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Show Results For
- All HBS Web
(6,976)
- News (1,210)
- Research (4,343)
- Events (115)
- Multimedia (62)
- Faculty Publications (2,995)
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- February 2016 (Revised February 2017)
- Case
The Climate Corporation
By: David E. Bell, Forest Reinhardt and Mary Shelman
Climate Corporation is a San Francisco–based data analytics company focused on agricultural applications. It was acquired by Monsanto in 2013. In 2015, Climate's decision support platform was used on 75 million acres of farmland in the U.S.; however, most of those... View Details
Keywords: Agribusiness Industry; Farming; Big Data; Data Analytics; Agriculture; Agribusiness; Decision Making; Analytics and Data Science; Agriculture and Agribusiness Industry
Bell, David E., Forest Reinhardt, and Mary Shelman. "The Climate Corporation." Harvard Business School Case 516-060, February 2016. (Revised February 2017.)
- August 2020
- Case
Chesapeake Conservancy: Democratizing Data to Protect 30% of the Planet by 2030
By: Lynda M. Applegate and Ankita Panda
Applegate, Lynda M., and Ankita Panda. "Chesapeake Conservancy: Democratizing Data to Protect 30% of the Planet by 2030." Harvard Business School Case 821-017, August 2020.
- 1998
- Working Paper
The Consequences of Labour Market Flexibility: Panel Evidence Based on Survey Data
By: Rafael Di Tella and Robert MacCulloch
- January 2016
- Case
Acxiom
By: John Deighton
Acxiom built the market for personal data, yet sales have been flat for a decade during which marketing's appetite for data has exploded. Will the acquisition of a digital data onboarder LiveRamp give marketers what they want from a data broker? View Details
- January 2022
- Article
Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems
By: David R. Clough and Andy Wu
Gregory, Henfridsson, Kaganer, and Kyriakou (2020) highlight the important role of data and AI as strategic resources that platforms may use to enhance user value. However, their article overlooks a significant conceptual distinction: the installed base of... View Details
Keywords: Artificial Intelligence; Data Strategy; Ecosystem; Value Capture; Digital Platforms; Analytics and Data Science; Strategy; Learning; Value Creation; AI and Machine Learning; Technology Industry; Information Technology Industry; Video Game Industry; Advertising Industry
Clough, David R., and Andy Wu. "Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems." Academy of Management Review 47, no. 1 (January 2022): 184–189.
- August 2018 (Revised September 2018)
- Supplement
LendingClub (C): Gradient Boosting & Payoff Matrix
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default... View Details
Keywords: Data Analytics; Data Science; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (C): Gradient Boosting & Payoff Matrix." Harvard Business School Supplement 119-022, August 2018. (Revised September 2018.)
- July 9, 2019
- Article
Setting Better Sales Goals with Analytics
By: Doug J. Chung, Isabel Huber, Vinay Murthy, Varun Sunku and Marije Weber
Sales compensation is a critical lever in motivating a salesforce and driving growth in the business-to-business sector: Studies show that revising compensation in line with market trends can have a 50% greater impact on sales than advertisements have, for instance. A... View Details
Keywords: Analytics; Salesforce Management; Compensation and Benefits; Motivation and Incentives; Goals and Objectives
Chung, Doug J., Isabel Huber, Vinay Murthy, Varun Sunku, and Marije Weber. "Setting Better Sales Goals with Analytics." Harvard Business Review (website) (July 9, 2019).
- January 1999
- Exercise
Seneca Systems (B): General and Confidential Instructions for Dr. D. Monosoff, Vice President, Data Devices Division
Seneca is a three-party negotiation-mediation simulation. The context is a product failure crisis in a manufacturing company with highly autonomous units. The heads of two divisions are in a dispute over who has responsibility for failures in a key product. The head of... View Details
Watkins, Michael D. "Seneca Systems (B): General and Confidential Instructions for Dr. D. Monosoff, Vice President, Data Devices Division." Harvard Business School Exercise 899-173, January 1999.
- January 1999
- Exercise
Seneca Systems (A): General and Confidential Instructions for Dr. D. Monosoff, Vice President, Data Devices Division
Seneca is a three-party negotiation-mediation simulation. The context is a product failure crisis in a manufacturing company with highly autonomous units. The heads of two divisions are in a dispute over who has responsibility for failures in a key product. The head of... View Details
Watkins, Michael D. "Seneca Systems (A): General and Confidential Instructions for Dr. D. Monosoff, Vice President, Data Devices Division." Harvard Business School Exercise 899-170, January 1999.
