Analytics and Data Science - Publications - Faculty & Research - Harvard Business School

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September 2020 (Revised June 2023)
Supplement

Spreadsheet Supplement to Artea Teaching Note

By: Eva Ascarza and Ayelet Israeli

Spreadsheet Supplement to Artea Teaching Note 521-041. 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... View Details

Keywords: Targeted Advertising; Algorithmic Data; Bias; Advertising; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

August 2020 (Revised September 2020)
Technical Note

Assessing Prediction Accuracy of Machine Learning Models

By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne

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

2021
Working Paper

Time and the Value of Data

By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti

Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details

Keywords: Economics Of AI; Machine Learning; Non-stationarity; Perishability; Value Depreciation; Analytics and Data Science; Value

August 2020
Technical Note

Comparing Two Groups: Sampling and t-Testing

By: Iavor I Bojinov, Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih and Michael W. Toffel

This note describes sampling and t-tests, two fundamental statistical concepts. View Details

Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods

July 2020
Case

Applying Data Science and Analytics at P&G

By: Srikant M. Datar, Sarah Mehta and Paul Hamilton

Set in December 2019, this case explores how P&G has applied data science and analytics to cut costs and improve outcomes across its business units. The case provides an overview of P&G’s approach to data management and governance, and reviews the challenges associated... View Details

Keywords: Data Science; Analytics; Analysis; Information; Information Management; Information Types; Innovation and Invention; Strategy; Analytics and Data Science; Consumer Products Industry; United States; Ohio

June 2020
Background Note

Customer Management Dynamics and Cohort Analysis

By: Elie Ofek, Barak Libai and Eitan Muller

The digital revolution has allowed companies to amass considerable amounts of data on their customers. Using this information to generate actionable insights is fast becoming a critical skill that firms must master if they wish to effectively compete and win in today’s... View Details

Keywords: Cohort Analysis; Customers; Analytics and Data Science; Segmentation; Analysis; Customer Value and Value Chain

2021
Working Paper

The Project on Impact Investments' Impact Investment Database

By: M. Diane Burton, Shawn Cole, Abhishek Dev, Christina Jarymowycz, Leslie Jeng, Josh Lerner, Fanele Mashwama, Yue (Cynthia) Xu and T. Robert Zochowski

Impact investing has grown significantly over the past 15 years. From a niche investing segment with only $25 billion AUM in 2013 (WEF 2013), it experienced double-digit growth and developed into a market with an estimated $502 billion AUM (Mudaliapar and Dithrich... View Details

Keywords: Impact Investing; Investment; Analytics and Data Science; Analysis

May 8, 2020
Article

Which Covid-19 Data Can You Trust?

By: Satchit Balsari, Caroline Buckee and Tarun Khanna

The COVID-19 pandemic has produced a tidal wave of data, but how much of it is any good? And as a layperson, how can you sort the good from the bad? The authors suggest a few strategies for dividing the useful data from the misleading: Beware of data that’s too broad... View Details

Keywords: COVID-19 Pandemic; Health Pandemics; Analytics and Data Science

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2020
Article

Public Sentiment and the Price of Corporate Sustainability

By: George Serafeim

Combining corporate sustainability performance scores based on environmental, social, and governance (ESG) data with big data measuring public sentiment about a company’s sustainability performance, I find that the valuation premium paid for companies with strong... View Details

Keywords: Sustainability; ESG; ESG (Environmental, Social, Governance) Performance; Investment Management; Investment Strategy; Big Data; Machine Learning; Environment; Environmental Sustainability; Corporate Governance; Performance; Asset Pricing; Investment; Management; Strategy; Human Capital; Public Opinion; Value; Analytics and Data Science

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March 2020 (Revised June 2022)
Case

GreenLight Fund

By: Brian Trelstad, Julia Kelley and Mel Martin

As Tara Noland, the Executive Director (ED) of GreenLight Cincinnati, reflected on her first few years on the job. Noland had delivered on what she had been hired to do in the city: work with leading philanthropists and nonprofit executives to use data and evidence to... View Details

