Big Data Investment, Skills, and Firm Value

Abstract

T his paper analyzes how labor market factors have shaped early returns on big data investment using a new data source-the LinkedIn skills database. The data source enables firm-level measurement of the employment of workers with technical skills such as Hadoop, MapReduce, and Apache Pig. From 2006 to 2011, Hadoop investments were associated with 3% faster productivity growth, but only for firms (a) with significant data assets and (b) in labor markets where similar investments by other firms helped to facilitate the development of a cadre of workers with complementary technical skills. The benefits of labor market concentration decline for investments in mature data technologies, such as Structured Query Language-based databases, for which the complementary skills can be acquired by workers through universities or other channels. These findings underscore the importance of geography, corporate investment, and skill acquisition channels for explaining productivity growth differences during the spread of new information technology innovations.

Big Data‐Savvy Teams’ Skills, Big Data‐Driven Actions and Business Performance

“…The characteristics of big data (e.g. definition, volume, variety, velocity, complexity) and the usefulness of big data analytics are well documented in the literature (Cohen et al, 2009;Davenport and Patil, 2012;Sheng, Amankwah-Amoah and Wang, 2017;Tambe, 2014). However, little is known about the links between the use of big data skills in multi-disciplinary teams, big data-driven (BDD) actions and business performance -a gap that this paper aims to fill.…”

“…Unfortunately, our understanding of the linkages between the use of BDS teams' skills and business performance is severely limited. Studies from different domains such as information technology (IT) (Katal, Wazid and Goudar, 2013;LaValle et al, 2011;Liben-Nowell and Kleinberg, 2007), information management and business intelligence (Chen, Chiang and Storey, 2012), machine learning (Lantz, 2015;Wu et al, 2008) and operations management (Davenport and Patil, 2012;Schoenherr and Speier-Pero, 2015;Tambe, 2014;Tan et al, 2015) have emphasized the importance of specific big data skills and capabilities for organizational success. For instance, those researchers highlighted the benefits of the effective use of Hadoop technology (Tambe, 2014), text mining (Liben-Nowell and Kleinberg, 2007), algorithms and statistical techniques (Wu et al, 2008) for business success.…”

Influence of Big Data on Manufacturing Industry and Strategies of Enterprises: A Literature Review

“…Hence, it is hard for manufacturing firms to get the comprehensive information of consumers, and that is doubtless a barrier for them to create value of data [3]. Not only cannot enterprises obtain the whole data they need, but their abilities also cannot grasp, manage and analyze such highvolume and high-variety big data [6,24,26,29,30]. Furthermore, there may be semi-structured or unstructured data does not fit with the type their techniques can process [29].…”

“…Although there are still a lot of challenges in the face of the enterprises in the big data era, there will be abundant benefits to them because of investing in the techniques relevant big data [30,33].…”

Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data

“…"Businesses appear to be on the cusp of a data-driven revolution in management. Firms capture enormous amounts of fine-grained data on social media activity, RFID tags, web browsing patterns, consumer sentiment, and mobile phone usage, and the analysis of these data promises to produce insights that will revolutionise managerial decision-making" (Tambe, 2014(Tambe, , p. 1452. These finegrained data play an additional economic function: generating wishful content and unwitting meta-data surrounding main content (Orlikowski, 2015;Kallinikos et al, 2010).…”