Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?
TL;DR: Evidence that the effect of DDD on the productivity do not appear to be due to reverse causality is found, providing some of the first large scale data on the direct connection between data-driven decision making and firm performance.
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.
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Book•
13 May 2011
TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
Abstract: The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
4,700 citations
13 Feb 2013
TL;DR: It is argued that there are good reasons why it has been hard to pin down exactly what is data science, and that to serve business effectively, it is important to understand its relationships to other important related concepts, and to begin to identify the fundamental principles underlying data science.
Abstract: Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.
1,023 citations
Cites background from "Strength in Numbers: How Does Data-..."
...Economist Erik Brynjolfsson and his colleagues from MIT and Penn’s Wharton School recently conducted a study of how DDD affects firm performance.(3)...
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TL;DR: A new kind of digital divide is created in the use of data-based knowledge to inform intelligent decision-making in developing countries by long-standing structural shortages in the areas of infrastructure, economic resources and institutions.
Abstract: Big Data for Development: A Review of Promises and Challenges Martin Hilbert, University of California, Davis; hilbert@ucdavis.edu Author’s version Hilbert, M. (2016). Big Data for Development: A Review of Promises and Challenges. Development Policy Review, 34(1), 135–174. http://doi.org/10.1111/dpr.12142 Abstract The article uses a conceptual framework to review empirical evidence and some 180 articles related to the opportunities and threats of Big Data Analytics for international development. The advent of Big Data delivers the cost-effective prospect to improve decision-making in critical development areas such as health care, economic productivity, and security. At the same time, all the well-known caveats of the Big Data debate, such as privacy concerns and human resource scarcity, are aggravated in developing countries by long-standing structural shortages in the areas of infrastructure, economic resources, and institutions. The result is a new kind of digital divide: a divide in data-based knowledge to inform intelligent decision- making. The article systematically reviews several available policy options to foster the opportunities and minimize the risks. Keywords: Big Data, decision-making, innovation, ICT, digital divide, digital, international development. Acknowledgements: The author thanks International Development Research Centre Canada (IDRC) for commissioning a more extensive study that laid the groundwork for the present article. He is also indebted to Manuel Castells, Nathan Petrovay, Francois Bar, and Peter Monge for food for thought, and to Matthew Smith, Rohan Samarajiva, Sriganesh Lokanathan, and Fernando Perini for helpful comments on draft versions, and thanks the United Nations Economic Commission for Latin America and the Caribbean (UN-CEPAL), where part of the research was undertaken. The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.
458 citations
Cites background from "Strength in Numbers: How Does Data-..."
...Brynjolfsson et al. (2011) found that US firms that adopted Big Data Analytics have output and productivity that is 5– 6% higher than their other investments and information technology usage would lead analysts to expect....
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01 Jul 2014
TL;DR: It is argued against the assertion that theory no longer matters and some new research directions are offered that can allow business analysts and researchers to achieve frequent, controlled and meaningful observations of real-world phenomena.
Abstract: article i nfo Available online xxxx The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of new data col- lection technologies, advanced data mining and analytics support, there seems to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts in- clude social networks and blogs, political discourse, corporate announcements, digital journalism, mobile tele- phony, home entertainment, online gaming, financial services, online shopping, social advertising, and social commerce. The changing costs of data collection and the new capabilities that researchers have to conduct re- search that leverages micro-level, meso-level and macro-level data suggest the possibility of a scientifi cp aradigm shift toward computational social science. The new thinking related to empirical regularities analysis, experimen- tal design, and longitudinal empirical research further suggests that these approaches can be tailored for rapid acquisition of big data sets. This will allow business analysts and researchers to achieve frequent, controlled and meaningful observations of real-world phenomena. We discuss how our philosophy of science should be changing in step with the times, and illustrate our perspective with comparisons between earlier and current re- search inquiry. We argue against the assertion that theory no longer matters and offer some new research directions.
300 citations
Cites background from "Strength in Numbers: How Does Data-..."
...[18] have pointed out that firms that emphasize decision-making based on business analytics have higher performance in productivity, asset utilization, return on equity and market value....
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TL;DR: In this article, the authors bring together diverse perspectives, coming from different geographical locations with different core research expertise and different affiliations and work experiences, to share the data analytics opinions and perspectives of the authors relating to the new opportunities and challenges brought forth by the big data movement.
Abstract: "Big Data" as a term has been among the biggest trends of the last three years, leading to an upsurge of research, as well as industry and government applications. Data is deemed a powerful raw material that can impact multidisciplinary research endeavors as well as government and business performance. The goal of this discussion paper is to share the data analytics opinions and perspectives of the authors relating to the new opportunities and challenges brought forth by the big data movement. The authors bring together diverse perspectives, coming from different geographical locations with different core research expertise and different affiliations and work experiences. The aim of this paper is to evoke discussion rather than to provide a comprehensive survey of big data research.
266 citations
References
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Posted Content•
TL;DR: In this paper, the authors developed an evolutionary theory of the capabilities and behavior of business firms operating in a market environment, including both general discussion and the manipulation of specific simulation models consistent with that theory.
