Team 2: Annie Ackroyd, Victoria Adair, Bret Petersen
Topic Overview
Big Data - defined by Lohr (2012) as “advancing trends in technology that open the door to a new approach to understanding the world and making decisions” - has the potential to revolutionize the way we understand ourselves and the behavior of humans across the world.
Some key findings from this chapter’s readings regard:
- Big data growth - Back in 2012, Lohr estimated that data collection was growing at at least 50% per year. This staggering amount of collection means there are figurative piles of data just waiting for sifting. The potential implications of these data sets are just as massive as the sets themselves.
- Big data as a predictor - Both Lohr (2012) and Pentland (2016) discuss the potential for big data as a predictor of human and other behaviors. In particular, Pentland highlights the distinction between what people report to be true about themselves (i.e. what they share on social media) and what is factually true about themselves (i.e. who they interact with, where they go, what they spend money on, etc.) (p. 8). Algorithms and models can be used to more accurately predict what humans will do (i.e. human output) when big data can provide more accurate input than what we self-report.
- Big data as higher quality data - In addition to the arguments mentioned above, Pentland (2016) effectively demonstrates in Figure 1 (p. 11) that one of big data’s greatest strengths and potentials is its capacity for an increased number of data samples (i.e. increased measurements per data subject), even over a long-term study.
Meier (2015) provides an example of big data “done right” with his lived experience of digital humanitarianism during the 2010 Haitian earthquake and its aftermath. Ultimately, the efforts of Meier and volunteers saved lives and improved the actions taken on-the-ground in the immediate after-effect of this natural disaster. As Meier stated, digital humanitarianism is an outlet for “global goodwill” requiring few advanced skills or connections. The role of big data, then, is to “connect the dots and channel this [global goodwill] toward positive social goals” (p. 18).
Chapter Summaries
Social Physics CH1 From Ideas to Action
The key concept of social physics is to better understand how ideas are shared between individuals & throughout groups, and what outcomes can be reasonably expected as a result (Pentland, 2015, p. 3-4). Social physics recognizes that a significant amount of data (known here as “big data”) is available given today’s technological advancements (computers, smart phones, electronic payment options, GPS tracking) and can be analyzed to inform comprehensive decisions, strategies and accurately predict plausible outcomes. It has the potential to differentiate between what people portray about their lives on Facebook (i.e. a selective depiction of one’s life) and the unbiased analytical data defining where we spend our time and our money (Pentland, 2015, p. 8). Pentland argues that the two most important concepts of social physics are:
- Idea Flow within social networks, including exploration and engagement, and
- Social Learning of new strategies or beliefs through observation or experiences (2015, p. 15).
Additionally, Pentland includes definitions for 15 key terms as they relate to social physics in an effort to meet his goal of providing a more robust vocabulary for understanding human interactions, organizations and societies (2015, p. 8, 19-21).
Digital Humanitarians CH1 Rise of Digital Humanitarianism
This chapter used Meier’s personal experience with what is now known as “digital humanitarianism” during the aftermath 2010 Haitian earthquake to illustrate the potential power of lay-person access to data to save lives. In summary:
- Meier’s wife was in Port-Au-Prince when the earthquake hit. Out of desperation to contact his wife (and a desire to do something), he set up a “crisis map” of Haiti using information he found from tweets online and from the Open Street Map (in his words, the “Wikipedia of maps”) (2015, p. 2-4).
- To more fully flesh out the map, he created a multiplatform account which could receive incoming email, tweets and SMS texts about the disaster.
- He soon recruited others around him - and, ultimately, throughout the world - to help sift through the data and update important information about aftermath details in Haiti from his dorm room in Boston, MA.
- FEMA and other branches of the US government used the information crowd-sourced from Meier’s volunteers to coordinate on-the-ground care immediately after the earthquake.
Meier argues that this experience highlights the promise of big data (i.e. it can literally save lives) and some of the complications (i.e. balancing personal privacy with collected data, what to use it for [i.e. should they stop their work once triage for the event was completed or continue on to show accountability for relief efforts?], etc.). He ended with a preview of remaining chapters in the book, which would explore other topics such as Big (False) data, policies and technology, AI, and more.
NYT The Age of Big Data
This article is about the explosion of available data in recent years, its applications across fields, and how to harness it. The impact of Big Data will extend far beyond the tech companies of Silicon Valley; there are implications for business, government, and academia. According to the article, “There is no area that is going to be untouched” (qtd. in Lohr 2012). The surge in available data has also created a rapidly growing need for workers with data expertise.
The author defines Big Data as “…advancing trends in technology that open the door to a new approach to understanding the world and making decisions” (Lohr 2012). Large quantities of data are rapidly becoming more available and understandable. Most of Big Data exists as “unstructured data.” However, we are developing new tools, like AI, to gain insight from unstructured data.
The article compares the impact of Big Data to that of the microscope in the 16th century. We are able to measure behavior and sentiment in fine detail in real time using data from platforms like Google and social media sites.
In business, decision making is becoming more analytical, and it’s paying off. Research shows that data-driven decision making increases productivity. Using Big Data to make predictions also has implications for preventing (or minimizing) public health disasters and economic downturns.
There are risks associated with Big Data. Privacy is at the top of the list, of course. Other potential pitfalls include an increased risk of false discoveries, biased fact-finding, and unfair or discriminatory statistical inferences. However, the author concludes that, “Despite the caveats, there seems to be no turning back. Data is in the driver’s seat. It’s there, it’s useful and it’s valuable, even hip” (Lohr 2012).
Key Take-Aways
Discussion Questions
- How is your organization collecting and analyzing data?
- Are the decisions currently being made supported by quantitative feedback?
- In the reading, Meier approached several different experts to determine whether it was an invasion of privacy to post the personal phone number and names of Haitians who had texted the emergency SMS line that he and other digital humanitarians were responding to. Who should be involved in these decisions? Would you want your name and phone number posted? Why or why not?
- What barriers to act do you think exist for potential digital humanitarians?
- How does Big Data inform your work? How might it inform your work in the future?
- Has your organization improved processes or outcomes using Big Data?
- How are you/your organization minimizing the risks associated with Big Data?
References
- Pentland, A. (2015). Social Physics: How social networks can make us smarter. Penguin. CH1
- Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH1
- Lohr, S. (2012). The Age of Big Data. Retrieved from: https://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html