- March 2019
- Case
Wattpad
By: John Deighton and Leora Kornfeld
How to run a platform to match four million writers of stories to 75 million readers? Use data science. Make money by doing deals with television and filmmakers and book publishers. The case describes the challenges of matching readers to stories and of helping writers... View Details
Keywords: Platform Businesses; Creative Industries; Publishing; Data Science; Machine Learning; Collaborative Filtering; Women And Leadership; Managing Data Scientists; Big Data; Recommender Systems; Digital Platforms; Information Technology; Intellectual Property; Analytics and Data Science; Entertainment and Recreation Industry; Entertainment and Recreation Industry; Canada; United States; Philippines; Viet Nam; Turkey; Indonesia; Brazil
Deighton, John, and Leora Kornfeld. "Wattpad." Harvard Business School Case 919-413, March 2019.
- Spring 2016
- Article
The Billion Prices Project: Using Online Prices for Inflation Measurement and Research
By: Alberto Cavallo and Roberto Rigobon
New data-gathering techniques, often referred to as “Big Data” have the potential to improve statistics and empirical research in economics. In this paper we describe our work with online data at the Billion Prices Project at MIT and discuss key lessons for both... View Details
Keywords: Billion Prices Project; Online Scraped Data; Online Price Index; Economics; Research; Price; Analytics and Data Science
Cavallo, Alberto, and Roberto Rigobon. "The Billion Prices Project: Using Online Prices for Inflation Measurement and Research." Journal of Economic Perspectives 30, no. 2 (Spring 2016): 151–178.
- 1997
- Working Paper
What Really Happens in Creative Projects: Event Sampling Through Electronic Data Collection
By: Teresa M. Amabile, Dean Whitney, Jeremiah Weinstock, Lynn Miller and Chelley Fallang
- Article
Vungle Inc. Improves Monetization Using Big-Data Analytics
By: Bert De Reyck, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin and Andrew Kritzer
The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry,... View Details
Keywords: Big Data; Monetization; Data and Data Sets; Advertising; Mobile Technology; Customization and Personalization; Performance Improvement
De Reyck, Bert, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin, and Andrew Kritzer. "Vungle Inc. Improves Monetization Using Big-Data Analytics." Interfaces 47, no. 5 (September–October 2017): 454–466.
- 01 Jan 1977
- Conference Presentation
Short Term Natural Gas Consumption Forecasts: Optimal Use of National Weather Service Data
By: James K. Sebenius and Richard Lehman
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
- April 2002
- Case
In vivo to in vitro to in silico: Coping with Tidal Waves of Data at Biogen
By: Juan Enriquez-Cabot, Gary P. Pisano and Gaye Bok
Biogen is a successful biotech company facing a critical juncture. CEO John Mullen ponders how technological changes introduced into the research function will shape larger corporate decisions. This world in which biotechnology companies operated had changed... View Details
Keywords: Change; Decisions; Product Development; Research and Development; Expansion; Technology; Biotechnology Industry
Enriquez-Cabot, Juan, Gary P. Pisano, and Gaye Bok. "In vivo to in vitro to in silico: Coping with Tidal Waves of Data at Biogen." Harvard Business School Case 602-122, April 2002.
- March 2022 (Revised January 2025)
- Technical Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- Fall 2012
- Article
How 'Big Data' Is Different
Many people today in the information technology world and in corporate boardrooms are talking about "big data." Many believe that, for companies that get it right, big data will be able to unleash new organizational capabilities and value. But what does the term "big... View Details
Keywords: Big Data; Analytics; Mathematical Methods; Information Management; Information Technology Industry
Davenport, Thomas H., Paul Barth, and Randy Bean. "How 'Big Data' Is Different." MIT Sloan Management Review 54, no. 1 (Fall 2012).
- May 16, 2018
- Other Article
How Companies Can Use the Data They Collect to Further the Public Good
By: Edward L. Glaeser, Hyunjin Kim and Michael Luca
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "How Companies Can Use the Data They Collect to Further the Public Good." Harvard Business Review (website) (May 16, 2018).
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).