Jargon and technical names can be downright intimidating and confusing to the uninformed, isn’t it? Those involved in the field of computers, data and technologies, have to deal with redundant sounding terminology that is often puzzling. Take the fields of Big Data and Data Analytics for instance. Both have something to do with data, but are seemingly different!
We are sure that any sports fan will be familiar with the term analytics. They made a whole movie about baseball analytics and almost won an Oscar for that. Yes, we are referring to the popular Hollywood flick of Moneyball starring Brad Pitt.
And Big Data is catching all the attention and creating a huge impact on organizations using them. If you are in the technology field you are sure to have heard this buzzword.
So, what is it about the word data that is present in both and puts us all at such unease? Let’s get to sorting out these two terms, the distinct skill sets required for them and what it all means. For it is important for aspirants to know them to move ahead.
What is Big Data?
As implied by its name, big data refers to an immense volume of raw and unstructured data from diverse sources. Owing to its high volume and high veracity nature, it often requires more computing power to gather and analyze. The data is usually deciphered through various digital channels like mobile, internet, social media, etc. and are then used by business to make strategic decisions.
What is Data Analytics?
Data analytics seek to provide operational insights into the business. It considers historical data and then draws out inferences from them to find better solutions to complex business problems. Data analysis is conducted at a more basic level, wherein data related to the problem is specifically scanned through and parsed out with a specific goal in mind.
What is the difference between Big Data & Data Analytics?
Think of Big Data like a library that you visit when the information to answer your question is not readily available. Whilst, data analytics is like the book that you pick up and sift through to find answers to your question. This is the basic difference between them.
Data analytics is generally more focused than big data because instead of gathering huge piles of unstructured data, data analysts have a specific goal in mind and sort through relevant data to look for ways to gain support. On the other hand, big data is a collection of a huge volume of data that requires a lot of filtering out to derive useful insights from it.
Another notable difference between the two is that Big data employs complex technological tools like parallel computing and other automation tools to handle the “big data”. Data analytics use predictive and statistical modelling with relatively simple tools.
Application of Big Data and Data Analytics
Data is the baseline for almost all activities performed today. So much so that businesses now are forced to adopt a data-focused approach to be successful. This only means that there are great career prospects for the data experts now. Aspirants, who want to take up a career in Big Data, should enrol for big data analytics courses online to become an expert. Here is what Big Data professionals do:
- Analyze bottlenecks in the system
- Detection of fraudulent transactions
- Build large scale data processing system
- Architect highly scalable distributed systems
- Find unexpected relationships between different variables
- Real-time analysis to monitor the situation as it develops
Here is what Data Analysts do:
- Acquire process and summarize data
- Package data to derive valuable insights
- Design and create data reports using reporting tools
- Spotting patterns to make recommendations and see trends over time
Now, it is evident from this table that any type of business to gain a competitive edge can adopt both these technologies. While big data is largely helping the retail, banking and other industries to take strategic directions, data analytics allow healthcare, travel and IT industries to come up with new advancements using the historical trends.
Skill Sets Required for Big Data and Data Analytics
- Grasp of technologies and distributed systems,
- Creativity to gather, interpret and analyze a data strategy
- Programming languages like Java, Scala and Frameworks like Apache or Hadoop
- Mathematical and Statistic skills to help with number crunching
- Programming languages like R, Python
- Data wrangling skills to gather raw data and convert it to a presentable format
- Statistical and mathematical skills to draw inferences
- Data visualization skills
As seen, each field requires a diverse set of skills to become an expert at it. If you would like to become an expert in data analytics, it is highly recommended to opt for data analytics courses to acquire the skills required for the same.
With industry recommended learning paths, access to diversified information prepared by experts in the industry, enrolling for data analytics courses and ‘big data analytics’ courses are the way to go.