Business analytics: Why now and what next?

Business analytics is a revolution that is impossible to miss. At its core, business analytics is about leveraging value from data. Instead of being referred to as the ‘sludge of the information age,’ data has recently been deemed ‘the new oil.’ While data can be employed for purposes such as detecting new opportunities, identifying market niches, and developing new products and services, it is also notoriously amorphous and hard to extract value from. In this Guest Editors’ Perspective, we first present a structural framework for deriving value from business analytics. Extracting value from data requires aligning strategy and desirable behaviors to business performance management in conjunction with analytic tasks and capabilities. We then introduce three special articles that provide in-depth insights regarding how business analytics is being employed in the management of healthcare, accounting, and supply chains.     

The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources

Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.     

Big data analytics and firm performance: Effects of dynamic capabilities

Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed.     

Implementing big data strategies: A managerial perspective

Despite considerable recent advances in big data analytics, there is substantial evidence that many organizations have failed to incorporate them effectively in their own decision-making processes. Advancing the existing understandings, this article lays out the steps necessary to implement big data strategies successfully. To this end, we first explain how the big data analytics cycle can provide useful insights into the characteristics of the environments in which many organizations operate. Next, we review some common challenges faced by many organizations in their uses of big data analytics and offer specific recommendations for mitigating them. Among these recommendations, which are rooted in the findings of strategy implementation research, we emphasize managerial responsibilities in providing continued commitment and support, the effective communication and coordination of efforts, and the development of big data knowledge and expertise. Finally, in order to help managers obtain a fundamental knowledge of big data analytics, we provide an easy-to-understand explanation of important big data algorithms and illustrate their successful applications through a number of real-life examples.     

Correcting analytics maturity myopia

Companies face increasing pressure to compete in the practice of analytics and strive for analytics maturity to sustain their competitive advantage. A single-minded, narrow focus on gaining analytics maturity, however, leads to analytics maturity myopia. Based on our studies of analytical capabilities and numerous conversations with executives and managers, we offer a scorecard for organizations to identify the presence of analytics maturity myopia and propose a framework for organizations to correct this issue. The proposed framework partially explains the mixed and conflicting results regarding the relationship between analytics maturity and business value found in the literature. Specifically, we recommend that companies focus on three factors that are critical to realizing value from analytics initiatives: (1) a balanced view of value to different stakeholders, (2) a continuous expansion of the business ecosystem beyond current stakeholders to identify and pursue new opportunities, and (3) use of an emergent strategy to take advantage of unexpected opportunities and develop organizational agility.     

Exploring a Measurement of Analytics Capabilities

Changes in the volume and velocity of data have led many organizations to consider assessing and improving analytics capabilities. The purpose of this research is to describe a methodology developed to assess organizations’ analytics capabilities and explore the empirical value of data collected using this methodology. The measurement for analytics capabilities was developed by IBM during 2009–11 marketing efforts. To assess the data’s empirical value, we investigate whether measurements of analytics capabilities are internally consistent, associated with decisions to invest in analytics software and hardware, and able to explain firm profitability. In analyzing consistency, we find a natural sequence in the development of analytics capabilities. Exploring decisions to invest in analytics, we discover that firms with higher levels of capabilities are more likely to invest, as are firms that are larger and located in more profitable industries. However, we find no relationship between analytics capabilities and firm profitability.     

How talent capability can shape service analytics capability in the big data environment?

We know very little about how big data-driven service analytics capabilities (SAC) are built in data-driven service organizations and the potential role of talent capability in facilitating overall SAC and the impact of both on firm performance (FPER). Drawing on the dynamic capabilities (DC) approach, this study investigates the link between SAC and FPER examining the mediating role of talent capability and the moderating influence of a firm’s strategic alignment. On the basis of two Delphi studies and survey data from 267 service analysts in the US and France, the findings show that even though SAC are built on technology, talent and information capabilities, their overall impact on firm performance is mediated by the level of talent capability of service analytics managers. The findings also confirm the critical moderating impact of strategic alignment between dynamic talent capability and firm performance in the big data environment.     

Leadership succession planning for today’s digital transformation economy: Key factors to build for competency and innovation

Digital transformation is rapidly changing the competitive landscape and the war on talent for today’s organizations. As part of this economy, organizations and their HR units must continuously reevaluate leadership structures and practices that exploit core competencies while allowing for innovation (i.e., leadership ambidexterity) and incorporate big data with predictive analytics. In this vein, understanding how HR executives can create better solutions around this problem remains sparse. Specifically, what frameworks can HR executives apply to identify potential alignment failures in leadership succession planning in light of newer emerging markets? What internal decision-making traps need to be recognized? Finally, what specific forms of data and evidence must test these plans for relevance and recharge and renew the talent-to-strategy pipeline? In this article, we examine these questions by reviewing the gaps in the literature and identifying through our four-step model how organizations can incorporate ambidexterity-building as a leadership succession planning practice.     

Comparing Business Intelligence and Big Data Skills

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.     

Big data dreams: A framework for corporate strategy

The phenomenon of big data—large, diverse, complex, and/or longitudinal data sets—is having a stark influence on organizational strategy making. An increase in levels of data and technological capabilities is redefining innovation, competition, and productivity. This article contributes to both practical strategic application and academic research in the strategic management domain by presenting a framework that identifies how big data improves functional capabilities within organizations, shapes entirely new industries, and is a key component of innovative and disruptive strategies used by learning organizations to diversify and break down barriers of traditionally defined industries. This framework provides an appropriate basis for internal corporate strategy discussions that surround big data investments by explaining how firms create value through various approaches. In addition, we offer guidance for how firms might derive their own big data approach through the merits of aligning data strategy aspirations with data strategy authenticity.     

