{"id":10245,"date":"2025-03-20T21:38:28","date_gmt":"2025-03-21T01:38:28","guid":{"rendered":"https:\/\/solutionsreview.com\/business-intelligence\/?p=10245"},"modified":"2025-03-21T07:58:50","modified_gmt":"2025-03-21T11:58:50","slug":"blurring-the-lines-between-data-science-and-optimization","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/","title":{"rendered":"Blurring the Lines Between Data Science and Optimization"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-10254\" src=\"https:\/\/solutionsreview.com\/business-intelligence\/files\/2025\/02\/blurring-lines-data-science-optimization-2.jpg\" alt=\"\" width=\"786\" height=\"393\" srcset=\"https:\/\/solutionsreview.com\/business-intelligence\/files\/2025\/02\/blurring-lines-data-science-optimization-2.jpg 786w, https:\/\/solutionsreview.com\/business-intelligence\/files\/2025\/02\/blurring-lines-data-science-optimization-2-406x203.jpg 406w, https:\/\/solutionsreview.com\/business-intelligence\/files\/2025\/02\/blurring-lines-data-science-optimization-2-768x384.jpg 768w\" sizes=\"(max-width: 786px) 100vw, 786px\" \/><\/p>\n<p><em><strong>Gurobi Optimization&#8217;s Jerry Yurchisin offers insights on blurring the lines between data science and optimization. <\/strong><\/em><em><strong>This article originally appeared on <a href=\"https:\/\/insightjam.com\/feed\" target=\"_blank\" rel=\"noopener\">Solutions Review&#8217;s Insight Jam<\/a>, an enterprise IT community enabling the human conversation on AI.<\/strong><\/em><\/p>\n<p id=\"isPasted\" style=\"text-align: justify;\">Research suggests that the average person makes somewhere in the ballpark of\u00a0<a href=\"https:\/\/www.psychologytoday.com\/intl\/blog\/stretching-theory\/201809\/how-many-decisions-do-we-make-each-day\" target=\"_blank\" rel=\"noopener noreferrer\">33,000-35,000 decisions every single day<\/a>. Some of these are simple \u2014 what shirt should I wear, do I want a hot or iced coffee \u2014 but others are much more complex.<\/p>\n<p style=\"text-align: justify;\">Consider, for example, the myriad choices that businesses need to make on a regular basis. What is a reasonable timeline for this project? How can we best appeal to this customer base? How can I use my budget effectively for this effort? These kinds of decisions \u2014 ones that can impact your team\u2019s performance and business objectives \u2014 are likely the result of more deliberation, discussion, and data analysis than whatever you decided to eat for breakfast.<\/p>\n<p style=\"text-align: justify;\">The more complex the question, the more factors may impact the final decision. When unchecked, these various moving parts can quickly gridlock your team\u2019s capacity to make timely and effective choices. This is why as data sources, analytics tools, and business roles continue to evolve, the future of decision-making is becoming increasingly data-driven.<\/p>\n<h4><strong>An Increase in Decision-Making Roles<\/strong><\/h4>\n<p>One way to examine this claim is to observe the types of employees that play a key role in making data-based business decisions at modern organizations. Consider the two following roles that are present across industries:<\/p>\n<ul>\n<li><strong>Data Scientists:<\/strong>\u00a0These are problem solvers who leverage statistical models and machine learning (ML) to derive important insights from complex forms of data.<\/li>\n<li><strong>Operations Researchers:<\/strong>\u00a0These are problem solvers who leverage various mathematical and analytical techniques like data mining, optimization modeling, simulation, and statistical analysis in order to help businesses make informed and efficient decisions.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">On their own, each of these roles is becoming increasingly sought-after by employers. In fact, the\u00a0<a href=\"https:\/\/www.bls.gov\/opub\/mlr\/2024\/article\/industry-and-occupational-employment-projections-overview-and-highlights-2023-33.htm\" target=\"_blank\" rel=\"noopener\">U.S. Bureau of Labor Statistics<\/a>\u00a0estimates that U.S. employers will need an additional 24,200 operations researchers and an additional 40,500 data scientists by 2031, making these two of the top 30 fastest-growing jobs of this decade.<\/p>\n<p style=\"text-align: justify;\">The roles of data scientists and operations researchers are also starting to become increasingly intertwined. In fact,\u00a0<a href=\"https:\/\/www.gurobi.com\/resources\/report-state-of-mathematical-optimization-2024\/\" target=\"_blank\" rel=\"noopener\">55 percent of respondents to one survey<\/a>\u00a0noted that they experience operations research and data science team collaboration on a weekly basis. This comes as businesses look for ways to ensure that their decisions are informed from all possible angles.<\/p>\n<h4><strong>Combining Decision-Making Technologies<\/strong><\/h4>\n<p style=\"text-align: justify;\">Beyond the closer alignment of decision-making staff, businesses are also up leveling the joint capabilities of their decision-making tools and technologies. Some popular examples of these tools include:<\/p>\n<ul>\n<li><strong>Machine Learning (ML):<\/strong>\u00a0A branch of artificial intelligence (AI) that enables computers to ingest, analyze, and learn from data autonomously in order to share predictive insights.<\/li>\n<li><strong>Mathematical Optimization (MO):\u00a0<\/strong>A process that leverages a mathematical model of your problem and advanced algorithms to examine complex problems and generate prescriptive solutions.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">While ML has flourished in the spotlight of the artificial intelligence boom,\u00a0<a href=\"https:\/\/www.gurobi.com\/resources\/report-state-of-mathematical-optimization-2024\/\" target=\"_blank\" rel=\"noopener\">94 percent of survey respondents<\/a>\u00a0indicated that MO was gaining traction or remaining steady with decision-makers at their organization. This demonstrates that both ML and MO are crucial to contemporary decision making in their own manner.