2011 StrengthinNumbersHowDoesDataDri
- (Brynjolfsson et al., 2011) ⇒ Erik Brynjolfsson, Lorin Hitt, and Heekyung Kim. (2011). “Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?.” Social Science Research Network (SSRN). doi:10.2139/ssrn.1819486
Subject Headings: Data-Driven Decision Making.
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Abstract
We examine whether firms that emphasize decision making based on data and business analytics (“data driven decision making” or DDD) show higher performance. Using detailed survey data on the business practices and information technology investments of 179 large publicly traded firms, we find that firms that adopt DDD have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage. Furthermore, the relationship between DDD and performance also appears in other performance measures such as asset utilization, return on equity and market value. Using instrumental variables methods, we find evidence that the effect of DDD on the productivity do not appear to be due to reverse causality. Our results provide some of the first large scale data on the direct connection between data-driven decision making and firm performance.
1. Introduction
How do firms make a better decision? Today, organizational judgment is in the midst of a fundamental change - from a reliance on a leader’s “gut instinct” to increasingly data-based analytics. At the same time, we have been witnessing a data revolution; firms gather extremely detailed data and propagate knowledge from their consumers, suppliers, alliance partners, and competitors. In particular, since 1993, most large companies have invested in large enterprise resource planning (ERP), Supply Chain Management (SCM), Customer Relationship Management (CRM) and similar enterprise information technology (Aral et al., 2006; McAfee, 2002). These systems collect terabytes of detailed data on operations, suppliers, customers and other aspects of the businesses, increasing the amount of data by 10-fold to 1000-fold. Mobile phones, automobiles, factory automation systems and other devices are routinely instrumented to generate streams of data on their activities, making possible an emerging field of “reality mining” to analyze this information (Pentland and Pentland, 2008). Manufacturers and retailers use RFID tags to deliver terabits of data on inventories and supplier interactions and then feed this information into analytical models to optimize and reinvent their business processes. Similarly, clickstream data and keyword searches collected from websites generate a plethora of data, making visible interactions and patterns that previously could only be guessed at.
According to economic theory, as information becomes more fine-grained and current, decisionmakers should optimally put more weight on it and the overall quality of decision should improve on average, changing from intuitive management to more numbers-driven decision-making. As a Microsoft researcher memorably put it, objective, fine-grained data are now replacing HiPPOs (Highest Paid Person’s Opinions) as the basis for decision-making at more and more companies (Kohavi et al., 2009). Managers conduct active experiments with their new business ideas and base their decisions on scientifically valid data. It is common for companies to purchase a “business intelligence” module to try to make use of the flood of data that they now have on their operations. From banks such as PNC, Toronto-Dominion, and Wells Fargo to retailers such as CKE Restaurants, Famous Footwear, Food Lion, Sears, and Subway to online firms such as Amazon, eBay, and Google, firms test many business ideas through a randomized test before launch, called as “information-based strategy” (Davenport, 2009). The innovative process in an online business is now being transformed by the information-based strategy.
While there is a great deal of anecdotal evidence of firms’ using data to gain a competitive edge in the business press and popular books (See e.g. Davenport and Harris, 2007; Ayres, 2008; Loveman, 2003), there has been virtually no systematic data analysis of the productivity effects of data-driven decisionmaking (or DDD) using statistical methods. We seek to address this gap by examining in detail business practices and the information technology investments of 179 publicly traded large firms in the US. We find that DDD can explain a 5-6% increase in their output and productivity, beyond what can be explained by traditional inputs and IT usage. DDD is also associated with significantly higher profitability and market value. While these correlations are consistent with the case evidence, as well as economic theory, econometrics alone cannot rule out the possibilities of reverse causality or omitted variables bias. However, our basic findings remain robust when we use instrumental variables and explore a number of alternative variables that might explain our results. To the best of our knowledge, this is the first study to report a large scale econometric analysis of the relationship between DDD and firm performances.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2011 StrengthinNumbersHowDoesDataDri | Erik Brynjolfsson Lorin Hitt Heekyung Kim | Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? | 10.2139/ssrn.1819486 | 2011 |