Applications of business analytics in healthcare

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics: the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently to improve healthcare delivery and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and ways in which analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.     

Cloud computing capability: its technological root and business impact

Cloud computing can help organizations create business value for long-term viability and sustainability by providing flexibility and versatility. We report a systematic analysis of the central role of cloud computing capability in bridging the information technology (IT) features of cloud computing and its business value. We posit that the IT features of cloud computing lead to measureable increase in business value on both dimensions of performance benefit and collaboration benefit through cloud computing capability. Survey data collected from 174 firms largely support our hypotheses. This study offers fine-grained insights into the mechanisms of how the IT features of cloud computing influence the business value stemming from cloud computing. Firms should focus more on cultivating organizational capabilities to effectively deploy cloud computing in order to harvest the benefits promised by cloud computing.     

Data Science,Predictive Analytics,and Big Data in Supply Chain Management: Current State and Future Potential

While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large‐scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large‐scale survey, and offer insight into its future potential via the discussion of how a research university is training next‐generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.     

Big Data and Information Processing in Organizational Decision Processes

Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing mechanisms and their complementary relationship to data-centric mechanisms. Third, it demonstrates how information processing theory can be applied to assess the dynamics of mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.     

Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research

Increased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression‐based process for analyzing big data.     

A strategic approach to workforce analytics: Integrating science and agility

Workforce analytics is a major emerging trend in human resource management. Yet, despite the enthusiasm, there exists a misunderstanding of how organizations can successfully use workforce analytics to achieve important organizational outcomes. This article proposes ways to overcome this execution dilemma and achieve organizational success with workforce analytics through the integration of agile development with scientific research. We use a number of company examples to outline five key parts of an agile workforce analytics process: (1) prioritizing issues, (2) integrating deductive and inductive approaches, (3) preparing and validating data, (4) applying multiple methods in concert to support decisions, and (5) transforming insight into action to improve business outcomes.     

Click Here for a Data Scientist: Big Data,Predictive Analytics,and Theory Development in the Era of a Maker Movement Supply Chain

Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.     

Revisiting customer analytics capability for data-driven retailing

Customer analytics is one of the most dominant strategic weapons in today's competitive retail environment. In spite of its strategic importance, there is scant attention to investigating customer analytics capabilities in the retail context. Drawing on a systematic literature review and thematic analysis, this study proposes a multidimensional customer analytics capability model by identifying relevant dimensions and sub-dimensions in retail settings. The principal contribution of this study is that the model links a customer analytics perspective to a resource-based view (RBV)-capability of the retailers by proposing six customer analytics capability dimensions and twelve sub-dimensions in the spectrum of market orientation and technology orientation. The customer analytics capability dimensions depict three crucial themes of marketing, such as value creation (offering capability and personalization capability), value delivery (distribution capability and communication capability), and value management (data management capability and data protection capability). By incorporating this capability dimensions, practitioners will likely be able to engage customers and enhance customer equity.     

Advanced technologies and international business: A multidisciplinary analysis of the literature

Advanced digital technologies, such as the Internet of Things, blockchain, big data analytics and augmented reality, are gradually transforming the way multinational firms do business. Due to the extent of this transformation many scholars argue that the integration of these technologies marks the commencement of the fourth industrial revolution (Industry 4.0). However, the question how these advanced technologies impact international business activities needs further attention. To this end, we adopt a multidisciplinary approach to review the related literature in international business (IB), general management, information systems, and operations research. We include the two latter fields, because advanced technologies have received more attention in these bodies of literature. Based on our analysis, we discuss the implications of these technologies for international business. Further, we highlight the drivers of technology utilisation by multinational firms and likely outcomes. We also provide future research avenues.     

知识动态能力视角的电商平台大数据分析价值链战略

电子商务领域信息技术的开放性提高了服务创新的可见性,为适应多维度要素交互的平台竞争环境,需要基于电商平台丰富的数据资源,通过大数据分析提高战略规划的动态性、灵活性和响应敏捷性,形成一种能够快速集合与组织资源的模式,实现业务价值传递的可持续,满足竞争对抗与互动以及时间轴动态演化的需要。鉴于此,基于大数据分析、知识管理、动态能力、业务流程理论和指向性网络调查数据,构建大数据分析价值链战略研究模型,探讨大数据分析、动态能力、流程级创新与核心竞争力及战略绩效之间的关联。研究结果表明,大数据分析能实现有效的内生源和外生源知识管理,帮助企业形成动态能力,构建核心竞争力,进而提高战略绩效;大数据分析能提高企业组织的灵活性,可作为企业在竞争中赖以生存发展的战略投资;外生源知识管理和内生源知识管理均可单独运作产生知识动态能力,但外生源知识管理的作用更显著有效,更值得重视;知识共享是流程级创新的潜在障碍,与合作商进行知识共享需要选择合适的路径;动态能力既可直接影响流程级创新与核心竞争力,也可调节知识资产对竞争力的影响。总之,大数据分析能够通过影响动态能力和流程级创新来提高核心竞争力,且动态能力在知识管理与流程级创新及核心竞争力(战略绩效)间具有中介作用,电商平台应客观认识大数据分析潜在价值,将之纳入信息技术战略,通过梳理大数据分析→动态能力→核心竞争力→战略绩效的价值链过程,形成战略协同,最终提高知识创新的边际绩效。