{"id":73025,"date":"2022-07-23T14:25:00","date_gmt":"2022-07-23T18:25:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=73025"},"modified":"2022-07-27T13:54:24","modified_gmt":"2022-07-27T17:54:24","slug":"disadvantages-of-big-data","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/disadvantages-of-big-data\/","title":{"rendered":"The 2 Crucial Disadvantages of Big Data"},"content":{"rendered":"\n

What are the disadvantages of big data<\/a>? At what point does data get in the way?<\/p>\n\n\n\n

In Everybody Lies<\/em>, Seth Stephens-Davidowitz warns that good data science isn\u2019t just a matter of amassing a giant data set. When working with data, he says it\u2019s important to keep data\u2019s shortcomings in mind and not lose sight of the bigger picture.<\/p>\n\n\n\n

Read more so you can be aware of big data’s disadvantages.<\/p>\n\n\n\n\n\n\n\n

Disadvantage #1: False Correlations<\/h2>\n\n\n\n

Stephens-Davidowitz says that when a dataset is too detailed, it can lead to predictive errors. The problem, he says, is one of the disadvantages of big data: the<\/strong><\/em> curse of dimensionality<\/em>. It’s a phenomenon whereby the more details a dataset contains, the more likely it is to suggest false positives when you look for predictive correlations.<\/strong> <\/p>\n\n\n\n

Stephens-Davidowitz gives the example of flipping coins to try to predict the stock market<\/a>. Say you flip a coin every day, record whether it was heads or tails, and then record whether the stock market went up or down that day. Stephens-Davidowitz says that if you perform this test using 1,000 coins for two years, it\u2019s likely that by pure chance, at least one coin\u2019s results will appear to correlate with market performance. Obviously this correlation is false. But Stephens-Davidowitz says this problem happens any time you test a lot of variables against a small number of outcomes\u2014such as when trying to predict the stock market or link gene variations to disease likelihood.<\/p>\n\n\n\n

(Shortform note: In addition to the risk of drawing conclusions based on random noise<\/a> as Stephens-Davidowitz describes, the curse of dimensionality<\/a> can make it hard to draw any meaningful conclusions at all. That happens when you classify data into so many parameters that all data points appear equidistant from each other and there are fewer \u201cclusters\u201d of data to draw your attention\u2014in other words, you can no longer see useful similarities<\/a> between items.)<\/p>\n\n\n\n

Disadvantage #2: Data for Data\u2019s Sake<\/h2>\n\n\n\n

Stephens-Davidowitz points out that it\u2019s easy to fall in love with data for its own sake. When that happens, we\u2019re likely to lose sight of what the data was supposed to be doing for us in the first place<\/strong>. He gives the example of standardized testing in education, which aims to make teaching and learning measurable by generating data on student outcomes. But in many cases, schools end up focusing on improving their test scores (which are tied to schools\u2019 reputation and funding) by any means necessary\u2014means that include limiting the curriculum in order to focus on test prep and, in extreme cases, cheating on the tests. <\/p>\n\n\n\n

Stephens-Davidowitz says that studies suggest the best way to use data to measure teacher quality is to combine test scores with other factors like student evaluations and classroom observation. He says that many fields are finding that this combination of big data and traditional, small-scale information works better than focusing on big data alone<\/strong>.<\/p>\n\n\n\n

Big Data vs. Small Data<\/strong><\/h3>\n\n\n\n

Similarly, in Small Data<\/em><\/a>, author and branding consultant Martin Lindstrom argues that big data on its own is misleading and that it should be coupled with what he calls \u201csmall data\u201d\u2014in-person observation of people\u2019s desires and motivations. <\/p>\n\n\n\n

Lindstrom gives the example of LEGO, which tried to address struggling sales by turning to big data research. That research convinced the company that millennials would be easily bored by toy building blocks, so the company simplified its sets in an attempt to offer instant gratification<\/a>. This approach failed. But when market research interviews<\/em> with actual children revealed that kids like mastering hobbies, LEGO found more success than ever before by making more <\/em>complicated sets\u2014an approach that directly contradicted what big data had told them.<\/p>\n\n\n\n

Lindstrom\u2019s definition of \u201csmall data\u201d is not the only one. Other researchers use the term to describe small-scale measurements of specific attributes<\/a>\u2014such as wind direction sensors on wind turbines or smart bottle labels that track a medicine\u2019s remaining shelf life. This kind of small data can work on its own (for example, by telling the turbine to adjust its blades to maximize electricity output) or integrate with big data techniques (for example, to track when, where, and why medicines expire on shelves).<\/p>\n","protected":false},"excerpt":{"rendered":"

What are the disadvantages of big data? At what point does data get in the way? In Everybody Lies, Seth Stephens-Davidowitz warns that good data science isn\u2019t just a matter of amassing a giant data set. When working with data, he says it\u2019s important to keep data\u2019s shortcomings in mind and not lose sight of the bigger picture. Read more so you can be aware of big data’s disadvantages.<\/p>\n","protected":false},"author":14,"featured_media":13770,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[45,25],"tags":[691],"class_list":["post-73025","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business","category-statistics","tag-everybody-lies","","tg-column-two"],"yoast_head":"\nThe 2 Crucial Disadvantages of Big Data - Shortform Books<\/title>\n<meta name=\"description\" content=\"To use big data effectively, you have to be aware of its disadvantages. 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