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(959)
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- Faculty Publications (527)
Show Results For
- All HBS Web
(959)
- News (132)
- Research (690)
- Events (6)
- Multimedia (4)
- Faculty Publications (527)
- September 2019
- Case
Starling Trust Sciences: Measuring Trust in Organizations
By: Aiyesha Dey, Jonas Heese and James Weber
Stephen Scott needed to decide whether to keep his behavioral analytics startup in the people analytics sector or shift his company into the RegTech sector. Starling had develop technology that enabled its customers to anticipate and shape the behavior of their... View Details
Keywords: Behavioral Analytics; Financial Institutions; Banks and Banking; Entrepreneurship; Strategy; Banking Industry; Consulting Industry; Information Technology Industry; United States; United Kingdom
Dey, Aiyesha, Jonas Heese, and James Weber. "Starling Trust Sciences: Measuring Trust in Organizations." Harvard Business School Case 120-006, September 2019.
- July 2023
- Case
HealthVerity: Real World Data and Evidence
By: Satish Tadikonda
Andrew Kress (CEO and founder) and his team had built a promising marketplace business at HealthVerity serving its core market in healthcare, with a focus on pharmaceutical R&D and services. Thus far, HealthVerity’s products had been unique to the pharma and pharma... View Details
Tadikonda, Satish. "HealthVerity: Real World Data and Evidence." Harvard Business School Case 824-019, July 2023.
- Winter 2016
- Article
Analytics for an Online Retailer: Demand Forecasting and Price Optimization
By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi
We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time... View Details
Ferreira, Kris J., Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization." Manufacturing & Service Operations Management 18, no. 1 (Winter 2016): 69–88.
- 2010
- Book
The New Science of Retailing: How Analytics Are Transforming the Supply Chain and Improving Performance
By: Marshall Fisher and Ananth Raman
Retailers today are drowning in data but lacking in insight: They have huge volumes of information at their disposal. But they're unsure of how to sort through it and use it to make smart decisions. The result? They're struggling with profit-sapping supply chain... View Details
Keywords: Profit; Knowledge Use and Leverage; Logistics; Supply Chain Management; Mathematical Methods; Retail Industry
Fisher, Marshall, and Ananth Raman. The New Science of Retailing: How Analytics Are Transforming the Supply Chain and Improving Performance. Harvard Business Press, 2010.
- November–December 2022
- Article
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Marketing; Retail Industry
Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Marketing Science 41, no. 6 (November–December 2022): 1074–1096.
- 2021
- Working Paper
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Retail Industry
Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Harvard Business School Working Paper, No. 21-067, November 2020. (Revised December 2021. Accepted at Marketing Science.)
- 01 Mar 2018
- News
Democratizing Data to Favor Farmers
as much for the same seeds. That knowledge would help farmers haggle with dealers, but the real insight would come from analytics that go beyond consolidating seed prices to measuring a seed variety’s potential success. The most important... View Details
Keywords: Sasha Issenberg
- July 2021
- Article
Big Data for Social Benefits: Innovation as a Mediator of the Relationship between Big Data and Corporate Social Performance
By: Goran Calic and Maryam Ghasemaghaei
Over the last decade, the use big data in firms has seen a rapid increase. Whilst scholars have begun to unpack the relationship between big data utilisation and financial performance, significant uncertainty exists about the ethical uses of this new asset. Whether... View Details
Keywords: Analytics and Data Science; AI and Machine Learning; Performance Improvement; Organizational Change and Adaptation
Calic, Goran, and Maryam Ghasemaghaei. "Big Data for Social Benefits: Innovation as a Mediator of the Relationship between Big Data and Corporate Social Performance." Journal of Business Research 131 (July 2021): 391–401.
- October 2017 (Revised November 2017)
- Case
NYC311
By: Constantine E. Kontokosta, Mitchell Weiss, Christine Snively and Sarah Gulick
Joe Morrisroe, executive director for NYC311, had some gut instincts but no definitive answer to the question he was just asked by one of the mayor’s deputies: “Are some communities being underserved by 311? How do we know we are hearing from the right people?” Founded... View Details
Keywords: New York City; NYC; 311; NYC311; Big Data; Equal Access; Bias; Data Analysis; Public Entrepreneurship; Urban Informatics; Predictive Analytics; Chief Data Officer; Data Analytics; Cities; City Leadership; Analytics and Data Science; Analysis; Prejudice and Bias; Entrepreneurship; Public Sector; City; Public Administration Industry; New York (city, NY)
- 15 May 2017
- Sharpening Your Skills
The Promises and Limitations of Big Data
Source: peterhowell Although many people claim we have entered the era of big data, research firms tell us that most collected information is never used. It sits uncleaned, unanalyzed, unused in databases. But when data View Details
- Teaching Interest
Overview
By: John A. Deighton
I teach about the ecosystem of big data, the role of data in advertising and creative industries, and customer management and personal privacy in an era of individual addressability. View Details
- Article
Algorithms Need Managers, Too
By: Michael Luca, Jon Kleinberg and Sendhil Mullainathan
Algorithms are powerful predictive tools, but they can run amok when not applied properly. Consider what often happens with social media sites. Today many use algorithms to decide which ads and links to show users. But when these algorithms focus too narrowly on... View Details
Keywords: Machine Learning; Algorithms; Predictive Analytics; Management; Big Data; Analytics and Data Science
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101.
- 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.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
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
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- November 1998
- Article
Modeling Large Data Sets in Marketing
By: Sridhar Balasubramanian, Sunil Gupta, Wagner Kamakura and Michel Wedel
Balasubramanian, Sridhar, Sunil Gupta, Wagner Kamakura, and Michel Wedel. "Modeling Large Data Sets in Marketing." Special Issue on Large Data Sets in Business Economics. Statistica Neerlandica 52, no. 3 (November 1998).
- Article
Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business
By: Marco Iansiti and Karim R. Lakhani
When Google bought Nest, a maker of digital thermostats, for $3.2 billion just a few months ago, it was a clear indication that digital transformation and connection are spreading across even the most traditional industrial segments and creating a staggering array of... View Details
Keywords: Digital Innovation; Digitization; Industrial Internet; Technological Innovation; Production; Competitive Strategy; Engineering; Aerospace Industry
Iansiti, Marco, and Karim R. Lakhani. "Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business." Harvard Business Review 92, no. 11 (November 2014): 90–99.
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- 22 May 2014
- News
For Website Personalization, Simple Is the New Sexy
- July 2013
- Case
Sample6: Innovating to Make Food Safer
By: Robert F. Higgins and Kirsten Kester
Tim Curran, CEO of Sample6, a start-up biotechnology company developing a novel food safety diagnostics platform, must decide how to partner with food industry players. How can he best convince leaders in this mature industry to adopt a new technology and improve food... View Details
Keywords: Data Analytics; Food Safety; Biotechnology; Nutrition; Entrepreneurship; Product; Partners and Partnerships; Food; Technological Innovation; Business Startups; Governing Rules, Regulations, and Reforms; Product Development; Agribusiness; Information Technology; Globalization; Performance Improvement; Safety; Technology Adoption; Agriculture and Agribusiness Industry; Food and Beverage Industry; Biotechnology Industry; Information Industry; United States; Boston; Massachusetts
Higgins, Robert F., and Kirsten Kester. "Sample6: Innovating to Make Food Safer." Harvard Business School Case 814-014, July 2013.