what they mean after they’re counted is far more difficult. Susan Etlinger, an industry analyst, urges us as consumers, coders, and analysts to think critically about the data we receive and try to interpret.

Data goes beyond numbers — it includes images, text, audio, and video. All the disparate types of data we have created require people to think more contextually about what it means and how we use it. “We are not passive consumers of data and technology,” she says. “We shape the role it plays in our lives and the way we make meaning from it. But to do that, we have to pay as much attention to how we think as how we code.”

7. Photographing the World Through Data

Though a bit dated (2014 is a data lifetime ago), Dan Berkenstock’s TED Talk is a fascinating combination of data and passion. Once a data scientist who chased nuclear tech smugglers, Berkenstock moved into satellite imagery, building satellites that cost a fraction of what their predecessors had.

Berkenstock explains the uses beyond Google Mapping your own house. “We see ourselves as pioneers of a new frontier, and beyond economic data, unlocking the human story, moment by moment,” he says. “For a data scientist that just happened to go to space camp as a kid, it just doesn’t get much better than that.”

8. Skills You Need When Working With Data

Collecting data, inputting it into Excel spreadsheets, and writing algorithms aren’t the only skills required of data analysts and scientists. Jose Miguel Cansado argues that those who work with data should also have an artistic mindset.

Cansado, VP of sales at an intelligence company, says data prevents crime, predicts political revolutions, and creates fine art. Data has enhanced our lives, but we still need to know how to use it properly. “It’s a paradigm shift,” Cansado says. “Where we made decisions based on intuition and guesswork, now we can manage based on evidence and we can move based on data-driven decisions.”

9. Influencing Decisions that Matter

Prukalpa Sankar tells the story of data-based decision-making through the lens of two 2014 events: that year, Germany leveraged data analytics to win the World Cup football tournament, and Myanmar had to halt its census because it ran out of pencils.

“It’s crazy that big data is used to solve some kinds of problems and not others,” says Sankar, who has founded two companies that seek to democratize data. Sankar envisions a world in which data can predict traffic patterns or determine if and when children might drop out of school before they even know it.

10. How Should We Use Data to Make Decisions?

Christina Orphanidou assesses the ways people should use data to make decisions. Individually, we make many decisions largely on impulse; our moods and biases inform those decisions.

But Orphanidou, Senior Manager of the Data and Artificial Intelligence Lab at PricewaterhouseCoopers (PwC), says people are beginning to process decisions based on data, just like large corporations. “We will move away from impulse and intuition and toward decisions based on data and evidence,” she says. “Our partners in making decisions will be intelligent machines.”

11. Small Farms and Big Data

Erin Baumgartner worked at MIT for 11 years before starting a food delivery service called Family Dinner. Baumgartner used her experience in data analytics to build menus, cut food waste, and help small farmers be more competitive.

Baumgartner says the food industry is “broken” and she’s out to change it by using analytics to create a community around local food. “I believe that the story of local food needs to be understood, told, and elevated. And in many ways, I think that nerds like us are really uniquely poised to tell it.”

12. Tech Companies, Data, and Our Children

Did you know that tech companies know about your children before they’re born? By conducting a web search for “ways to get pregnant”, downloading tracking apps, or posting ultrasound photos, parents give companies all sorts of data about their unborn children. And that’s just the beginning.

Veronica Barassi, author of the book Child Data Citizen, explains to parents why this data about their children matters. “All of these technologies transform the baby’s most intimate behavioral and health data into profit by sharing it with others,” she says.

13. The Human Insights Missing From Big Data

In the 2000s, a global digital-products manufacturer watched business sharply decline because of a decision based on in-house analytics. This company’s data said people were not interested in buying smartphones.

Tricia Wang, a data ethnographer, says that the company wasn’t analyzing the right data. Wang proceeds to delineate the difference between “big data” and “thick data,” which contains human stories, emotions, and interactions that can’t be quantified as easily. She also outlines the importance of combining these data forms into a complete model.

14. What Data Analytics Can Teach Successful Organizations About Success

Is it possible to outthink the competition and not outspend them? Rasmus Ankersen makes his argument through the lens of European football, suggesting that many organizations can benefit.

Ankersen, an author and entrepreneur who lends his analytics expertise to football teams in England and Denmark, further explores how a sports gambler became so successful that he bought two European teams — and ran those teams using the analytic models that made him a successful gambler.

15. Crime and Data

As attorney general of New Jersey, Anne Milgram employed data analytics that helped lower crime in Camden. Later, she worked at a foundation that built a data analytics tool that enables judges to assess the risk of jailing or freeing people who have been arrested.

Milgram says that data is among the most important forces in public safety. She also references how a Major League Baseball team used data analytics to transform itself, which was detailed in the book Moneyball.

“I wanted to introduce data and analytics and rigorous statistical analysis into our work,” says Milgram. “In short, I wanted to Moneyball criminal justice. It worked for the Oakland A’s, and it worked for the state of New Jersey.”

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