Food delivery generates huge amounts of data

“Everybody’s gotta eat” isn’t just a funny depiction of the world but also a universal truth. That being said, it is not surprising that the entire food delivery industry is on the rise, with the UK food delivery market being expected to grow from $48.21 billion in 2024 to $66.78 billion in 2028.

However, the real reason behind this market growth is that food ordering and delivery platforms offer a very important commodity to the modern consumer; namely, convenience. How much does convenience matter these days? Apparently, it matters a lot considering that people are willing to wait longer and pay extra for a more convenient experience.

Given the current behavioural trend, many food ordering and delivery apps are gaining momentum, becoming more popular by the day. Ride-sharing is another industry sector that follows the exact same trajectory for the exact same reason as the food delivery market, being expected to increase by approximately 150% by 2028.

While the upsurge in the habit of getting everything (even the “bus”) at our doorstep is the most important driving force behind the rapid growth of these two markets, any individual with an inquisitive mind cannot help but wonder if there is something else that fuels this growth. Could that “something else” be somehow related to data collection? Let’s find out.

Assessing the Real Purpose of Data Collection

Data collection and analysis, which allow businesses to stay competitive by better understanding their customers and predicting future trends, may seem like a completely innocent thing at first. However, we cannot deny the privacy concerns that are inherently connected. Here, we’re referring not only to the risk of unauthorised access to sensitive customer information, which could occur due to unsecured networks, outdated software, or poor security practices but also to ethical concerns, including unintended and even intended actions with harmful consequences, especially for the end consumer.  

An example of unintended action is the utilisation of autonomous AI systems, which could lead to violations of privacy arising from the behaviour of these systems as a result of how they are designed, implemented, and deployed.

Coming down to intended actions, numerous reports have confirmed that the majority of food delivery platforms collect customer information, including contact details, address, age, marital status, order history, payment details, interests, preferences, messages, and feedback, and share it with third-party companies, like Google, Microsoft, Facebook, LinkedIn, Twitter, Snapchat, and many others, particularly for advertising purposes. 

Considering that many food delivery platforms have extensive networks that operate in hundreds or even thousands of cities around the world, fully understanding the huge amount of data these businesses can capitalise on along with all of the implications is quite a difficult thing to do.

Going One Step Further

In addition to privacy concerns and potential ethical implications, there is another aspect that many tend to ignore. Currently, new software solutions allow companies to easily collect, extract, analyse, create new data sets, and transfer them as needed, even without the intervention of engineers. Using advanced technologies that make data more accessible, simplify its management, and streamline data transfers can provide tremendous benefits to data brokers and other businesses interested in capitalising on data as a new asset class.

But since data collection, sharing, and monetisation practices worry many consumers, knowing how far the companies using and sharing our data could actually go is an issue that must be addressed. While highly speculative, could all the data that’s currently being collected about us, sometimes even without our knowledge, be used against us one day?

With that in mind, in addition to potential privacy and security threats, data itself may represent another matter of concern. One example of how data could be used against us comes from the insurance giant Aetna, which uses an Apple Watch app to gather health information about the customers who download the app and reward them for their healthy behaviour. While receiving custom-tailored insurance plans and lower premiums may seem beneficial at first, what about those who don’t meet the eligibility requirements or, worse, whose profiles are accidentally connected to unhealthy lifestyle choices? How will those people get affordable health insurance plans? As suggested by this example, the way different companies process and use data can have a significant impact not only on our health insurance plans but also on many other areas of our lives—now, that’s some food for thought!   
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