This is how Big Data Analytics differ from Regular analytics

Analytics refers to the process of analysis of raw data or statistics in order to discover, communicate and interpret its meaningful patterns. Analytics is helpful for businesses in decision-making, improvement of performance, and much more.

Sometimes, the amount of data is huge and normal analytics cannot be used for such complex data. Such data is termed ‘Big Data and big data analytics are used for its analysis. However, how does big data analytics differ from regular data analytics?

The main difference between the two is the type of data that is to be analyzed. The tools and techniques used for big data analytics are way more advanced than the ones used for regular data analytics. The application of both of the analytics varies, also, according to what level of advancement and accuracy is required.  

Through this article, you will get to know in detail how big data analytics is different from regular analytics.

Big data Analytics vs. Regular analytics: 6 key Areas to Look at

1.    Nature of Data

“Big data is not just big. It’s also diverse data types and streaming data”

PHILIP RUSSOM

As mentioned earlier, big data analytics is a complex form of data analytics in which complex and huge data is examined to extract useful information. The data is usually unstructured and its size is quite heavy, in terabytes and petabytes. Big data can also be structured or semi-structured, but its analysis is still different and complicated as compared to regular data of course, because of its size.

The characteristics of big data are generally described through 3Vs

  1. Volume: Volume or size is the main characteristic of big data. The name ‘big data’ itself shows that the amount of data will be enormous.
  1. Variety: Variety that is found in big data also differentiates it from regular data. In big data, there are numerous sources of data and that, in various forms. Also, large inconsistencies are shown by big data. 
  1. Velocity: The velocity of Big data refers to the speed of data at which it flows from source to source and also, the speed at which it is generated and operated.   

On the other hand, the data in regular or traditional analytics is usually structured and is measured in Megabytes or Gigabytes. It is easier to analyze such data in which consistency can be observed and prediction of trend is not hectic.

So, the difference between the source data itself makes the analysis procedure different. It is mainly the nature of data that differs big data analytics from regular data analytics. An example of big data can be data generated at the New York Stock Exchange. 

Want to learn more? Check out the HBS website and learn about Big Data.

2.    Tools used:

As the nature of data is different in big data analytics from regular data analytics, different kinds of tools are used for big data analytics. These tools have sophisticated technology like parallel computing or automation tools to deal with data as it is difficult to manage a huge amount of big data.  Some well know tools are listed below:

  1. Xplenty
  2. Analytics
  3. Microsoft HDInsight
  4. Skytree
  5. Talend
  6. Splice Machine
  7. Spark
  8. Plotly
  9. Apache SAMOA
  10. Lumify

More about features of Best Tools for Big Data Analysis.

Of course, tools used for big data can be used for regular data but it is not the same in the opposite case. Tools used for routine data analytics are simple that can be conveniently used for predictive modeling etc. as the data to be analyzed is not complicated.  Some popular data analytics tools are:

  1. Python
  2. SAS
  3. Excel
  4. R
  5. Power BI
  6. Tableau
  7. Apache Spark

3. Application

Big data analytics is often used in industries like banking, retail, telecommunication, etc., where there are usually loads of data to be processed, stored, and analyzed to gather insights, these types of industries enable themselves through such insights to make better data-driven decisions.

  • Many financial services use big data analytics. Credit cards, insurance companies, etc. have the problem of a huge amount of data that is multi-structured. So, they use big data analytics for customer analytics, fraud analytics, compliance analytics, operational analytics, etc.
  • In the communication industry, when customers subscribe to their services, again, a massive data management problem arises which they resolve through big data analytics. It helps them in combining and examining customer and machine-generated data that is produced daily.    
  • In retail, the competition is very tough between different retailers. To remain in this competition, retailers use big data analytics for their website management, transactions, card details, social media, and different rewarding programs management.

 

Regular data analytics is often used by health care, travel industries,  etc. where there is no complicated data management involved.

  • In healthcare centers, the main challenge is to treat their patients effectively. They have records of patient disease history and treatments. Also, they have to manage the equipment used in healthcare centers. So, for these efficiencies, data analytics is used.
  • As far as travel agencies are concerned, they deal with the record of travelers and data from ticket buying mediums. So, they adopt regular data analytics for managing travelers’ records, correlating sales, and getting insight into the preference of customers. Other than that, data analytics is used in gaming, energy management, etc, fields.

