The Importance of Storytelling in Business Intelligence

The Importance of Storytelling in Business Intelligence

Richard T. Herschel (Decision and System Sciences Department, Saint Joseph's University, Philadelphia, PA, USA) and Nicolle Clements (Decision and System Sciences Department, Saint Joseph's University, Philadelphia, PA, USA)
Copyright: © 2017 |Pages: 14
DOI: 10.4018/IJBIR.2017010102
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This paper examines the relevance and importance of storytelling to business intelligence. Business intelligence provides analytics to inform a decision-making process. However, there are often times issues with understanding the analytics presented and contextualizing the analytics to the overall decision-making process. This paper examines these issues and then assesses the value of storytelling in conveying BI findings. The SOAP protocol used for physician/patient clinical encounters is presented to illustrate the benefit of providing structure to a decision-making process. The importance of structure to storytelling is then further examined and shown to help facilitate the communication of data insights. Media richness is also examined to reveal the impact that it has on BI storytelling, helping to explain why visualizations have become so important to BI. An example of BI storytelling with a high level of media richness is then presented. The paper concludes that storytelling is inextricably linked to BI success.
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Today, firms compete in an economy where big data is generated by people and devices at unprecedented quantities and this data is being scrutinized with analytics to drive data-driven decision-making. Organizations have the capability to store and analyze data generated from such sources as Internet data, online surveys, consumer data, location data, image data, supply chain data, and device data (e.g., sensors, RFID). By combining data from these various sources, organizations can use this data to predict people’s likely behavior with astonishing accuracy.

New technologies and applications such a Hadoop, tablets, cloud computing, software as a service (SaaS), crowdsourcing, in-data analytics, dashboards, and solid-state drives enable new channels for data capture and business intelligence (BI) evaluation capabilities. Minelli, Chambers, & Dhiraj (2013) note these technologies and applications generate more data with less expensive and faster hardware and software, thereby creating a fundamental transformation: the ability to do real-time analyses (BI) on complex data sets and models. The result is that we live in a hyper knowledge-driven economy where there is constant pressure to react to new information.

A great deal of the data now being captured is unstructured text that tends to grow exponentially. This is especially true in social media platforms. Firms try to apply analytics to this text to evaluate it to enable deeper and broader descriptive and descriptive insights. However, this is an imperfect science and oftentimes employee intervention is required to try to ascertain understanding and value from this information.

At the same time, while BI-related technology is radically changing the way data is captured, stored, processed, analyzed, and consumed, human beings continue to play a vital role. Oftentimes reports are created that provide dashboard visualizations that inform employee understanding about what is happening so they can monitor critical activities and decide if they need to act. Moreover, the rule-based techniques themselves must be constantly evaluated and assessed by employees to insure their ongoing accuracy, timelines, relevance, continuity, and value. These processes enhance an organization’s ability to make informed decisions.

The point is, BI and Big Data create new benefits for businesses and their employees. There are improved operational efficiencies, including reduced risks and costs, time savings, reduced complexity, and new customer self-service capabilities. Companies can increase their revenues in many ways, including selling to microtrends, improved customer targeting, enriched customer experiences, and via enhanced fraud detection capabilities. They can also realize greater competitive differentiation derived from new product design optimization capabilities by employing new channels for offering new services, by enhanced product market targeting activities, and through new online customized customer experiences.

Figure 1.

Business intelligence/business analytics breakdown (Klimberg & Miori, 2010)


Figure 1 presents a vision of BI as an integrative application of technologies, models, techniques, and practices. In Miori and Klimberg’s (2010) framework, each of the three circles of the Venn diagram represent applications that had previously been considered quite distinct, which include (1) information systems and technology, (2) statistics, and (3) operation research/management science.

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