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Data Analytics Concepts – A Detailed Guide

In today’s fast-paced, digital world, to succeed as a leader, one has to undertake numerous strategic decisions that cannot be based on assumptions, but need to be data-driven and informed decisions. That’s the key to driving growth in any industry. This catalytic change was brought about by the introduction of Data Analytics to the business world. In this article let’s delve into the core principles and fundamentals of Data Analytics Concepts, and understand what is it all about. Whether you are a student or a businessman understand the intricacies and the real-world application of Data Analytics through this article.

Data Analytics Concepts

Introduction to Data Analytics 

With the introduction of computers in the 1990s, so was the concept of data introduced. Slowly people started realizing the potential of this data. The data collected from the companies was stored, organized, and then analyzed to gain important insights about where the company had been, where it was now, and where it needs to go in the future, in terms of overall productivity, revenue, and other business functions. This was integral to companies to make strategically informed decisions, as long gone were the days of assumption-based decisions. This entire process comes under the Data Analytics Concepts. 

Data Analytics soon gained a lot of confidence in the business world, such that at present approximately 59% of companies, whether big or small depend on Data Analytics to channel their growth in the right direction. Let’s take a look at the reasons why Data Analytics has garnered so much fame: 

Better Understanding of the Customer:

The digital footprint of the customer gives a better insight into their browsing pattern, needs, likes, dislikes, purchasing pattern, and purchasing behaviour. This helps companies to increase their target audience correctly.

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Create Targeted Marketing Strategies:

Once the companies have the entire data on their target audience, with the aid of Data Analytics, targeted marketing campaigns can be created. With the availability of real-time feedback on the success or failure of the campaign, it can be tweaked at a faster pace to increase customer engagement.

Cost Optimization:

Once the company becomes aware of the trends and patterns of the target audience and product performance, it saves them huge costs from carrying out ineffective marketing campaigns, strategies, and operations bound to cause losses. This also gives an opportunity to innovate new products and services based on Data Analytics, which in turn reduces the chances of failure as it is validated by data.

Enhanced Operational Efficiency:

Whether it is a company planning to make strategic investment plans, a non-profit organization planning for raising funds, or a political candidate planning to fight the election, Data Analytics, helps increase operational efficiency by reducing the risk of failure, many notches down.

Evolution of Data Analytics

Data Analytics has been there since pre-historic times, for which archeologists have found evidence, that humans kept a tally of things on pieces of bones. The invention of Data Analytics on a larger documented scale was done in the 17th Century. In the present day 21st Century Data Analytics Concepts have come a far way, from being just a buzzword to being an integral part of the entire global scientific, business, and government community. Here is a brief view of the chronologically listed events that led to the development of the Data Analytics world, to where it is now: 

  • 1663 – Joh Graunt in England carried out the first data analysis of the public health record during the Bubonic plague pandemic by publishing the data of the death rates and the variations in the data.
  • 1884- Herman Hollerith developed the first Punch Card Tabulating Machine for employees. In 1911 he founded the company Computing- Tabulating – Recording – Company. This was later renamed IBM.

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  • 1943- During World War II, the UK developed the first data-analyzing machine called Colossus. This was used to analyze the data for cracking Nazi Codes. 
  • 1969- The Advanced Research Projects Agency Network (ARPANET) invented a wide area network which was the foundation of the Internet.
  • 1989-1990 – Tim Berners Lee and Robert Cailleau were the founders of the World Wide Web, HTML, HTTP, and URL.
  • 1997- Google was founded by Larry Page and Sergey Brin and thus started the concept of data availability, data storage, and big data.
  • 2005- Roger Mougalas came up with the term Big Data. Hadoop was invented to handle Big Data.

