{"id":27911,"date":"2018-04-24T19:27:13","date_gmt":"2018-04-25T02:27:13","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&p=27911"},"modified":"2024-02-02T14:26:37","modified_gmt":"2024-02-02T22:26:37","slug":"data-analysis-pitfalls","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/data-analysis-pitfalls","title":{"rendered":"Data-Driven Business: Avoiding the Pitfalls"},"author":501,"featured_media":0,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","castos_file_data":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[1001,791,931],"tags":[871,311],"department":[476822],"wherenext-category":[],"industry":[],"class_list":["post-27911","wherenext","type-wherenext","status-publish","format-standard","hentry","category-commercial","category-digital-transformation","category-spatial-analysis","tag-big-data","tag-data-driven","department-data-and-ai"],"acf":{"short_description":"Business decision-makers are using data to make strategic choices for their companies. What if they\u2019re misinformed?","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"It\u2019s the beginning of Q4, and a retailer with hundreds of stores across North America is forecasting next year\u2019s sales in the San Francisco Bay Area. The business, a first-mover in the area, has established a dominant presence with four brick-and-mortar locations. But competitors are expected to open nearby locations within the next year.\r\n\r\nExecutives recognize that the competitor stores will impact sales, and they want to factor those effects into revenue projections. The analytics team creates a model that probes the consequences of having a competititor store located in the same ZIP code as their store. They display the data on a map to show the relationships between stores.\r\n\r\nThis approach would be applauded by most executive teams. But what if it doesn\u2019t reveal the whole story? What if the data ends up misleading executives, resulting in business decisions that hurt the company strategically and financially?"},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Left","content":"

Five Steps for Successful Location Analysis<\/h2>\r\n
    \r\n \t
  1. Ask and explore\u2014Always begin with a well-framed question\u2014getting the question right is key to deriving meaningful results.<\/li>\r\n \t
  2. Model and compute\u2014Choose analysis tools that transform data into new results. Question the analytics team on the techniques they use.<\/li>\r\n \t
  3. Examine and interpret\u2014Seek explanations for the patterns revealed. Executives should be encouraged to add their perspective and interpretation.<\/li>\r\n \t
  4. Make decisions\u2014Assess whether the results provide a useful answer to the original question. Often, new questions arise, spurring further analysis.<\/li>\r\n \t
  5. Share results\u2014Use maps, pop-ups, graphs, and charts to communicate the information efficiently and effectively.<\/li>\r\n<\/ol>","snippet":""},{"acf_fc_layout":"content","content":"I\u2019ve seen it happen. Digital transformation<\/a> has opened new doors for companies, introducing a wealth of information and big data. In this new terrain where data abounds and analysis guides decisions, small missteps can lead an executive\u2014and a business\u2014astray. In this article, we\u2019ll examine how executives can use new information and tools to drive valuable business decisions, while avoiding the pitfalls of one-dimensional analysis.\r\n\r\nData-Driven Leadership <\/strong>\r\n\r\nData-driven decision-making is a hallmark of executive leadership. In a recent PwC survey, 39 percent of senior executives said decision-making at their organizations is \u201chighly data driven,\u201d<\/a> while another 53 percent characterized their decision-making as \u201csomewhat data driven.\u201d That means over 90 percent of business leaders are using some form of data to drive day-to-day decisions.\r\n\r\nBut the process of turning big data into data-driven decisions is laced with challenges, and some are easily overlooked. After all, data can mislead. Whether the data is flawed, analyzed incorrectly, or lacks key ingredients, it can lead executives to questionable decisions.\r\n\r\nAs George Box<\/a>, an eminent statistician, said, \u201cAll models are wrong; some models are useful.\u201d\r\n\r\nExecutive Decisions: Three Data-Driven Business Scenarios <\/strong>\r\n\r\nIn a world of rapid digital transformation, it is increasingly easy to confuse data with insight. By analyzing the where<\/em> and even the when<\/em> behind certain data, executives can add critical elements to business planning. Still, while mapping is a step in the right direction, there are pitfalls to avoid. That\u2019s why every executive should be armed with tools and techniques to convert big data into information\u2014and action.\r\n\r\nWhat are the best techniques for converting data into useful, intelligent business decisions? To answer that question, we\u2019ll explore three business scenarios where datasets, analyzed two ways, yield different results\u2014and could trigger divergent business decisions. While the particulars are hypothetical, each scenario is drawn from real-world analysis.\r\n\r\nBusiness Scenario 1: Adding Another Variable <\/strong>\r\n\r\nLet\u2019s examine a fairly straightforward example of data-driven decision-making.\r\n\r\nIn the business scenario that opens this article, a North American retailer is projecting annual sales based on multiple datasets. Executives know that two competitors are building a total of four stores in this geographic market. The executive team wants to include the impact of these new stores in financials projections for the next fiscal year.\r\n\r\nThe company\u2019s sales data, mapped by ZIP code, is shown here, with the yellow dots representing new competitor stores:"},{"acf_fc_layout":"image","image":28481,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"A member of the geographic information system (GIS) team has some reservations about the results. She suggests deepening the analysis by factoring in the physical distance between each store and its nearest competitor. To do so, the team uses GIS to analyze a new variable: a 15-minute drive time from each of the company\u2019s stores, independent of ZIP code. The map below reveals the results."},{"acf_fc_layout":"image","image":28491,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"The drive-time analysis shows that competitors will affect sales differently than first anticipated. The new map underscores that while ZIP codes are a common organizing variable, they can create artificial relationships within data. After all, customers cross ZIP codes at will\u2014why wouldn\u2019t the company\u2019s data?"},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"

