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.

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 businesses to make strategic decisions.

Also Read: Kickstart your Career in Analytics with Data Science Program

What is Data Analytics?

Data analytics seeks 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 uses 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

Also Read: 6 reasons for you to opt for big data analytics certification

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:

Big Data:

• 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 Statistical skills to help with number crunching

Data Analytics:

• 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.

Big Data describes a large volume of structured and unstructured data that holds the potential to be mined for valuable information and that can be incorporated in advanced analytics applications.

Also Read: How to Build a Successful Career in Data Science?

Why does it matter in The World of Business?

Data plays an important role in gaining insights into customer preferences and target demographics. From every interaction with technology, data is generated that can prove to be significantly helpful to businesses. If analyzed correctly, this information can provide a lot of valuable information about customer personalities, behaviours, and life events that can help in improving products, business strategies, and marketing campaigns.

Following are the ways Big Data is transforming the business realm:-

• Reputation

Big Data has streamlined the process of scaling a business reputation in the marketplace. The collected data offers insights to formulate strategies that help a brand’s public reputation. Companies can refer back to the data from various social media platforms to gauge how their customers feel about a particular product or service. The information gathered from social platforms can also provide insights into customer interactions and satisfaction, thereby allowing companies to make necessary modifications.

• Productivity

When companies focus on increasing profitability, it must work on boosting productivity and overall operational efficiency. Companies can use multiple ERP programs in order to evaluate business functions. Big Data tools can be implemented to record the transactions in real-time and store data effectively into the software. The data collected can offer details about productivity and operational efficiency, which can help in identifying more productive employees, highlight better processes, and machines that have led to increased performance.

• Operations

An organization can make significant decisions with regards to its existing operations using Big Data. Not all business operations are profitable and worth investing in. However, until and unless operations are analyzed, it becomes difficult to identify inefficient processes. Big Data can help organizations differentiate between beneficial methods and ineffective procedures. This way businesses can make better decisions that continue to foster profitable results.

How to Become a ‘Big Data’ Analyst?

Additionally, the role of Big Data analysts is not limited to the analysis of raw data. It varies from ETL operations, working with reporting and visualization tools and machine learning algorithms. The responsibility of a data scientist is purely mathematical and statistical. A smart way to acquire the required skill set is to enrol in business analytics online certification training programs. Yes, individuals can even learn business analytics online at their own comfort and pace. Acquiring a professional certification certainly adds credibility to the professional’s portfolio and positions one at a competitively advantageous position over their peers.

Big Data is well on its way to transforming the way businesses collect data and store it. Irrespective of the field or size of the operation, data acquisition and interpretation is becoming critical to identify what the customers demand and subsequently allowing the companies to cater to those expectations in order to sustain in this ever-evolving marketplace.

More Information:

Prosperous Data Science Careers in the Digital Age

How is Big Data Analytics Different from Data Science?

What is the importance of the integration of big data and data science?

Why Enrolling for an Online Big Data Course is an Ideal Career Move?