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 collection.
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:
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:
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 collection.
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.
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.
The Big Data industry has expanded significantly in a short time. It went from being worth $169 billion in 2018 to reaching $274 billion in 2022, marking a substantial 62% growth. Looking ahead, the worldwide Big Data market is expected to produce a revenue of around $103 billion by the year 2027.
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.
You can collect big data in the 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.
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:
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.
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.
Flight crew voices, microphone recordings, and aircraft performance data are all included in black box data used by companies to collect big data.
Companies collect big data from stock exchanges about customer share selling and buying decisions.
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.
Web scraping involves the extraction of data from web pages and its subsequent transfer to file formats like XLS, CSV, or JSON. Yet, the efficient extraction of data, distinguishing the vital information, and ensuring compliance with data protection rules necessitates expertise and a skill set that takes time to acquire. To streamline the process and reduce costs, utilizing a data collection tool is often the most prudent choice.
It is one of the most used method of collecting data. By using Crawlbase Crawler, 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.
Cookies and web beacons are two commonly used ways to collect big data about users, such as which web pages they visit and when. They offer simple statistics about how people use a website. It’s important to note that cookies and web beacons don’t in any way harm your privacy; they simply aim to customize your experience with a particular website or online source.
Gamification is a popular way to engage people, often used alongside loyalty programs. The idea is to encourage users to interact with a brand by playing small games, and in return, they might get a reward. Because games are often quite addictive, it’s a good way for businesses to collect big data about users while they play. The potential of gamification is big. Right now, there are more than 1.1 billion gamers in the world. This large group of users generates a huge amount of data that businesses can use to create successful marketing strategies.
Among the various big data collection methods, satellite imagery stands out as it can capture information from all around the world within just 30 minutes. Notably, Google Maps and Google Earth update their data between 50 to 70 times daily. Employing satellites as one of the big data collection strategies enables businesses to keep their information up to date, especially for vast areas and long distances.
More e-commerce leaders agree that big data holds the secret to the e-commerce industry’s great future. Big data collection works for E-commerce businesses in better decision making, acquiring a competitive advantage, and improving their performance.
E-commerce sites collect big data related to consumers, 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 collection 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.
Google is most likely responsible for introducing the advantages of analyzing and interpreting how Big Data collection methods work 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 collection strategies and technologies to understand our needs based on factors such as search history, geography, trends, and interests.
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.
Finding useful data sources is just the beginning of the big data collection process. After that, an organization must create a system that moves data from where it’s created to places where the organization can store and use it. This process usually involves three main steps known as “extract, transform, and load” or ETL:
- Extraction: This is when big data is taken from its original location.
- Transformation: Here, big data is cleaned up and organized so that it’s useful for business purposes.
- Loading: This is when the big data is moved into a database, data warehouse, or data lake where it can be accessed and used.
At each of these steps, data management teams have to think about important things like making sure the data they want to use is reliable and getting it ready for use.
It’s no surprise that many businesses find themselves battling with a bunch of questions. There are various challenges, including technical, organizational, and sometimes issues related to compliance.
Here are some of the common challenges:
- Figuring out and managing all the big data that an organization has.
- Getting access to all the data sets you need and breaking down data barriers, whether it’s inside or outside the organization.
- Making sure the big data is of good quality and keeping it that way.
- Choosing and using the right tools for the ETL (extract, transform, load) jobs.
- Having the right skills and enough skilled people to do the work needed to achieve the organization’s goals.
- Keeping all the collected data safe and following privacy and security rules while letting the right people use the data for business needs.
These challenges in big data collection process are pretty similar to the challenges that stop executives from making their big data collection and execution plans work. For instance, a study by NewVantage found that 91.9% of the people claimed that the biggest challenge in becoming a data-driven organization is all about culture - that’s the people, how the business works, and managing changes.
Experts worldwide recommend that business leaders should create a strong big data governance program to deal with the challenges, especially those related to security and privacy.
A good data governance program should set up the procedures for how data is collected, stored, and used. It should also make sure the organization does the following:
- Identifies data that’s regulated and sensitive.
- Sets up controls to stop unauthorized access to it.
- Puts in place checks to keep track of who’s accessing it.
- Creates systems to make sure that everyone follows the rules and guidelines for governance.
These steps are vital to keeping data safe and ensure the organization follows the rules. Plus, experts suggest that these measures help the business trust its data, which is a key part of becoming an organization that relies on data.
When it comes to a secure and successful big data collection, experts suggest these top practices:
- Start with a Solid Framework: Right from the beginning, create a plan to collect big data that focuses on security, following rules, and governing the data properly.
- Know Your Data: Early on, make a catalog of all the data in your organization’s data system. This helps you understand what you’ve got.
- Let Business Needs Decide: Decide what data to collect based on what the business needs, not just what’s available.
- Adjust as You Go: As you use data and your data plan grows, make changes to improve big data collection and governance. Figure out what data is missing and what data isn’t valuable.
- Automate the Process: Use big data collection tools to make the process as smooth as possible. This helps it move faster and makes sure it follows the rules set by the governance program.
- Detect Issues: Put tools in place that can find problems in the data collection process, like missing data sets.
These practices help make sure your data collection works well and helps your organization meet its goals.
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 Crawlbase Crawler, so that your business can obtain accurately scraped data on massive scale, with minimal effort.