Data Analytics Concepts:

Data Analytics Concepts are scientifically deduced techniques that are applied to collect, process, analyze and interpret Big Data collected from sources. Here is a brief description of all the key Data Analytics Concepts: 

Data Types and Data Collection

The large volume of data which is complex and cannot be handled by traditional computing techniques is known as Big Data. As Doug Laney termed in 2001, Big Data arrives in big Volumes, at great Velocity, and has lots of Variety. This was known as the 3Vs of Data. This is further proven by the fact that every second 2.5 quintillion of data are generated globally, from various online sources. The first process in Data Analytics Concepts is identifying the type of data and then using various forms of collecting the data to analyze it. Let’s begin with the first step: 

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Data Types:

The data collected for analysis comes in two major types, which are as follows: 

  • Qualitative Type: Data that cannot be measured in numbers is known as Qualitative Data or also known as Categorical Data. This can be collected in the form of images, symbols, audio clips, or answers and texts. A few examples of this kind of data are gender, favorite language, colors, and opinion on something (answers in the form of agree, disagree, and neutral). This can further be classified into two types: 
  • Nominal Data: This data has no order and is not in any numeric order. For ex., Color of hair, gender, eye color, marital status, etc.
  • Ordinal Data: Qualitative data which has some kind of numbering presence is known as ordinal data. For example, Customer satisfaction numbering between 1-10, the grading system in educational institutes (A, B, C), and the academic level (graduate, PG, undergraduate).
  • Quantitative Data: Data that is in numbers and can be counted is known as Quantitative Data. They are of the two following types: 
  • Discreet Data: Data that is in whole numbers and cannot be divided into further smaller parts, or fractions is known as discrete data. A few examples of this kind of data are the number of students in a class, number of cars, number of employees in a company, number of tickets sold per day, etc,
  • Continuous Data – Data that can be divided into fractions or smaller numbers is known as continuous data. This data set has many ranges due to the presence of fractions, a few examples being, the height of students in a class, the time required to complete a task by a set of people, the time needed to read a particular book by the study group, etc.

Data Collection:

The data collection method depends upon the objective, the time, and the resources available. The collection method can be divided into two categories: 

  • Primary Data Collection: In this method, the data is freshly collected and has not been used in any past methods. It can be classified into the following two methods: 
  • Quantitative Method: In this method, the past data of the company is available, which is collected to predict trends and patterns. This method is generally used to forecast sales, demand and supply. The methods under this technique are as follows: 
  • Time Serie Analysis: When the historical data is available to collect the data on patterns and trends, time intervals, and frequency, then this method is applied.
  • Smoothing Technique: When the historical data is present, but it doesn’t show any trends, then the data is collected to check any random patterns occurring that could affect the final analysis.
  • Barometric Method: When there is no past data available for analysis, then the data on current events is collected and the analysis is based on it.

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  • Qualitative Method: When there is no past data available and the data is based on the feedback from the target audience, this is known as a qualitative method of collection, and it includes the following methods of collecting data: 
  • Survey: The main motive of a survey is to collect data on the target audience set’s preferences, feedback, and opinions on any product or service offered by the client company.
  • Polls: These are single or multiple-choice questionnaire sets which are not very lengthy and are easier to collate.
  • Interviews: This method of data collection is generally used when the respondent group is small, as the interviewer asks all the questions directly to the respondent.
  • Delphi Method: In this method, the assumptions and opinions of different market experts are collected and then the analysis is based on the collected assumptions.
  • Focus Group: In this method, a small set of people are chosen to be evaluated and the results are forecasted on the basis of their observations, and discussions.
  • Questionnaires: Questionnaires are a set of open-ended or close-ended questions.

Data Cleaning 

 This is step one and the most important Data Analytics Concepts. Since strategic decisions are based on the accuracy of the results of the data collected, it is very important to clean the data before processing it. Cleaning refers to removing rogue data like incomplete, inaccurate, duplicate, irrelevant, corrupt, and wrongly formatted data. This is an inevitable occurrence during data collection, thus, checking such irregularities is of utmost importance as incorrect data can lead to normal looking significantly wrong data analysis. The following are the steps for data cleaning: 

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  •  Remove Duplicate Data – Deduplication of data happens when it is collected from multiple sources, different data sets are combined, and when scraping data.  This affects the effective data analysis calculation process. Irrelevant data is another area that has to be considered. This process of cleaning makes the final data more manageable, and relevant to the primary objective and creates a minimum distraction from the primary target.
  • Structural Errors – Data is collected usually in three types, boolean, number, and strings. Sometimes the numbers can be written in texts instead of numerics, there can be typing errors, misuse of capitalization, and strange spellings. These all need to be identified and put in the correct format.
  • Unwanted Outliers: Outliers are set of data or observations which do not adhere to the values or objectives of the target data set. 
  • Missing Data – Missing rows and columns of data interfere with the proper data analysis process, sometimes even causing the analysis to fail. The missing data can be either observed and then extracted from the original data source or can be replaced by assumptions inferred from the original data set (although this method can be tedious and not always correct). 

