1. Introduction
In the era of big data, firms must effectively analyze complex data sets to inform strategic decision-making. Interactive data visualization has emerged as a powerful tool to address this challenge, enabling users to explore data, uncover insights, and communicate findings more effectively [1, 2]. However, the impact of interactive data visualization on decision-making processes in business intelligence remains understudied [3].
This research aims to analyze how interactive data visualization affects decision-making in business intelligence. It seeks to investigate the benefits, challenges, and best practices associated with the use of interactive data visualization tools within companies. The study will employ a mixed-methods approach, combining a literature review, case studies, and empirical research. The findings are expected to contribute to both theory and practice, providing insights into the role of interactive data visualization in business intelligence and offering guidance on how to effectively leverage these tools to support data-driven decision-making.
2. The Role of Data Visualization in Business Intelligence
2.1. The Importance of Data Visualization in Decision-Making
Data visualization determines efficient and effective decision-making by allowing users to understand and interpret complex data sets in seconds [4]. Through the graphical representation of this data in the form of charts, graphs, and dashboards, decision-making authorities can reveal the existing trends, patterns, and outliers that cannot be noticed in the raw data [5].
2.2. Types of Data Visualization Techniques
Data visualization techniques come in various forms, each suited for different types of data and applications. Some of the most common techniques include:
-Bar charts: Bar charts are used to compare categorical data, where each bar's height or length represents the category's value. They effectively display data with discrete values and highlight differences between categories [6].
-Line charts: Line charts are best suited for displaying trends and changes in data over time. They connect individual data points with lines, making it easy to see patterns, peaks, and valleys in the data [6].
-Pie charts: Pie charts are used to show the proportional composition of a whole, with each slice representing a category's relative size. While they can be visually appealing, pie charts are often criticized for being less effective than other techniques for accurate comparisons.
-Scatter plots: Scatter plots are used to display the relationship between two continuous variables, with each data point represented by a marker. They can reveal correlations, clusters, and outliers in the data [7].
-Heat maps: Heat maps use color intensity to represent the magnitude of values in a matrix or grid. They are effective for identifying patterns and hot spots in large, complex datasets [8].
-Treemaps: Treemaps display hierarchical data as nested rectangles, with the size of each rectangle proportional to the value it represents. They are useful for visualizing the relative sizes of categories and subcategories within a larger structure [9].
The choice of visualization technique depends on the nature of the data, the insights to be conveyed, and the intended audience. Interactive data visualizations often combine multiple techniques, allowing users to switch between different views and explore the data from various perspectives.
2.3. The Evolution of Data Visualization in Business Intelligence
Data visualization, nowadays, has transformed extensively in the times of computer and network technology progress and the growing complexity of business data. Once the static data represented graphically was all that people could do, now we can interact with the data using advanced dynamic applications that enable us to explore big data in real-time and draw more bottomless pictures.
3. Interactive Data Visualization
Interactive data visualization involves the utilization of graphics that enable the users to select, filter, and manipulate the data elements [10]. Interactive data visualization possesses vital features like user control, live response, and simultaneously finding data on different levels. Interactive data visualization enables users to investigate the data more intuitively and interactively. This type of visualization sought to uncover the hidden patterns and relationships that may not be apparent in static visualizations [11]. With people becoming able to see what is happening and answer questions on the spot, interactive data visualization contributes to data-based decision-making. It reduces the time and effort needed to analyze data.
Technology is advancing fast: Tableau, PowerBI, D3.js, and R Shiny are just a few available tools for generating interactive data visualizations [12]. These tools have different features, including but not limited to drag-and-drop interfaces, customizable dashboards, and interactive charts and graphs, that are essential as they simplify the process of creating and sharing interactive visualizations.
4. The Impact of Interactive Data Visualization on Decision-Making
4.1. Enhancing the Speed and Quality of Decision-Making
Interactive data visualization may do a lot for faster and better decision-making because it can offer users real-time access to essential data and enable them to play various scenarios and options [13]. Interactive data visualization, eliminating the cognitive overload related to comprehension of complex datasets, sharpens the mindset of the decision-makers towards implementing correct assumptions and taking action faster.
A prime example of this can be seen in the case of Procter & Gamble (P&G), a multinational consumer goods corporation. P&G implemented an interactive data visualization platform called "Decision Cockpit" to support its supply chain management decisions [14]. The platform integrates data from various sources and provides real-time, interactive dashboards for decision-makers.
Using the Decision Cockpit, P&G's managers can quickly identify potential supply chain disruptions and evaluate different scenarios to determine the best course of action. By leveraging interactive data visualization, P&G has been able to significantly improve the speed and quality of its supply chain decisions, reduce costs, and improve customer satisfaction [14].
This case study demonstrates how interactive data visualization can enhance decision-making by providing decision-makers with real-time access to critical data and enabling them to quickly evaluate different scenarios and make informed decisions, ultimately leading to better business outcomes.
4.2. Facilitating Collaboration and Communication among Stakeholders
Through interactive data visualization, it is possible to stimulate effective collaboration and communication among the stakeholders by providing a common vocabulary and platform for idle talk about data-specific insights [15]. Users can adequately share and annotate visualizations through interactive data visualization to cooperate correctly and make more effective decisions.