<\/p>\n<p style=\"text-align: justify;\">Even so, teams are beginning to lean more into the combined capabilities of these tools in order to drive the most informed, prescriptive choices possible. In 2020, less than half of survey respondents (46 percent) claimed that their organization combined ML and MO capabilities. This number has nearly doubled since then, with\u00a0<a href=\"https:\/\/www.gurobi.com\/resources\/report-state-of-mathematical-optimization-2024\/\" target=\"_blank\" rel=\"noopener\">81 percent of 2024 survey respondents<\/a>\u00a0indicating that their organization combined the capabilities of ML and MO for at least one project. The combination of ML and MO enables decision-makers to automate data analysis, gain predictive insights, and feed these into algorithms that output informed prescriptive suggestions.<\/p>\n<h4><strong>What Does This Mean for Your Business?<\/strong><\/h4>\n<p style=\"text-align: justify;\">Growing your ranks of decision-oriented staff and combining capable tools is great, but what does it mean for your company\u2019s decision-making bottom line? By recognizing and acting on opportunities to combine technical and operational capabilities and drive improved decision-making, organizations can:<\/p>\n<ul>\n<li><strong>Improve Efficiency<\/strong>: Gone are the days of getting bogged down in lengthy strategy discussions or debates. Matching predictive insights from ML with prescriptive solutions from MO helps remove the guesswork from decisions and streamline your processes, making your operations and staff more efficient.<\/li>\n<li><strong>Optimize Employee Capabilities:<\/strong>\u00a0Make sure that your skilled employees have the best possible chance to leverage their skills and work cross-functionally to drive improved insights. This can include collaboration between teams and\/or upskilling of existing employees to add new capabilities (MO or ML) to their existing training.<\/li>\n<li><strong>Minimize Costs and Maximize Profits:<\/strong>\u00a0Improved decision-making can help teams spend less and make more. When fewer resources like time, tools, and employee focus need to be dedicated to analyzing data and deliberating over complex decisions, your team saves on the related costs. Similarly, when company decisions can be made at the speed of the market instead of the speed of internal deliberation, it becomes easier to jump on opportunities and achieve more profit-driving business goals.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">A streamlined, efficient, and low-cost business model is ultimately better suited to keep pace with the speed of business and achieve more lasting and concrete results.<\/p>\n<h4><strong>The Future of Decision Making<\/strong><\/h4>\n<p style=\"text-align: justify;\">Taking into account the growth of operations research and data scientist roles, their increasing overlap, and the synergistic relationship between ML and MO, it\u2019s clear that businesses are committed to making the most informed and reliable decisions possible.<\/p>\n<p style=\"text-align: justify;\">As teams advance their decision-making capacity, it\u2019s crucial that they understand how each piece of the puzzle \u2014 employee capabilities, predictive analytics tools (ML), and prescriptive analytics tools (MO) \u2014 can play its critical role in the streamlining of these processes. By continuing to invest in skilled employees and capable tools, these teams will be empowered to solve whichever complex problems come their way.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Gurobi Optimization&#8217;s Jerry Yurchisin offers insights on blurring the lines between data science and optimization. This article originally appeared on Solutions Review&#8217;s Insight Jam, an enterprise IT community enabling the human conversation on AI. Research suggests that the average person makes somewhere in the ballpark of\u00a033,000-35,000 decisions every single day. Some of these are simple [&hellip;]<\/p>\n","protected":false},"author":1264,"featured_media":10254,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[4],"tags":[1909,1908],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Blurring the Lines Between Data Science and Optimization<\/title>\n<meta name=\"description\" content=\"Gurobi Optimization&#039;s Jerry Yurchisin offers insights on blurring the lines between data science and optimization.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Jerry Yurchisin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/\",\"url\":\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/\",\"name\":\"Blurring the Lines Between Data Science and Optimization\",\"isPartOf\":{\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/solutionsreview.com\/business-intelligence\/files\/2025\/02\/blurring-lines-data-science-optimization-2.jpg\",\"datePublished\":\"2025-03-21T01:38:28+00:00\",\"dateModified\":\"2025-03-21T11:58:50+00:00\",\"author\":{\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/#\/schema\/person\/73a62c03fe1687a7850e813f8f3d8c38\"},\"description\":\"Gurobi Optimization's Jerry Yurchisin offers insights on blurring the lines between data science and optimization.\",\"breadcrumb\":{\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/#primaryimage\",\"url\":\"https:\/\/solutionsreview.com\/business-intelligence\/files\/2025\/02\/blurring-lines-data-science-optimization-2.jpg\",\"contentUrl\":\"https:\/\/solutionsreview.com\/business-intelligence\/files\/2025\/02\/blurring-lines-data-science-optimization-2.jpg\",\"width\":786,\"height\":393},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/blurring-the-lines-between-data-science-and-optimization\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/solutionsreview.com\/business-intelligence\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Blurring the Lines Between Data Science and Optimization\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/solutionsreview.com\/business-intelligence\/#website\",\"url\":\"https:\/\/solutionsreview.com\/business-intelligence\/\",\"name\":\"Business Intelligence &amp; 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