THE AUTHOR:
WHAT IS BIG DATA COLLECTION?
The era of digital intelligence has led to a substantial shift towards fast data generation. With the rise of social media and eCommerce, 2.5 quintillion bytes of data are produced every day. For example, the New York Stock Exchange generates about one terabyte of new trade data per day. This amount of information is not easy to process, especially insofar as 90% of it comes unstructured and disorganized. This continuous generation of huge data volumes that are difficult to analyze using traditional data mining techniques is called big data, and it is one of the most conspicuous phenomena in the 21st-century world.
WHAT DATA IS BEING COLLECTED?
The big data includes information produced by humans and devices. Device-driven data is largely clean and organized, but of far greater interest is human-driven data that exist in various formats and need more exquisite tools for proper processing and management.
The big data collection is focused on the following types of data:
– Network data. This type of data is gathered on all kinds of networks, including social media, information and technological networks, the Internet and mobile networks, etc.
– Real-time data. They are produced on online streaming media, such as YouTube, Twitch, Skype, or Netflix.
– Transactional data. They are gathered when a user makes an online purchase (information on the product, time of purchase, payment methods, etc.)
– Geographic data. Location data of everything, humans, vehicles, building, natural reserves, and other objects are continuously supplied with satellites.
– Natural language data. These data are gathered mostly from voice searches that can be made on different devices accessing the Internet.
– Time series data. This type of data is related to the observation of trends and phenomena taking place at this very moment and over a period of time, for instance, global temperatures, mortality rates, pollution levels, etc.
– Linked data. They are based on HTTP, RDF, SPARQL, and URIs web technologies and meant to enable semantic connections between various databases so that computers could read and perform semantic queries correctly.
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MOST COMMON BIG DATA COLLECTION METHODS
1. Online Marketing Analytics
Online marketing analytics is the driving force for digital marketing. The biggest eCommerce companies such as Amazon, eBay, and IKEA serve millions of customers per day and have to deal with tons of data gathered in their buying experiences. Basically, customers are asked to fill out an order form where they should indicate some personal information. The insights drawn from these data are needed to personalize customer journeys and improve customer service. While it allows these businesses to learn their audiences better and boost their sales even further, such data volumes require effective big data collection tools that enable fast and accurate data processing.
2. Loyalty Programs and Cards
Loyalty programs are a popular practice among retailers that strive to build brand loyalty. The point of any loyalty program is to encourage a customer to collect points with every single purchase and swap them for some rewards. It allows the business to create a buyer’s profile with detailed consumer preferences and habits. This profile may be sold to advertisers or used to achieve more effective merchandising.
3. Gameplay
Gamification is another popular engagement strategy employed alongside loyalty programs. It is aimed to make users interact with one or another brand through mini-games as a result of which a customer can get an incentive award. Since gameplay is often addictive, it gives businesses an opportunity to collect big data on users as long as they are involved with the game. The potential of gamification is high. To date, there are over 2.2 billion gamers in the world. Such a large number of users produce tons of big data every minute which businesses should analyze to build their marketing strategies successfully.
4. Satellite Imagery
Out of all the common methods of big data collection, satellite imagery covers the entire globe within 30 minutes. Google Maps and Google Earth both update their data 50 to 70 times per day. The use of satellites in big data collection allows companies to continuously update the information on long distances.
5. Social Media Activity
Social network users on average spend 2-3 hours daily. They are considered the main suppliers of unstructured data in the form of video, audio, photo, texts, etc. Although all users share these data willingly, big data tools are a must to process the content shared on social media as well as gather the data on user activity. This massive flow of data coming from social networking is expected to grow exponentially and constitutes a major opportunity for creating detailed user profiles. What is currently the primary interest of big tech companies like Facebook that collects around 63 distinctive pieces of data for API.
HOW IS BIG DATA COLLECTED?
There are different ways of how to collect big data from users. These are the most popular ones.
1. Asking for It
The majority of firms prefer asking users directly to share their personal information. They give these data when creating website accounts or buying online. The minimum information to be collected includes a username and an email address, but some profiles require more details.
2. Cookies and Web Beacons
Cookies and web beacons are two widely used methods to gather the data on users, namely, what web pages they visit and when. They provide basic statistics about how a website is used. Cookies and web beacons in no way compromise your privacy but just serve to personalize your experience with one or another web source.
3. Email tracking
Email trackers are meant to give more information on the user actions in the mailbox. In particular, an email tracker allows detecting when an email was opened. Both Google and Yahoo use this method to learn their users’ behavioral patterns and provide personalized advertising.
BIG DATA AND THE BUSINESS LANDSCAPE
Companies turn to big data collection for many reasons, but mostly for a variety of advantages they get in business.
Better customer service
Big data collection lets businesses gain insights into their audience’s behavior, discover their consumer habits and preferences and based on that launch effective marketing campaigns. Knowing the buyer personas better also allows eCommerce businesses to cultivate brand loyalty and improve their presence on social media and the web overall.
Turn data into cash flow
Some large companies sell big data gathered on scalable web sources. Having access to a good customer base is a big advantage to any eCommerce startup these days. Data dealers are in high demand as they assist companies in reaching out to the right audience. Keep in mind, however, that they don’t sell customer data but only access to these customers. This is the reason why you see relevant ads in accordance with your searches.
Enhanced security
Finance companies, however, have to deal with the collection of big data so as to provide a higher level of security. For example, some online banking systems authorize users through voice recognition data, which considerably reduces the risk of identity theft and cyberattack as a whole.
For further information
To learn more about big data tools and techniques, feel free to get in touch with Computools’s expert team via info@computools.com. They keep up with big data trends and specialize in the most common methods of big data collection.
Computools was selected through an RFP process. They were shortlisted and selected from between 5 other suppliers. Computools has worked thoroughly and timely to solve all security issues and launch as agreed. Their expertise is impressive.