Big Data vs. Crowdsourcing Ventures - Revolutionizing Business Processes

Big Data vs. Crowdsourcing Ventures - Revolutionizing Business Processes

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.

Big Data Analytics + Crowdsourcing = A Happy Couple

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 Big Data

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.

How crowdsourcing helps ease the process of  big data analytics?

• Generally, a data scientist spends 78% of his time in preparing the data for big data analytics. Thus, an intelligent and cost effective strategy for big data companies would be to hand over the unstructured data sets to a well-managed crowdsourcing platform so that the crowd will tell more about the information contained within the data points collected. For example, before the analysis the crowd can tell whether the data points are a Tweet or updates from Facebook and whether it carries a negative, positive or neutral connotation.
• Crowd helps provide structure (document editing, audio transcription, image annotation) to big data thereby helping analysts improve their analysis predictive models by 25%.
• Crowdsourcing along with big data analytics can help reveal hidden insights from dispersed but connected information quickly.
• Big data problems can be solved with more accuracy with crowdsourcing as a reliable medium.
• The results from the crowd can be used by data scientists to enhance the efficiency of the machine learning algorithms.

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Big Data Analytics + Crowdsourcing in Action-

• San Francisco startup Kaggle launched in 2010 has built an online community of close to 140,000 data scientists. Kaggle hosts contests for solving complex big data problems for BIG big data companies like Expedia, GE and Facebook for data scientists to compete for rewarding cash prizes.
• CyberInfrastructure for Billions of Electronic Records (CI-BER) is a government initiative that explores the potential of crowd to draw meaningful insights from big data. South side neighbourhood of Asheville, North Carolina is among the largest urban renewal area in south east US where several people are banished and close to 1000 homes have been lost since 1960’s.CI-BER is working on a “citizen-led” outsourcing initiative on this project to assemble and make sense of the documents governing the renewal and development policies.
• With 400 million viewers after just 13 episodes and close to 1.2 billion connected through Twitter and Facebook. The secret to success of Satyamev Jayate, India’s top rated TV show is – it harnesses the power of big data for crowdsourcing social innovations in various ways to bring about a meaningful change in the society. The show analyses millions of messages on various controversial issues such as female foeticide, freedom from domestic violence, affordable healthcare that helps the producers of the TV show plan for future episodes and push the society towards a political change. People watching the show express their opinions on various social and political issues in the form of data on their thoughts expecting action from the political leaders.
• Refugees need to adapt to a new culture, find jobs and learn a new language - which are challenging barriers and a very difficult undertaking. Refugee crisis in United Nations is being tackled with the intersection of big data and crowdsourcing. United Nations Refugee Agency has partnered with Mindjet’s SpigitEngage platform that uses cloud computing to find out and solve problems through crowdsourcing. The SpigitEngage platform leverages game mechanics (leader boards) and big data analytics to help United Nations with its philanthropic work.
• Potholes are common on Boston roads however the problem generally is seen to cause extreme accidents during monsoons. Mayor’s office of new urban mechanics in Boston has developed Street Bump, an application for Smartphone that automatically detects any road hazards in the city as soon as it hears the unfortunate “thud” sound. The first version of the app was not successful as it reported several false potholes. A Massachusetts based firm InnoCentive that specializes in innovation and crowdsourcing turned it into a contest for 400,000 experts for a rewarding share of $25,000 in prize moneyto get innovate ideas on improving the Street Bump app. The result is Streep Bump 2.0 is almost perfect making the city of Boston more like a progressive business than operating as a government agency.

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.