Big Data vs Data Analytics: Difference Between Big Data and Data Analytics

What is Big Data?

Big Data refers to the massive volumes of unstructured and raw data from various sources. Big Data comes with high veracity and is in high volume, and this requires high computing power to collect and process. All these data are collected through various means such as social media, internet, mobile, computer and many more. These data are later processed and analyzed to take strategic decisions in businesses.

Check out the scope of a career in big data.

What is Data Analytics?

Data Analytics means analyzing of data. Data collected from various sources through the internet are processed and then analyzed so that businesses can get operational insights. Complex business problems can easily be solved by analyzing the historical data collected, and that is why Data Analytics is essential. The data related to the issues in business are particularly processed and analyzed to find the solution to a specific problem. Check out our data science courses if you are eager to get into data science.

What is the difference between Data Analytics and Big Data?

  1. Nature: Let’s understand the fundamental difference between Big Data and Data Analytics with an example. Data Analytics like a book where you can find a solution to your problems, on the other hand, Big Data can be considered as a Big Library where all the answers to all the questions are there but difficult to find the answers to your questions.
  1. Structure of Data: In data analytics, one will find that the data will be already structured and it is easy to find an answer to a question. But, on the other hand, Big Data is a mostly unstructured set of data that has to be sorted out to find an answer to any question, and it is not very easy to process those enormous volumes of data. Lots of filters have to be applied to find some meaning insight into Big Data.
  1. Tools used in Big Data vs Data Analytics: In Data Analytics, one will use simple tools for statistical modelling and predictive modelling because the data to analyze is already structured and not complicated. In Big Data, one will need to use sophisticated technological tools such as automation tools or parallel computing tools to manage the Big Data because it is not easy to process the enormous volume of Big Data. More about Big Data Tools. 
  1. Type of Industry using Big Data and Data Analytics:

Data Analytics is mainly used by industries like IT Industries, Travel Industries, and Healthcare Industries. Data Analytics helps these industries to create new developments which are done by using historical data and analyzing past trends & patterns. Whereas, Big Data is used by industries such as banking industries, retail industries and many more. Big Data helps these industries in many ways to take some strategic business decisions.

Application of Data Analytics and Big Data

For all kinds of decisions taken today, Data is the base for it. Without the Data, No decisions or actions can be made today. All the companies are now using an approach called a data-focused approach to get success. There are lots of career opportunities in the field of data nowadays, like Data Scientist, Data Experts, etc.

Job Responsibilities of Data Analysts

  1. Analyzing Trends and Patterns: Data Analysts have to predict and forecast what may happen in the future, which could be very helpful in strategic decision making to the businesses. In this case, a data analyst has to spot the trends that have happened over time. He also has to make specific recommendations by analyzing the patterns.
  1. Creating and Designing Data Report: The reports given by a data scientist is the essential prerequisite in the decision making of a company. Data scientists will need to create the data report and design it in such a way that it is very easily understandable by the decision-maker. Data can be represented in many ways like pie-charts, graphs, charts, diagrams and many more. Reporting of Data can also be done in the form of a table depending on the nature of data to be shown.
  1. Deriving valuable insights from the Data: The Data Analysts will need to derive useful and meaningful insights from the package of Data to bring some benefits to the organizations. The organization will be able to use those meaningful and unique insights to make the best decision for the success of their company.
  1. Collection, Processing and Summarizing of Data: A Data Analyst has first to collect the data and then process it using the required tools and then summarize the data to be easily understood. The summarized data can tell a lot about the trends and patterns which will be used to predict things and forecasting.

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Job Responsibilities of Big Data Professionals

  1. Analyzing Real-time Situations: Big Data Professionals are very much required to analyze and monitor situations that are happening on a real-time basis. It will help a lot of companies to take prompt and timely action to counter any issue or problem and to benefit from the opportunity. In this way, many organizations can reduce losses and increase profits and become more successful.
  2. Building a System to Process Large Scale Data: It is not a very easy task to process the Big Data, which is in very High Volume. Big Data is also unstructured data that cannot be processed by any simple tool. A Big Data Professional is required to build a sophisticated technological tool or system using which the Big Data can be processed and then analyzed for better decision making.
  1. Detecting Fraud Transactions: Fraud is increasing day by day, and it is essential to counter this problem. Big Data professionals should be able to identify any fraudulent transactions happening. These are the significant responsibilities for many industries, especially banking of the banking sector. Many fraudulent transactions are happening in banking sectors every day, and it is a considerable need of the hour for banks to solve this issue. Else, people will start losing their trust in the banking system to save their hard-earned money in banks.

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Skills Required for Data Analytics

  1. The skill of Data Visualization: It is one of the most critical skills for Data Analytics. The Data has to be visually represented to the decision-maker which they can easily understand. The data visualization can be done through many diagrams such as charts, graphs, pie-chart and many more.
  1. Good Skill of Mathematics Calculation and Statistical Knowledge: A Data Analytics shall have excellent skills in statistics and mathematics to conclude the data analyzed.
  1. Wrangling skill for Data: The Data might be in a messed up format, and a Data Scientist should be able to solve the messed & complex data and present it in a format that can be given to the decision-makers or concerned people.
  1. Programming Knowledge: Good Knowledge of Python Programming language and R.

Read: Big data jobs and its career opportunities

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Skills Required for Big Data Professionals

  1. Statistical and Computation Skills
  2. Good Knowledge of Frameworks such as Hadoop or Apache
  3. Excellent understanding of Scala and Java programming language
  4. Ability to create Good Data Strategy by collection, interpretation and analyzing of Data
  5. Excellent knowledge of Distributed Systems and Technologies. 

Learn: Mapreduce in big data

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So here are the major difference between Big data and Data analytics in terms of what they fundamentally are, their applications and job responsibilities. We hope this article has been informative to you. 

If you are curious to learn about big data, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

What are the constraints of big data in terms of making management decisions?

Business Intelligence uses data with a high information density to assess things or discover patterns. Big data has the power to change the way decision makers see company challenges in general and affect strategic decisions. Thus, they can rely on objective facts. Big data frequently leads to managers relying too much on data and deferring decision-making. Using data to support a thoughtful choice is admirable, but simply adopting it without inquiry or leaving room for experience and gut instincts can result in poor judgments.

What sort of data analytics provides us with the most useful data?

Prescriptive analytics is the most useful yet underutilized type of data analysis. Prescriptive analysis considers a variety of options and makes recommendations based on the findings of descriptive and predictive analytics on a particular dataset. A prescriptive model, in essence, examines all of the various choice patterns or paths that a firm may follow, as well as their anticipated results.

What is the most common programming language used by data analysts?

Python has a number of useful libraries for dealing with data science applications. Python's popularity in the scientific and research sectors stems from its ease of use and straightforward syntax, which makes it simple to learn even for those without a technical background.

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