2.5 quintillion bytes of data is generated on a daily basis - and business giants like Amazon and Google want to be the first ones to get their hands on this big data!

Why is that the case?

Because this big data is the answer to every single one of their marketing requirements. Over 97.5% of large and growing businesses are already investing in Big Data and AI…and there’s a reason behind it. Every business wants to understand what their targeted customer requires, and for that, they need data tracking online activity, purchase history, and demographics of their ideal user. All this information can be extracted through big data.

But what it is, how it works, and most importantly, how to collect big data, we will discuss all this in today’s article. So without further ado, let’s begin:

What is Big Data?

Data room

Big Data is a term used to describe a tremendous volume of organized and unstructured data that is too large to process using typical database and software techniques.

The 3Vs often used to characterize big data summary:

1. Volume:

Big data is all about volume. It refers to the data volumes that potentially reach new heights. For example, Facebook has 2 billion users, YouTube has 1 billion, Twitter has 350 million, and Instagram has 700 million.

An enormous amount of data is generated by these sources, cell phones, sensors, and public releases in the form of public data, images, and videos. This data is so big that it cannot be stored using typical data storage and analysis techniques. This is why businesses use different methods like cloud storage to store big data.

2. Velocity:

No one loves it when data is processed slowly; thus, speed is essential. The velocity of data is the rate at which it is collected, stored, and processed.

Big data technologies now enable us to examine data without storing it in traditional databases and assist organizations in accepting the incoming flood of data while processing it quickly.

3. Variety:

The massive volume of data can take several forms like video, text, PDF, and graphics. We now have types of data unstructured, semi-structured, and structured.

Texts, tweets, photographs, and videos are the most frequently added data types. Unstructured data, such as emails, voicemails, handwritten text, audio recordings, and are also vital components of variety.

What is Big Data Collection?

What is big data collection

According to a 2021 survey by NewVantage Partners, 99% of senior C-level executives at Fortune 1000 businesses are pursuing a big data approach.

Big data collection is a strategy of gathering and measuring large volumes of data from many sources.

Big data collection entails structured, semi-structured, and unstructured data generated by people and computers.

The big data collection comes in the following forms:

  • Structured data is well-organized and comes in standard formats such as names, dates, addresses, credit card numbers, and stock information.
  • Unstructured data exists in its original forms, such as video, audio, image, and log files.
  • Semi-structured data combines structured and unstructured data, such as Email, CSV, XML, and JSON documents.

There are two main types of data:

  • Quantitative Data: When a researcher is attempting to solve a problem or address the “what” or “how many” components of a research subject, quantitative data is used. It is data that can be counted or compared on a numerical scale.
  • Qualitative Data: Qualitative data describes characteristics or attributes. It is gathered by questionnaires, interviews, or observation and is typically presented in narrative form.

How Do Companies Collect Big Data?

Companies can now wrap our lives into increasingly substantial data sets, from online activity and apps to credit cards and satellite imaging through extensive data programs. Now the question is how to collect big data. Following are some big data collection examples:

1. Internet of Things

Streaming data is generated by the Internet of Things (IoT) and other connected devices, and it enters IT systems via wearables, intelligent automobiles, medical gadgets, industrial equipment, and other devices.

Companies examine this extensive data as it comes in, determining which data to keep or discard.

2. Social Media

Interactions on Facebook, YouTube, Instagram, and other social media platforms generate social media data. These platforms include massive volumes of big data in the form of photographs, videos, speech, text, and sound, which may be used for marketing, sales, and support. Companies can use the information-driven from these sites.

3. Black Box Data

Flight crew voices, microphone recordings, and aircraft performance data are all included in black box data used by companies to collect big data.

4. Stock Exchange Data

Companies get big data from stock exchanges about customer share selling and buying decisions.

5. Tracking Via Email

Another technique is to track emails. Companies can learn not just that a recipient opened a cold email but also where it was opened and on what devices. Over 40% of emails sent worldwide are tracked.

6. Data Collection Tool

One of every business’ favorite method of collecting data is through data collection tools. By using Crawler by ProxyCrawl, for example, your firm can extract web data at scale while also handling data pipelines, proxies and queues. This is especially useful to carry out crawling on a massive scale where the business also requires granular monitoring.

How Do eCommerce Sites(like Amazon) Collect Big Data?

Data from ecommerce sites

More e-commerce leaders agree that big data holds the secret to the e-commerce industry’s great future. Big data works for E-commerce businesses in better decision making, acquiring a competitive advantage, and improving their performance.

E-commerce sites collect customer data, which includes predictive analytics, consumer base data, and third-party product sales. Big data enables eCommerce businesses to sustain millions of visitors daily without fear of data loss.

For Amazon, collecting customer data comes from its very own search engine. This e-commerce giant collects individual data by tracking every customer’s purchases, shipping addresses and purchase reviews.

Big data has helped e-commerce firms in many facets; for instance, there is increased strategic decision making (69%), improved operational processes (54%), a better understanding of customers (52%), and cost reductions (47%)!

By using big data analysis tools, Amazon and other ecommerce companies are not stepping up every stage of operations, from customer behavior to supply chain efficiency to social media trends.

How Does Google Collect Big Data?

Google is most likely responsible for introducing the advantages of analyzing and interpreting how Big Data works in our daily life.

Not only has Google made Big Data solutions more accessible, but also available and usable. This online tycoon uses Big Data technologies and approaches to understand our needs based on factors such as search history, geography, trends, and interests.

Google mainly uses cookies to track and collect user data. By using cookies, Google can monitor visitors around the web. Google can collect user data such as preferences, interests, favorite things, and requirements. When a user searches for something on Google, it takes all of that information into account before displaying the results.

Google products also have access to your GPS, IP address, and sensor data for various purposes. So whether you are using Google Maps or engaging with a Firebase-integrated app, big data is being collected by the company.

Final Verdict

Big data collection might seem like pushing a boulder up a hill. But no company can compete in today’s saturated market without having a good big data set on their hands which is why you need to get this work done by hook or crook.

We have shared some of the best ways that companies employ to make this happen - and you can use them too. The best option, of course, is to take help of a powerful data collection tool - like ProxyCrawl’s Crawler, so that your business can obtain accurately scraped data on massive scale, with minimal effort.