How to Collect Big Data ? : Yes we know you would be having a number of questions in your mind like Collection of Big Data, How companies collect Big Data, how to collect data for quantitative research so don’t worry, if you are here to search for these questions here then you are on the right webpage as here we are going to provide you a complete article on Collection of Big Data methods briefly.
Amazing Facts about Rise of Big Data Collectection
- Every day consumers make around 11.5 million payments by using Paypal
- Every hour, Walmart (chain of discount department stores) handles more than 1 million customer transactions
- 510 comments, 293000 status and 136000 updates are posted on Facebook every minute
- Every second, ~7000 tweets are made on Twitter
Just image the amount of data generated if the above stats are calculated for 24 hours? Whoa! That’s colossal.
The term ‘Big Data’ is commonly associated with 4V’s namely, Velocity, Volume, Variety, Veracity. These 4V’s aptly represents the true nature of Big Data. Every ‘V’ has a significant role to play in the existence of Big Data. If joined together, these 4V paints a beautiful explanation of Big Data which can be understood as ‘ Big Data as a concept refers to high velocity collection of data in large volumes which emanates from variety of sources causing different forms of data which is uncertain in nature’.
Big Data Collection Methods
The process of dealing with big data is quite different from handling traditional data. Big Data is handled at different stages namely, collection, storing, organizing, analyzing etc. with a motive of deciphering useful insights useful to make business decisions. Here’s a quick explanation of these stages:
1. Collection: This stage involves collection of data from several types of data sources, data marts and data warehouses.
2. Storing: This stage involves storing the data into distributed database systems and servers. The data is stored in such a manner that for every data stored, the backup is simultaneously created. This stage involves setting up physical infrastructure or setting up cloud for data storage.
3. Data Organization: This stage involves categorizing and arranging the data on the basis of structured, unstructured and semi-unstructured data which is easy to access and analyze. This data can be accessed using big data technologies such as NoSQL, Hadoop Distributed File System(HDFS).
4. Data Analysis: After the data has been organized (stored and arranged properly), this stage involves extracting the data and applying statistical & business analytics concepts to carve out the hidden insights from the data helpful for decision making.
5. Data Visualization: Once the insights have been carved out from data, the very next stage is to represent it. The representation is generally done using Data Visualization. Since, not everyone feels comfortable with numbers, but everyone can effortlessly the pattern when represented on graph. This stage involves using of tools such as D3.js, Qlikview, Tableau etc.
6. Actions or Result : Once the insights have been presented to the clients, the last step calls for the necessary future course of action.
Social Media forms an indispensable source of generating Big Data. Consumer good companies such as Nestle, Parle, HUL, P&G, Marico actively scan social media websites such as Facebook, Instagram, Twitter, Pinterest to decipher the preferences, choices, perception of the customers towards their brands. Not only consumer good, but healthcare, financial services companies have also pro-actively embraced social media and utilized it for brand building.
Supporting this big data waves, national governments have also started uploading their data on open source servers such that any user can access them and use them to build any useful applications. But privacy becomes a concern which is tackled by installing advanced level privacy support. Furthermore, the data generated from Hospital in terms of patients, medicines, staffs and the data generated from insurance companies, railways, advertising agencies gives birth to big data.
The data sources of big data can be categorized into internal and external. The internal data includes sources such as Customer Relationship Management (CRM), enterprise resource, customers details, products and sales data, OLTP and operational data. The external data sources includes data collected from business partners, syndicate data suppliers, internet, government and market research organizations.
Precisely speaking, the commonly used data for insights is collected from 3 major sources: Social Data, Machine Data and Transactional Data.
1. Social Data refers to the data generated from Facebook, Twitter, Google+, Linkedin.
2. Machine Data includes data generated from RFID chip readings, global positioning system(gps) results.
3. Transactional Data includes data generated from ebay, amazon, walmart, Ikea etc
In this article, we discussed the sources from which big data emanates. The facts discussed in this article have been taken from internet stats.com. These sources of big data have exponentially multiplied in all spheres with the advent of IT, the internet and globalization. Undoubtedly, big data has a bright future nearby.