How Do We Use Data for Good? Add Context.
In this series of commentaries on analytics-related TED Talks, we examine how insights from predictive analysis can solve real-world problems and how various experts are making that happen.
When it comes to data analytics, Susan Etlinger says facts can be stupid and stubborn things. They can provide us with the business intelligence metrics we long for, but without predictive analytics based on contextual interpretation, we may find ourselves using skewed quantitative analysis that produces less-than-desirable results. In her TED Talk: What Do We Do With All This Big Data, this data analyst discusses how the appropriate use of context in analytics makes all the difference toward achieving optimal results.
Citing Neil Postman's book, Amusing Ourselves to Death, she notes that while technology has brought us so much, it also taps into many of our deepest fears. "In a nutshell, it's a choice between Big Brother watching you and you watching Big Brother." But, Etlinger says, we can opt out of having to make this choice. Instead of being passive consumers of data and technology, we can shape the role it plays in our lives and the way we derive meaning from it. In order to do so, we must challenge ourselves to ask the difficult questions that will move us "past counting things, to understanding them" in the context in which disparate types of data are created.
As a prime example, she describes an initiative called the Health Media Collaboratory, a group of data scientists and analysts at the University of Illinois-Chicago who are collaborating with the CDC to better understand how people talk about quitting smoking and electronic cigarettes, and what can be done collectively to help them quit. These data scientists and analysts are taking the time to understand the related language across social media platforms—and the context in which it is created—in order to help develop solutions that will really make a difference. She cites this as "a fantastic example of courage in the face of a sea of irrelevance."
On a personal level, Etlinger talks about her son, Isaac, who was diagnosed with autism at the age of 2. Describing him as "this happy, hilarious, loving, affectionate little guy," she says that although the metrics on his developmental evaluations were factually correct, they didn't tell the whole story in the context of Isaac's capabilities. When she found him typing search terms into Google, she realized that he'd found his own workaround in his struggle to communicate—an ability that the data would never have been able to adequately capture. "He was teaching himself to communicate, but we were looking in the wrong place, and this is what happens when assessments and analytics overvalue one metric—in this case, verbal communication—and undervalue others, such as creative problem-solving."
Etlinger says this is a perfect example of the challenge before us—to take advantage of the opportunity to try to create meaning out of the data, because in and of itself, data doesn't create meaning. Citing the increasingly rapid speed of data processing, she notes that this works in parallel with the increased speed at which we can make bad decisions based on an incorrect interpretation of the data. This is where skilled data analysts come in, as they can help us avoid this hazard. Etlinger says we need to hone our critical thinking skills—focusing in tandem on disciplines such as the humanities, sociology, the social sciences, rhetoric, philosophy, and ethics—"because they give us context that is so important for big data, and because they help us become better critical thinkers."
Etlinger says that deriving valuable meaning from data will help us to ask the most difficult question of all: "Did the data really show us this, or does the result make us feel more successful and more comfortable?" She says that if we are to truly unlock the power of data, we have to optimize critical thinking in order to use the power of data analytics for good.
The importance of interpreting data contextually is what Health Care Without Harm believes in as well. Described as "leading the global movement for environmentally responsible health care," the organization collects massive amounts of data on hospitals around the world related to energy reduction, waste management, and healthy food. In a column for the Huffington Post, authors Christina Quint and Benn Grover note: "Given the health care sector's moral mission and massive buying power, how can the sector shift our entire economy toward sustainable, safer products and practices?"
By aggregating the data they collect, and analyzing it within context, the numbers begin to tell individual stories regarding the outcomes and impacts of various initiatives. "Big data is a funny thing. On the one hand, it represents the aggregate of a bunch of similar actions. But without context, the power of those numbers is lost. It's not the data that drives strategy or funding. It's the implications of that data—the story the data is telling—that determine decisions."
While the approach to using context may differ slightly between Etlinger's description, and that of Health Care Without Harm, the premise is the same—that siloed data analysis can lead to less-than-optimal decision-making. Depending on the environment, the results of such decisions could have a wide array of negative ramifications. As Etlinger both cautions and encourages, we need to ensure that we are using our big data powers for good.
Susan Etlinger is an industry analyst with Altimeter Group, where she focuses on data and analytics. She also advises global clients on how to work measurement into their organizational structure and how to extract insights from the social web, which can lead to tangible actions. In addition, she works with technology innovators to help them refine their roadmaps and strategies. Etlinger is on the board of The Big Boulder Initiative, an industry organization dedicated to promoting the successful and ethical use of social data.