How to Approach Big Data Efficiently - 3 Resourceful Ways

By: Alex Chrum | Published: September 26, 2013

In the past two years, we have collectively created 90 percent of the world’s data, and that explosion of new information shows no signs of slowing down. Nearly every sector — from the healthcare industry to the government to e-commerce — is poised to benefit from the actionable insights that big data can provide, but the data sets are so large and, in many cases, so messy, that the prospect of filtering out useful information is overwhelming.

While data is a sustainable asset that, unlike other assets, does not deplete with use, an efficient approach to data analysis is essential. The actionable insights you gain about your business practices, consumer behavior, products and other aspects of your business allow you to supercharge the customer experience, troubleshoot issues and — most importantly — jump ahead of your competitors. Here are three ways to make your data analysis more efficient.

1. Have a goal in mind

Trying to analyze big data with no goal is like panning for gold — sure, there might be some nugget of interest buried in all of that information, but you’re going to waste a lot of time, money and effort to find it, and it might not be all that helpful in the grand scheme. To avoid frustration, you must approach overwhelming amounts of data with a specific goal or question in mind. Do you want to know…

    … why customers are abandoning their shopping carts?

 

    … what the optimal price for a given product is?

 

    … whether your customer service is effective?

 

    … how to personalize the user experience on your site?

When you approach big data with a goal in mind, you blast through the noise and zero in on the information that helps you best meet your objective.

2. Use the data you already have to its fullest extent

Don’t rush for a second helping without finishing what’s on your plate. You probably have access to plenty of unstructured data, such as emails, social media posts, location-based data, user-generated content, application data and other types of information that do not fit naturally into neat, organized databases. If you start collecting even more data without first isolating, filtering and organizing the information currently at your disposal, you are wasting your time and missing out on opportunities to act on time-sensitive information.

3. Outsource analysis and organization

Data

Computers do a swell job of handling structured data sets, but organizing and filtering large sets of unstructured data require the eyes and minds of human beings. Crowd-based outsourcing solutions help you process large volumes of unstructured data to improve your internal organization and give you insights that help you respond to time-sensitive information immediately.

With crowdsourcing, you can:

transcribe

    images, video or audio files to create searchable text files

 

tag

    customer questions or images to enhance searchability and organization

 

analyze sentiments

    in social media posts to give you immediate, actionable insights about your business or products

 

cleanse data

    to remove duplicate entries, improve standardization and correct inaccuracies

 

categorize

    products, attributes, locations, articles and other information to improve site taxonomy

 

moderate

    large amounts of user-generated content

Outsourcing these and other data-processing microtasks to a crowdsourcing agency preserves internal resources, giving you the time and manpower to focus on the big picture.

Businesses of all types and sizes are using crowdsourcing to make sense of big data. The crowd’s capabilities in handling data are limited only by creativity. If you have a problem, our client solutions specialists can design the tasks necessary to solve it.

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