Big data is a common term these days. Practically everyone has heard it at least once and even used it. But few look into the topic in depth to find out why some companies improve their performance by using big data analytics, while others fail to derive business value from big data analytics?

How can we use big data analytics effectively to maximize its value?

Our ZJUSOM professor LIU Yuan, PhD student ZHENG Yuzhu, and their team from the School of Management at Zhejiang University, recently published a paper entitled "Using Inside-Out and Outside-In Big Data Analysis on Electronic Platforms: Performance Impact and Heterogeneity Analysis". In it, they explain the micro-process of companies using big data analytics to create business value and lift the curtain between the use of big data analytics and the improvement of business performance.

Click  here  to access the journal article

Big Data has brought unprecedented opportunities and challenges that are driving global organizations to invest more in Big Data Analytics (BDA), especially in a turbulent business environment.

In the "information age", computers and internet technologies have transformed the way we live, work and study. Big data analytics should have led to a significant improvement in business performance, but the actual performance is not the same.

A study has shown that 91% of enterprises have not yet reached transformation maturity in the use of big data analysis, which means that they are not yet fully and effectively using big data analysis.

From an academic perspective, existing studies have examined the relationship between organizational performance and investment in big data analytics, big data analytics capabilities and the adoption of big data analytics technologies, but have neglected research on the use of big data analytics. At the same time, there is also a lack of sufficient empirical evidence on whether and how the use of big data analytics can create business value.

The question for enterprises is therefore how they can effectively use big data analytics and turn it into knowledge resources to achieve potential value Research on these questions is of great importance both in theory and in practice.

In practice, some companies have improved their performance through the use of big data analytics, but others have failed to derive business value from big data analytics. Why is this the case?

The Knowledge-Based View (KBV) theory states that an organization is a knowledge-processing system and the source of its core capabilities is the tacit knowledge within the organization.

As big data becomes an increasingly important strategic resource for enterprises, the big challenge for enterprises is whether they can use big data analytics to turn big data into knowledge and insights for decision making to create sustainable competitive advantage, which the knowledge-based view theory refers to as the ability of enterprises to acquire tacit knowledge.

In practice, the business benefits that companies derive from the use of big data analytics vary greatly. The main reason for this is that companies have different capabilities to use big data analytics effectively.

LIU Yuan’s team has found that the effective use of big data analytics is critical to creating business value for organizations. Based on the knowledge-based view, this article develops a micro-process for using big data analytics to create business value:

First, Big Data analytics transforms Big Data into valuable information through various analytical techniques. Then, organizations acquire knowledge assets by transforming information into knowledge through big data analytics. Finally, business value and competitive advantage is created by using the absorbed knowledge for planning, decision making, execution, management and learning.

Based on the theory of knowledge-based view, LIU Yuan’s team has elaborated the hierarchical structure of "data-information-knowledge-value", opening the "black box" between the utilization of big data and the value of enterprise performance, explaining the basic mechanism of using big data analytics to create enterprise value, and filling a major gap in the research on the utilization of big data analytics.

With the rapid development of digital technology, big data analysis is now being used in more and more areas of management. E-commerce platforms in particular, with their sophisticated digital infrastructure and diverse data sources, can offer enterprises on the platform extensive functions through big data analysis. Sophisticated big data analytics solutions usually combine data from multiple sources and offer a variety of analytical functions to meet complex practical requirements.

When data analysts use different types of big data analytics capabilities in operations management, they need to consider the complex impact that the combination of these big data analytics capabilities can have on business performance.

To unravel these complex influences, we first it is necessary to understand the different types of functions that big data analytics can perform.

The article innovatively divides the functions of big data analytics into two categories, namely:

Research conceptual model

It is clear that these two different big data analytics functions have different impacts on business performance, which raises questions:

Based on the theory of knowledge-based view, LIU Yuan’s team derived two impacts on performance when organizations use different types of big data analytics: individual impact and combined impact.

The research results show that on e-commerce platforms, the use of inside-out and outside-in big data analytics has a positive independent and joint impact on the sales performance of enterprises, but the degree of impact on different enterprises varies. The use of inside-out big data analytics has a greater impact on enterprises with a lower level of performance, while the use of outside-in big data analytics has a greater impact on enterprises with a higher level of performance.

It should be noted that the article uses the transaction index instead of the profit variables to measure the performance of enterprises on e-commerce platforms. Although this index can reflect the comprehensive level of sales in a more detailed and accurate way, it cannot reflect the profitability of enterprises.

Based on empirical studies, LIU Yuan’s team has made four suggestions for enterprises on the use of big data analysis:

- We extend our appreciation to Professor LIU Yuan for his invaluable contribution to the study of big data analytics, providing critical insights that bridge the gap between theory and practice in the field of business performance.

- You can read the original article in Chinese  here