Big data is many things: it is large, deeply necessary in today's society with so much consumer and citizen data to store and evaluate and it is a trend in data management that is said to be the future. Having said this, it is also prone to error, sterile and difficult to manage without outside influences.
In some ways, crowdsourcing is the opposite because it offers a human element to data collection and management yet it too is being heralded as the future. So which is it? Is big data analysis too problematic, meaning crowd-sourced data will overtake, or can they co-exist to create an even more effective form of data management?
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Big data Vs Crowdsourcing ventures:
For years, big data has been the system to be touted by industries experts as the future of data collection and management. Companies can create massive databases of information on trends and customers in order to locate problems and solutions and improve the business and this, allegedly, makes everything easier and more manageable. This automated streamline approach sounds ideal on the surface but the cracks are beginning to show in the regulation of misfiled data and the effects of administrators.
In comparison, the concept of crowd-sourced data is one that has really taken off in recent years and is seen by many as having the legs to really succeed and become the undisputed future of data collection. The concept of reaching out to others for help, of making a project a community effect, may be fashionable right now – crowdsourcing instantly making people think of crowdfunding – but it seems that this use of numerous, keen data collectors from different walks of life could also be highly beneficial for collecting high quantities of information and regulating data.
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How does crowdsourcing actually compare to big data when looking at results?
With so much interest in the two forms, and so many questions being asked about their future, it is important to put them side by side where possible and there have been some interesting studies looking at the functions and benefits of both. While Big Data is criticized for its potential lack of objectivity and altered results, crowdsourcing projects have shown the potential of using a wide group of real people to collect useful and accurate data. One such study was carried out by the University of Colorado Boulder, where data from thousands of amateurs counting craters through CosmoQuest was compared with the results of eight NASA scientists. The results were statistically the same.
Data collection projects using non-professionals and the average man on the street have a lot more potential than we may assume because it seems that a desire to collect and submit the data effectively is much more important for useful results than a college education on the subject. The city of Boston showcased this power of everyday citizens through a new app that let users report city problems and damage like potholes. Giving these people walking the streets a tool to have their say – and potentially help fix neighborhood problems – seems to be a great incentive and a helpful tool as the city became aware of issues more quickly, carried out more repairs than before the app's launch and saved money.
Is crowdsourcing the better option is it simply a necessary part of making big data a success?
Many would lean towards crowdsourcing as the future of data collection because of these numerous successes and the simplicity of the schemes; however, there are others that would argue that crowdsourced data is actually a necessary tool within big data collection and we cannot really have one without the other.
To begin with, there cannot be reliable data management without some human intervention to check for errors and the tiny group of administrators currently in place can easily make their own mistakes and put their own subjective slant on the filing of information. Big data companies need crowdsourcing in their operations to ensure objectivity and diversity, prevent against errors more effectively and let realistic social trends play a part in data analysis. Examples of crowdsourcing aiding big data collection are everywhere. CrowdFlower pays 5 million data organizers to help clean up the system, crowdsourcing them through the internet in a much laid back way. Similarly, Kaggle posts problems jobs online so they can reach the right people for the most reliable data – an act that can cause great competition among scientists.
This combination of big data and crowdsourcing leads to another important aspect of modern data collection – crowd science.
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Cold, objective scientific data is a valuable part of big data analysis due to the hands-free storage and understanding of data by customer type and other concrete factors, but there is more to understanding modern trends and current big data processes can sometimes overlook the emotive side of consumer trends and the influence of fashion and other people. Crowd science uses data mining, statistics and algorithms but allows for this human, behavioral side of consumer data to present and analyze information with social influences and social media in mind too. Crowd science may sound like a new term latching on to these established ideas but it’s a form of data collection that is already widely used, from the buyer recommendations of online retailers like Amazon to the analysis of social and political trends through hash tags on Twitter.