4. Working domain

The main duty of a data analyst is to analyze the current trend and to forecast and predict the possible events that might happen in the future. He can give certain recommendations for strategic decision-making by analyzing the ongoing business trend.

The purpose of data analysis is to get that information that is beneficial for the business. So, a data analyst must derive those insights from the data that are meaningful for the business. After all this analysis, he designs data reports. It is important for him to make the report presentable and clear so that the decision-makers can understand it easily. These reports may include charts, tables, graphs, and diagrams, etc.

After collecting, processing, and analyzing data, he summarizes the results of the data through which future patterns and trends can be predicted. The skills required by a data analyst are:

  1. Data Visualization Skills
  2. Wrangling skills
  3. Mathematical knowledge
  4. Statistical knowledge
  5. Programing knowledge

But for a big data analyst, he is required to analyze real-time situations. Through the analysis of events happening on a real-time basis, business companies are able to take timely actions to solve any issue and to take advantage of the opportunity.

Another important duty of big data analysts is to detect fraud and fraud transactions. This responsibility is prominent in sectors like banking, retail as many fraud transactions happen in banks, etc on a daily basis. So, this particular task is very crucial as it directly relates to customers’ trust. More skills that must be acquired by the big data analyst are:

  1. Knowledge of frameworks
  2. Computation and Statistical skills
  3. Programming skills with languages
  4. Knowledge of technologies
  5. Knowledge of distributed system

Based on the skills and effort put in by both specialists, obviously, there’s a major salary difference. According to the job insight company, Glassdoor, the average salary for a big data analyst is $103,000 annually and for a data analyst, it is $62,453 annually. Well, that’s an average and can vary on the basis of experience.

If you want to learn more about the career of data analyst, the different industries, jobs, pay grades by country, and level of experience, check out this in-depth article on a Data Analyst Career and Salary Potential. Also, you can learn about How long it takes to Learn Data Analytics in this article.

5. Procedures Involved

The basic steps in any form of data analytics are:

  1. Defining Question
  2. Setting Goals
  3. Collecting Data
  4. Analyzing Data
  5. Interpreting Results

Now, how are these primary steps performed differently in Big Data Analytics? We know that big data is generally in scattered form. So, firstly the important and relevant information is compiled and then questions arise from the data.

After the establishment of the business’ goals and objectives, data is collected from various sources and it is often unstructured. The sources from where the data is collected can be internet clickstream, mobile apps, cloud apps, web servers, social media, feedback, and emails of customers or machine data that might have been captured by sensors.

Then, after storing this data in data warehouses, it is examined and processed. It is categorized and organized so that it could be understandable and easier to operate. As high-level outputs are expected, this data is cleansed with the help of different software and tools. Through data cleansing, the data gets purified from inconsistencies and any sort of errors that may include formatting mistakes, repetition, etc. 

Then the analysis is done using various tools for the various tasks, like tools for data mining, predictive analysis, deep learning, AI, etc. These tools are generally part of advanced software. We have an entire article on data analytics tools and how to use them, go ahead and read it Here.

6. Trends and Examples

The robots used for taking orders and transactions, live support, etc. are most popular in big data analytics. Precise product searching, Internet of Things (IoT), and Artificial Intelligence AI are also in trend.

In data analytics, there is a high demand for machine learning. Also, data visualization, predictive analytics, data curation, data engineers, metadata management trend in data analytics.

Now, take an example of a streaming service that generates a lot of unstructured data that includes video, audio, text, etc. The management of this data will be done by big data analytics. But the content that will be recommended to the users will be managed by regular data analytics. 

Conclusion:

From the above discussion, it is established that big data analytics is used where there is more complexity in data and important decisions are to be taken from it. Big data analytics assist your business and help in effective marketing, newer opportunities, providing efficiency with accuracy, and customer personalization.

Regular data analytics are being used in almost every type of business and it is easier to learn, whereas big data analytics is specific and complicated. But through the complicated procedures, your business can stand well in the market.  

REFERENCES

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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