As many big data companies ramp up huge investments in big data to capture business insights by scrambling to employ data scientists, data engineers and data analysts-big data crowdsourcing can add value to an organizations investment plans. For Silicon Valley startups launching a big data platform, the best way to reduce expenses is to pay remote workers so that they can distribute tasks to people who have internet access anywhere in the world. Big Data holds the promise of changing how businesses and people solve real world problems and Crowdsourcing plays a vital role in managing big data. Let’s understand how crowdsourcing big data can revolutionize business processes.
"When we think of big data, we think of enterprise crowdsourcing. We’re looking at the next evolution ... a different way of business getting done."- said Martha Crow, Senior VP of Global Testing at Lionbridge
Big data is all the rage these days as various organizations dig through large datasets to enhance their operations and discover novel solutions to big data problems. Sorting through the flood of information is a challenge even for large IT organizations.
Big data solutions that once took several hours for computations now can now be done just in few seconds with various predictive analytics tools that analyse tons of data points. Organizations need to collect thousands of data points to meet large scale decision challenges. For most of the enterprises today, data collection is a major tailback in decision making. Organizations are using crowdsourcing-obtaining data by imploring contributions from a large online community or group of people to overcome the bottlenecks withdata collection methods.
Crowdsourcing has gained significance as an interesting practice for yielding meaningful insights from big data. Crowdsourcing has eased the process of performing tasks that are hard to crack for computers including audio transcriptions, sentiment analysis, document summaries, document editing, entity resolution and image annotation. Enterprises that completely crowdsource data to make critical business decisions, definitely does have some loopholes.
When making business decisions using crowdsourcing alone, from diverse network sources, businesses need to judge the quality of various data points, find different ways to overcome geographical differences if any and then relate to the goals of an organization. This is when big data analytics proves to be highly beneficial in coagulating crowdsourcing success. Organizations can identify the real titbits in crowd sourced data that drive innovation, development decisions and market practices by making use of well-established big data principles. Big data analytics is a sternly intertwined trend to crowdsourcing.
We live in the age of digital big data where companies like Amazon, Walmart, Facebook and Google have popularized the thought of investing millions every year to draw meaningful insights from big data they receive daily, to improve their services and ultimately the profitability of the business. However, it is important to understand the fact that big data analytics is not merely for big corporate IT giants. Big data analytics in combination with crowdsourcing can revolutionize business processes to add value even for enterprises with budget constraints.
Organizations with on-premise or in cloud big data managements systems will not merely have to invest in hardware or software costs but also will have to incur various other significant startup costs. Thus, companies might not be willing hire employees for data management tasks paving way for crowdsourcing in big data ventures.
The big data crowdsourcing model is a good match as organizations can distribute data related tasks or projects to workers. The goal of a big data crowdsourcing model is to accomplish the given tasks quickly and effectively at a lower cost. Crowdsource workers can perform several tasks for big data operations like- data cleansing, data validation, data tagging, normalization and data entry.
Businesses invest in the “crowd” in some way or the other as it gives them an opportunity to accelerate the ROI. The exquisiteness of crowdsourcing is that the longer a business does it, more are the data points collected. There is greater potential for the organization to benefit by leveraging big data analytics if more data points are gathered through crowdsourcing.
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Crowdsourcing is an innovative approach in the age of big data as it improves distributed processing and big data analytics. Big benefits can be reaped by pairing up crowdsourcing with big data-
1.Crowdsourcing big data helps organizations save their internal resources-Why hire over qualified staff for big data processes that crowdsource workforce can tackle more efficiently, quickly and cost effectively.
2.Crowdsourcing big data helps organizations capitalize on the human element-Content moderation and sentiment analysis from feedback of customers, social updates, reviews or comments with crowd sourced workforce results in highly accurate, actionable and meaningful insights when compared to machines.
3.The distributed nature of crowdsourcing ensures that big data is processed at an unexpected speed which would not be possible to achieve in-house.
4.Organizations can build applications based on real time analytics as crowd sourced workforce produce big data analysis at real time. Enterprises do not have to be bothered about being unfashionably late to the big data party.
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Crowdsourcing and Big data analytics together can help organizations exploit information for making informed business decisions that are a worthy quest.
What’s your opinion? How can big data analytics and crowdsourcing make the most of big data to prove mutually beneficial? Let us know in comments below.