Keywords: Philanthropy; Venture Philanthropy; Replication; Philanthropy and Charitable Giving; Venture Capital; Social Issues; Decision Making; Analytics and Data Science; Cincinnati

March 2020
Supplement

People Analytics at Teach For America (B)

By: Jeffrey T. Polzer and Julia Kelley

This is a supplement to the People Analytics at Teach For America (A) case. In this supplement, situated one year after the A case, Managing Director Michael Metzger must decide how to apply his team's predictive models generated from the previous year’s data. View Details

Keywords: Analytics; Human Resource Management; Data; Workforce; Hiring; Talent Management; Forecasting; Predictive Analytics; Organizational Behavior; Recruiting; Analytics and Data Science; Forecasting and Prediction; Recruitment; Selection and Staffing; Talent and Talent Management

March 2020
Article

Diagnosing Missing Always at Random in Multivariate Data

By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin

Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first... View Details

Keywords: Missing Data; Diagnostic Tools; Sensitivity Analysis; Hypothesis Testing; Missing At Random; Row Exchangeability; Analytics and Data Science; Mathematical Methods

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2020
Book

The Power of Experiments: Decision-Making in a Data-Driven World

By: Michael Luca and Max H. Bazerman

Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of changes to an... View Details

Keywords: Experiments; Randomized Controlled Trials; Organizations; Decision Making; Analytics and Data Science; Management Analysis, Tools, and Techniques

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2020
Working Paper

A General Theory of Identification

By: Iavor Bojinov and Guillaume Basse

What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree on a definition in the context of parametric statistical models — roughly, a parameter θ in a model P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective.... View Details

Keywords: Identification; Econometric Models; Analytics and Data Science; Theory

February 2020 (Revised April 2021)
Case

StockX: The Stock Market of Things

By: Chiara Farronato, John J. Horton, Annelena Lobb and Julia Kelley

Founded in 2015 by Dan Gilbert, Josh Luber, and Greg Schwartz, StockX was an online platform where users could buy and sell unworn luxury and limited-edition sneakers. Sneaker resale prices often fluctuated over time based on supply and demand, creating a robust... View Details

2020
Article

Assessing the Impact of Big Data on Firm Innovation Performance: Big Data is not Always Better Data

By: Maryam Ghasemaghaei and Goran Calic

In this study, we explore the impacts of big data’s main characteristics (i.e., volume, variety, and velocity) on innovation performance (i.e., innovation efficacy and efficiency), which eventually impacts firm performance (i.e., customer perspective, financial... View Details

Keywords: Big Data; Analytics and Data Science; Performance; Innovation and Invention

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

May 2020
Article

Scalable Holistic Linear Regression

By: Dimitris Bertsimas and Michael Lingzhi Li

We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details

Keywords: Mathematical Methods; Analytics and Data Science

2019
Article

Does Big Data Enhance Firm Innovation Competency? The Mediating Role of Data-driven Insights

By: Maryam Ghasemaghaei and Goran Calic

Grounded in gestalt insight learning theory and organizational learning theory, we collected data from 280 middle and top-level managers to investigate the impact of each big data characteristic (i.e., data volume, data velocity, data variety, and data veracity) on... View Details

Keywords: Analytics and Data Science; Innovation and Invention; Learning

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August 2019 (Revised February 2020)
Teaching Note

Sidewalk Labs: Privacy in a City Built from the Internet Up

By: Leslie John and Mitch Weiss

Email mking@hbs.edu for a courtesy copy.

The case serves as a microcosm of issues of digital privacy: the availability of data – personal data in particular – has tremendous potential to improve people’s lives... View Details

Keywords: Privacy; Privacy By Design; Privacy Regulation; Platforms; Data; Data Security; Behavioral Science; Analytics and Data Science; Safety; Entrepreneurship; Business and Government Relations; Consumer Behavior; Digital Platforms

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