CINXE.COM

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Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you鈥檙e measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for鈥攐r undertake鈥攐ther research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions." data-testid="meta"/><meta name="item-name" content="Where Data-Driven Decision-Making Can Go Wrong" data-testid="meta"/><meta name="page-category-name" content="Decision making and problem solving" data-testid="meta"/><meta name="shopper-attributes" content="state:guest" data-testid="meta"/><meta name="twitter:image" content="/resources/images/article_assets/2024/07/R2405D_DOCUBYTE.jpg" data-testid="meta"/><meta name="article:section" content="Decision making and problem solving" data-testid="meta"/><meta name="item-id" content="tag:hbr.org,1922-01-01:202409.388594-R2405D" data-testid="meta"/><meta property="article:publisher" content="https://www.facebook.com/HBR" data-testid="meta"/><meta property="og:site_name" content="Harvard Business Review" data-testid="meta"/><meta property="og:type" content="article" data-testid="meta"/><meta property="og:description" content="When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you鈥檙e measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for鈥攐r undertake鈥攐ther research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions." data-testid="meta"/><meta property="og:image" content="/resources/images/article_assets/2024/07/R2405D_DOCUBYTE.jpg" data-testid="meta"/><meta property="og:title" content="Where Data-Driven Decision-Making Can Go Wrong" data-testid="meta"/><meta property="article:published_time" content="2024-09-01T04:00:00.000+00:00" data-testid="meta"/><meta property="page-id" content="320" data-testid="meta"/><meta property="article:modified_time" content="2024-08-12T16:00:00Z" data-testid="meta"/><meta property="page-category-id" content="391767" data-testid="meta"/><meta property="intromercial" content="false" data-testid="meta"/><meta property="issue-id" content="[object Object]" data-testid="meta"/><meta property="primary-editor" content="57" data-testid="meta"/><meta property="article:tag" content="Analytics and data science" data-testid="meta"/><meta property="article:tag" content="Data management" data-testid="meta"/><meta property="article:tag" content="Leadership styles" data-testid="meta"/><meta property="article:tag" content="Organizational decision making" data-testid="meta"/><meta property="article:tag" content="Interpersonal skills" data-testid="meta"/><script>window.digitalData = {"pageInstanceID":"Where Data-Driven Decision-Making Can Go Wrong:production","siteID":"hbrprod","page":{"pageInfo":{"language":"en","contentType":"Magazine Article","pageName":"Where Data-Driven Decision-Making Can Go Wrong","pageType":"Article","pageCategoryID":"391767","pageCategoryName":"Decision making and problem solving","pageDescription":"When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. 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Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you鈥檙e measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for鈥攐r undertake鈥攐ther research that might confirm or contradict the evidence. 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class="DekBylineContainer_dek-byline-content__jety2 DekBylineContainer_premium__Y5cte"><span class="Dek_dek__7kUre">Five pitfalls to avoid</span><span class="Byline_byline__Fsu0D"> by <a href="/search?term=Michael Luca" class="Byline_author__SdVEf">Michael Luca</a> and <a href="/search?term=Amy C. Edmondson" class="Byline_author__SdVEf">Amy C. Edmondson</a></span></p></div><div class="PublicationDate_magazine-date-container__0OWx5"><a class="Anchor-module_anchor__uhpem PublicationDate_magazine-date__fyw4I" href="/archive-toc/BR2405">From the Magazine (September鈥揙ctober 