Many businesspeople fear that crowdsourcing – or cloud labor – poses a threat to US jobs and thus our economy. In fact, the opposite is true. Using crowdsourcing to take care of time-consuming research, data collection, and other simple tasks frees skilled employees to focus on higher-value work, increasing employers’ ROI on labor costs. Furthermore, the easy scalability and low cost of cloud labor eliminate process bottlenecks and let US firms deliver their solutions more quickly and at more attractive prices, boosting top-line revenues and profit margins.
Soon, cloud labor will be an important competitive tool without which a firm will be at a serious disadvantage.
So how do companies get on the cloud labor bandwagon? Cloud labor campaigns demand either a substantial up-front commitment to an enterprise cloud labor platform or an arrangement with a “managed crowdsourcing” vendor like Information Evolution. To justify this step, an analysis is usually first undertaken to assess the “points of pain” in existing or upcoming high-volume, low-complexity processes. This frank assessment of existing or future work processes helps identify tasks suitable for cloud labor like:
- Data maintenance for customer or other mission critical databases. One common use case for maintaining databases in virtual real-time is developing multi-stage processes:
- “alert services” that monitor web sites and news feeds for mentions of companies, people, or products;
- routing alerts to cloud or in-house resources who determine the relevance of the alert; and,
- routing relevant changes to in-house or cloud resources who update internal databases.
- Making digital data discoverable by appending appropriate metadata. This is most often used to make searching for image and audio files possible.
- Data acquisition and normalization. Gathering pricing data is one of the more popular applications of cloud-based data harvesting and normalizing routines.
Enabling cloud-based projects means setting up the system, including designing the workflow, specifying labor pools, establishing payment terms, connecting machine and human processes, configuring software, and writing custom code to connect sub-processes. After that, it’s necessary to select and train the workforce(s), design QA routines, and monitor and manage processes to ensure speed and accuracy. Routines can be used at will once they’re built, running quickly through massive amounts of data. They also can be tweaked over and over again to increase the percentage of work handled via automation and run ever faster and cheaper.