Sarosh Bhatti

Over recent decades, it has become crucial to develop and refine skills that allow us to understand and act on rapid changes in our environment. The world has become increasingly interconnected and complex, with increased expectations and a lot of unknowns. Design thinking offers a means to grapple with all this change in a human-centric way.

Analytics is an essential component of organizations’ success and is being applied to business and big data to describe, predict, and improve business performance. While there are mature and innovative frameworks for the concepts of business intelligence (BI) and analytics, this article will explore how using a design thinking framework, can enable BI & analytics to contribute more towards creating organization and shareholder value.

First, let’s look at what design thinking is. 

Brief Intro to Design Thinking

Design thinking is a design methodology that provides a solution-based approach to solving problems. It’s extremely useful in tackling complex problems that are ill-defined or unknown by understanding the human needs involved, re-framing the problem in human-centric ways, brainstorming many ideas, and adopting a hands-on approach in prototyping and testing.

Design thinking is a process for creative problem-solving that has a human-centered core. It encourages organizations to focus on the publics they serve, which leads to better products, services, and internal processes. When you sit down to create a solution for a business need, the first question should always be: “What's the human need behind it?”

Design thinking revolves around the following five stages.

·      Empathizing: In this stage, you will perform tasks to understand people’s context in reference to the problem. You will gain a new perspective and empathy for who they are, and what is important to them. In order to gain a better understanding of users, you can break the empathy stage into three parts: Observe, Engage, and Watch and Listen.

·      Defining: During the second stage, your goal is to create a succinct definition of the exact problem. You will also create an actionable problem statement using the information and data that was collected in the empathy stage.

·      Ideate: In this stage, you will explore a broad range of possible solutions while keeping the problem statement front-of-mind. By the end of this stage, you should have generated various ideas with a diverse selection of potential solutions.

·      Prototyping: In this stage, you will create a physical representation of your ideas. The goal here should be to test the design of possible solutions to gather inputs without investing too much resources to any of the solutions.

·      Testing: This is the fifth stage, in which you would like to create authentic experiences to test the prototypes. This should not be referred as final stage, as design thinking is an iterative process, and you might need to revisit other stages to refine your solution.

Now that we have touched upon design thinking, lets look at analytics and business intelligence.

Brief Intro to Aviation Analytics

Even though analytics is a common buzzword, the concept itself is an essential component of any successful business. Let’s start with the definition, and the one I like to use is: “Analytics uses statistics, finance, economics, data science, software, and hardware to convert the raw data into insights for businesses to tap into emerging opportunities and mitigating risks.” Analytics is the process of discovering patterns in contextual business data for effective decision-making. It is an iterative process and needs continuous improvement to be refined for best outcomes.

While there are quite a few frameworks for analytics, Figure 2 above shows the crucial steps to follow specifically for aviation analytics.

Any data analytics approach starts with what the business needs are and what kind of problems/opportunities the organization needs to address. This is the most important step and needs to be properly articulated, or else the outcomes will not align with the issue at hand.

Once the business need has been properly identified, you would then identify the relevant data sources and prepare them for analysis. Airlines and airports are rich with data, so identifying which data set is needed is crucial. For example. if you are looking to drive efficiencies in on-time departures or turn-around times, knowing the scheduled and actual time of departure, processing times of various processes, etc. is mandatory.

Once the data has been transformed, it is then ready for analysis to discover any opportunities that might arise, or any challenges the aviation industry might face. 

Aviation Analytics through Design Thinking Lenses

We will start off by mapping both frameworks side by side.

While the define phase in design thinking aligns with identifying business problem in within the analytics framework, and other steps in both frameworks can align in one way or the other, the main differentiator through which analytics can be enhanced is by adding an empathy. While some might argue that this is being done when functional and non-functional requirements are captured in analytics and in other stages of the process, but I would say that the true spirit and essence of empathy is missing from the structure. As highlighted earlier, by adding empathy to analytics, we need to see who the users are, how they interact with data & analytics, what is important to them, and we need to put ourselves in users’ shoes and understand how they will feel about their problems, how will they connect with dashboards and what their experience will be like in order to get deeper insights for the business. The empathy stage can help the analytics team gather insights to human context of how users will be using analytics and will help to define the business problem better.

One other significant similarity between design thinking and aviation analytics is that it is an iterative process which is defined as “an iterative process, or on-going process, is systematic repetition of sequences or formulas that aims to achieve a given result. It is a process where different data is tested until the desired result is obtained.” Both design thinking and analytics uses the iterative process, opting to solve the problem until the time an optimized solution has been achieved.

    From my perspective in the aviation industry, integrating a design thinking approach to existing analytics methods would create a more strategic system for everyone involved. The focus on empathy and an iterative process would mean end users are being heard, and the work performed by analytics professionals is valued throughout the organization and industry.

References

https://www.interaction-design.org/literature/article/5-stages-in-the-design-thinking-process

https://www.ideou.com/blogs/inspiration/what-is-design-thinking

https://uxdesign.cc/user-experience-is-design-thinking-2428a0a360c2

What is an Iterative Process?