Did COVID-19 teach us to wear our data hats more?
The past few years have seen the hype in the phrase “data-driven decision making”, urging companies to use historical data to make informed decisions. During the pandemic of COVID-19, many industries demonstrated excellent examples of this. State governments’ use of contact tracing, and detailing measures of success for reopening is a demonstration of the use of data to combat COVID-19. On the other hand, transit optimization, route optimization, and contactless deliveries during COVID-19 have taken advantage of data they already collect to adapt to the pandemic.
Contact Tracing to contain COVID-19: As soon as the government declared COVID-19 as a pandemic and health officials started to learn about how this virus spreads, the need for contact tracing became very crucial and urgent. Even though the concept of contact tracing has long existed for tracking communicable diseases, COVID-19 made use of GPS data to achieve this goal. There was a global race to develop smartphone apps to track the spread. Unprecedented times saw unprecedented collaboration with Google and Apple exploring options to track and alert exposure to possible COVID-19 infection. (Joint announcement on April 10, 2020). They have released APIs that will make it possible for countries to develop contact tracing apps. This is clearly based on cell-tower signals, wi-fi signals, and satellite-based GPS (this data has always been collected by telecom companies). Public health agencies from 22 countries and some US states have asked to test the system (Read here). Since this is an opt-in model, user adoption will dictate the success of tracing. This data will also help with projections and forecasts of potential cases based on the “rate of reproduction” metric. Data privacy could be debated but using this data is crucial to alert possible exposure and track any “super spreaders”.
SMART Goals to phased approach of reopening: While states have published guides for a phased approach to reopening, the measure of success is widely different amongst states. I am most impressed with New York’s plan. I say this with no political bias in mind or affiliations to NY state. They have clearly defined 7 SMART goals in 3 different categories which have to be attained in each phase of reopening to qualify for the next phase and it is region-based. (Scroll to section “Metrics to Guide Reopening” in "NY Forward Reopening Guide).
Transit optimization to meet changing demands: During this period of lockdown, transit authorities had to face myriad challenges. They had to cater to critical mobility for essential workers and the general public while dealing with reduced drivers and other workforce. Transit authorities in various states are using welfare data to optimize routes as a significant population continues to use public transportation to get to work, receive health services, get food, and avail of social programs. They also accounted for ease of access to school lunch distribution sites for children from a low-income background. Transit optimization was driven by data in this case!
Route optimization in transportation and logistics: While the above use cases of data are at the forefront, there is so much more that is not evident to the daily consumer. Every company that delivers goods or services, whether B2B (delivery of goods to grocery stores) or B2C (Amazon, UPS, FedEx, utility companies), has been using route optimization software and algorithms for decades while factoring in hurricanes and other disasters. But COVID-19 further complicated that optimization to include additional factors, as COVID-19 hot spots varied across international borders. Apps such as Google Maps and Waze optimize routes based on accidents and road closures but COVID-19 hot-spots have been at a different scale and unaccounted for in terms of avoidance of those routes. Planning for the delivery of goods and services for COVID-relief work vs non-COVID-19 differently created an additional complexity. Logistics companies have been successful in avoiding and navigating COVID-19 hotspots while keeping up with the demands of delivery using this data.
Contactless delivery leads to a better cost-effective option: Attempts from restaurants and businesses trying to provide services while keeping up with COVID-19’s social distancing measures necessitated contactless delivery. Last-mile delivery costs are typically high. Contactless delivery with electronic proof of delivery has proven to be more efficient for the service provider as less time is spent in delivering and as a result, they have been able to meet the higher demands of delivery during this time.
Work-from-home a more lucrative option: India’s largest IT firm, Tata Consultancy Service, is seeing the benefits of the work-from-home culture forced by COVID. Given the savings in real-estate cost and utility cost, they are changing their operating model to 75% of the workforce working from home by 2025 where the current industry average is 20%. (TCS Reference article). There is evidence on the internet of more companies following the same pattern.
Data drives pivot for Airbnb: The tourism industry is one of the industries hugely affected by COVID-19. Plummeting revenue numbers made Airbnb pivot their “Experience” platform from 100% in-person to being an online experience where people can share their experience at a very reasonable cost. Though the Experience platform was only generating a fraction of their revenue in the pre-COVID-19 era (reference here) but this is the only part of their services that could be pivoted during COVID-19 times. They had enough previous information to support projections for a “better-than-nothing” revenue. “Experience” ranges from travel to food and cooking to exercising. As a result of this pivot, some hosts of the Online Experience platform have earned as much as $150,000 a month as quoted in this Forbes article. Airbnb gets 20% of the amount earned by hosts of the Experience platform as stated in this article from Dec 2019. To pivot to “Online Experience” was a decision based on existing data and the risk of pivoting. This may not be enough for sustainability but during the pandemic, like most organizations, any income was better than none.
Conclusion: Organizations are increasingly becoming data-driven. But what COVID-19 has brought to the forefront is that it is important to not only evaluate descriptive analytics but also use the data in predictive and furthermore, prescriptive analytics.
Organizations today have a lot of data. The bigger challenge that we see as Cleartelligence, Inc. is not about collecting data but harvesting it and converting that into intelligence and insights for improved predictive and prescriptive analytics. To do so, organizations need to establish a robust data foundation, understand the implications and opportunities of data quality, and then use that substratum of rich and high-quality data, combined with machine learning to power data-driven strategies. This is where you need a partner like Cleartelligence, Inc. to be on your side and work as an integral part of your team to drive success. Please feel free to contact me if we can be of assistance to your organization.