Publications

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2022
Working Paper

The Limits of Decentralized Administrative Data Collection: Experimental Evidence from Colombia

By: Natalia Garbiras-Diaz and Tara Slough

States collect vast amounts of data for use in policymaking and public administration. To do so, central governments frequently solicit data from decentralized bureaucrats. Because central governments use these data in policymaking, decentralized bureaucrats may face... View Details

Keywords: Decentralization; Policy-making; Policy/economics; Policy Evaluation; Governance; Government Administration; Government and Politics; Government Legislation; Policy; Public Opinion; Analytics and Data Science; Latin America; South America; Colombia

March 27, 2017
Article

How the Water Industry Learned to Embrace Data

By: Frank V. Cespedes and Amir Peleg

Most current talk about “big data” seems to assume the disintermediation or replacement of physical assets by digital technologies. But a bigger and more impactful trend is the use of online tools to improve physical asset utilization in many traditional off-line... View Details

Keywords: Information Technology; Analytics and Data Science; Organizational Change and Adaptation; Utilities Industry

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Fast Answer

Investing in Life Sciences - SIP Resources

href="https://www.library.hbs.edu/services/learn-with-baker-library#" target="_blank">Learn with Baker Library LSEG Workspace Introduction. Startup and Private company profiles: Pitchbook has detailed profiles on startups View Details

May 2021 (Revised February 2024)
Teaching Note

THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)

By: Ayelet Israeli and Jill Avery

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; 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

June 2020
Article

Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure

By: Omar Isaac Asensio, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer and Sooji Ha

By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to... View Details

Keywords: Environmental Sustainability; Transportation; Infrastructure; Behavior; AI and Machine Learning; Demand and Consumers

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21 Aug 2017
Lessons from the Classroom

Companies Love Big Data But Lack the Strategy To Use It Effectively

analyzing that data and designing strategy around it. That’s one reason eight HBS professors pooled resources in June to launch the Competing on Business Analytics View Details

Keywords: by Dina Gerdeman

12 Apr 2022
Research & Ideas

Swiping Right: How Data Helped This Online Dating Site Make More Matches

some estimates, with players such as Bumble, Tinder, and OKCupid vying to help people find love. While McFowland is not a dating expert, his work in machine learning and social View Details

Keywords: by Kara Baskin

July 2018
Article

Reimagining Health Data Exchange: An Application Programming Interface-Enabled Roadmap for India

By: Satchit Balsari, Alexander Fortenko MD, MPH, Joaquin A. Blaya PhD, Adrian Gropper MD, Malavika Jayaram LLM, Rahul Matthan LLM, Ram Sahasranam, Mark Shankar MD, Suptendra N. Sarbadhikari PhD, Barbara Bierer, Kenneth D. Mandl MD, Sanjay Mehendale MD, MPH and Tarun Khanna

In February 2018, the Government of India announced a massive public health insurance scheme extending coverage to 500 million citizens, in effect making it the world’s largest insurance program. To meet this target, the government will rely on technology to... View Details

Keywords: Health Information Exchange; India; Health APIs; Health Care and Treatment; Information; Analytics and Data Science; Information Technology; Health Industry; India

Web

Blavatnik Fellowship in Life Science Entrepreneurship - Health Care

offers Harvard Business School (HBS) alumni and Harvard-affiliated postdoctoral researchers the opportunity to advance new ventures around promising life science technologies View Details

Web

Entrepreneurship in Life Sciences - Course Catalog

exploration may include emerging opportunities in pharma & biotech, medical device & diagnostics advances, and related topics such as digital therapeutics, platform technologies, use of real-world evidence, View Details

January 2021 (Revised February 2021)
Case

Tech with a Side of Pizza: How Domino's Rose to the Top

By: Boris Groysberg, Sarah L. Abbott and Susan Seligson

After hitting an all-time low in 2008, Domino’s Pizza underwent a vigorous rebranding, product development, and embraced innovative technologies to become the world’s leading international fast-food retailer. Domino’s considered itself as much a tech company as it was... View Details

Educators

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

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

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14 Sep 2018
Blog Post

10 Things I Learned During My First Month in the MS/MBA: Engineering Sciences Program

skills with big-picture business vision. I chose Harvard’s MS/MBA: Engineering Sciences Program to become a strong leader while also refining the skills I would need to succeed in deeply technical fields. I arrived in Boston on an... View Details

December 1996 (Revised November 2006)
Background Note

General Mills, Inc.: Appendix of Comparable Company Data

By: William J. Bruns Jr.

Financial ratios for comparable companies to be used in conjunction with an analysis of the General Mills Annual Report. View Details

Keywords: Business Conglomerates; Analytics and Data Science; Food and Beverage Industry

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February 25, 2016
Article

The Hodgepodge Principle in U.S. Privacy Policy

By: John A. Deighton

Data, says Professor Lawrence Summers, is the new oil, "a hugely valuable asset essential to economic life." Personal data, the kind of data that invites thoughts of privacy, is a big part of that. The European Union saw this economic fuel source coming long ago and... View Details

Keywords: Data; Privacy; Technology; Big Data; Personal Data; Marketing; Information Technology; Analytics and Data Science

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

March–April 2017
Article

Advancing Conservation by Understanding and Influencing Human Behavior

By: Sheila M. Reddy, Jensen Montambault, Yuta J. Masuda, Ayelet Gneezy, Elizabeth Keenan, William Butler, Jonathan R. Fisher and Stanley T. Asah

Behavioral sciences can advance conservation by systematically identifying behavioral barriers to conservation and how to best overcome them. Behavioral sciences have informed policy in many other realms (e.g., health, savings), but they are a largely untapped resource... View Details

Keywords: Adaptive Management; Awareness; Behavioral Economics; Behavioral Science; Conservation Intervention; Conservation Planning; Decision-making; Incentives; Nudge; Management; Motivation and Incentives; Behavior; Marketing; Decision Making; Environmental Sustainability; Economics

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