Are you interested in the very popular tech world topics of big data and data analytics? Interested to pursue a big data Montreal career, but not quite sure what each one stands for and what their differences are. Read on to find out the answers to all your questions.
What is Big Data?
Big data is large volumes of unstructured and structured data that gets generated every day worldwide. And the size of this data generation is as large as a quintillion bytes every day. This generated data is so confusing and complex when unorganized that often it’s impossible to analyze it manually or use standard data management applications and tools.
What is Analytics?
Business analytics is taking in these humongous quantities of data and processing a business’s historical data. They analyze such data to identify trends, leading causes, and patterns. In short, business analytics helps to make data-driven business decisions for organizations to work efficiently.
Big data vs. Analytics
Now, let us look at some differences between Big Data and Analytics.
All major unicorns – Facebook, Apple, Google, Amazon, etc., have been investing millions in business analytics and big data due to its importance and significance in successful large-scale businesses. If we look at the two words from a layman’s point of view, there isn’t much difference. However, the experts will tell you otherwise. These concepts stand on different and far ends of the entire spectrum.
Therefore, if you are interested in a career in either of these fields or add more competency skills to your CV, you need to understand the basic differences between the two.
Inherent differences between Big Data and Analytics
-
The Volume of The Data
An enormous amount of data is generated every day from numerous platforms or sources, like business transactions, online interactions and clicks, machines, and social media.
Variety of the data formats
Data comes in many different formats, and it could be structured, unstructured, or texts, emails, websites, videos, GPS, etc.
-
The Velocity of Data Generation
The speed at which data gets generated and the need to take a competitive advantage in decision-making make all the difference. Depending on how this big data is developed on these three vs. analytics cannot be applied to define the necessary metrics for measuring it. Only when an organization is leveraging all these three V’s efficiently its analytics game will become robust and invincible.
-
Machines v/s People
At the bottom of it all, machines generate the big data, but it is the human touch, the people who are putting their brains to perform the analysis, and the device itself cannot complete it. Data specialists work hours on spreadsheets and do all sorts of number crunching to offer insightful data and suggestions to business decision-makers. While computers and machines are at the center of data generation and remain in the background of data mining, it is data interpreters who need to do the task of analysis.
Now that you understand the difference between big data and analytics enrolling in a course will add further value to your skills and knowledge. Enroll today.
