Intelligent Crowdsourcing: A Big Data Solution

Intelligent Crowdsourcing: A Big Data Solution

IDG (International Data Group) predicts the use of unstructured Big Data will steadily grow at a rate of 62 percent annually. They also predict by the year 2022, 93 percent of all data being processed will be unstructured. The issue of processing unstructured Big Data is currently one of the most challenging problems facing Data Scientists, and Spare5 has met the problem head on it with their Intelligent Crowdsourcing solution. The clients who have signed on with Spare5 (and have various use cases available for perusal) include Getty Images, Avvo, GoPro, IBM Expedia, Sentient Technologies, and many others.

Spare5 is a pioneering company. Its staff is creative and focused on being flexible and adaptable, as they look for new applications for their services. They have created a unique program, in that it combines Machine Learning with human insights. Spare5 uses a community of experts and specialists to perform micro-tasks tailored to the client’s needs. After being screened for quality, Spare5 clients can use their platform to train Artificial Intelligence (AI) systems, improve on their browse-and-search experiences, enhance their directories, and generally work more efficiently.

Read Also:
A perfect illustration of how the big data value chain works

Myles Brundage, the Director at Sentient Technologies, said:

“Spare5 is a valuable part of our product development for Sentient Aware, our AI-powered shopping assistant. With Spare5’s unique ability to access people with specific domain experience, we are able to quickly validate our AI-generated models by comparing them to how people perceive certain nuances between different retail products.”

Spare5’s platform uses a variation of the crowdsourcing technique, which they call “Intelligent Crowdsourcing.”  Intelligent Crowdsourcing uses a network of qualified individuals as their “crowd,” while traditional crowdsourcing uses its customer base or the general population. This model allows Spare5 to engage the right person quickly and efficiently, as they deliver insights on unstructured data. Spare5 has built a community of specialists (called “Fives” within the organization) with a wide range of skills and interests, and has also developed a library of pre-screening, game-like tasks. The pre-screening process assures the right person is assigned the right task. This approach provides the best micro-task advice available from the community.

Read Also:
How to Kick-Start Your Company's Analytics Culture

“A micro-task is a bite-size question, or challenge, that usually takes anywhere from a few seconds to a few minutes. Part of what Spare5 specializes in, is taking these enormous data challenges and sticking them into tiny little tasks that people can complete in their free time. For example, waiting for an airplane, or for a coffee, or while they commute on a bus. We call them micro-tasks because they’re so small, and we do our best to create a game-like experience.”

A detailed quality assurance process then filters the task results for accuracy. The process includes Spare5’s Machine Learning algorithms. Machine Learning takes place while the client uses their platform, making the process becomes faster, better, and smarter. By combining human insights and Machine Learning, customers receive clean, labeled, in-house data that otherwise would remain unused and unidentified. A variety of APIs and SDKs process the data and integrate it into existing data workflows, which can then be used to study customer bases and produce highly functional business reports.

Read Also:
Making Predictive Analytics a Routine Part of Patient Care


Leave a Reply

Your email address will not be published. Required fields are marked *