Established in conjunction with MIT's Operations Research Center – an interdisciplinary research center established in 1953– the MBAn program is tailored for current students or recent college graduates who plan to pursue a career in the data science industry, as well as those seeking career advancement or change, especially engineers, mathematicians, physicists, computer programmers, and other high-tech professionals. The program answers the industry’s demand for a skilled pool of graduates who can apply data science to solve business challenges. MIT offers an analytics degree at the undergraduate, graduate, and doctoral levels. The MIT Sloan MBAn curriculum is designed to prepare you for the challenges you will face as a business leader in today's rapidly changing environment. We strive to provide you with fundamental skills and cutting edge business knowledge that will equip you to lead innovative thinking in today's organizations. At MIT Sloan, we not only welcome diverse perspectives but see them as critical in our collaborative and creative environment. When people from different backgrounds come together to develop and test an idea, invention happens....

In these difficult times, we’ve made a number of our coronavirus articles free for all readers. To get all of HBR’s content delivered to your inbox, sign up for the Daily Alert newsletter. When stay-at-home orders took hold this past March, retail sales dropped dramatically — as everybody knows. But that change in customer behavior has resulted in a phenomenon that hasn’t been talked about much: the flow of sales information to retailers’ data repositories has dried up. That’s a significant problem, because a healthy flow of that information is the lifeblood of customer loyalty programs, AI-driven product recommendations, and a wide array of critical business decisions. What this change means is that many retailers – independent or chain, brick-and-mortar or e-commerce, startup or legacy – are now facing an information deficit. That’s what happens when the data and intelligence derived from customer transactions becomes scarce or unusable due to a sudden change in buyer behavior. Today the problem is widespread: Even businesses that had amassed great volumes of customer data before Covid-19 are finding themselves in the same cold-start position as businesses venturing into unknown markets or reaching out to new audiences....

We’re in the middle of an analytics revolution. The change is being driven by numerous factors, but two are more important than all of the rest. The first is an explosion in the amount of valuable digital data generated by workers and consumers as we go about our daily lives. The second is advances in technology, such as machine learning, artificial intelligence, and cloud-computing platforms that allow us to interpret and leverage these vast amounts of data. Yet companies that excel with analytics have more than access to great data and technology. These companies recognize that success with analytics also requires an analytical mindset among its executives and an analytical culture in the business. In other words, it’s critical to bring the people along with the technology. There are two easy steps firms can take to do this. It’s only when deep expertise exists at the top of the org charts that a penchant for evidence-based decision-making will develop pervasively throughout the organization. It’s too common to devolve responsibility for analytics modeling to junior positions, which prevents analytical mindsets from really taking hold. Like any other discipline, becoming good at analytics requires seasoning and experience, which junior employees are unlikely to have. Essential areas of expertise include a deep understanding of the data sets being analyzed, and the ability to recognize their limitations and potential biases. An understanding of proper research design is a must, as are knowledge of basic measurement concepts, such as reliability and validity. Without this in-depth knowledge of data and the appropriate application of analytical techniques, the wrong conclusions could be reached, and suboptimal decisions and actions could be taken....

In these difficult times, we’ve made a number of our coronavirus articles free for all readers. To get all of HBR’s content delivered to your inbox, sign up for the Daily Alert newsletter. The events of spring 2020 have turned marketers’ worlds upside down. In light of an unprecedented health and economic crisis, teams scrambled to adjust their advertising messages, campaigns, and offers. They could no longer rely on previous assumptions about their customers, including what, why, and how they buy. In a matter of days, stores closed, e-commerce sales ramped up, and contact center interactions exploded. Meanwhile, media consumption changed as more people began working from home, spending more time online and watching TV, and less time interacting in person. Rapid change is the new normal, and more than ever, marketers need to make decisions quickly that are nonetheless anchored in data. As a result, companies are pouring money into marketing analytics — last year, CMOs invested more in this category than any other, according to Gartner. Yet, even as marketers bury themselves in data, they are getting an incomplete picture of performance and their customers. Below are four approaches that marketing teams should be using more often to better orient themselves around the truth of what their customers are experiencing and which strategies are actually working....