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Does Big Data Provide the Answer? CASE STUDY oday's companies are dealing with an ava- mean that the right information is being collected lanche of data from social media, search, and or that people will make smarter decisions. Experts sensors, as well as from traditional sources. in big data analysis believe that too many compa- According to one estimate, 2.5 quintillion bytes of nies, seduced by the promise of big data, jump into data per day are generated around the world. Making big data projects with nothing to show for their efsense of "big data" to improve decision making and forts. They start amassing mountains of data with business performance has become one of the pri- no clear objective or understanding of exactly how mary opportunities for organizations of all shapes analyzing big data will achieve their goal or what and sizes, but it also represents big challenges. questions they are trying to answer. Organizations Businesses such as Amazon, YouTube, and Spotify also won't benefit from big data that has not been have flourished by analyzing the big data they col- properly cleansed, organized, and managed-think lect about customer interests and purchases to create data quality. millions of personalized recommendations for books, Big Data does not always reflect emotions or intuifilms, and music. A number of online services ana- tive feelings. For example, when LEGO faced banklyze big data to help consumers, including services ruptcy in 2002 to 2003, the company used big data for finding the lowest price on autos, computers, mo- to determine that Millennials have short attention bile phone plans, clothing, airfare, hotel rooms, and spans and easily get bored. The message from the many other types of goods and services. Big data is data led LEGO to de-emphasize their small iconic also providing benefits in sports (see the Interactive bricks in favor of large simplistic building blocks. Session on Management), education, science, health This change only accelerated LEGO's decline, so the care, and law enforcement. company decided to go into consumers' homes to Healthcare companies are currently analyz- try and reconnect with once-loyal customers. After ing big data to determine the most effective and meeting with an 11-year-old German boy, LEGO diseconomical treatments for chronic illnesses and covered that for children, playing and showing mascommon diseases and provide personalized care tery in something were more valuable than receivrecommendations to patients. For example, the ing instant gratification. LEGO then pivoted again state of Rhode Island has been using InterSystems' to emerge after its successful 2014 movie into the HealthShare Active Analytics tool to collect and an- world's largest toy maker. Patterns and trends can alyze patient data on a statewide level. The state's sometimes be misleading. Quality Institute found that about 10 percent of Huge volumes of data do not necessarily provide major lab tests performed in over 25 percent of the more reliable insights. Sometimes the data being state's population were medically unnecessary - analyzed are not a truly representative sample of the a discovery that has since helped Rhode Island data required. For example, election pollsters in the tighten spending as well as improve quality of care. United States have struggled to obtain representaBig data analytics are helping researchers pinpoint tive samples of the population because a majority of how variations among patients and treatments in- people do not have landline phones. It is more timefluences health outcomes. For instance, big data's consuming and expensive for pollsters to contact mogranularity could help experts detect and diagnose bile phone users, which now constitute 75 percent of multiple variants of asthma, pointing physicians to some samples. U.S. law bans cell phone autodialing, the precise treatment plan called for by each pa- so pollsters have to dial numbers by hand individutient's unique case. ally and make more calls, since mobile users tend to There are limits to using big data. A number of screen out unknown callers. Opinions on Twitter do companies have rushed to start big data projects not reflect the opinions of the U.S. population as a without first establishing a business goal for this new whole. The elderly, poor people, or introverts, who information or key performance metrics to measure tend not to use social media-or even computerssuccess. Swimming in numbers doesn't necessarily often get excluded. Although big data is very good at detecting corre- The software searches through hundreds of thoulations, especially subtle correlations that an analysis sands of crime records across 77 precincts in the of smaller data sets might miss, big data analysis NYPD database to find a series of crimes likely to doesn't necessarily show causation or which correla- have been committed by the same individual or inditions are meaningful. For example, examining big viduals, based on a set of identifying characteristics. data might show that the decline in United States In the past, analysts had to manually review reports crime rate was highly correlated with the decline to identify patterns, a very time-consuming and inefin the market share of video rental stores such as ficient process. Some experts worry that Patternizr Blockbuster. But that doesn't necessarily mean there inadvertently perpetuates bias. The NYPD used 10 is any meaningful connection between the two phe- years of