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Show Results For
- All HBS Web
(6,697)
- News (1,182)
- Research (4,338)
- Events (107)
- Multimedia (62)
- Faculty Publications (2,958)
- 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.
- 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.
- 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).
- 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
- Web
HBS Alumni Research Tips: Finding Market Data & Insights | Baker Library
Help Center HBS Alumni Research Tips: Finding Market Data & Insights How can alumni search for market data, trends, and insights? Statista is a user-friendly aggregator of statistical View Details
- 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.)
- 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).
- 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.
- 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.)
- 15 Nov 2016
- News
Britain’s inflation data needs to be dragged out of the stone age
- 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
- Web
Research & Data Services for HBS Faculty & Doctoral Students | Baker Library
Research & Data Services for HBS Faculty & Doctoral Students We advance the intellectual ambition of HBS faculty and doctoral students by providing leading- edge services at every stage of the research... View Details
- 2015
- Chapter
Investigating Population Dynamics of the KUMBH MELA through the Lens of Cell Phone Data
By: Jukka-Pekka Onnela and Tarun Khanna
Onnela, Jukka-Pekka, and Tarun Khanna. "Investigating Population Dynamics of the KUMBH MELA through the Lens of Cell Phone Data." In Kumbh Mela, January 2013: Mapping the Ephemeral Mega City, edited by Rahul Mehrotra and Felipe Vera, 202–218. Ostfildern, Germany: Hatje Cantz Verlag, 2015.
- 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
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- January 2021
- Supplement
E-Commerce Analytics for CPG Firms (B): Optimizing Assortment for a New Retailer
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for... View Details
Keywords: Data Analysis; Data Analytics; CPG; Consumer Packaged Goods (CPG); Online Channel; Retail; Retail Analytics; Retailing Industry; Data; Data Sharing; Ecommerce; Assortment Optimization; Assortment Planning; Analytics and Data Science; Retention; Retail Industry; Consumer Products Industry; United States
- Article
DEA Model with Shared Resources and Efficiency Decomposition
By: Yao Chen, Juan Du, H. David Sherman and Joe Zhu
Data envelopment analysis (DEA) has proved to be an excellent approach for measuring performance of decision making units (DMUs) that use multiple inputs to generate multiple outputs. In many real world scenarios, DMUs have a two-stage network process with shared input... View Details
Chen, Yao, Juan Du, H. David Sherman, and Joe Zhu. "DEA Model with Shared Resources and Efficiency Decomposition." European Journal of Operational Research 207, no. 1 (November 2010): 339–349.