Building Organizational Resilience: A Data Driven Approach

Decision-making in emergency situations or dealing with a disaster, crisis or disruption may involve a significant degree of complexity and uncertainty in terms of unknown or changing circumstances and time pressure. Decision making is the ability to react to information about the environment and organizational factors, and choosing or consider one of the few different alternatives available. Through a data driven approach, organizations can better predict incidents, identify vulnerable areas of a business in the case of disaster, analyze how effective the current recovery process works and develop ways to restore systems, critical infrastructure and human resources faster.?

All decisions, especially those made under time pressure, need to be considered carefully since they could have an impact on a series or chain of subsequent events (Khorram-Manesh, Berlin and Carlstr?m, 2016). It is all too easy to confuse or mix up complicated and complex. Despite having many moving parts, complicated systems follow a highly predictable pattern. Consequently, in a complicated environment outcomes are relatively easy to model. Complex environments on the other hand, involve many unknowns and are composed of many interacting or interrelated components or factors. Outcomes of complex systems or complex environments are therefore unpredictable.?

The complexity of a system or environment is influenced or determined by three factors, namely multiplicity (the number of interacting elements), interdependencies (how closely those elements are linked) and diversity (the degree of heterogeneity of the elements). Complexity increases with multiplicity, interdependence and diversity (Sargut and McGrath, 2011). In complex environments, even small changes can have unanticipated or unintended consequences or outcomes. The Cynefin framework groups problems that leaders or executives are facing on the basis of cause and effect relationships into four categories, namely simple, complicated, complex and chaotic. If a situation can not be assigned to one of these four categories, or when it is unclear which of the four is predominant, a situation is labeled as “disorder”. The Cynefin framework is useful in providing context and can assist leaders in seeing and thinking through situations from a new or different perspective (Snowden and Boone, 2007).

The first step in decision-making or managing an emergency situation or dealing with a disaster, crisis or disruption is gaining an understanding of the full extent of what’s happening. Situational awareness provides context and an enhanced understanding of what the internal and external environment look like and how events and actions may potentially impact the realization of an organizations mission or goals, both now and in the future. On the most basic level, situational awareness is about developing a clear understanding about what is happening in the environment and organization, and what this means in terms of possible outcomes or scenarios (Endsley, 1988). Seeing the bigger picture of what the external environment looks like is critical in making informed strategic, tactical and operational decisions. In today's modern world, focusing efforts to mitigate the impacts and protect against the complex, interacting, or compounding threats and hazards necessitates a deeper understanding of the social, economic, and environmental systems that surround us. Compound events are two or more events that occur simultaneously or sequentially, for instance a hurricane during a world-wide pandemic. Combinations of events and their underlying conditions may amplify their impact, resulting in enormously complex challenges. Individually these events may not be significant, but when combined, they can lead to an extreme event or have a significant impact on the organization (Seneviratne et al, 2012).

Seeing and understanding the breath of information, relationships and causations gives organizations the opportunity to predict and decide on their next move (Cotgreave, 2021; Girard, 2019; Deoras, 2020). Situational awareness is difficult to achieve in emergency situations or when dealing with a disaster, crisis, or disruption because there are so many things happening at the same time, things are changing quickly, different cause and effect relationships may be plausible, and impacts and potential impacts may be unclear and unpredictable. In other words, these situations are inherently “VUCA”: volatile, uncertain, complex and ambiguous (Tiefenbacher, 2019). Analytics and business intelligence can help predict what might happen by analyzing different incident scenarios. Adopting data analytics can help making more informed decisions in the event of an emergency or dealing with a disaster, crisis situation or disruption.

Data-driven decision-making is the process of using data to inform and validate your decision-making process before committing to a course of action (Stobierski, 2019). Data-driven analytical methods can be categorized into three types: descriptive, predictive and prescriptive. Descriptive analytics provides insights into the “what happened” and “what is happening” and uses current and historical data. Predictive analytics uses historical data to construct a mathematical model that captures key trends. After that, the predictive model is applied to current data to predict "what will happen next.” Prescriptive analytics takes things a step further by using data to predict where things might go and how the system might react to different scenarios. Prescriptive analytics is supporting decision-makers by informing them about the potential consequences of their decisions and recommending actionable strategies aimed toward improving business performance (Bi?er, Tarakci and Kuzu, 2022).?

A better understanding of causality and fundamental uncertainties arising from complex system dynamics and interdependencies can be gained by examining alternative explanations using probability. Historical data combine with statistical probability based modeling can help identify trends and patterns and predict future outcomes (Walsh, 2020). Probabilistic reasoning can assist us in determining the most likely outcomes of events and the best course of action, even when those events are determined by an infinitely complicated set off variables. Rare ("black swan") events pose special challenges for those attempting to make sense of complex systems because they do not repeat themselves frequently enough for us to learn how they will affect the system. The ultimate objective of preparing for low probability/high consequence events is to concentrate on the effects of risk realization and the organization's capacity and capability to withstand risk realization, rather than trying to simulate the probabilities of these events.?

The facts or details from which information is derived are referred to as data. Individual pieces of data are rarely useful or informative on their own. For data to become information useful to inform decision making, it must be placed in context. Most companies have taken steps to go from being data-aware to data-driven and use analytics tools and business intelligence to understand what the data is telling them and provide actionable insights. A wealth of data is however often not being utilized in a meaningful way, which can hinder or slowdown decision making, including decision making in the event of an emergency or dealing with a disaster, crisis situation or disruption. Business Continuity Management in organizations has often a focus on the need to protect data in the event of a business interruption. However, data also plays an important role in making plans and strategic, tactical and operational business continuity decisions. Through a data driven approach, organizations can better predict incidents, identify vulnerable areas of a business in the case of disaster, analyze how effective the current recovery process works and develop ways to restore systems, critical infrastructure and human resources faster.?

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