manually identified pattern data to train nomena. Data analysts need some business knowl- Patternizr, removing attributes such as gender, race, edge of the problem they are trying to solve with big and specific location from the data. Nevertheless data. such efforts may not eliminate racial and gender Just because something can be measured doesn't bias in Patternizr if race and gender played any role mean it should be measured. Suppose, for instance, in past police actions used to model predictions. that a large company wants to measure its web- According to Gartner Inc. analyst Darin Stewart, site traffic in relation to the number of mentions Patternizr will sweep up individuals who fit a profile on Twitter. It builds a digital dashboard to display inferred by the system. At best, Stewart says, some the results continuously. In the past, the company people identified by Patternizr will be inconvehad generated most of its sales leads and eventual nienced and insulted. At worst, innocent people will sales from trade shows and conferences. Switching be incarcerated. to Twitter mentions as the key metric to measure Companies are now aggressively collecting and changes the sales department's focus. The depart- mining massive data sets on people's shopping ment pours its energy and resources into monitoring habits, incomes, hobbies, residences, and (via mowebsite clicks and social media traffic, which pro- bile devices) movements from place to place. They duce many unqualified leads that never lead to sales. are using such big data to discover new facts about All data sets and data-driven forecasting models people, to classify them based on subtle patterns, to reflect the biases of the people selecting the data and flag them as "risks" (for example, loan default risks performing the analysis. Google developed what it or health risks), to predict their behavior, and to mathought was a leading-edge algorithm using data it nipulate them for maximum profit. Privacy experts collected from web searches to determine exactly worry that people will be tagged and suffer adverse how many people had influenza and how the dis- consequences without due process, the ability to ease was spreading. It tried to calculate the number fight back, or even knowledge that they have been of people with flu in the United States by relating discriminated against. people's location to flu-related search queries on Insurance companies such as Progressive offer Google. Google consistently overestimated flu rates, a small device to install in your car to analyze your when compared to conventional data collected after- driving habits, ostensibly to give you a better insurward by the U.S. Centers for Disease Control (CDC). ance rate. However, some of the criteria for lower Several scientists suggested that Google was "tricked" auto insurance rates are considered discriminatory. by widespread media coverage of that year's severe For example, insurance companies like people who flu season in the United States, which was further don't drive late at night and don't spend much time amplified by social media coverage. The model de- in their cars. However, poorer people are more likely veloped for forecasting flu trends was based on a to work a late shift and to have longer commutes flawed assumption-that the incidence of flu-related to work, which might increase their auto insurance searches on Googles was a precise indicator of the rates. number of people who actually came down with the More and more companies are turning to comflu. Google's algorithm only looked at numbers, not puterized systems to filter and hire job applicants, the context of the search results. especially for lower-wage, service-sector jobs. The The New York Police Department (NYPD) re- algorithms these systems use to evaluate job candicently developed a tool called Patternizr, which uses dates may be preventing qualified applicants from pattern recognition to identify potential criminals. obtaining these jobs. For example, some of these algorithms have determined that, statistically, people and Ernest Davis, "Eight (No, Nine!) Problems With Big Data," New with shorter commutes are more likely to stay in a York Times, April 6, 2014. job longer than those with longer commutes or less reliable transportation or those who haven't been at CASE STUDY QUESTIONS their address for very long. If asked, "How long is 6-13 What business benefits did the organizations your commute?" applicants with long commuting described in this case achieve by analyzing and times will be scored lower for the job. Although such using big data? considerations may be statistically accurate, is it fair 6-14 Identify two decisions at the organizations to screen job applicants this way? described in this case that were improved by Sources: Grant Wernick, "Big Data, Small Returns," Data-Driven using big data and two decisions that big data Investor, January 13, 2020; "Big Data 2020: The Future, Growth did not improve. and Challenges of the Big Data Industry," www.i-scoop.com, ac- 6-15 Describe the limitations to using big data. AnalysisToolRaisesAIBiasConcerns,"searchbusinessanalytics.com,March14,2019;LisaHedges,"WhatIsBigDatainHealthcareanalyzebigdata?Whyorwhynot?Whatman- and How Is It Already Being Used?" October 25, 2019; Alex Bekker, agement, organization, and technology issues "Big Data: A Highway to Hell or a Stairway to Heaven? Exploring should be addressed before a company decides Big Data Problems," Sciencesoft, May 19, 2018; and Gary Marcus to work with big data