4.3. Enabling Data-Driven Decision-Making Culture in Organizations
By opening the data to more people who can make sense of it, interactive data visualization can lead the way to a style of management response to these data [16]. Visual and interactive data representation supports citizens and non-technical stakeholders in joining the data conversation and being part of the decision-making processes.
5. Challenges and Considerations in Implementing Interactive Data Visualization
5.1. Data Quality and Data Governance Issues
One of the most critical challenges that preclude the practical application of interactive data visualization is the accuracy and leadership in the data architecture of the original data. Incorrect, incomplete, or a departure type from the data may lead to misleading exhibits and wrong decision-making. Organizations must set up robust data governance structures to guarantee that the information used in the interactive visualization is reliable and without compromising integrity.
5.2. User Adoption and Training Challenges
Data visualization's other obstacle is the adoption of users and their training. Users can be chary of new visualization instruments and techniques, which are particularly unacceptable if used to the traditional reports. Companies have to offer users proper training and support for using interactive data visualization to benefit from.
5.3. Designing Effective and User-Friendly Visualization Interfaces
Interactive data visualization effectiveness is also based on the design of the visualization interface, which plays a significant role. Interfaces that are not well-designed can often confuse users, frustrate, and disengage. Apart from that, practices in data visualization design need to be followed, which comprises using appropriate visual encodings, providing clear labels and annotations, and keeping the interface intuitive and easy to navigate [17].
5.4. Balancing Interactivity and Cognitive Load
On the one hand, interactivity is one of the main advantages of interactive data visualization; on the other hand, excessive interactivity should be restricted by all means to avoid cognitive load. When interactivity becomes too excessive, users might tend to be overwhelmed and face the problem of mental overload, resulting in a less effective visualization. Designers must be very selective in creating easy-to-use and understandable visuals while providing users with help and support guidelines.
6. Best Practices and Future Directions
6.1. Integrating Interactive Data Visualization with Business Intelligence Workflows
The impact of interactive data visualization can be augmented by integrating it with the company's existing data processing/reporting processes and data delivery channels. This comprises the congruency of visualization tools and technological aids with the organization's data platform and ensuring they align with business objectives and decision-making processes.
6.2. Leveraging Advanced Visualization Techniques, Such as Visual Analytics and Data Storytelling
Furthermore, another application for organizations is the use of advanced visualization methods, including visual analytics and data storytelling, which further increase the impact of interactive data visualization. Visual analytics integrates interactive visualization and computational analysis to make the data explore and analyze more conveniently, and data storytelling uses storytelling techniques to help present data-driven observations more creatively and persuasively [2].
6.3. Evaluating the Effectiveness of Interactive Data Visualization through User Studies and Metrics
Organizations should combine user studies and measurements regularly to ensure the complete success of an interactive data visualization project. This covers asking for user feedback about the efficacy and practicality of the visualizations and tracking essential metrics like time to insight, decision accuracy, and user engagement [17, 18].
By conducting regular user studies and tracking relevant metrics, organizations can gain a deeper understanding of how interactive data visualization impacts decision-making processes and identify opportunities for improvement. This ongoing evaluation process is crucial for ensuring the long-term success and value of interactive data visualization initiatives in business intelligence contexts.
6.4. Emerging Trends and Future Research Directions in Interactive Data Visualization for Business Intelligence
While the sector of data visualization is rapidly gaining ground, there are several issues of emerging trends and future research aspects that organizations need to keep in mind. Among the most influential technological innovations in data visualization are virtual and augmented reality for truly immersive data experience, machine learning, artificial intelligence to enrich data exploration and analysis, and the development of co-learning and multimodal interfaces. It is incumbent upon researchers and practitioners to keep up to date with advancing trends and investigate these latest trends to maximize interactivity in business intelligence.
7. Conclusion
In this research, the advantages data visualization can have on decision-making in the BI community have been discussed, as well as on data exploration, the process of making decisions, and the collaboration of stakeholders. Nevertheless, data quality, information design skills, and the visualization interface are crucial elements for the effectiveness of interactive data visualization. Therefore, Organizations should carefully consider these obstacles and apply the best practices to harness the BI systems' full potential for data visualization. Although this line of research remains insightful, future studies might look at the long-term impacts linking interactive data visualization and organizational performance, explore the involvement of user experience design in visualization adoption, and examine the influence of new technologies like virtual and augmented realities on data visualization for business intelligence.
References
[1]. Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. Queue, 10(2), 30-55.
[2]. Kirk, A. (2016). Data visualisation: a handbook for data driven design. Sage.
[3]. Few, S. (2007). Save the pies for dessert. Visual business intelligence newsletter, 1, 1-14
[4]. Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media, Inc.
[5]. Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013). What makes a visualization memorable?. IEEE transactions on visualization and computer graphics, 19(12), 2306-2315.
[6]. Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
[7]. Friendly, M., & Denis, D. J. (2005). The early origins and development of the scatterplot. Journal of the History of the Behavioral Sciences, 41(2), 103-130.
