How has Data Science positively and negatively impacted healthcare?
JEAN FRANCOIS PODEVIN/THEISPOT.COM SPRING 2021 MIT SLOAN MANAGEMENT REVIEW 85 D AT A A N A L Y T I C S M ore and more companies are embracing data science as a function and a ca- pability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learn- ing.1 Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.2 To better understand the mistakes that companies make when implementing profitable data science projects, and discover how to avoid them, we conducted in-depth studies of the data science activities in three of India’s top 10 private-sector banks with well-established analytics departments. We identified five common mistakes, as exemplified by the following cases we encountered, and below we suggest corresponding solutions to address them. Mistake 1: The Hammer in Search of a Nail Hiren, a recently hired data scientist in one of the banks we studied, is the kind of analytics wizard that organizations covet.3 He is especially taken with the k-nearest neighbors algorithm, which is useful for identifying and classifying clusters of data. “I have applied k-nearest neighbors to several simulated data sets during my studies,” he told us, “and I can’t wait to apply it to the real data soon.” Why So Many Data Science Projects Fail to Deliver Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles. BY MAYUR P. JOSHI, NING SU, ROBERT D. AUSTIN, AND ANAND K. SUNDARAM 86 MIT SLOAN MANAGEMENT REVIEW SPRING 2021 SLOANREVIEW.MIT.EDU D AT A A N A L Y T I C S Hiren did exactly that a few months later, when he used the k-nearest neighbors algorithm to identify es- pecially profitable industry segments within the bank’s portfolio of business checking accounts. His recom- mendation to the business checking accounts team: Target two of the portfolio’s 33 industry segments. This conclusion underwhelmed the business team members. They already knew about these segments and were able to ascertain segment profitability with simple back-of-the-envelope cal- culations. Using the k-nearest neighbors algorithm for this task was like using a guided missile when a pellet gun would have sufficed. In this case and some others we examined in all three banks, the failure to achieve business value resulted from an infatuation with data science solutions. This failure can play out in several ways. In Hiren’s case, the problem did not require such an elaborate solution. In other situations, we saw the successful use of a data science solution in one arena become the justification for its use in another arena in which it wasn’t as appropriate or effective. In short, this mistake does not arise from the technical execution of the analytical technique; it arises from its misapplication. After Hiren developed a deeper understanding of the business, he returned to the team with a new recommendation: Again, he proposed using the k-nearest neighbors algorithm, but this time at the customer level instead of the industry level. This proved to be a much better fit, and it resulted in new insights that allowed the team to target as-yet untapped customer segments. The same algorithm in a more appropriate context offered a much greater potential for realizing business value. It’s not exactly rocket science to observe that analytical solutions are likely to work best when they are developed and applied in a way that is sensitive to the business context. But we found that data science does seem like rocket science to many managers. Dazzled by the high-tech aura of analytics, they can lose sight of context. This was more likely, we discovered, when managers saw a solution work well elsewhere, or when the solution was accompanied by an intriguing label, such as “AI” or “machine learning.” Data scientists, who were typically focused on the analytical methods, often could not or, at any rate, did not provide a more holistic perspective. To combat this problem, senior managers at the banks in our study often turned to training. At one bank, data science recruits were required to take product training courses taught by domain experts alongside product relationship manager trainees. This bank also offered data science training tailored for business managers at all levels and taught by the head of the data science unit. The curriculum included basic analytics concepts, with an emphasis on questions to ask about specific solution techniques and where the techniques should or should not be used. In general, the training interventions designed to address this problem aimed to facilitate the cross-fertilization of knowledge among data scientists, business managers, and domain experts and help them develop a better understanding of one another’s jobs. In related fieldwork, we have also seen process- based fixes for avoiding the mistake of jumping too quickly to a favored solution. One large U.S.-based aerospace company uses an approach it calls the Seven Ways, which requires that teams identify and compare at least seven possible solution approaches and then explicitly justify their final selection. Mistake 2: Unrecognized Sources of Bias Pranav, a data scientist with expertise in statistical modeling, was developing an algorithm aimed at producing a recommendation for the underwriters responsible for approving secured loans to small and medium-sized enterprises. Using the credit approval memos (CAMs) for all loan applications processed over the previous 10 years, he compared the borrowers’ financial health at the time of their application with their current financial status. Within a couple of months, Pranav had a software tool built around a highly accurate model, which the underwriting team implemented. Unfortunately, after six months, it became clear that the delinquency rates on the loans were higher after the tool was implemented than before. Perplexed, senior managers assigned an experienced underwriter to work with Pranav to figure out what had gone wrong. The epiphany came when the underwriter discov- ered that the input data came from CAMs. What the underwriter knew, but Pranav hadn’t, was that CAMs were prepared only for loans that had already been SLOANREVIEW.MIT.EDU SPRING 2021 MIT SLOAN MANAGEMENT REVIEW 87 prescreened by experienced relationship managers and were very likely to be approved. Data from loan applications rejected at the prescreening stage was not used in the development of the model, which produced a huge selection bias. This bias led Pranav to miss the import of a critical decision parameter: bounced checks. Unsurprisingly, there were very few instances of bounced checks among the borrowers whom relationship managers had prescreened. The technical fix in this case was easy: Pranav added data on loan applications rejected in prescreening, and the “bounced checks” parameter became an important element in his model. The tool began to work as intended. The bigger problem for companies seeking to achieve business value from data science is how to discern such sources of bias upfront and ensure that they do not creep into models in the first place. This is challenging because laypeople — and sometimes analytics experts themselves — can’t easily tell how the “black box” of analytics generates output. And analytics experts who do understand the black box often do not recognize the biases embedded in the raw data they use. The banks in our study avoid un- recognized bias by requiring that data scientists become more familiar with the sources of the data they use in their models. For instance, we saw one data scientist spend a month in a branch shadowing a relationship manager to identify the data needed to ensure that a model produced accurate results. We also saw a project team composed of data scientists and business professionals use a formal bias-avoidance process, in which they identified potential predictor variables and their data sources and then scrutinized each for potential biases. The objective of this process was to question assumptions and otherwise “deodorize” the data — thus avoiding problems that can arise from the circumstances in which the data was created or gathered.4 Mistake 3: Right Solution, Wrong Time Kartik, a data scientist with expertise in machine learning, spent a month developing a sophisticated model for analyzing savings account attrition, and he then spent three more months fine-tuning it to improve its accuracy. When he shared the final product with the savings account product team, they were impressed, but they could not sponsor its implementation because their annual budget had already been expended. Eager to avoid the same result the following year, Kartik presented his model to the product team before the budgeting cycle began. But now the team’s mandate from senior management had shifted from account retention to account acquisition. Again, the team was unable to sponsor a project based on Kartik’s model. In his third year of trying, Kartik finally got approval for the project, but he had little to celebrate. “Now they want to implement it,” he told us, with evident dismay, “but the model has decayed and I will need to build it again!” The mistake that blocks banks from achieving value in cases like this is a lack of synchronization between data science and the priorities and processes of the business. To avoid it, better links between data science and the strategies and systems of the business are needed. Senior executives can ensure the alignment of data science activities with organizational strategies and systems by more tightly integrating data science practices and data scientists with the business in physical, structural, and process terms. For example, one bank embedded data scientists in business teams on a project basis. In this way, the data scientists rubbed elbows with the business team day to day, becoming more aware of its priorities and deadlines — and in some cases actually anticipating unarticulated business needs. We have also seen data science teams colocated with business teams,