Big Data Analytics and Firm Productivity
- A Literature Review
In the last decade, firms have made significant investments in acquiring the technology and human resources necessary to make full use of the capabilities of big data analytics (BDA). Through a systematic review of 94 published studies, this report analyzes the concept of BDA, explores its diverse impact on firm productivity, and offers practical insights for firms and policymakers to leverage the full potential of BDA.
Over the last decade, a growing body of research has examined the adoption and impact of big data analytics (hereafter BDA) for organizations. The research has been somewhat fragmented and has frequently fallen short of delivering clear and tangible findings that would be beneficial for businesses and policymakers. This has been attributed to the conceptual opacity surrounding the term BDA, and the fact that research has used and examined different performance indicators and mechanisms. This study aims, through applying a systematic literature review of 94 published studies over the past ten years, to provide a conceptual analysis of the concept of BDA, examine the impact that BDA has on firm productivity through different mechanisms and provide actionable insight for firms and policymakers.
BDA is a distinct component of the digital transformation process
BDA not only represents and defines data by specific attributes but also encompasses the underlying technologies supporting data management, analysis, and decision processes. Furthermore, it influences the organizational structure of firms and the skill sets of individuals involved in the process. BDA represents a novel paradigm of digital transformation, requiring investments in technological infrastructure, employee training and education in data handling, analytical techniques, and data-driven management and operational decision-making. The research community is also discussing the distinction between BDA and Artificial Intelligence (AI). BDA aims to make sense of data for predictions and decision-making, whereas AI develops applications that emulate human-like intelligence and behavior.
BDA has a positive effect on firm productivity
The analysis of past studies highlights that investing in BDA can produce annual gains of 3-7 percent in firm productivity. The effects of BDA are more pronounced in industries that are highly competitive or more technologically oriented. In noncompetitive industries, no significant effects are observed. Other studies show that there is a positive and significant effect between the adoption of BDA and innovation growth and that firms that leverage BDA in their operations realized a 10-25% performance improvement in comparison with key competitors. Gains are mostly found in more technologically oriented industries, such as the service sector, the manufacturing industry, and IT/technology industries.
Productivity gains from BDA are often realized indirectly
Another important finding in from the empirical studies emphasize that the value of BDA to firm productivity is often realized indirectly through improvements in organizations’ operational or competitive strategies. These are realized through different “mechanisms” such as enhanced decision processes, improved operational efficiencies, more accurate forecasting, and cost reduction. The productivity gains are contingent on how efficient firms are in utilizing these. By effectively monitoring and optimizing internal operations, sensing changing customer beliefs and requirements, and perceiving emerging opportunities and competitive actions, BDA can be a strategic tool for firms to make data-driven decisions. Thus, it is important to understand the organizational shifts that BDA enables and how those indirectly impact productivity and value-generation.
The effects of BDA are contingent upon industry and application uses
Studies suggest that the malleability of BDA depends on both internal and external factors. BDA applications can be applied to a wide range of industries, and their ability to create value depends on both the specific industry in which they are implemented and the processes they are intended to automate or enhance. For instance, certain industries that use physical devices to generate and collect data can more closely monitor and optimize processes. In addition, firms that operate in the same industry can have vastly different uses for their BDA investments, resulting in differentiating productivity gains.
Lag effects in realizing productivity gains from BDA investments
Following the adoption of BDA projects, according to studies it typically takes at least one year, with an average of two years, before most firms begin to witness tangible improvements in terms of performance gains. This delay differs from the adoption of other technologies that these firms commonly embrace. The lag effects are attributed to the multifaceted requirements of BDA, which necessitate substantial preparatory work, experimentation, and testing before its applications can be effectively integrated into operations.
Investments in complimentary skills and organizational structure increase BDA-adoption
One of the most important findings from the study of academic literature, is that generating value through BDA requires more than just investing in technological infrastructure. It also involves building organizational capability, often referred to as BDA capability. This includes necessary investments in data management and technology infrastructure, ensuring that employees at all levels have the required knowledge and skills, and establishing an organizational structure and strategic direction that recognizes data as a fundamental resource.
When companies view BDA as a central strategic initiative that permeates the entire organization, it promotes transparency and access to critical data sources, which ensures that projects can leverage the technologies optimally.
To unlock the full potential of productivity growth and value creation, three complementary drivers of BDA adoption should be taken into account: individuals' knowledge and skills, organizational barriers at the firm level, and facilitators at the industry level. Individuals’ skillset in working with BDA is in high demand and frequently constitutes a lack of competence among employees in small and medium enterprises (SMEs). This requires more on-the-job training opportunities, a reevaluation of educational curricula to foster cross-disciplinary skills, and incentives to develop practical experience through industry-related projects.
Many firms, particularly SMEs, encounter substantial barriers when attempting to adopt BDA. These barriers primarily revolve around insufficient funding for technological infrastructure, as well as a shortage of expertise and available time for experimentation. At the industry level, some firms operating in less dynamic industries have been slower to adopt BDA, primarily due to the difficulty of identifying the tangible benefits of implementing such technologies. It has been demonstrated that stimulus and support incentives can be instrumental in boosting the adoption of BDA.
More data and analyses in specific contexts are needed
Despite the recent increase in the number of empirical studies on the value of BDA, there remains a significant gap in terms of context-specific insights into the productivity effects of BDA and access to objective data sources. Currently, there is no large-scale study using data from Swedish firms that provides a comprehensive understanding of how these technologies affect specific sectors or through which key performance indicators they influence them.
For a better understanding of how policy measures can boost BDA adoption and, in turn, enhance the productivity of Swedish firms, future studies should prioritize analyzing these effects while considering investments, not only in technology, but also in other complementary resources like human capital and educational programs. As more data becomes accessible, it would be beneficial for more studies to assess the value of BDA adoption systematically, evaluating its impact on different aspects of productivity and the underlying mechanisms.