Abstract: This study develops an evolutionary theory of the capabilities and behavior of business firms operating in a market environment. It includes both general discussion and the manipulation of specific simulation models consistent with that theory. The analysis outlines the differences between an evolutionary theory of organizational and industrial change and a neoclassical microeconomic theory. The antecedents to the former are studies by economists like Schumpeter (1934) and Alchian (1950). It is contrasted with the orthodox theory in the following aspects: while the evolutionary theory views firms as motivated by profit, their actions are not assumed to be profit maximizing, as in orthodox theory; the evolutionary theory stresses the tendency of most profitable firms to drive other firms out of business, but, in contrast to orthodox theory, does not concentrate on the state of industry equilibrium; and evolutionary theory is related to behavioral theory: it views firms, at any given time, as having certain capabilities and decision rules, as well as engaging in various ‘search' operations, which determines their behavior; while orthodox theory views firm behavior as relying on the use of the usual calculus maximization techniques. The theory is then made operational by the use of simulation methods. These models use Markov processes and analyze selection equilibrium, responses to changing factor prices, economic growth with endogenous technical change, Schumpeterian competition, and Schumpeterian tradeoff between static Pareto-efficiency and innovation. The study's discussion of search behavior complicates the evolutionary theory. With search, the decision making process in a firm relies as much on past experience as on innovative alternatives to past behavior. This view combines Darwinian and Lamarkian views on evolution; firms are seen as both passive with regard to their environment, and actively seeking alternatives that affect their environment. The simulation techniques used to model Schumpeterian competition reveal that there are usually winners and losers in industries, and that the high productivity and profitability of winners confer advantages that make further success more likely, while decline breeds further decline. This process creates a tendency for concentration to develop even in an industry initially composed of many equal-sized firms. However, the experiments conducted reveal that the growth of concentration is not inevitable; for example, it tends to be smaller when firms focus their searches on imitating rather than innovating. At the same time, industries with rapid technological change tend to grow more concentrated than those with slower progress. The abstract model of Schumpeterian competition presented in the study also allows to see more clearly the public policy issues concerning the relationship between technical progress and market structure. The analysis addresses the pervasive question of whether industry concentration, with its associated monopoly profits and reduced social welfare, is a necessary cost if societies are to obtain the benefits of technological innovation. (AT)
22,566 citations
Book•
01 Jan 1998
TL;DR: The definitive primer on knowledge management, this book will establish the enduring vocabulary and concepts and serve as the hands-on resource of choice for fast companies that recognize knowledge as the only sustainable source of competitive advantage.
Abstract: From the Publisher:
The definitive primer on knowledge management, this book will establish the enduring vocabulary and concepts and serve as the hands-on resource of choice for fast companies that recognize knowledge as the only sustainable source of competitive advantage. Drawing on their work with more than 30 knowledge-rich firms, the authors-experienced consultants with a track record of success-examine how all types of companies can effectively understand, analyze, measure, and manage their intellectual assets, turning corporate knowledge into market value. They consider such questions as: What key cultural and behavioral issues must managers address to use knowledge effectively?; What are the best ways to incorporate technology into knowledge work?; What does a successful knowledge project look like-and how do you know when it has succeeded? In the end, say the authors, the human qualities of knowledge-experience, intuition, and beliefs-are the most valuable and the most difficult to manage. Applying the insights of Working Knowledge is every manager's first step on that rewarding road to long-term success. A Library Journal Best Business Book of the Year. "For an entire company...to have knowledge, that information must be coordinated and made accessible. Thomas H. Davenport...and Laurence Prusak... offer an elegantly simple overview of the 'knowledge market' aimed at fulfilling that goal.... Working Knowledge provides practical advice about implementing a knowledge-management system....A solid dose of common sense for any company looking to acquire -- or maintain -- a competitive edge."--Upside, June 1998
10,791 citations
"Strength in Numbers: How Does Data-..." refers background in this paper
...Online firms such as Amazon, eBay, and Google also rely heavily on field experiments as part of a system of rapid innovation, utilizing the high visibility and high volume of online customer interaction to validate and improve new product or pricing strategies....
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TL;DR: In this paper, the authors assume that firms invest in R&D not only to generate innovations, but also to learn from competitors and extraindustry knowledge sources (e.g., university and government labs).
Abstract: The authors assume that firms invest in R&D not only to generate innovations, but also to learn from competitors and extraindustry knowledge sources (e.g., university and government labs). This argument suggests that the ease of learning within an industry will both affect R&D spending, and condition the influence of appropriability and technological opportunity conditions on R&D. For example, they show that, contrary to the traditional result, intraindustry spillovers may encourage equilibrium industry R&D investment. Regression results confirm that the impact of appropriability and technological opportunity conditions on R&D is influenced by the ease and character of learning. Copyright 1989 by Royal Economic Society.
7,980 citations
TL;DR: In this paper, the authors consider structural inertia in organizational populations as an outcome of an ecological-evolutionary process and define structural inertia as a correspondence between a class of organizations and their environments.
Abstract: Considers structural inertia in organizational populations as an outcome of an ecological-evolutionary process. Structural inertia is considered to be a consequence of selection as opposed to a precondition. The focus of this analysis is on the timing of organizational change. Structural inertia is defined to be a correspondence between a class of organizations and their environments. Reliably producing collective action and accounting rationally for their activities are identified as important organizational competencies. This reliability and accountability are achieved when the organization has the capacity to reproduce structure with high fidelity. Organizations are composed of various hierarchical layers that vary in their ability to respond and change. Organizational goals, forms of authority, core technology, and marketing strategy are the four organizational properties used to classify organizations in the proposed theory. Older organizations are found to have more inertia than younger ones. The effect of size on inertia is more difficult to determine. The variance in inertia with respect to the complexity of organizational arrangements is also explored. (SRD)
6,425 citations
TL;DR: In this article, the authors combine the concept of weak ties from social network research and the notion of complex knowledge to explain the role of weak links in sharing knowledge across organization subunits.
Abstract: This paper combines the concept of weak ties from social network research and the notion of complex knowledge to explain the role of weak ties in sharing knowledge across organization subunits in a...
5,947 citations