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Show Results For
- All HBS Web
(1,913)
- News (289)
- Research (1,262)
- Events (23)
- Multimedia (10)
- Faculty Publications (741)
- Fall 2012
- Article
How 'Big Data' Is Different
Many people today in the information technology world and in corporate boardrooms are talking about "big data." Many believe that, for companies that get it right, big data will be able to unleash new organizational capabilities and value. But what does the term "big... View Details
Keywords: Big Data; Analytics; Mathematical Methods; Information Management; Information Technology Industry
Davenport, Thomas H., Paul Barth, and Randy Bean. "How 'Big Data' Is Different." MIT Sloan Management Review 54, no. 1 (Fall 2012).
- April 2020
- Case
Ment.io: Knowledge Analytics for Team Decision Making
By: Yael Grushka-Cockayne, Jeffrey T. Polzer, Susie L. Ma and Shlomi Pasternak
Ment.io was a software platform that used proprietary data analytics technology to help organizations make informed and transparent decisions based on team input. Ment was born out of founder Joab Rosenberg’s frustration that, while organizations collected ever... View Details
Keywords: Decision Making; Information Technology; Knowledge; Knowledge Acquisition; Knowledge Management; Operations; Information Management; Product; Product Development; Entrepreneurship; Business Startups; Communications Industry; Information Industry; Information Technology Industry; Web Services Industry; Middle East; Israel
Grushka-Cockayne, Yael, Jeffrey T. Polzer, Susie L. Ma, and Shlomi Pasternak. "Ment.io: Knowledge Analytics for Team Decision Making." Harvard Business School Case 420-078, April 2020.
- August 2018 (Revised April 2019)
- Case
Chateau Winery (A): Unsupervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers’ purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (A): Unsupervised Learning." Harvard Business School Case 119-023, August 2018. (Revised April 2019.)
- 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
Bojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing Missing Always at Random in Multivariate Data." Biometrika 107, no. 1 (March 2020): 246–253.
- May 2018
- Case
The Multiple Myeloma Research Foundation's Answer Fund
By: Richard G. Hamermesh and Matthew G. Preble
Keywords: Data Analytics; Customer Focus and Relationships; Customer Relationship Management; Cost vs Benefits; Investment Return; Health Care and Treatment; Innovation Leadership; Intellectual Property; Knowledge Sharing; Knowledge Dissemination; Leadership; Leading Change; Resource Allocation; Goals and Objectives; Marketing Communications; Performance; Programs; Projects; Business and Community Relations; Business and Stakeholder Relations; Networks; Partners and Partnerships; Research and Development; Genetics; Behavior; Motivation and Incentives; Social and Collaborative Networks; Nonprofit Organizations; Strategy; Health Industry; Pharmaceutical Industry; Biotechnology Industry; United States
- August 2015 (Revised May 2017)
- Case
TSG Hoffenheim: Football in the Age of Analytics
By: Feng Zhu, Karim R. Lakhani, Sascha L. Schmidt and Kerry Herman
In 2015, Dietmar Hopp, owner of Germany's Bundesliga football team TSG Hoffenheim and co-founder of the global enterprise software company SAP, was considering how to ensure long-term sustainability and competitiveness for TSG Hoffenheim. While historically a small... View Details
Zhu, Feng, Karim R. Lakhani, Sascha L. Schmidt, and Kerry Herman. "TSG Hoffenheim: Football in the Age of Analytics." Harvard Business School Case 616-010, August 2015. (Revised May 2017.)
- 07 Aug 2000
- Research & Ideas
Rocket Science Retailing
Marshall Fisher of the Wharton School at the University of Pennsylvania, Ananth Raman of HBS and their colleague Anna Sheen McClelland recently completed a survey of 32 retail companies focusing on their practices View Details
- 12 Jul 2010
- News
Rocket Science Retailing: A Practical Guide
- April 29, 2014
- Column
Corporate Reporting in the Big Data Era
By: George Serafeim
Advancements in information technology can improve corporate communication with shareholders, but not through incessant data dumps. Instead, companies will more likely be poised for continued success if they use digital platforms for long-term oriented engagement and... View Details
Keywords: Integrated Reporting; Big Data; Corporate Reporting; Sustainability; Corporate Social Responsibility; Corporate Governance; Accounting; Reporting; Organizational Change and Adaptation; Corporate Accountability; Analytics and Data Science; Information Technology; Communication; Financial Reporting; Business and Shareholder Relations
Serafeim, George. "Corporate Reporting in the Big Data Era." IIRC Blog (April 29, 2014).