    Network Analysis<\/h2>\r\nA network is a system that represents possible routes from one location to another. People, resources, and goods tend to travel along networks: cars and trucks travel on roads, while airliners fly on predetermined flight paths. By using network analysis, it\u2019s possible to examine factors like the drive time for customers, market areas covered by a store, the most efficient delivery routes, and response times for police and fire departments. Learn more<\/a>.","snippet":""},{"acf_fc_layout":"content","content":"Neither analysis introduced here is objectively right or wrong, but each has business consequences. A less-precise sales projection will change where and how the retailer markets itself and staffs its customer service and sales teams\u2014and even the merchandise assortment for each store.\r\n\r\nGIS technology and other tools can uncover vital insight, but only when executives and analysts ask the right questions and think critically about how they analyze data.\r\n\r\n(This scenario benefits from the science of network analysis. To learn about the technique, see the sidebar on the right.)\r\n\r\nBusiness Scenario 2: Make New Friends, but Keep the Old \u00a0<\/strong>\r\n\r\nAs businesses undergo rapid digital transformation, the tools, techniques, and data they can use to their strategic advantage are evolving quickly. At the forefront is a new wave of customer data that is helping drive business decisions.\r\n\r\nMobile apps, in particular, have remade the landscape of consumer behavior and predictive marketing. IHS Markit, a business intelligence consulting firm, predicts that consumer spending on mobile apps<\/a> will reach $74 billion worldwide by 2020, up from $54 billion in 2016. The firm also expects the number of global smartphone users to eclipse six billion in 2020, up from four billion in 2016."},{"acf_fc_layout":"image","image":19991,"image_position":"left","orientation":"horizontal","hyperlink":"http:\/\/www.esri.com\/digital-transformation?adumkts=branding&aduc=advertising&adum=house_ad&utm_Source=advertising&aduca=branding&aduco=dt_leading_companies&adut=wherenext_data_bias&adupt=awareness"},{"acf_fc_layout":"content","content":"Clearly, mobile apps are big business. But perhaps even more intriguing than their promise of direct revenue is the treasure trove of customer data they can yield, from insight on spending habits to a window into foot-traffic patterns around retail locations. But turning digital data into business insight isn\u2019t always as simple as it seems.\r\n\r\nConsider a fast-casual restaurant chain that offers a make-your-own salad bar and quick counter service. The company developed a mobile app that allows customers to access special discounts and place orders before they arrive at the store. Several months after the app\u2019s release, the company analyzed customers\u2019 food choices and coupon use as well as their anonymized movements before and after visiting the company\u2019s restaurants.\r\n\r\nThe data showed that a large proportion of mobile app users purchased soft serve ice cream after eating at the company\u2019s urban locations\u2014a finding supported by Google trend data, which showed rising North American interest in soft serve. Based on this analysis, the executive team began brainstorming ways to capitalize on the soft serve phenomenon. One solution they considered was to partner with a gourmet soft serve startup, piloting a joint marketing effort in a North Carolina city.\r\n\r\nFor a deeper understanding, the company analyzed mobile app data to show customer purchasing habits within an hour of visiting the restaurant, then mapped that data to reveal how far customers were traveling for soft serve.\r\n\r\nThis map shows that initial analysis."},{"acf_fc_layout":"image","image":28461,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"In light of the findings, some executives pushed to roll out in-store soft serve kiosks across the company\u2019s footprint. But other team members wondered if that was the right course of action. Examining the mapped data, they realized they were perhaps too eager to capitalize on the fruits of the data, and were overlooking a key fact: only a small fraction of the company\u2019s customers were actively using the app. While the app data was easy to collect and valuable in its own way, it didn\u2019t necessarily paint a full picture of customers\u2019 purchasing habits.\r\n\r\nTo balance the view, the company added a more traditional dataset, conducting in-store polls of customers who didn\u2019t use the mobile app. Those results showed the executive team that less than 5 percent of all customers were purchasing soft serve\u2014but more than 15 percent were buying coffee nearby after their meal.\r\n\r\nThis map shows how the combined data sets alter the view in just one section of the city:"},{"acf_fc_layout":"image","image":28471,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"By examining the relationship between the datasets, the company gained a more nuanced view of customer behavior, empowering the executive team to make data-driven decisions based on customer preferences in certain store locations or geographic regions."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Left","content":"

