10 TED Talks That Will Inspire Every Data Professional

As data professionals, we have the privilege and responsibility of working with the most powerful asset of the 21st century: data. With 2.5 quintillion bytes of data created each day, and 90% of the world‘s data generated in the last two years alone, the potential for data-driven insights and solutions is staggering.[^1]

But with great power comes great responsibility. How can we harness the immense possibilities of data science and AI while still keeping human values and ethics at the center? These 10 TED Talks offer compelling ideas and inspiration for using data to make a positive difference in the world.

The Power of Data for Global Good

Many of the speakers highlighted the incredible potential for data science and AI to solve global challenges. For example:

• Mallory Soldner of UPS shared how company data can be used for "data philanthropy." By combining UPS‘s logistics data with data from food banks and relief organizations, Soldner estimated that wasted food could be rerouted to feed 825 million undernourished people worldwide.[^2]

• Will Marshall of satellite company Planet explained how their "searchable database of Earth" is helping to track deforestation, monitor natural disasters, and improve agricultural yields. Planet‘s satellites generate 6 terabytes of data per day.[^3]

• Anthony Goldbloom predicted that data science and machine learning will increasingly automate routine cognitive tasks. However, he emphasized that human creativity and social intelligence will become even more valuable. One study estimated that while 45% of jobs could be automated with current technology, only 5% of jobs are fully automatable.[^4]

The main takeaway is that data offers immense leverage for positive change – but data professionals need to actively steer these technologies toward humane and ethical ends. As Soldner put it: "It‘s time for the private sector to step up…Your company‘s data could literally save lives."

Confronting Bias and Prioritizing Inclusivity

Another major theme that emerged was the urgent need to identify and combat bias in our data and algorithms. Several speakers powerfully illustrated the real-world harms caused by unchecked AI bias:

• Joy Buolamwini showed how commercial facial recognition systems had an error rate up to 34% higher for dark-skinned women compared to light-skinned men.[^5] She co-founded the Algorithmic Justice League to create more inclusive and accountable AI.

• Tricia Wang warned about the pitfalls of "big data hubris" – thinking we don‘t need human insights because we have big data. She shared examples of major business failures caused by neglecting the "thick data" of human experience, from Nokia missing the smartphone revolution to retailers misunderstanding rural Chinese consumers.

• Dan Ariely used behavioral economics to show how big data often fails to capture irrational human behavior. Even with petabytes of personal data, companies still struggle to accurately predict basic things like what consumers will buy or what content will go viral.

The takeaway is that data is never "objective" – it is created by biased human processes. As data professionals, we have an obligation to critically examine our data, be transparent about limitations, and actively include diverse perspectives. If we don‘t, we risk magnifying and automating societal inequities through our algorithms.

Making Data Human-Centered and Meaningful

Many speakers pushed back against the idea of reducing people to data points. They asserted the need to keep data human-centered and maintain space for creativity, individuality and serendipity:

• Data artist Giorgia Lupi created hand-drawn "data portraits" tracking everything from her emotions to wardrobes to digital detoxes with a friend (aka "Dear Data"). She showed how "data humanism" can create deeper connections and self-understanding.

• Researcher Mona Chalabi used hand-drawn data visualizations to make often hidden statistics visceral and meaningful, from the racial disparities in the U.S. prison system to plastic waste to pay gaps. "The numbers are not enough," she said. "I need you to feel something."

• Data storyteller RJ Andrews showed how data visualization can unearth fascinating historical insights, like Florence Nightingale‘s 1859 "rose diagram" that revolutionized sanitary conditions in hospitals, or W.E.B. Du Bois‘s 1900 Paris Exposition graphs that powerfully depicted black life post-slavery.

The key insight here is that working with data is ultimately a creative, human, and storytelling endeavor. While AI and algorithms are immensely powerful, they will never replace our need for meaning-making. As data professionals, combining the quantitative and the qualitative, the analytical and the emotional, the measurable and the meaningful is vitally important.

Envisioning the Future of Data Science

Where is the data science field heading, and what skills will be essential? Several speakers offered thought-provoking predictions and calls-to-action:

• Futurist Amy Webb foresaw a coming "Big Nine" of tech giants (U.S. and China) that will dominate AI development. She called for more long-term and proactive thinking to harness AI for good and prevent catastrophic risks.[^6]

• Data scientist Cathy O‘Neil, author of "Weapons of Math Destruction," asserted the need for algorithmic audits and regulation. "Algorithms are opinions embedded in code," she said. "Algorithms are not inherently fair, because the world isn‘t fair."[^7]

• Ben Wellington showed how open data and open-source tools like Python and Google Sheets are democratizing data science. His blog "I Quant NY" used NYC‘s open data to uncover everything from obscure parking laws to the fastest subway routes.

The speakers envisioned a future where data literacy is a basic skill for all citizens, where "black box" algorithms are unacceptable, and where data and AI systems are co-designed by diverse coalitions to benefit humanity. Getting there will require data professionals to become much more interdisciplinary, bridging technical skills with domain knowledge, ethical reasoning, creativity, and emotional intelligence.

Looking Ahead

Data science has already transformed our world in astonishing ways – but it‘s clear we are still in the early innings. As data tools and technologies continue to rapidly advance, it‘s on us as data professionals to ensure they advance in the right direction.

These 10 TED speakers offer a roadmap for the future: one grounded in using data for good, for inclusion, for human connection and understanding. While not shying away from hard topics like algorithmic bias and automation risks, they ultimately strike a tone of determined optimism. With the right values and intentional stewardship, data can be a profoundly positive force.

As Hans Rosling, the late statistician and TED speaker, put it: "The world cannot be understood without numbers. But the world cannot be understood with numbers alone." [^8] Data professionals have an exciting but weighty task ahead: to wrangle the numbers in service of human needs and wisdom. If we succeed, the potential benefits are limitless.

[^1]: Source: IBM. "10 Key Marketing Trends for 2017." https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=WRL12345USEN
[^2]: Source: Mallory Soldner. "Your company‘s data could help end world hunger." TED, 2020. https://www.ted.com/talks/mallory_soldner_your_company_s_data_could_help_end_world_hunger
[^3]: Source: Will Marshall. "The mission to create a searchable database of Earth‘s surface." TED, 2018. https://www.ted.com/talks/will_marshall_the_mission_to_create_a_searchable_database_of_earth_s_surface
[^4]: Source: Carl Benedikt Frey and Michael A. Osborne. "The future of employment: How susceptible are jobs to computerisation?" Technological Forecasting and Social Change, 2017. https://www.sciencedirect.com/science/article/abs/pii/S0040162516302244
[^5]: Source: Joy Buolamwini and Timnit Gebru. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." Proceedings of Machine Learning Research, 2018. http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
[^6]: Source: Amy Webb. "How we can find ourselves in data." TED, 2018. https://www.ted.com/talks/amy_webb_how_we_can_find_ourselves_in_data
[^7]: Source: Cathy O‘Neil. "The era of blind faith in big data must end." TED, 2017. https://www.ted.com/talks/cathy_o_neil_the_era_of_blind_faith_in_big_data_must_end
[^8]: Source: Hans Rosling. Factfulness: Ten Reasons We‘re Wrong About the World–and Why Things Are Better Than You Think. Flatiron Books, 2018.