Sustainable growth in the aviation industry needs data-driven decision-making

The COVID-19 virus has spread worldwide without acknowledging borders. It has?impacted all industries, all sectors, and all aspects of our lives with devastating blows to economies, financial losses,?and significant uncertainties. Aviation has been one of the hardest hit industries. International air traffic had reduced by more than 60% in 2020 as compared to 2019, and as you can see in the image below, the financial impacts are astounding.?

This industry’s recovery is going to be slow amid various pandemic waves and lockdowns, and it’s projected that it’s going to take two to three years for global traffic to return to 2019 levels.

The aviation industry is a highly resource- and capital-intensive industry, and it’s quite evident that the traditional ways of decision-making will not work going forward, whether that is capital investments for aircraft purchases or airport expansion or maintenance, new route development, operational efficiencies, or asset or resource utilization and allocation, to name a few.

This is where data-driven decision-making is so critical in aviation. But before we get into that model, let’s begin by looking at the traditional organizational decision-making models.

?Organizational decision-making?refers to making choices among alternative courses of action — which may also include inaction. Therefore, increasing effectiveness in decision-making is an important part of maximizing effectiveness in business. There are various decision-making models that can help organizations make effective decisions, including:?

1.?????Rational decision-making model. Follows a general approach, such as defining the problem, identifying criteria for decision making, listing possible solutions, evaluating alternatives, and determining the best solution.

?2.?????Bounded rationality decision-making model. You can use bounded rationality when you don't have enough time or information to follow the full rational decision-making model. Sometimes it's better to have a good enough decision sooner vs. a "perfect" decision that's delayed.

?3.?????Vroom-Yetton decision-making model. The first part of this model uses seven yes-or-no questions. The answers to these questions then guide you toward one of five decision-making processes to use. Options range from making the decision based on what you know now without consulting your team to reaching a group consensus with your team.

?4.?????Intuitive decision-making model. Intuitive decision-making model yields good results when you're dealing with areas where you have a lot of expertise or experience. But in the current environment where there are so many moving pieces, this might not produce the desired organizational results. Nevertheless, it’s quite surprising that this model is still extensively used.?

?5.?????Recognition-primed decision-making model. This is quite similar to the intuition-based model. Like the intuitive model, the recognition-primed model works best in situations where you can draw on deep experience or expertise. In those cases, it's an especially handy model to use when you are pressed for time.

Now that we have gone through various decision-making models (which are quite subjective in nature), lets talk about the data-driven decision-making (DDDM) models.

First let’s start with what does it mean to be “data-driven?” This term describes a decision-making process that involves collecting data, extracting patterns and facts from those data, and utilizing those facts to make inferences that influence decision-making.

Data-driven decision making (or DDDM)?is the process of making organizational decisions based on actual data rather than intuition or observation alone. Data-based business decisions arise from a complex process that involves people, relationships, analytics, culture, software, and problem-solving.

Having data on-hand empowers you to answer crucial business questions such as: “why?”, “how to sell?”, “how to grow your business?”, “how to understand customers?”, “are customers going to buy your new product?”, “where to invest?”, “what differentiates your product?”, etc.

Data-driven aviation organizations can gain a range of benefits such as: a better understanding of the market, which new routes to start, greater customer acquisition and retention, operational efficiencies in terms of turn-around-times and on-time performance, lower costs, higher profitability, and improved pricing.

The following diagram outlines the key steps followed in a data-driven decision-making model.

Not all organizations have implemented DDDM. As with all organizational changes, some businesses and industries are quicker to adapt to new methods. Within the aviation industry, we are seeing many different companies adopting DDDM in their operations. Some characteristics of aviation organizations that effectively implement DDDM are:?

·??????They understand the data very well. They know where the data came from, its quality, ?the best data analysis methods, how reliable the data is, how to measure its reliability, etc.

·??????They stimulate the ongoing sharing of information and collaboration. Everyone in the organization has access to the appropriate data.

·??????They keep their data clean. The data must be well organized, documented, and error-free. With the help of the right data cleansing approaches, companies make a good basis for decision-making.

·??????They have the right set of tools and skills to?make insights?into structured and unstructured data?and thus to come up with strong business decisions and make predictions.

·??????They pay serious attention to data collection tools?and processes as primary elements of the whole data environment.

·??????More importance is given to data and analysis rather than having the perfect infrastructure

·??????Data-driven businesses are?able to apply the insights?in a manner that supports business goals.

By adopting a DDDM and following these characteristics, aviation-based organizations can position themselves for economic recovery after one of the worst downturns to ever hit the industry. New data-gathering technologies are being developed and implemented around the world to provide organizations data on their customers, freight, finances, and much more. Taking advantage of these tools is key for any industry, and especially the aviation industry, if they want to increase the speed and efficacy of recovery.?

References

Atlassian. (n.d.). 5 decision-making models to try if you're Stuck: Team Central. Atlassian. https://www.atlassian.com/work-management/strategic-planning/decision-making/models

ICAO. (2021). Economic impacts of covid-19 on civil aviation. Economic Impacts of COVID-19 on Civil Aviation. https://www.icao.int/sustainability/Pages/Economic-Impacts-of-COVID-19.aspx

Miller, K. (2021, April 23). Data-Driven decision making: A primer for beginners. Northeastern University Graduate Programs. https://www.northeastern.edu/graduate/blog/data-driven-decision-making/ ?

Valcheva, S. (2020, May 24). Data driven decision making in business: Process and model. Blog For Data-Driven Business. https://www.intellspot.com/data-driven-decision-making/

https://www.mercurymediatechnology.com/en/blog/how-to-really-transition-to-data-driven-decision-making/

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