2024)</a></div></div><div class="Premium_hero-image__nv5tq"><div class="Hero_premium__9aZvk"><img class="Hero_img__EpkVw" src="/resources/images/article_assets/2024/07/R2405D_DOCUBYTE.jpg" sizes="(min-width: 64em) 84vw, 100vw" srcSet="/resources/images/article_assets/2024/07/R2405D_DOCUBYTE.jpg 2000w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-300x181.jpg 300w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1024x616.jpg 1024w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-768x462.jpg 768w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1536x925.jpg 1536w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-383x231.jpg 383w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-500x301.jpg 500w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-700x421.jpg 700w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-850x512.jpg 850w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1200x722.jpg 1200w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1900x1144.jpg 1900w" alt="" role="presentation"/><div class="Hero_container-caption-credits__w3p3u Hero_premium__9aZvk"><span class="Hero_credits__Ll8_0">Docubyte</span></div></div></div><div class="Premium_summary__OyWNf"><section class="Summary_container__LHBTn Summary_initial-layout__EtsKc"><h2 class="Summary_summary-header__NtM4x">Summary.聽聽聽</h2><span class="Summary_summary-text__eMqXD"><span class="Summary_summary-text__eMqXD">When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question.</span> <span class="Summary_summary-text__eMqXD">Such conversations should explore the <em>internal validity</em> of any analysis (whether it accurately answers the question) as well as its <em>external validity</em> (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you鈥檙e measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for鈥攐r undertake鈥攐ther research that might confirm or contradict the evidence.</span> <span class="Summary_summary-text__eMqXD">By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.</span></span></section><div class="TranslateMessage_translate-message__iUF3m"><a class="Anchor-module_anchor__uhpem TranslateMessage_link__KubhN TranslateMessage_translate__mUd5F" href="/2024/09/where-data-driven-decision-making-can-go-wrong?language=es" data-analytics="lang-es">Leer en espa帽ol</a><a class="Anchor-module_anchor__uhpem TranslateMessage_link__KubhN TranslateMessage_translate__mUd5F" href="/2024/09/where-data-driven-decision-making-can-go-wrong?language=pt" data-analytics="lang-pt">Ler em portugu锚s</a></div></div><div class="Premium_left-rail__MMwHB"><ul class="PageUtils_container-left__whOYQ PageUtils_premium__mI6T2"><li class="PageUtils_list-item__HK3pt PageUtils_vertical__SqN7k"><a class="PageUtils_item-button__UHILB" href="https://twitter.com/intent/tweet?&original_referer=&ref_src=twsrc%5Etfw&related=twitterapi%2Ctwitter&tw_p=tweetbutton&url=" target="_blank" rel="noopener noreferrer" data-analytics="share-on-x"><img src="/assets-mfe-articles/_next/static/media/Twitter.7ac71c2c.svg" aria-hidden="true" alt="" width="24" height="24"/><span class="PageUtils_utility-name__y5qBD PageUtils_premium__mI6T2">Post</span></a></li><li class="PageUtils_list-item__HK3pt PageUtils_vertical__SqN7k"><button class="PageUtils_item-button__UHILB" type="button" data-analytics="share-on-facebook"><img src="/assets-mfe-articles/_next/static/media/Facebook.f94faecd.svg" aria-hidden="true" alt="" width="24" height="24"/><span class="PageUtils_utility-name__y5qBD PageUtils_premium__mI6T2">Post</span></button></li><li class="PageUtils_list-item__HK3pt PageUtils_vertical__SqN7k"><button class="PageUtils_item-button__UHILB" type="button" data-analytics="share-on-linkedin"><img src="/assets-mfe-articles/_next/static/media/LinkedIn.c9a4114a.svg" aria-hidden="true" alt="" width="24" height="24"/><span class="PageUtils_utility-name__y5qBD