- June 2012
- Article
A Reexamination of Tunneling and Business Groups: New Data and New Methods
By: Jordan I. Siegel and Prithwiraj Choudhury
One of the most rigorous methodologies in the corporate governance literature uses firms' reactions to industry shocks to characterize the quality of governance. This methodology can produce the wrong answer unless one considers the ways firms compete. Because... View Details
Keywords: Corporate Governance; Mergers And Acquisitions; Business Economics; Firm Organization; Firm Performance; Groups and Teams; Analytics and Data Science
Siegel, Jordan I., and Prithwiraj Choudhury. "A Reexamination of Tunneling and Business Groups: New Data and New Methods." Review of Financial Studies 25, no. 6 (June 2012): 1763–1798.
- Article
Private and Civil Society Governors of Mercury Pollution from Artisanal and Small-scale Gold Mining: A Network Analytic Approach
By: Kristin Sippl
Artisanal and small-scale gold mining (ASGM) is both a subsistence livelihood for millions of people and the leading source of mercury pollution globally. The United Nation’s 2013 Minamata Convention on Mercury aims to address this challenge, but such public regulatory... View Details
Keywords: Artisanal And Small-scale Mining (ASM); Private Governance; Gold; Mercury; Mining; Governance; Networks; Pollutants; Research
Sippl, Kristin. "Private and Civil Society Governors of Mercury Pollution from Artisanal and Small-scale Gold Mining: A Network Analytic Approach." Extractive Industries and Society 2, no. 2 (April 2015): 198–208.
- Research Summary
Reforming Social Science
By: Max H. Bazerman
Social science research affects all of us. When researchers learned organ donation rates are higher in countries where human organs are automatically available for donation unless you specifically “opt-out” of the system, as opposed to countries like the U.S., where... View Details
- February 2024
- Module Note
Data-Driven Marketing in Retail Markets
By: Ayelet Israeli
This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,... View Details
Keywords: Data; Data Analytics; Retail; Retail Analytics; Data Science; Business Analytics; "Marketing Analytics"; Omnichannel; Omnichannel Retailing; Omnichannel Retail; DTC; Direct To Consumer Marketing; Ethical Decision Making; Algorithmic Bias; Privacy; A/B Testing; Descriptive Analytics; Prescriptive Analytics; Predictive Analytics; Analytics and Data Science; E-commerce; Marketing Channels; Demand and Consumers; Marketing Strategy; Retail Industry
Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.
- 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.
- Web
Online Business Analytics Course | HBS Online
and can be completed in as little as nine months. How does Business Analytics differ from Data Science Principles View Details
- 2021
- Working Paper
Corporate Environmental Impact: Measurement, Data and Information
By: David Freiberg, DG Park, George Serafeim and T. Robert Zochowski
As an organization’s environmental impact has become a central societal consideration, thereby affecting industry and organizational competitiveness, interest in measuring and analyzing environmental impact has increased. We develop a methodology to derive comparable... View Details
Keywords: Environment; Impact; Measurement; Environmental Ratings; Corporate Valuation; Financial Materiality; Sustainability; Environmental Impact; Environmental Strategy; Impact-Weighted Accounts; IWAI; Environmental Sustainability; Corporate Social Responsibility and Impact; Measurement and Metrics; Valuation
Freiberg, David, DG Park, George Serafeim, and T. Robert Zochowski. "Corporate Environmental Impact: Measurement, Data and Information." Harvard Business School Working Paper, No. 20-098, March 2020. (Revised February 2021.)
- 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
- February 2016
- Article
Bridging Science and Technology Through Academic-Industry Partnerships
By: Sen Chai and Willy C. Shih
Partnerships that foster the translation of scientific advances emerging from academic research organizations into commercialized products at private firms are a policy tool that has attracted increased interest. This paper examines empirical data from the Danish... View Details
Keywords: Economic Development; Technological Change; Government Policy; Technological Innovation; Research and Development; Information Technology; Policy; Technology Industry; Denmark
Chai, Sen, and Willy C. Shih. "Bridging Science and Technology Through Academic-Industry Partnerships." Research Policy 45, no. 1 (February 2016): 148–158.
- October 2020
- Article
The Elasticity of Science
By: Kyle Myers
This paper identifies the degree to which scientists are willing to change the direction of their work in exchange for resources. Data from the National Institutes of Health are used to estimate how scientists respond to targeted funding opportunities. Inducing a... View Details
Myers, Kyle. "The Elasticity of Science." American Economic Journal: Applied Economics 12, no. 4 (October 2020): 103–134.
- March 2022 (Revised July 2022)
- Technical Note
Linear Regression
This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares, and how to... View Details
Keywords: Data Science; Linear Regression; Mathematical Modeling; Mathematical Methods; Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Linear Regression." Harvard Business School Technical Note 622-100, March 2022. (Revised July 2022.)