[8]. Wilkinson, L., & Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63(2), 179-184.
[9]. Shneiderman, B. (1992, June). Tree visualization with tree-maps: 2-d space-filling approach. ACM Transactions on graphics (TOG), 11(1), 92-99.
[10]. Yi, J. S., ah Kang, Y., Stasko, J. T., & Jacko, J. A. (2007). Toward a deeper understanding of the role of interaction in information visualization. IEEE transactions on
[11]. Keim, D. A. (2002). Information visualization and visual data mining. IEEE transactions on Visualization and Computer Graphics, 8(1), 1-8.
[12]. Nair, L. R., Shetty, S. D., & Shetty, S. D. (2018). Interactive visual analytics on Big Data: Tableau vs D3. js. Journal of e-Learning and Knowledge Society, 14(4).
[13]. Kang, Y. A., Gorg, C., & Stasko, J. (2009, October). Evaluating visual analytics systems for investigative analysis: Deriving design principles from a case study. In 2009 IEEE Symposium on Visual Analytics Science and Technology (pp. 139-146). IEEE.
[14]. Camm, J. D., Unlu, F., Kuvvetli, O., & Pekny, J. (2020). Interactive data visualization for supply chain planning and management. In Supply Chain Engineering and Logistics Handbook: Inventory and Production Control (pp. 319-338). CRC Press.
[15]. Isenberg, P., Elmqvist, N., Scholtz, J., Cernea, D., Ma, K. L., & Hagen, H. (2011). Collaborative visualization: definition, challenges, and research agenda. Information visualization, 10(4), 310-326.
[16]. Davenport, T. H., & Kim, J. (2013). Keeping up with the quants: Your guide to understanding and using analytics. Harvard Business Press.
[17]. Kelleher, C., & Wagener, T. (2011). Ten guidelines for effective data visualization in scientific publications. Environmental Modelling & Software, 26(6), 822-827.
[18]. Lam, H., Bertini, E., Isenberg, P., Plaisant, C., & Carpendale, S. (2011). Empirical studies in information visualization: Seven scenarios. IEEE transactions on visualization and computer graphics, 18(9), 1520-1536.
Cite this article
Zhang,Q. (2024). The Impact of Interactive Data Visualization on Decision-Making in Business Intelligence. Advances in Economics, Management and Political Sciences,87,166-171.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. Queue, 10(2), 30-55.
[2]. Kirk, A. (2016). Data visualisation: a handbook for data driven design. Sage.
[3]. Few, S. (2007). Save the pies for dessert. Visual business intelligence newsletter, 1, 1-14
[4]. Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media, Inc.
[5]. Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013). What makes a visualization memorable?. IEEE transactions on visualization and computer graphics, 19(12), 2306-2315.
[6]. Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
[7]. Friendly, M., & Denis, D. J. (2005). The early origins and development of the scatterplot. Journal of the History of the Behavioral Sciences, 41(2), 103-130.
[8]. Wilkinson, L., & Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63(2), 179-184.
[9]. Shneiderman, B. (1992, June). Tree visualization with tree-maps: 2-d space-filling approach. ACM Transactions on graphics (TOG), 11(1), 92-99.
[10]. Yi, J. S., ah Kang, Y., Stasko, J. T., & Jacko, J. A. (2007). Toward a deeper understanding of the role of interaction in information visualization. IEEE transactions on
[11]. Keim, D. A. (2002). Information visualization and visual data mining. IEEE transactions on Visualization and Computer Graphics, 8(1), 1-8.
[12]. Nair, L. R., Shetty, S. D., & Shetty, S. D. (2018). Interactive visual analytics on Big Data: Tableau vs D3. js. Journal of e-Learning and Knowledge Society, 14(4).
[13]. Kang, Y. A., Gorg, C., & Stasko, J. (2009, October). Evaluating visual analytics systems for investigative analysis: Deriving design principles from a case study. In 2009 IEEE Symposium on Visual Analytics Science and Technology (pp. 139-146). IEEE.
[14]. Camm, J. D., Unlu, F., Kuvvetli, O., & Pekny, J. (2020). Interactive data visualization for supply chain planning and management. In Supply Chain Engineering and Logistics Handbook: Inventory and Production Control (pp. 319-338). CRC Press.
[15]. Isenberg, P., Elmqvist, N., Scholtz, J., Cernea, D., Ma, K. L., & Hagen, H. (2011). Collaborative visualization: definition, challenges, and research agenda. Information visualization, 10(4), 310-326.
[16]. Davenport, T. H., & Kim, J. (2013). Keeping up with the quants: Your guide to understanding and using analytics. Harvard Business Press.
[17]. Kelleher, C., & Wagener, T. (2011). Ten guidelines for effective data visualization in scientific publications. Environmental Modelling & Software, 26(6), 822-827.
[18]. Lam, H., Bertini, E., Isenberg, P., Plaisant, C., & Carpendale, S. (2011). Empirical studies in information visualization: Seven scenarios. IEEE transactions on visualization and computer graphics, 18(9), 1520-1536.