PageUtils_premium__mI6T2">Share</span></button></li><li class="PageUtils_list-item__HK3pt PageUtils_vertical__SqN7k"><button class="PageUtils_item-button__UHILB" type="button" data-analytics="open-save-flyout"><img src="/assets-mfe-articles/_next/static/media/Save.635e3a71.svg" aria-hidden="true" alt="" width="24" height="24"/><span class="PageUtils_utility-name__y5qBD PageUtils_premium__mI6T2">Save</span></button></li><li class="PageUtils_list-item__HK3pt PageUtils_vertical__SqN7k PageUtils_premium__mI6T2"><a class="Anchor-module_anchor__uhpem PageUtils_item-button__UHILB" href="/product/where-data-driven-decision-making-can-go-wrong/R2405D-PDF-ENG" rel="noopener noreferrer" target="_blank" data-analytics="buy-reprint-link"><img src="/assets-mfe-articles/_next/static/media/BuyCopy.088ae0cd.svg" aria-hidden="true" alt="" width="24" height="24"/><span class="PageUtils_utility-name__y5qBD PageUtils_premium__mI6T2">Buy Copies</span></a></li><li class="PageUtils_list-item__HK3pt PageUtils_vertical__SqN7k PageUtils_premium__mI6T2"><button class="PageUtils_item-button__UHILB" type="button" data-analytics="print-article"><img src="/assets-mfe-articles/_next/static/media/Print.61d005bb.svg" aria-hidden="true" alt="" width="24" height="24"/><span class="PageUtils_utility-name__y5qBD PageUtils_premium__mI6T2">Print</span></button></li></ul></div><div class="Premium_content__Y7Ewd"><p class="Paragraph_text__yGFNO Paragraph_mag-icon-decision-making-problem-solving-w30__wl9ir" data-first-paragraph="true"><span class="LeadIn_lead-in__xiKdI">Let鈥檚 say you鈥檙e leading a </span><span class="LeadIn_lead-in-large__tE9w8">meeting about </span>the hourly pay of your company鈥檚 warehouse employees. For several years it has automatically been increased by small amounts to keep up with inflation. Citing a study of a large company that found that higher pay improved productivity so much that it boosted profits, someone on your team advocates for a different approach: a substantial raise of $2 an hour for all workers in the warehouse. What would you do?</p><iframe title="Article audio player" class="NOAPlayer_player__CEZEV" src="https://embed-player.newsoveraudio.com/v1?key=uir3z2&id=tag:hbr.org%2C1922-01-01%3A202409.388594-R2405D&bgColor=F5F5F5&color=282828&simplified=true&quot;"></iframe></div><div class="Premium_right-rail__lBJUV"><div class="RelatedTopics_container__2Nkb4"><span class="RelatedTopics_related-topics__zEjID">Read more on <a href="/topic/subject/decision-making-and-problem-solving?ab=articlepage-topic" class="RelatedTopics_topic-link__u_bsi"><strong>Decision making and problem solving</strong></a> or related topics <a href="/topic/subject/analytics-and-data-science?ab=articlepage-topic" class="RelatedTopics_topic-link__u_bsi"><strong>Analytics and data science</strong></a>, <a href="/topic/subject/data-management?ab=articlepage-topic" class="RelatedTopics_topic-link__u_bsi"><strong>Data management</strong></a>, <a href="/topic/subject/leadership-styles?ab=articlepage-topic" class="RelatedTopics_topic-link__u_bsi"><strong>Leadership styles</strong></a>, <a href="/topic/subject/organizational-decision-making?ab=articlepage-topic" class="RelatedTopics_topic-link__u_bsi"><strong>Organizational decision making</strong></a> and <a href="/topic/subject/interpersonal-skills?ab=articlepage-topic" class="RelatedTopics_topic-link__u_bsi"><strong>Interpersonal skills</strong></a></span></div></div><div class="Premium_author__kBrx_"><div data-analytics="end-of-article" data-testid="container"></div><div class="PublicationDate_bottom-magazine-date-container__8lgoi"><span class="PublicationDate_bottom-magazine-date__JyTxT">A version of this article appeared in the聽<a class="Anchor-module_anchor__uhpem PublicationDate_bottom-magazine-date-anchor__iCH_p" href="/archive-toc/BR2405">September鈥揙ctober 2024</a>聽issue of <em>Harvard Business Review.