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

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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

2019
Article

History, Micro Data, and Endogenous Growth

By: Ufuk Akcigit and Tom Nicholas

The study of economic growth is concerned with long-run changes, and therefore, historical data should be especially influential in informing the development of new theories. In this review, we draw on the recent literature to highlight areas in which study of history... View Details

Keywords: Economic Development; Growth; Innovation; Economic Growth; History; Analytics and Data Science; Innovation and Invention

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2019
Article

Can Big Data Improve Firm Decision Quality? The Role of Data Quality and Data Diagnosticity

By: Maryam Ghasemaghaei and Goran Calic

Anecdotal evidence suggests that, despite the large variety of data, the huge volume of generated data, and the fast velocity of obtaining data (i.e., big data), quality of big data is far from perfect. Therefore, many firms defer collecting and integrating big data as... View Details

Keywords: Big Data; Analytics and Data Science; Decisions; Quality

2019
Book

Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity

By: Karen G. Mills

Fintech, Small Business & the American Dream describes the needs of small businesses for capital and demonstrates how technology—novel data sources, artificial intelligence, machine learning—will transform the small business lending market. This market has been... View Details

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March 2019
Case

HOPI: Turkey's Shopping Companion

By: Sunil Gupta, Donald Ngwe and Gamze Yucaoglu

The case opens in 2017 as Onur Erbay, CEO of HOPI, a multi-vendor loyalty platform, is contemplating a critical decision. The case chronicles the origins of Boyner Group, the parent company of HOPI and a major retailer in Turkey, and development of retail and customer... View Details

Keywords: Loyalty Programs; Multi-vendor Platform; Retail; Big Data; Customer Relationship Management; Mobile and Wireless Technology; Business Model; Analytics and Data Science; Competitive Strategy; Decision Making; Applications and Software; Digital Platforms; Technology Industry; Retail Industry; Turkey

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

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

2020
Working Paper

Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach

By: Eva Ascarza

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details

Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry

February 2019 (Revised August 2019)
Case

Maccabitech: The Promise of Israel's Healthcare Data

By: Scott Duke Kominers and Carin-Isabel Knoop

Dr. Varda Shalev bridges technology and medicine through Maccabitech, a "research and innovation wing" of Israel's Maccabi Healthcare Services (MHS) that partners with research institutions, pharmaceutical companies, and startups. Shalev hopes to scale Maccabitech's... View Details

Keywords: Big Data; Healthcare; Analytics and Data Science; Digital Platforms; Health Care and Treatment; Innovation and Invention; Research; Entrepreneurship; Risk Management; Israel

Article

Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

By: Daniel Elsner, Pouya Aleatrati Khosroshahi, Alan MacCormack and Robert Lagerström

Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning... View Details

Keywords: Big Data; Data Science And Analytics Management; Governance And Compliance; Organizational Systems And Technology; Anomaly Detection; Application Performance Management; Machine Learning; Enterprise Architecture; Analytics and Data Science

January 2019
Supplement

Understanding the Brand Equity of Nestlé Crunch Bar (B): Data Analysis

By: Jill Avery and Gerald Zaltman

In early 2018, Nestlé announced the sale of its U.S. candy-making division and a select collection of 20 of its confectionery brands, including the Nestlé Crunch Bar, to Ferrero SpA for $2.8 billion. Luckily, an old consumer research study on the Nestlé Crunch Bar... View Details

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