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By: Jacob L. Cybulski (jacob.cybulski@deakin.edu.au) and Morteza Namvar (mory.namvar@deakin.edu.au)
Department of Information Systems and Business Analytics, Deakin University, Australia

Introduction

Many modern organisations face constant environmental volatility and change, and thus the creation of massive amounts of corporate data. To master this change, many organisations try to turn huge volumes of data into genuine business understandings. Consequently, such organisations can base their decisions on insights derived from facts – a process known as business intelligence (BI). BI consists of several capabilities: capturing, storing and integration of data; data analytics; and insight creation and presentation (Sabherwal and Becerra-Fernandez, 2009). BI is also one of the most important and most underutilized aspects of modern decision-support systems (Chen et al., 2012). Davenport (2010), for example, claims that BI is often limited to simple decision support for short-term operations. However, Sabherwal and Becerra-Fernandez (2009) suggest BI could enhance managers’ understanding of business, foster organisational learning and support decision-making. Along these lines, we propose to use the theory of organizational sensemaking (Namvar et al., 2016; Namvar and Cybulski, 2014), defined by Weick as a structured process of removing ambiguity and uncertainty from executive decision-making (Weick, 1995; Weick et al., 2005).

Weick discusses sensemaking at organisational and individual levels and considers it to be characterised by seven unique properties: the sensemaker’s capacity for retrospection (giving meaning to past events), cue extraction (detection of important events), dealing with plausibility (inexactness and fuzziness of observations), the social and ongoing nature of sensemaking, as well as the willingness to create and enact identity (capacity for sensemaking).

This study aimed to explore how BI and business analytics technologies could assist decision-makers in a continuous process of sensemaking to gain business insights.

Results and discussion

Focusing on decision-makers’ practice and lived experience using BI to improve organisational sensemaking, we undertook 23 detailed interviews with 27 participants from 17 organisations. In order to untangle a confounding web of socio-technical context and personal opinion, we used hermeneutic phenomenology (Van Manen, 1998) as an overarching theoretical perspective and method of inquiry. From the analysis of interview transcripts, a model of BI-based sensemaking emerged (Figure 1).
In traditional (non BI-driven) organisations, sensemaking provides a framework for comprehending organisational enactment, where exchanges between actors (enactment) in their environments (ecological change) are made meaningful (selection) and preserved as lessons (retention). BI in this context alters the process of meaning creation and recognises the primacy of data-based identity for individuals and organisations.

The collaboration of BI stakeholders and their interaction with BI tools ultimately leads to an evolution of BI identity at individual and organisational levels, supporting shared knowledge retention and individual experience (the ‘identity’, ‘enactment’ and ‘selection’ loops). Through individual experience and shared knowledge, BI can improve decision-making and indirectly help to enact business objectives (feedback from ‘identity’ to ‘enactment’) on external environments. In a modern, digital environment, ecological change starts with the creation of new data, as well new business activities. At the same time, enactment starts the reflective process of meaning (‘selection’), which feeds into strengthening BI identity.

At organisational levels, stronger BI identity can be associated with process improvement, and in particular new requirements for better BI-related processes (‘new needs’). Organisational BI identity helps in the development of workplace practices and culture. It further leads to scope refinement for BI use, aligning business processes with BI requirements, BI tool based on real needs and expectations, and the integration of data definition with business rules (‘evolution of organisational identity’).

At individual levels, interaction with BI provides the chance to gather experience, knowledge, attitudes and skills that help define an individual’s role (‘evolution of individual identity’). Through BI self-service, end users in modern BI settings (e.g. executive decision-makers) can interact with high-level business reports, rather than relying on specialist data analysts. More individualised reports can be generated at relevant levels, which lets end users view and explore data directly and immediately, eventually to generate new insights and actions effectively and efficiently.
However, decision-makers must establish clear communication in groups and with data analysts (who may better understand data and its concepts; e.g. statistical and mathematical grounding). This communication not only leverages decision-makers’ BI identity, but also educates data analysts about business rules. It can lead, gradually, to a shared understanding of business needs and expectations, and help define the scope of BI in an organisation.

Then, when turning fact-based decisions into action, organisations can collect more data and provide their BI systems with higher quality data (‘ongoing updating’). This helps with updating data sources, preparing the right data for analytics and improving data quality. Thanks to data-driven meaning creation (the link between ‘BI’ and ‘selection’), as well as leveraging experience (the link between ‘identity’ and ‘selection’), decision-makers can rely on extracted environmental cues and plausible outcomes, to develop backward- and forward-looking views of the organisation.
BI systems that incorporate data for ongoing ecological change also allow users to reflect on the past, and filter incoming data to extract cues via summaries, charts and alerts for busy decision-makers or those needing (or willing) to justify their actions. Prediction models and self-service BI motivate wary decision-makers to interact with information, and articulate insights that should yield improved decisions. Lastly, a collaborative BI platform helps decision-makers to share insights and learn from each other (see the interaction of all feedback loops).

Conclusions

The research reported in this paper brings together two distinct theoretical fields – sensemaking and enterprise decision-making. It shows how the two approaches can be put to use, with sound applications of BI and analytics. Insights derived from BI provide benefits to organizations in support of their decision-making processes.

References

Chen, H., Chiang, R.H.L., Storey, V.C., 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Q. 36, 1165–1188.
Davenport, T.H., 2010. Business Intelligence and Organizational Decisions. Int. J. Bus. Intell. Res. IJBIR 1, 1–12.
Namvar, M., Cybulski, J., 2014. BI-based Organizations: A Sensemaking Perspective. Presented at the International Conference on Information Systems (ICIS), New Zealand.
Namvar, M., Cybulski, J., Perera, L., 2016. Using business intelligence to support the process of organizational sensemaking." Commun Assoc Inf Syst. 38(1), Paper 20.
Sabherwal, R., Becerra-Fernandez, I., 2009. Business intelligence. Wiley.
Van Manen, M., 1998. Researching lived experience: Human science for an action sensitive pedagogy, 2nd ed. The Althouse Press, Ontario, Canada.
Weick, K.E., 1995. Sensemaking in organizations. Sage Publications, Inc.
Weick, K.E., Sutcliffe, K.M., Obstfeld, D., 2005. Organizing and the process of sensemaking. Organ. Sci. 16, 409–421.

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