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Decline in Dashboards





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The decline of the dashboard. Data stories, (not dashboards) will become the most widespread way of consuming analytics by 2025, and 75% of these stories will be automatically generated using augmented analytics techniques. AI and machine learning techniques are making their way into business intelligence platforms. In dashboards, users have to do a lot of manual work to dive into further insights. But these data stories provide the insights without requiring the user to perform their analysis.

Decline in Dashboards

In recent times the world has experienced quite numerous technological innovations, especially in data analysis. The developments and innovations in the analysis of data stories have been quite effective and seek to replace the conventional dashboards. According to Verbert et al., (2013, p. 1501), a dashboard is a graphical user interface that is strategically designed to provide at a glance the KPI (key performance indicators) of a business or entire company. Dashboards are mostly accessed through browsers that are regularly updated such as Google Analytics (Ledford et al., 2009). In recent times, technological innovations such as Artificial intelligence (AI) and machine learning have greatly revolutionized the data analysis process and provided better solutions that do not require manual procedures like the traditional systems that necessitated constant human input (Jivet et al., 2017, p. 86).

Digital dashboards have been in use for a long-time helping managers in data analysis and also assisting managers in monitoring the performance of the organizations (Verbert et al., 2013, p. 1506). Dashboards have been a crucial tool enabling managers to evaluate and monitor performance through visual graphics and simplified data presentation tools. The traditional dashboards require great understanding, and skills to deal with the complexities of modern-day data.

In an attempt to overcome the shortcomings of dashboards in data analysis, researchers have come up with automated processes such as machine learning and artificial intelligence that don’t require human intervention to analyze. During the Gartner symposium, Rita Sallam proposed that by 2025, 75% of data stories will be automatically generated using augmented analytical techniques such as machine learning and artificial intelligence (Benaissa et al., 2020). Machine learning is the study of computer algorithms and statistical models to establish trends. Machine learning is a form of Artificial intelligence and allows software applications to become more accurate without being programmed to do so (El Naqa & Murphy, 2015, p. 4). Machine learning is a crucial tool in data analysis since it frees the data analyst to focus on more value-adding activities to the company.

Artificial intelligence is one of the most recent technological innovations that has played quite a crucial role in transforming the data analysis sector. Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to perform basic human functions. According to (Dubey et al., 2020) AI is the data science that makes use of advanced algorithms thus enabling computers to run on their own. On the other hand, data analysis is a process of turning raw data into meaningful and actionable insights. In recent times, scholars have integrated artificial intelligence in the data analysis process thus enabling computers to perform data analysis and come up with solutions without human intervention.

In data analysis, artificial intelligence has been immensely embraced and utilized in performing various analytical works. A case example is data stories where artificial intelligence and machine learning are programmed to conduct accurate analysis and also make decisions based on the analysis. Artificial intelligence and machine learning bring about the following advantages to data analytics (Dillenberger et al., 2019);

  1. Accelerates data preparation,

  2. Automates generation of insights,

  3. Allows data querying,

  4. Empowers everyone to use analytics, and

  5. Automates the report automation and dissemination process.

Artificial intelligence with machine learning plays a crucial role in data analysis only requiring initial human input. Human intervention is required to feed machine learning algorithms samples (Silva & Fonseca, 2018, p. 312). The computerized systems then determine trends and learn from the training data and use it as a benchmark for future data analysis. Using AI-guided systems for data analysis is crucial since it enables the user to automatically clean, visualize, explain and finally visualize the data.

In recent times, businesses have realized the importance of AI and greatly embraced it in obtaining accurate detailed insights, automating analytical processes, and making data-dependent decisions. Researchers believe that artificial intelligence is one of the greatest accomplishments that will greatly revolutionize the data analysis processes and increase efficiency in the management and decision-making process for businesses and companies.

Critiques have also come forward to criticize the use of artificial intelligence in business analytics. Nonetheless, artificial intelligence is one of the greatest innovations of all time that will augment the monitoring and evaluation process for businesses worldwide (Zhao et al., 2019). AI’s capabilities are poised aimed at augmenting analytical processes and thereby enabling companies to internalize data-driven decisions. In addition, the process is aimed at enabling all individuals in an organization to easily deal with data. AI in data analysis plays a crucial role in democratizing data across the organization and thereby saves data analysts and other users from spending time on repetitive manual processes. Based on the numerous advantages artificial intelligence has over dashboards, I also agree with Gartner that 75% of the stories will be automatically generated using augmented analytic techniques.


Benaissa, K., Bitam, S., & Mellouk, A. (2020). BSM-data reuse model based on in-vehicular computing. Applied Sciences10(16), 5452. https://doi.org/10.3390/app10165452

Dillenberger, D. N., Novotny, P., Zhang, Q., Jayachandran, P., Gupta, H., Hans, S., Verma, D., Chakraborty, S., Thomas, J. J., Walli, M. M., Vaculin, R., & Sarpatwar, K. (2019). Blockchain analytics and artificial intelligence. IBM Journal of Research and Development63(2/3), 5:1-5:14. https://doi.org/10.1147/jrd.2019.2900638

Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organizations. International Journal of Production Economics226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599

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Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. Data-Driven Approaches in Digital Education, 82-96. https://doi.org/10.1007/978-3-319-66610-5_7

Ledford, J., Teixeira, J., & Tyler, M. (2009). Google analytics (3rd ed.).

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Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist57(10), 1500-1509. https://doi.org/10.1177/0002764213479363

Zhao, Y., Li, T., Zhang, X., & Zhang, C. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges, and the future. Renewable and Sustainable Energy Reviews109, 85-101. https://doi.org/10.1016/j.rser.2019.04.021

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