</em></span></div><aside class="AuthorBioList_container__qBo_1"><div class="AuthorBio_author-info-item__0G_0z"><div class="AuthorBio_image-container___c3yQ"><img src="/resources/images/article_assets/2011/11/110-mike-luca-1.jpg" alt="" class="AuthorBio_author-picture__2ElG9" role="presentation"/></div><div class="AuthorBio_author-bio-container__u5vHF"><a class="AuthorBio_author-name__VPl47" href="/search?term=Michael%20Luca&amp;search_type=search-all">Michael Luca </a><span class="AuthorBio_biography__CPmNZ"> is a professor of business administration and the director of the Technology and Society Initiative at Johns Hopkins University, Carey Business School.</span></div></div><div class="AuthorBio_author-info-item__0G_0z"><div class="AuthorBio_image-container___c3yQ"><img src="/resources/images/article_assets/2015/06/110-Amy_Edmondson.jpeg" alt="" class="AuthorBio_author-picture__2ElG9" role="presentation"/></div><div class="AuthorBio_author-bio-container__u5vHF"><a class="AuthorBio_author-name__VPl47" href="/search?term=Amy%20C.%20Edmondson&amp;search_type=search-all">Amy C. Edmondson </a><span class="AuthorBio_biography__CPmNZ"> is the Novartis Professor of Leadership and Management at Harvard Business School. Her latest book is <a rel="noopener" target="_blank" href="https://www.amazon.com/Right-Kind-Wrong-Science-Failing/dp/1982195061" class="ContentLink_anchor__Q6_U6"><em>Right Kind of Wrong: The Science of Failing Well</em></a> (Atria Books, 2023).</span></div></div></aside></div><div class="Premium_bottom-utils__4O9e0"><ul class="PageUtils_container-bottom__NBym_ PageUtils_premium__mI6T2"><li class="PageUtils_list-item__HK3pt"><a class="PageUtils_item-button__UHILB" href="https://twitter.com/intent/tweet?&original_referer=&ref_src=twsrc%5Etfw&related=twitterapi%2Ctwitter&tw_p=tweetbutton&url=" target="_blank" rel="noopener noreferrer" data-analytics="share-on-x"><img src="/assets-mfe-articles/_next/static/media/Twitter.7ac71c2c.svg" aria-hidden="true" alt="" width="24" height="24"/><span class="PageUtils_utility-name__y5qBD PageUtils_premium__mI6T2">Post</span></a></li><li class="PageUtils_list-item__HK3pt"><button class="PageUtils_item-button__UHILB" type="button" 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GWMZmIIUy3r1PDdnJlHY8bAQ/RPjBy/owaY7JuD7QPAL9X0jsWu1lu2J+OTdEDrOrHYrf/sDfN8cXmJnEjmmsCAJOrt2o6Sn9QXu+3ZS2HPtJ6KX0s3yeflAtw9Sw+iA6LGYJqIqfX3XtPZlkgy1EUj5o4L/KvYhSCUWmcEpeuUob1Q8YXkO1wbAgBD8TaqSrW7E8++R/BdeorATJWAwVARvEW16DISphp8pQiaxIYxUWIuH9c2ixv+f6T6cV8cpcACWcaceW7FruAHFc2TptsTCOLCoV1COhWYfwLjTdH+I166J96erj1vMLq2Er/mRl0amIOfzm2rm1bkX8UFxGaqVyYn8wr11XzNrcgwnmuFoGzlorZt5PetEEnLHBhnY2hvhK3tK0ecmn5fWPCl9sGFg6kM1xC0H6jj9jmTVJfTlIFp1uz/niixi5PtY+mxdiiVJ9P4UE2aa698nwrbg2Rinqa128jyewBN3USdso1w2xczEm67aovqZNxUfQ/pWhTg65d6d4616Ea6cR97Endf0+hQ7Pav31ecrB/4pLNmIrQ/Unyi9zDXUvE+4EJMpP1rA5hWPsLB5aSMyOFdq64ZDj3vKX1kvddp2Jbs1plg+/5F+Tj8grWxFPpFvIs9PB+nDVgJ+/HrlzX3d24QIH1hrifgymxZgSCoA7IigVRQiHEB92FdC/OAgVbibebyktuRZX0RzhQwy9R+xu+RAGgp5N7WWGSUaYxM4zMDYQQ1m24mIs4QqZtCkG4LD0LorRy090mJqku+TOPah0lLSUWw3dEM3z2uhXlA2GOFsJsiop7COG2tLPOKoU/CnAUetzCWX6JxEEwmPNTpg6UGhpI5cH/DF9s60h6WAK/HtmoIXz+zTRz84lOL1uHwWBvzMAZKmBNWq/OMiAlJhqlvflcNUpe0je292th4p5u413fqV2XUEeb+xXH+E52VUTP4nI/bK7KAbELyuOJA+0BITyiF6mpgnBGTJPuYCjP/yFncBvPcJmzuWzoX6T0CO73amYdF4LgGGlVgmp7COPN92NymsamnbW+BficGGsOvSRwZaPRBURE2oXk4A60YA12XYRz4FiPm47wXgabi7XZkcpJPymhC0xlFDjBw/K4R3xMaSDLQhdgd3Zh4+hD7ColhrtSwvzaS+aUmBG9TZbBtQBL/DBXzx46rzuoK77hlH0eFu5oY95wJhrKJpDKncEjBEsv6S1qA6yvMj2s6kFpHTgqUMzx7zbbqq9rnkMDkH8nFWnk06A8KijTGPFzN550+i10eXIB0iMQySXxrwlaL7eNe/YL3psJ7KkQgsp4ouRAHuC+ob7ZleAe7fWZzgBV4UcYciN1JF6GBoJIDY561MC+SFi46HzdASAuodzrw6km3fRb+zT1jnASS7dAmoIhwgoqM85MRhb1y6aJmp3lteJeJMC8sPhzShEFTl15vOm5oUFKynTAJd4M1kwCMM4Rko1C0XIw6O/Do1dyBudY1MiUH1TyuI4Q+9dQki0M8uh7uwbwoQNQa8xBXo7bYnEnOFyw6Uu5aKjQ1ug4RCgoCxtDGzyXaPRNvF+24humri8fQ/qYijJnr/LEAQVbA3BeHPIfwb+kEBh4kkox1ZnruqI3BsYIQXN1vJ/ij5amxmm0RCKuW8r9sm7tXu0qUL61Q6yFiPldK12eoWGTa4nDU8iDkAGSYdXmIKr6DD8sctC3hTW0KTbGbnxr3qENdB5+ccehgar6rVZrBMkxtq/I075ing8/OOBvhn1C7zdw+7lDYQQfejLMtlPKvDhcdfFb4888///fbb7/9N//z3/n1H4dHKLKYtB4w6KCDPTbhArFLRYqYlklwPahBrv8LMABEjF8VpViClwAAAABJRU5ErkJggg==" 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Harvard Business Publishing is an affiliate of Harvard Business School.