Filter Results:
(1,044)
Show Results For
- All HBS Web
(1,789)
- People (9)
- News (316)
- Research (1,044)
- Events (15)
- Multimedia (10)
- Faculty Publications (864)
Show Results For
- All HBS Web
(1,789)
- People (9)
- News (316)
- Research (1,044)
- Events (15)
- Multimedia (10)
- Faculty Publications (864)
Sort by
- February 2006 (Revised March 2008)
- Case
ChoicePoint (A)
By: Lynn S. Paine and Zack Phillips
The CEO of ChoicePoint, a leading company in the rapidly growing U.S. personal data industry, must reexamine the company's business model after a serious breach of data security affecting some 145,000 U.S. citizens. He must decide on steps to strengthen data protection... View Details
Keywords: Business Model; Safety; Rights; Analytics and Data Science; Ethics; Information Technology; Information Industry; United States
Paine, Lynn S., and Zack Phillips. "ChoicePoint (A)." Harvard Business School Case 306-001, February 2006. (Revised March 2008.)
- June 2021
- Article
Developing a Value Framework: Utilizing Administrative Data to Assess an Enhanced Care Initiative
By: Casey J. Allen, Jarrod S. Eska, Nikhil G. Thaker, Thomas W. Feeley, Robert S. Kaplan, Ryan W. Huey, Ching-Wei D. Tzeng, Jeffrey E. Lee, Steven J. Frank, Thomas A. Aloia, Vijaya Gottumukkala and Matthew H.G. Katz
We used national administrative data to assess multiple domains of value associated with enhanced recovery pathways for patients undergoing pancreatic surgery. Value metrics included in-hospital mortality, complication rates, length of stay (LOS), 30-day readmission... View Details
Keywords: Value-based Health Care; Health Care and Treatment; Analytics and Data Science; Outcome or Result; Measurement and Metrics; Performance Improvement
Allen, Casey J., Jarrod S. Eska, Nikhil G. Thaker, Thomas W. Feeley, Robert S. Kaplan, Ryan W. Huey, Ching-Wei D. Tzeng, Jeffrey E. Lee, Steven J. Frank, Thomas A. Aloia, Vijaya Gottumukkala, and Matthew H.G. Katz. "Developing a Value Framework: Utilizing Administrative Data to Assess an Enhanced Care Initiative." Journal of Surgical Research 262 (June 2021): 115–120.
- 2019
- Working Paper
Biometric Monitoring, Service Delivery and Misreporting: Evidence from Healthcare in India
By: Thomas Bossuroy, Clara Delavallade and Vincent Pons
Developing countries increasingly use biometric identification technology in hopes of improving the reliability of administrative information and delivering social services more efficiently. This paper exploits the random placement of biometric tracking devices in... View Details
Keywords: Biometric Technology; Health Care and Treatment; Technological Innovation; Analytics and Data Science; Quality; Performance Improvement; India
Bossuroy, Thomas, Clara Delavallade, and Vincent Pons. "Biometric Monitoring, Service Delivery and Misreporting: Evidence from Healthcare in India." NBER Working Paper Series, No. 26388, October 2019. (Revise and resubmit requested, Review of Economics and Statistics.)
- 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.
- Article
Considerations of Fairness and Strategy: Experimental Data from Sequential Games
By: V. Prasnikar and A. E. Roth
Prasnikar, V., and A. E. Roth. "Considerations of Fairness and Strategy: Experimental Data from Sequential Games." Quarterly Journal of Economics 107, no. 3 (August 1992): 865–888.
- 25 Apr 2012
- What Do You Think?
How Will the “Age of Big Data” Affect Management?
the Big Thinker." Gerald Nanninga cautioned us that "the big risk is that it gives executives a false sense of comfort." Clifford Francis Baker added, "My concern is primarily focused on the possibility of complacency data derived from data View Details
Keywords: Re: James L. Heskett
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
- October 1, 2021
- Article
An Evaluation of Cross-efficiency Methods: With an Application to Warehouse Performance.
By: B.M. Balk, M.R. De Koster, Christian Kaps and J.L. Zofio
Cross-efficiency measurement is an extension of Data Envelopment Analysis that allows for tie-breaking ranking of the Decision Making Units (DMUs) using all the peer evaluations. In this article we examine the theory of cross-efficiency measurement by comparing a... View Details
Keywords: Efficiency Analysis; Performance Benchmarking; Warehousing; Analytics and Data Science; Performance Evaluation; Measurement and Metrics; Mathematical Methods
Balk, B.M., M.R. De Koster, Christian Kaps, and J.L. Zofio. "An Evaluation of Cross-efficiency Methods: With an Application to Warehouse Performance." Art. 126261. Applied Mathematics and Computation 406 (October 1, 2021).
