Everywhere you look Big Data is watching. Every transaction you make, Big Data knows. Even as I am writing about it here, it can see every word I deleted and the number of times I edit this paper. No one can escape it – but what exactly is “It” anyway? Big Data is a term that people use to describe large amounts of data gathered together and analyzed to reveal trends associated with human behaviors. Should we be afraid of it? Yes, we should be afraid, and no; it is a double-edged sword, which can make our lives better OR miserable. It can be used to reduce emissions, lower our living costs, and research cancer, but in the wrong hands, it can be used to predict and manipulate social change on a massive scale.

In the article “Is Big Data Analytics Good or Evil?”, author Bill Frank describes Big Data as potentially scary, but “more good than evil.” Throughout the passage, Frank uses factual evidence and analogy to persuade his audience that Big Data should not be feared, for it has enormous benefits. Frank believes that Big Data will be misused, but the benefits of it outweigh all the bad. For example, it can save children from abusive families, which can prevent injuries or death. That is why he believes that people should focus on minimizing the risk of Big Data, rather than not using it at all. To convince his readers that using Big Data is a good idea, Frank uses an analogy:

“It’s a bit like driving. Every time we drive in a car we are risking our lives. At any moment, someone could slam into us and kill us even though we did nothing wrong. It is tragic when this occurs, but it occurs rarely enough that we consider it an acceptable risk.”

Through the car analogy, the reader can accept Big Data as one of the hundreds of things people do in a day that has a risk. Frank is convincing in that he uses examples, like child abuse, to make the readers sympathize, and tries to simplify Big Data to make the audience feel at ease, but nowhere does he actually explain in detail what Big Data is; he assumes that his readers will know enough from his analogies to make an informed decision as to what to believe about it.

The tech journalist Randy Bean is more sanguine; he relates in his article “Bloomberg’s Data Initiative: Big Data For Social Good In 2018” that Big Data is found everywhere now, and can be used in many different fields. Throughout Bean’s article, he focuses on three main points on why Big Data is helpful: his first is about how the use of data can “solve problems at the core of society,” linking to Bloomberg’s Data for Good Exchange to further support his claim; his second argument is that Big Data can help improve public health worldwide. He talks about how the Data For Health program “[enables] countries to improve public health data collection with the goal of addressing public health problems.” The program has partnered with more than 20 countries across the globe, reaching more than 1 billion people; Improving the health data collection can help address public health problems. The third claim Bean makes is that “Big data is making a difference in addressing disparities in criminal justice sentencing and in tackling the challenges of poverty and crime.” Using information from the data-driven Justice Initiative, he concludes that on average there are about 11 million people going through American’s local jails, costing governments about $22 billion annually, while most inmates suffer mental illnesses and chronic health problems. To combat this, Bean talks about how three experts (Mary McKernan McKay of the Brown School of Social Work at Washington University in St. Louis, and former professor of poverty studies and director of poverty policy at New York University, Kelly Jin, director of the data-driven justice initiative for the The Laura and John Arnold Foundation, and former policy advisor for the Obama White House data-driven Justice Initiative, and Rebecca Ackerman, a data scientist with New York Defender Services) “bring their perspectives on links between poverty, mental health, and racial discrimination.” Bean shows lots of statistical and factual evidence to persuade us that there are enormous benefits Big Data can bring.

Big Data – What is it Good For” is another pro-Big Data article, written by Dr. Kirk Borne. He talks about the “trio of D2D’s: Data-to-Discovery, Data-to-Decisions, and Data-to-Dollars.”  Data-to-Discovery has 4 main categories:

Correlation discovery – finding the hidden patterns and trends in the data

Novelty discovery – finding surprises, anomalies, outliers, and unexpected items in your data space

Association discovery – finding unusual, improbable co-occurring features or products in the data set

Class discovery – finding new categories and classes of items, events, or behaviors in your domain.

Borne believes that it is not just scientists who can use this data. For example, businesses could use this knowledge to discover “new marketing opportunities, new customers, new ways to engage existing customers, new signals that they are about to lose a customer, [and] new categories of customer interests.” Borne’s second point is Data-to-Decisions: while “joint human-computer cooperation is essential” to game- changing decisions, the trivial questions “such as the decision to deliver a discount coupon to a disengaged customer, or to send a welcome-back message to a returning customer” can be fully automated by guiding algorithms to make suggestions based on “your business rules and processes”. Borne is convinced that “Big Data is pure gold to business[es].” It shows “insights on customer behaviors, preferences, and responses to stimuli can be delivered to marketing teams in real-time, autonomously, at a person-specific level.” Overall, I think this article has a lot of evidence, lots of research, to support his positive opinion regarding Big Data.