    Density-Based Clustering<\/h2>\r\nDensity-based clustering uses unsupervised machine-learning algorithms to detect where points are concentrated and where they are sparse. Points that are not part of a cluster are labeled as noise. Clustering, grouping, and classification techniques are widely used in machine learning, a form of artificial intelligence. Learn more<\/a>.","snippet":""},{"acf_fc_layout":"content","content":"The restaurant\u2019s experience shows how business decisions based on siloed datasets can lead to shortsighted planning\u2014and how access to new digital datasets can exacerbate this tendency. By contrast, analyzing the full spectrum of data, integrated spatially, can reveal valuable insights and drive significant decisions on matters such as product development, partnerships and acquisitions, and personalized marketing plans.\r\n\r\n(This analysis relies on density-based clustering, a form of artificial intelligence. To learn more, see the sidebar on the left, or listen to part 1<\/a> and part 2<\/a> of a podcast on making data smarter.)\r\n\r\nBusiness Scenario 3: Presenting Data versus Analyzing Data<\/strong>\r\n\r\nA sales executive at a large manufacturer in the Chicago area saw the potential of digital transformation early on and has built a team of analysts to examine product sales.\r\n\r\nOne of the analysts used GIS to create an animated spatiotemporal map\u2014which combines where<\/em> and when<\/em> data\u2014showing sales over the past several quarters. The animation, which showed how sales in each location changed over time, was an improvement on the static maps the team had been using, according to the analyst.\r\n\r\nBelow is the analysis."},{"acf_fc_layout":"youtube","youtube_video_url":"https:\/\/www.youtube.com\/embed\/jWp0-SGSpvs?rel=0"},{"acf_fc_layout":"content","content":"Examining the animation, the sales team noticed patterns in the data and concluded that certain areas were ripe for sales expansion. These were rational inferences, but they may not serve the business well in the long run.\r\n\r\nWhen looking at an animated map, we draw inferences because the human brain is programmed to find patterns, even when meaningful relationships aren\u2019t present. The phenomenon is called \u201capophenia,\u201d a term coined by Klaus Conrad, a German psychologist. Dr. Michael Sherman, a science historian and author of The Believing Brain<\/em>, calls the same tendency \u201cpatternicity\u201d\u2014the tendency to find meaningful patterns in both meaningful and meaningless data. He has also researched what he calls \u201cagenticity,\u201d the tendency to infuse patterns with meaning, intention, and agency.\r\n\r\nWhen working with data to make decisions, executives and analysts need to acknowledge the biases of human thinking. Data may appear to be straightforward and objective. But it is not immune to our own patternicity and beliefs. At worst, data provides dubious evidence for the decisions we believe are correct.\r\n\r\nIn the case of the sales team, the lure of an animated tool may have opened the door to those biases. The same dataset, with a hotspot analysis applied, provides much richer insight:"},{"acf_fc_layout":"image","image":28501,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"By viewing this spatial analysis, we can see exactly where new hot spots appeared, identify where new and persistent cold spots exist, and understand\u2014scientifically\u2014where sales are growing and where they are falling off. Rather than asking the sales executive to make subjective judgments, the spatial analysis powers more objective conclusions and the ability to drive clear business decisions based on data."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"

    Emerging Hot Spots Analysis<\/h2>\r\nHot-spot analysis allows teams to identify trends within clusters of data points in space and time. Rather than leaving the identification of patterns to individuals, hot-spot analysis provides an objective view of trends, including new, intensifying, persistent, diminishing, and historical hot and cold spots. Learn more<\/a>.","snippet":""},{"acf_fc_layout":"content","content":"Depending on the approach used, the sales team may devote resources to different geographic areas. The time and money spent on establishing new sales leads represent a significant investment\u2014one that should be made based on accurate analysis.\r\n\r\n(This scenario draws on a technique called hot-spot analysis. To learn more, see the sidebar on the right.)\r\n\r\nMaking Data Work for Businesses <\/strong>\r\n\r\nFor business executives, it has never been more important to have a strong understanding of data analysis: its power, its weaknesses, and its applications. By analyzing data in ways that approximate reality as closely as possible\u2014approaches that are nuanced and rich in information\u2014business leaders can more closely predict sales, develop customized marketing plans, accurately allocate resources, enrich product development cycles, and more.\r\n\r\nData has tremendous potential to mislead. But in the right hands, and with the right tools, data has an infinite capacity to reveal the truth and drive better business decisions."}],"references":null},"yoast_head":"\nBig Data Analysis, Data-Driven Decisions, and Digital Transformation<\/title>\n<meta name=\"description\" content=\"Digital transformation and its resulting big data have given business executives more material for decisions. Are they using it wisely?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/data-analysis-pitfalls\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data-Driven Business: Avoiding the Pitfalls\" \/>\n<meta property=\"og:description\" content=\"Digital transformation and its resulting big data have given business executives more material for decisions. 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Through his writing, he explores the intersection of business strategy and location intelligence\u2014and strategic challenges like sustainability, growth, and risk. Prior to joining Esri, Chris managed internal content for PTC, a pioneer in IoT technology, smart products, and augmented reality. 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