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details
Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
- 2021
- Working Paper
Invisible Primes: Fintech Lending with Alternative Data
By: Marco Di Maggio, Dimuthu Ratnadiwakara and Don Carmichael
We exploit anonymized administrative data provided by a major fintech platform to investigate whether using alternative data to assess borrowers’ creditworthiness results in broader credit access. Comparing actual outcomes of the fintech platform’s model to... View Details
Keywords: Fintech Lending; Alternative Data; Machine Learning; Algorithm Bias; Finance; Information Technology; Financing and Loans; Analytics and Data Science; Credit
Di Maggio, Marco, Dimuthu Ratnadiwakara, and Don Carmichael. "Invisible Primes: Fintech Lending with Alternative Data." Harvard Business School Working Paper, No. 22-024, October 2021.
- 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.)
- 2024
- Working Paper
Scaling Core Earnings Measurement with Large Language Models
By: Matthew Shaffer and Charles CY Wang
We study the application of large language models (LLMs) to the estimation of core earnings, i.e., a firm's persistent profitability from its core business activities. This construct is central to investors' assessments of economic performance and valuations. However,... View Details
Keywords: Large Language Models; AI and Machine Learning; Accounting; Profit; Corporate Disclosure; Analytics and Data Science; Measurement and Metrics
Shaffer, Matthew, and Charles CY Wang. "Scaling Core Earnings Measurement with Large Language Models." Working Paper, November 2024.
- April–May 2024
- Article
Gone with the Big Data: Institutional Lender Demand for Private Information
By: Jung Koo Kang
I explore whether big-data sources can crowd out the value of private information acquired through lending relationships. Institutional lenders have been shown to exploit their access to borrowers’ private information by trading on it in financial markets. As a shock... View Details
Keywords: Analytics and Data Science; Borrowing and Debt; Financial Markets; Value; Knowledge Dissemination; Financing and Loans
Kang, Jung Koo. "Gone with the Big Data: Institutional Lender Demand for Private Information." Art. 101663. Journal of Accounting & Economics 77, nos. 2-3 (April–May 2024).
- August 2013 (Revised August 2014)
- Case
Catalina In the Digital Age
By: Robert J. Dolan and Uma R. Karmarkar
Catalina in the Digital Age considers how a company with a dominant market position should evolve its established product lines given the rise of novel digital technologies. Since its founding in 1983, Catalina had enjoyed a distinct position in the world of consumer... View Details
Keywords: Big Data; Digital Technologies; Marketing; Customer Relationship Management; Consumer Behavior; Analytics and Data Science
Dolan, Robert J., and Uma R. Karmarkar. "Catalina In the Digital Age." Harvard Business School Case 514-021, August 2013. (Revised August 2014.)
- February 2001 (Revised April 2001)
- Background Note
Note on Valuing Private Businesses
By: Dwight B. Crane and Indra Reinbergs
This case provides a brief overview of valuation for owners of closely held companies. The focus is on a comparable transactions approach, although rules of thumb and discounted cash flow are mentioned. Earnings multiples and their drivers are discussed. It uses... View Details
Keywords: Earnings Management; Finance; Cash Flow; Analytics and Data Science; Private Ownership; Valuation
Crane, Dwight B., and Indra Reinbergs. "Note on Valuing Private Businesses." Harvard Business School Background Note 201-060, February 2001. (Revised April 2001.)
- 24 Jul 2009
- Research & Ideas
Business Summit: Business Education in the 21st Century
data about the challenges facing the business education marketplace and presented qualitative information on innovations in top MBA programs. On the whole, MBA programs are in decline. Their value is being questioned, and they are seen as overly emphasizing View Details
- 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.
- April 2023
- Case
Fizzy Fusion: When Data-Driven Decision Making Failed
By: Michael Parzen, Eddie Lin, Douglas Ng and Jessie Li
This is a case about a fictional New York beverage company called Fizzy Fusion. The business is facing supply chain and inventory management challenges with its new product, SparklingSip. Despite seeking help from a data science consulting firm, the machine learning... View Details
Keywords: Supply Chain Management; Production; Risk and Uncertainty; Analytics and Data Science; Food and Beverage Industry
Parzen, Michael, Eddie Lin, Douglas Ng, and Jessie Li. "Fizzy Fusion: When Data-Driven Decision Making Failed." Harvard Business School Case 623-071, April 2023.
- May 2021
- Article
The Firm Next Door: Using Satellite Images to Study Local Information Advantage
By: Jung Koo Kang, Lorien Stice-Lawrence and Forester Wong
We use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store-level... View Details
Keywords: Satellite Images; Store-level Performance; Institutional Investors; Local Advantage; Overweighting; Processing Costs; Alternative Data; Big Data; Emerging Technologies; Information; Quality; Institutional Investing; Decision Making; Behavioral Finance; Analytics and Data Science
Kang, Jung Koo, Lorien Stice-Lawrence, and Forester Wong. "The Firm Next Door: Using Satellite Images to Study Local Information Advantage." Journal of Accounting Research 59, no. 2 (May 2021): 713–750.
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.