</p></div></div></div></footer></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"profile":{"guid":"60f970312c17d915548b4306f63fd6e0","firstName":"Guest","lastName":"User","displayName":"Guest User","accountLocked":false,"emailAddress":"anonymousUser","loggedIn":false,"lastVisitDate":"2024-11-23T17:36:51.290+00:00","linkedAccounts":[],"jobTitle":"","companySize":"","industry":"","folders":[],"newsletters":[],"marketingCampaigns":[],"topics":[],"subscriptions":[],"isGdprDeleted":false,"tiers":[],"pianoRegistered":false,"state":"Anonymous","status":"Anonymous","username":"ANONYMOUSUSER","customAttributes":[],"quota":{"count":1,"limit":2,"remaining":1,"ascend":false}},"article":{"title":"Where Data-Driven Decision-Making Can Go Wrong","type":"premium","tag":"hbr.org,1922-01-01:202409.388594-R2405D","dek":"Five pitfalls to avoid","summary":"\u003cp\u003eWhen considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question.\u003c/p\u003e\n\u003cp\u003eSuch conversations should explore the \u003cem\u003einternal validity\u003c/em\u003e of any analysis (whether it accurately answers the question) as well as its \u003cem\u003eexternal validity\u003c/em\u003e (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you\u0026#8217;re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for\u0026#8212;or undertake\u0026#8212;other research that might confirm or contradict the evidence.\u003c/p\u003e\n\u003cp\u003eBy employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.\u003c/p\u003e","topics":["Decision making and problem solving","Analytics and data science","Data management","Leadership styles","Organizational decision making","Interpersonal skills"],"primaryTopic":"Decision making and problem solving","content":"\u003carticle-ideainbrief class=\"article-ideainbrief\"\u003e\n \u003ch4 class=\"ideainbrief-title\"\u003eIdea in Brief\u003c/h4\u003e\n \u003cdiv class=\"ideainbrief-contents\"\u003e\n \u003ch5\u003eThe Problem\u003c/h5\u003e\n \u003cp\u003eWhen managers are presented with internal data or an external study, all too often they either automatically accept its accuracy and relevance to their business or dismiss it out of hand.\u003c/p\u003e\n \u003ch5\u003eWhy It Happens\u003c/h5\u003e\n \u003cp\u003eLeaders mistakenly conflate causation with correlation, underestimate the importance of sample size, focus on the wrong outcomes, misjudge generalizability, or overweight a specific result.\u003c/p\u003e\n \u003ch5\u003eThe Right Approach\u003c/h5\u003e\n \u003cp\u003eLeaders should ask probing questions about the evidence in a rigorous discussion about its usefulness. They should create a psychologically safe environment so that participants will feel comfortable offering diverse points of view.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/article-ideainbrief\u003e\u003cp class=\"mag-icon-decision-making-problem-solving-w30\"\u003eLet鈥檚 say you鈥檙e leading a meeting about the hourly pay of your company鈥檚 warehouse employees. For several years it has automatically been increased by small amounts to keep up with inflation. Citing a study of a large company that found that higher pay improved productivity so much that it boosted profits, someone on your team advocates for a different approach: a substantial raise of $2 an hour for all workers in the warehouse. What would you do?