Big Data Analytics and Visualization of Performance of Stock Exchange Companies Based on Balanced Scorecard Indicators

Big Data Analytics and Visualization of Performance of Stock Exchange Companies Based on Balanced Scorecard Indicators

Iman Raeesi Vanani, Maziar Shiraj Kheiri
DOI: 10.4018/978-1-5225-3142-5.ch029
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One of the major concerns of managers at stock exchange companies is the maximum and efficient use of limited resources to meet the unlimited users' demands and in particular, investors and company owners. Achieving this goal gets more complex everyday due to the changing environment and multidimensional economic pressures. It is necessary that managers know the process of effective data oriented measurement in every single aspect of a successful business. One of the most accredited and useful methods for evaluating performance is the Balanced Scorecard (BSC). In this chapter, researchers have focused on providing a model that evaluates the performance of companies based on a combination of BSC indicators and big data analytics and algorithms. The chapter's purpose is to indicate which analytics algorithms are most appropriate for each BSC indicator based on a deep review of broad literature as a measurement guideline for future researchers and practitioners.
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Balanced Scorecard (BSC) and algorithms for Big Data analytics are key tools to the implementation of an effective organizational evaluation strategy. The main advantage of Balanced Scorecard (BSC) is that it not only focuses on short term financial goals but it also helps to achieve the non-financial objectives to better achieve the strategic goals. A comprehensive evaluation that is reliant on BSC has four main aspects:

  • 1.

    Financial: Profitability and value created for shareholders.

  • 2.

    Customer: Success in the market in which the company operates based on customer satisfaction.

  • 3.

    Internal Business Processes: Operations within an organization that create value for customers.

  • 4.

    Learning and Growth: The capabilities of people and systems that support internal operations.

The conceptual framework of this research is based on a literature review of BSC oriented papers and books as well as helpful Big Data analytics algorithms utilized for analyzing the BSC indicators measurement. As a result, a rich table is developed for connecting the BSC indicators to possible Big Data algorithms. A simple brief table has been provided here to create a preliminary perception on the final outcome that is a much more in-depth endeavor useful for scholars and practitioners in the field.


Literature Review

The concept of the Balanced Scorecard (BSC) was first presented in the early 1990s. By 2000, some surveys indicated that a majority of firms in the United States, the United Kingdom and Scandinavia used scorecards or at least intended to do so soon. Others, like Bain’s management tools survey indicated a slight drop in usage to 36% but with a high average satisfaction with the tool. The number of software packages for scorecards on the market is growing and now exceeds 100. In only 10 years, the idea of the BSC has certainly made its mark. At the same time there are reports of high failure rates. We have seen firms abandon their scorecard efforts. Others are struggling against the perception of the BSC as ‘just another three-letter fad’ propagated by consultants such as Total Quality Management (TQM), Business Process Reengineering (BPR), and Activity-Based Costing (ABC).

Developing the scorecards usually makes people see their company and its business model in a new way. This often leads to new ideas about the company’s vision and to a reconsideration of its strategy. Scorecards help to introduce strategic thinking into planning and control. The task of each business unit has to be agreed upon and related to the overall purpose of a corporation. To provide strategic direction and to monitor progress, corporations with a strong common identity will usually want to introduce scorecards from the top. Strategic guidance is possible also in other ways and other types of controls can be used. If the strategic role is sufficiently clear, then parts of a corporation may embark on scorecard projects for their internal benefit. There are also cases where only the corporate level uses scorecards. This may be because the firm is in an early stage of its scorecard project and will later extend its use to successive levels and units.

Performance measurement systems that translate an organization's strategic goals into a set of interlinked financial and non-financial objectives are commonly used in organizations. The focus on multidimensional, long term performance is consistent with the international emergence of “strategic management accounting” as a major contributor to strategy formation and achievement (Cadez & Guilding, 2008). An example of a multidimensional performance evaluation system is the Balanced Scorecard (Kaplan & Norton, 1996, 2000).

Key Terms in this Chapter

Data Quality: Data quality refers to the fitness of data with respect to a specific purpose of usage. Data quality is critical to confidence in decision making. As data are more unstructured and collected from a wider array of sources, the quality of data tends to decline (Lee, 2017 AU148: The in-text citation "Lee, 2017" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Big Data Analytics: Big Data Analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions and support decision making ( Cao, M. et al., 2015 ).

Balanced Scorecard (BSC): The Balanced Scorecard (BSC) is a managerial tool that defines the current and potential status of the organization based on specific and targeted objectives and measurements. BSC goes beyond typical performance measurement to be a popular strategic management tool that has been used widely ( Rigby & Bilodeau, 2015 ).

Predictive Analytics: Predictive analytics include statistical models and other empirical methods that are aimed at creating empirical predictions (as opposed to predictions that follow from theory only), as well as methods for assessing the quality of those predictions in practice (i.e., predictive power). Aside from their practical usefulness, predictive analytics play an important role in theory building, theory testing, and relevance assessment ( Shmueli & Koppius, 2011 ).

Data Identification: Data identification refers to records that link two or more separately recorded pieces of information about the same individual or entity. When data are structured, identification is easy ( Zhang, J. et al., 2015 ).

Big Data: Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing the way businesses operate in many industries (Lee, 2017 AU147: The in-text citation "Lee, 2017" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Data Mining: Data mining (DM) is a computer-based information system (CBIS) devoted to scan huge data repositories, generate information and discover knowledge. DM pursues to find out data patterns, organize information of hidden relationships, structure association rules, and estimation unknown items’ values. DM outcomes represent a valuable support for decision making ( Pena-Ayala, 2013 ).

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