“Data is what you need to do analytics. Information is what you need to do business.” —John Owen
Business strategy is very crucial for a company or an organization. It creates a vision for the whole company to develop. It will also establish a clear goal for the people within the company and prevent individuals from losing sight of its aims. Analytics empowers companies and organizations to unlock potential business strategies through the interpretation of data. The strategic use of data analytics can improve an organization’s overall operational efficiency as well as revenue growth if applicable.
In this blog, I will specifically focus on Angel Flight West (AFW), a nonprofit provider of non-emergency medical transportation, and its analytical strategies. I am going to introduce:
- Angel Flight West background and its current strategies
- Associated applications of analytics and data science
- Possible impact and risks of such applications
Introduction of Angel Flight West
1,600 miles separated Mason and his family from Texas Children’s Hospital in Houston, where a new study awaited to treat his acute lymphoblastic leukemia. Having relapsed after 8 months of chemotherapy in LA, Mason needed this study to treat the particularly aggressive form of his condition. A 24-hour road trip to Houston would have been impractical for the 11-year-old’s family, and a commercial flight during the pandemic was both risky and expensive. Thankfully, Mason was able to reach the hospital with the generosity of a volunteer pilot who shared his condition, to whom he was matched by Angel Flight West.
This is one of the thousands of matches made possible by Angel Flight West, a nonprofit non-emergency medical transportation service based in Santa Monica, California. Since 1983, AFW has matched more than 23,000 patients with volunteer pilots and drivers in 13 states across the western United States. Their volunteers graciously donate their time, skills, and money to fund trips and fly patients to their necessary medical treatment. Patients facing financial and geographic barriers are referred to AFW by social and healthcare workers. Although the flight trips may be the only engagement between patients and the organization, AFW has successfully managed to build long-standing relationships with many recurring passengers whose needs are fully understood.
2. Current Strategies
“A satisfied customer is the best business strategy of all.” — Michael LeBoeuf
Angel Flight West’s organizational goal is to double its yearly flight count from 4,000 to 8,000 by 2023, with its “Ascent to 8000” initiative. In order to reach its goal, the organization values every customer, especially the existing customer base. To improve customers’ satisfaction and engagement, the AFW team and UCD practicum team have to obtain, analyze, and interpret historical data that will provide them with the ability to form strategies in key operational areas.
Associated Applications of Analytics and Data Science
Angel Flight West employed data analytics in many aspects within the organization, such as volunteer pilot orientation improvements and passenger intake analysis. However, in this blog, I will only cover the data analytics application in the Mission Operations Team, in which our practicum team has been involved.
The Mission Operations Team is responsible for processing incoming mission requests, coordinating mission logistics with volunteers and passengers, and facilitating onboarding for new passengers. A mission can be canceled in two situations: 1) A mission without volunteer pilot signs up; 2) Poor weather. The first situation refers to “No Pilot Cancellation” and the latter one considers as “Weather Cancellation”. In both cases, coordinators from the Mission Operations Team need to intervene and resolve the problems. For no pilot cancellations, the coordinators need to intervene two days before the scheduled departure by approaching potential volunteers through e-mail and SMS. There is a small chance that the mission will be staffed through this method. For weather cancellations, the coordinators can solicit other volunteers two days before the scheduled departure. However, if the weather is severe, AFW will never endanger its volunteers or its passengers by making volunteers fly against their best judgment. If the weather cancellation happens, the team will need to create a backup plan for the patients.
The organization wants to increase operational efficiency with respect to no pilot and weather cancellations. No pilot cancellations are preventable if the team can successfully solicit a volunteer, but 2 days is too short for the pilot response rate to be high. Weather Cancellations are unpreventable, but the team has the means to provide a backup plan. Knowing which missions might be prone to poor weather makes it easier to proactively back them up. This is where data analytics is applied.
Our practicum team has built machine learning models using historical data that predict whether or not an upcoming mission is at risk of being canceled or unstaffed. This model will assist coordinators in prioritizing missions that need special attention, eliminate preventable no-pilot cancellations, and systematically back up potential weather cancellations. We also found the ten most important features that have the biggest impact on predicted outcomes. Moving forward, coordinators need to spend more time recording these features as well as monitoring them.
Possible Impact and Risks of Such Applications
Although the model we built will help the Mission Operations Team to improve its day-to-day operations, there are still concerns and risks associated with the model.
- Model Bias
Professor James Johndrow of Wharton Business School once said, “Any biases that exist in the data, those things will eventually show up in the predictions. It is impossible to avoid that kind of stuff because the data that we care about in these areas is data that we find in the real world.”
Our model may potentially include unintentional bias because we trained the models on historical data. One example comes with the weather cancellation model. In areas in which there has been a large number of historical weather cancellations, the model would suggest using a backup plan to get the passengers to their destinations. However, what if the actual weather situation is not as pessimistic as our model suggests? Or, what if the places that used to have bad weather become better in the future? Then, it would cost unnecessary time and money for the organizations to prepare for the backups. These are common scenarios, and not addressing them would be a crucial mistake. So, it is important to understand these model’s limitations and incorporate human-judgment along the process.
2. Unexpected Behavior and Unintended Consequences
Another possible risk is unexpected behavior and unintended consequences. Human behavior tends to be unpredictable. In this case, volunteer pilots are humans who choose which missions to sign up for. Even though our model is more likely to make predictions more than 2 days in advance, there are still certain situations that are unforeseeable. For example, the model suggests that the mission is more likely to have pilot signs up, however, it is still possible that the mission is not filled by pilots. Moreover, certain extreme conditions such as covid-19 can not be predicted nor be prepared in advance. Such unexpected conditions will result in unintended consequences. Thus, it is necessary to continue monitoring the model, identify these unanticipated patterns, and make corresponding changes.
Through data analysis, organizations can make better, smarter, and more strategic decisions that help with business development. While data could have a positive impact on the business, there still exist flaws and concerns. We need to understand those limitations and have our own judgment to overcome these potential setbacks.
I hope this blog could provide an understanding of using analytics in business or organizational settings and help you to apply analytics in your careers!