Data Sharing and Privacy in the Gig Economy

Adults worldwide are swapping traditional full-time roles for contract positions in the gig economy, an exponentially expanding set of marketplaces where flexibility and service quality reign supreme.

According to software firm Intuit, an estimated 3.9 million Americans now work as freelancers in this space, contracting with companies such as Airbnb, Dolly, and TaskRabbit in an effort to achieve increased economic stability via multiple personal revenue streams.

Companies participating in the gig economy can easily scale their operations to meet demand and easily navigate economic tides. This approach works for customers, workers and businesses. However, what makes the machinery of the gig economy churn is also the industry’s biggest risks: sharing and storage of sensitive data.

Understanding the Flow of Data Sharing
Contract workers that circulate this arena communicate with clients and employers using proprietary mobile applications. This strategy, which streamlines operations and service delivery, requires companies to collect, analyze and manage huge amounts of data. For example, the property rental giant Airbnb markets more than 3 million listings spread across 65 cities worldwide, helping hosts connect with the more than 200 million renters populating its mobile portal. The company captures all of the interactions that occur on the platform, using the information to bolster service quality. Data also plays a significant role in vetting potential hosts. In short, data drives the gig economy. But sometimes, this information can lead to catastrophe.

Ride share innovator Uber learned this the hard way in 2016 when hackers tunneled into its servers and stole the personal data of 57 million drivers and riders, The New York Times reported. The firm was so embarrassed by the intrusion that it waited for more than a year to publicize the event, a decision that drew immense backlash from both customers and the contract employees that trusted Uber to keep their data safe. Businesses in the gig economy or those considering entrance into the space can easily avoid this kind of situation by carefully fine-tuning their data intake policies and embracing collection strategies that mitigate risk.

Taking the Solo Approach to Data Management
Uber maintains a closed data collection system, forcing riders and drivers to submit their information directly to internal servers. While this lends the company immense control, it also creates risk.

Internal information technology teams become responsible for securing that data, including any fiscal implications that come along with suffering a breach. Organizations must pay, on average, more than $3.6 million per breach event, according to research from IBM and the Ponemon Institute. Uber’s troubles in 2016 demonstrate the weakness of this methodology.

Taking the Collaborative Approach with Evident
The only way a modern business can prevent cybercriminals from accessing private customer and employee information is to not host such data in the first place. This creates an important question: If businesses need specific answers to keep their communities safe, how do they get that information without having to hold and secure it?

Ask UrbanSitter. UrbanSitter works with Evident to simplify data sharing and management for the contractors that power their business. Instead of collecting and storing pre-employment information themselves, they partner with Evident to responsibly access necessary, relevant details. With permission from the user, Evident verifies the relevant information for the provider and monitors for changes going forward. Businesses no longer have to hold and manage sensitive personal information, mitigating risk and preventing situations like the Uber data debacle from unfolding.

Businesses already navigating or just entering the gig economy have to get the answers they need while ensuring sensitive personal information stays safe. Evident makes this possible.

Schedule a call with us today to learn more about how you can start running verifications in minutes.

Sources:

Source 1 I Source 2 I Source 3 I Source 4 I Source 5 I Source 6 I Source 7 I Source 8 I Source 9