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Show Results For
- All HBS Web
(1,564)
- News (204)
- Research (1,137)
- Events (16)
- Multimedia (1)
- Faculty Publications (665)
- March 2023
- Supplement
Allianz Türkiye (C): Managing the 2017 Hail Storm
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe... View Details
Keywords: Insurance And Reinsurance; Natural Disasters; Turkey; Insurance; Climate Change; Analytics and Data Science; Insurance Industry; Financial Services Industry; Turkey
Macomber, John D., and Fares Khrais. "Allianz Türkiye (C): Managing the 2017 Hail Storm." Harvard Business School Supplement 223-084, March 2023.
- 2024
- Case
EPCorp: What Story Does the Data Tell?
By: Jacob M. Cook
In EPCorp: What Story Does the Data Tell?, the Quick Case begins with Shivani Bahl researching problems with her company's website so that she can begin to analyze which option would help EPCorp most: selling all its products on Amazon or improving its own data... View Details
Cook, Jacob M. "EPCorp: What Story Does the Data Tell?" Harvard Business Publishing Case, 2024.
- November–December 2022
- Article
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Marketing; Retail Industry
Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Marketing Science 41, no. 6 (November–December 2022): 1074–1096.
- 01 Jun 2017
- News
Better Hiring Through Brain Science
and the games, Polli notes, is like the difference between the scattershot of a dot matrix printer and the precision of a laser printer. HBS students were the first data... View Details
Keywords: Dan Morrell
- October 2017 (Revised November 2017)
- Case
NYC311
By: Constantine E. Kontokosta, Mitchell Weiss, Christine Snively and Sarah Gulick
Joe Morrisroe, executive director for NYC311, had some gut instincts but no definitive answer to the question he was just asked by one of the mayor’s deputies: “Are some communities being underserved by 311? How do we know we are hearing from the right people?” Founded... View Details
Keywords: New York City; NYC; 311; NYC311; Big Data; Equal Access; Bias; Data Analysis; Public Entrepreneurship; Urban Informatics; Predictive Analytics; Chief Data Officer; Data Analytics; Cities; City Leadership; Analytics and Data Science; Analysis; Prejudice and Bias; Entrepreneurship; Public Sector; City; Public Administration Industry; New York (city, NY)
- 2021
- Working Paper
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Retail Industry
Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Harvard Business School Working Paper, No. 21-067, November 2020. (Revised December 2021. Accepted at Marketing Science.)
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- December 2011
- Article
Data Impediments to Empirical Work on Health Insurance Markets
By: Leemore S. Dafny, David Dranove, Frank Limbrock and Fiona Scott Morton
We compare four datasets that researchers might use to study competition in the health insurance industry. We show that the two datasets most commonly used to estimate market concentration differ considerably from each other (both in levels and in changes over time),... View Details
Dafny, Leemore S., David Dranove, Frank Limbrock, and Fiona Scott Morton. "Data Impediments to Empirical Work on Health Insurance Markets." B.E. Journal of Economic Analysis & Policy 11, no. 2 (December 2011).
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.
- November 1998
- Article
Modeling Large Data Sets in Marketing
By: Sridhar Balasubramanian, Sunil Gupta, Wagner Kamakura and Michel Wedel
Balasubramanian, Sridhar, Sunil Gupta, Wagner Kamakura, and Michel Wedel. "Modeling Large Data Sets in Marketing." Special Issue on Large Data Sets in Business Economics. Statistica Neerlandica 52, no. 3 (November 1998).
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- November 2023
- Article
Federated Electronic Health Records for the European Health Data Space
By: René Raab, Arne Küderle, Anastasiya Zakreuskaya, Ariel Dora Stern, Jochen Klucken, Georgios Kaissis, Daniel Rueckert, Susanne Boll, Roland Eils, Harald Wagener and Bjoern Eskofier
The European Commission's draft for the European Health Data Space (EHDS) aims to empower citizens to access their personal health data and share it with physicians and other health-care providers. It further defines procedures for the secondary use of electronic... View Details
Keywords: Analytics and Data Science; Cybersecurity; Information Management; Knowledge Sharing; Knowledge Use and Leverage; Health Industry
Raab, René, Arne Küderle, Anastasiya Zakreuskaya, Ariel Dora Stern, Jochen Klucken, Georgios Kaissis, Daniel Rueckert, Susanne Boll, Roland Eils, Harald Wagener, and Bjoern Eskofier. "Federated Electronic Health Records for the European Health Data Space." Lancet Digital Health 5, no. 11 (November 2023): e840–e847.
- January 2021 (Revised March 2021)
- Case
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Jill Avery, Ayelet Israeli and Emma von Maur
THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on... View Details
Keywords: Data; Data Analytics; Artificial Intelligence; AI; AI Algorithms; AI Creativity; Fashion; Retail; Retail Analytics; E-Commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Preference Prediction; Predictive Analytics; App Development; ＂Marketing Analytics＂; Advertising; Mobile App; Mobile Marketing; Apparel; Online Advertising; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Analytics and Data Science; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Digital Marketing; Internet and the Web; Mobile and Wireless Technology; AI and Machine Learning; E-commerce; Digital Platforms; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Avery, Jill, Ayelet Israeli, and Emma von Maur. "THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)." Harvard Business School Case 521-070, January 2021. (Revised March 2021.)
- October 2015 (Revised October 2016)
- Case
Building Watson: Not So Elementary, My Dear! (Abridged)
By: Willy C. Shih
This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the... View Details
Keywords: Analytics; Big Data; Business Analytics; Product Development Strategy; Machine Learning; Machine Intelligence; Artificial Intelligence; Product Development; AI and Machine Learning; Information Technology; Analytics and Data Science; Information Technology Industry; United States
Shih, Willy C. "Building Watson: Not So Elementary, My Dear! (Abridged)." Harvard Business School Case 616-025, October 2015. (Revised October 2016.)
- July 2021
- Article
Electronic Trace Data and Legal Outcomes: The Effect of Electronic Medical Records on Malpractice Claim Resolution Time
By: Sam Ransbotham, Eric Overby and Michael C. Jernigan
Information systems generate copious trace data about what individuals do and when they do it. Trace data may affect the resolution of lawsuits by, for example, changing the time needed for legal discovery. Trace data might speed resolution by clarifying what events... View Details
Keywords: Analytics and Data Science; Lawsuits and Litigation; Digital Transformation; Welfare; Health Industry
Ransbotham, Sam, Eric Overby, and Michael C. Jernigan. "Electronic Trace Data and Legal Outcomes: The Effect of Electronic Medical Records on Malpractice Claim Resolution Time." Management Science 67, no. 7 (July 2021): 4341–4361.
- September 2020 (Revised June 2023)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analytics; Data Analysis; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Algorithmic Bias; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- Article
Some Uses of Happiness Data in Economics
By: Rafael Di Tella and Robert MacCulloch
Di Tella, Rafael, and Robert MacCulloch. "Some Uses of Happiness Data in Economics." Journal of Economic Perspectives 20, no. 1 (Winter 2006): 25–46.
- 13 Jun 2017
- Blog Post
MS/MBA: Engineering Sciences – A Q&A with Professor Robert Howe
The first MS/MBA: Engineering Sciences cohort will enroll in the MS/MBA program in August of 2018.The program is a major collaboration between HBS and the Harvard John A. Paulson School of Engineering View Details