\u003c/p\u003e","authors":[{"name":"Michael Luca","image":"/resources/images/article_assets/2011/11/110-mike-luca-1.jpg","bio":"Michael Luca is a professor of business administration and the director of the Technology and Society Initiative at Johns Hopkins University, Carey Business School."},{"name":"Amy C. Edmondson","image":"/resources/images/article_assets/2015/06/110-Amy_Edmondson.jpeg","bio":"Amy C. Edmondson is the Novartis Professor of Leadership and Management at Harvard Business School. Her latest book is \u003ca href=\"https://www.amazon.com/Right-Kind-Wrong-Science-Failing/dp/1982195061\" rel=\"noopener\" target=\"_blank\"\u003e\u003cem\u003eRight Kind of Wrong: The Science of Failing Well\u003c/em\u003e\u003c/a\u003e (Atria Books, 2023)."}],"published":"2024-09-01T04:00:00.000+00:00","issue":{"coverDate":"September鈥揙ctober 2024","issueId":"BR2405","reprintId":"R2405D"},"hasAudio":true,"hasDropCap":true,"paywallEligible":true,"giftedArticle":false,"contentKey":"6EiMWsRJoBY6ojaCeQhJTjF6J3rhllljsQ82+1QR31GiZiWwt1Kbi4CdTrnJXKCl1DGThkbDULOThzvAN8LwF3g7kmMQV/ao5yqy7AXUuKeHSNbD0+9D+fzdVxhutqErBmyOR+KbC3t7P8Pi0fIyMw==","productLink":"/product/where-data-driven-decision-making-can-go-wrong/R2405D-PDF-ENG","metaTags":{"page-id":"320","article:modified_time":"2024-08-12T16:00:00Z","primary-editor":"57","page-category-id":"391767","article-content-type":"Magazine Article","issue-id":"BR2405"},"stylesOverride":["audio-available","has-dropcap"],"editorTags":[{"tagValue":"Import Notice"},{"tagValue":"Missing Derivative"}],"configurations":{"configExemptFromPaywall":false,"configNoIntromercial":false,"configSubscriberOnly":false},"pianoConfigurations":{"pianoContentType":"premium"},"userProfile":{"guid":"60f970312c17d915548b4306f63fd6e0","firstName":"Guest","lastName":"User","displayName":"Guest User","accountLocked":false,"emailAddress":"anonymousUser","loggedIn":false,"lastVisitDate":"2024-11-23T17:36:51.290+00:00","linkedAccounts":[],"jobTitle":"","companySize":"","industry":"","folders":[],"newsletters":[],"marketingCampaigns":[],"topics":[],"subscriptions":[],"isGdprDeleted":false,"tiers":[],"pianoRegistered":false,"state":"Anonymous","status":"Anonymous","username":"ANONYMOUSUSER","customAttributes":[],"quota":{"count":1,"limit":2,"remaining":1,"ascend":false}},"hero":{"image":{"defaultSrc":"/resources/images/article_assets/2024/07/R2405D_DOCUBYTE.jpg","sizes":"(min-width: 48em) 55.7291667vw, 97.3924381vw","srcset":"/resources/images/article_assets/2024/07/R2405D_DOCUBYTE.jpg 2000w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-300x181.jpg 300w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1024x616.jpg 1024w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-768x462.jpg 768w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1536x925.jpg 1536w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-383x231.jpg 383w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-500x301.jpg 500w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-700x421.jpg 700w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-850x512.jpg 850w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1200x722.jpg 1200w, /resources/images/article_assets/2024/07/R2405D_DOCUBYTE-1900x1144.jpg 1900w"},"credits":"Docubyte"}},"initialDataLayer":{"pageInstanceID":"Where Data-Driven Decision-Making Can Go Wrong:production","siteID":"hbrprod","page":{"pageInfo":{"language":"en","contentType":"Magazine Article","pageName":"Where Data-Driven Decision-Making Can Go Wrong","pageType":"Article","pageCategoryID":"391767","pageCategoryName":"Decision making and problem solving","pageDescription":"When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you鈥檙e measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for鈥攐r undertake鈥攐ther research that might confirm or contradict the evidence. 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