Not everyone agrees that Big Data is beneficial. In fact, many writers believe overall that it is a major threat to society! “A Need to Manage Big Data’s Big Risks”, written by Ernest Davis, suggests that Big Data, if misused, can do serious damage to society. He starts off by stating that “having more data is no substitute for having high-quality data ” that is not prejudicial and inaccurate, that is impartial and accurate. To support his claim, he explains that when you search on Google for “unprofessional hairstyles for work”, most of the pictures are of black people, and when you search for “professional hairstyles for work”, the majority of the pictures are of white people. The programmers at Google are not the ones to blame for this, for that is how people in general label those pictures – but Big Data powered by AI in search engines like Google are not capable of rendering impartial and accurate search results. Davis moves on to relate how Big Data can skew the criminal justice system. A study done by ProPublica found that “[determining] sentences for convicted criminals systematically overestimates the likelihood that black defendants will commit crimes in the future, and underestimates the risk that white defendants will do so.” Next, Davis explicates how universities have expelled weaker students to boost their “retention rate”, and that any one of your online postings can show your “political opinions or even sexual preferences”, all because of the vast amount of personal data you put on the internet is used by a wide variety of institutions and businesses for their commercial and/or institutional purposes, but not necessarily will they use it in your best interest. In conclusion, Davis warns us that a large amount of data used anonymously is not often a public good. l think this article is pretty solid; it has lots of evidence to back up its claim.

In the article “The Dark Side of Big Data”, author Tom Goodwin points out that “[people] overestimate the importance of what we know, rather than focus on what data makes clear that we don’t actually know” much. Many are said to believe Big Data is “a cure-all”, but the real truth of wisdom according to Goodwin is that “the more you know, the more you know you don’t know.” Goodwin argues that people are not seeing the bigger picture. Big Data is used to advertise certain products to people by looking at their purchase and browser history, but the problem is that Big Data analytics does not understand how people actually think. For example, if “[Goodwin is] interested in a hotel in Chicago next week,” rather than “offering him boat tours or car rentals,” Big Data will constantly retarget him with the same hotels as the one he is thinking about booking. In 2016, it is easy for companies to fire someone based on a “terrible but rational decision” since any rational decision can be supported by data. However, “today, an irrational decision that works wonders could be thought to be reckless and lucky, or risky but that worked out.” Look at some of the greatest business successes – almost all happened as a result of “the remarkable not iterative.” Still, after all the negative things he says about the negative effects of Big Data, Goodwin still believes it has some positive use: for example, “it forces us to make things tidy, to get closer to an iterative solutions.” Sony was able to make Discman “cheaper and lighter” for the general public through gathering data. Throughout the article, Goodwin gives us some insights into why we could believe Big Data is bad, and some personal anecdotes to support his claims, but not enough solid evidence to be sure about Big Data’s efficacy as a positive force for good.

Finally, no matter what we think about Big Data, we cannot just push a magic button to get rid of it. It will always be there now, as there is no going back to some more innocent time without computing power, and it is up to us on how we want to use it. So why not just focus on what we can control and make it the best humanly possible? In “Is Big Data Analytics Good or Evil?”, “Bloomberg’s Data Initiative: Big Data For Social Good In 2018”, and “Big Data – What is it Good For”, the authors provided uses in various different fields in which we could apply Big Data to solve pressing social and economic problems, such as saving money or improving our health care system, and for those authors who oppose the use of this new technology, there is not enough evidence to rule Big Data as altogether a negative force. Often in our species’ evolution, we have risked greatly to achieve our destiny, and the use of Big Data will not the the last time we will leap into the future without knowing what it holds in store for us. we can only do our best, and let the chips fall where they may.

References

  1. Franks, Bill, and Teradata. “Is Big Data Analytics Good or Evil?” VentureBeat. June 15, 2015. Accessed September 24, 2018. https://venturebeat.com/2015/06/15/is-big-data-analytics-good-or-evil/.
  2. Guest, CIO Central, and Randy Bean. “Bloomberg’s Data Initiative: Big Data For Social Good In 2018.” Forbes. January 02, 2018. Accessed September 24, 2018. https://www.forbes.com/sites/ciocentral/2018/01/02/bloombergs-data-initiative-big-data-for-social-good-in-2018/#111710673a44.
  3. “Data For Good Exchange 2018.” Bloomberg.com. Accessed September 24, 2018. https://www.bloomberg.com/company/d4gx/.
  4. “Data for Health.” Bloomberg Philanthropies. Accessed September 24, 2018. https://www.bloomberg.org/program/public-health/data-health/#overview.
  5. “FACT SHEET: Launching the Data-Driven Justice Initiative: Disrupting the Cycle of Incarceration.” National Archives and Records Administration. Accessed September 24, 2018. https://obamawhitehouse.archives.gov/the-press-office/2016/06/30/fact-sheet-launching-data-driven-justice-initiative-disrupting-cycle.
  6. Borne, Dr. Kirk. “Big Data – What Is It Good For?” MapR Media Kit | MapR. Accessed September 24, 2018. https://mapr.com/blog/big-data-what-it-good/.
  7. Davis, Ernest. “A Need to Manage Big Data’s Big Risks.” The Straits Times. February 16, 2017. Accessed September 24, 2018. https://www.straitstimes.com/opinion/a-need-to-manage-big-datas-big-risks.
  8. Goodwin, Tom. “The Dark Side of Big Data.” Forbes. July 14, 2016. Accessed September 24, 2018. https://www.forbes.com/sites/tomfgoodwin/2016/07/14/the-dark-side-of-big-data/#6c561f985bd5.