There Are Methods of Data Cleaning Which Are as Follows: 

  • Binning Method – In this method, the data is divided into smaller sets or bins to make them easier to preprocess. It is also known as discretization and bucketing. The data when divided into smaller buckets is easy to manage, more accurate, and easy to interpret. 
  • Regression- In this technique, the data is predicted based on variables in the data set. It is used generally when the data sets are missing. 
  • Clustering – In this method, the data is grouped into clusters and then processed. The clustering makes it easier to identify outliers. The data can be easily processed.

Data Analysis

Now we come to the main section of the Data Analytics Concepts, and that is why have we done all the above-described processes. Data Analytics is converting raw data into comprehensible facts and figures with the main objective of evaluating trends, patterns, and variations. This entire exercise gives the support to take future informed decisions in business and solve recurring problems causing obstacles in the revenue and other business processes. There are four main methods of Data Analytics, which are as follows: 

  • Descriptive Analysis:  This is the most basic and first step of the analysis. In this process, the historical or past data is analyzed to identify trends and patterns. It basically answers, “what happened”. It is used for example to predict the figures of revenue generated per month or week, how many customers visited the store or the landing page, and the volume of sales in a particular period.
  • Diagnostic Analysis: The next step to predict the reason for the outcome of the descriptive analysis is, Diagnostic analysis, which supplies the reason behind the results of descriptive analysis. It examines the variables that go into predicting the object of the analysis and correlates the variables to evaluate either the growth in the revenue or the reason for the loss of revenue. For ex. A company has the objective of finding the reason for high sales, and this can be achieved by finding out the month of the highest revenue, the time period of high sales volume, and the time period and duration of an ad campaign related to the product that was floated.
  • Predictive Analysis: As the term suggests, this analysis is to forecast the future possibility of events to take place. The past data is related to variables like economic future events, market trends, and customer behavior. This method is carried out with the intention to predict and avoid unfavorable events and recreate and repeat favorable circumstances.
  •  Prescriptive Analysis: In this analysis, the objective is to make informed decisions in favor of favorable outputs based on the relation between all variables and the past data.

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Data Visualization

The final step in the Data Analytics Concepts is the representation of the analyzed data with the help of easily comprehensible methods, which even a non-technical person can understand. The methods make it easier to understand the trends, patterns, and outliers in the data. In a way, it is the amalgamation of storytelling with data analysis with the help of effective visual aids. 

The Different Types of Data Visualization Are as Follows:

  • Charts: In this method, the data, or the variables, and the correlation are displayed along two axes. They can be Bar Charts, which display numerical values and their co-relation. Gantt charts are used in project analysis to show the time spent and work completed. Pie Charts and Treemaps are other methods of data visualization.
  • Table: The results of the analysis are simply arranged in tables and columns.
  • Infographics: In this method, there is a combination of numerical values along with images. 
  • Geospatial: In this method, the assistance of a map is taken and with the help of colors and images the analysis results are displayed. Choropleths and Isopleths are two commonly used mapping techniques used in this method. Heat Maps is a method in which the analysis results are shown in different colors on the map.
  • Graphs: Different types of graphs like bullet graphs, linear, segment, etc. are used to show the relation of the variables.
  • Dashboards: In this method, the data analysis is presented in the form of visuals and data.


In conclusion, in this data driven world, for companies to get a competitive edge, unraveling the hidden potential of the data is a prerequisite to get a competitive edge. Gone are the days when businesses used to work on assumptions and guesses. Growth can be attained only when informed decisions are taken, that not only positively affect the revenue potential but also overall processes. The Data Analytics Concepts encompasses the process starting from data identification and its collection to data cleaning. The raw data which is now more comprehensible is analyzed through analytics methods like descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis and is transformed into results showing the trends and patterns in the data.

Data Visualization is story telling put in simple terms, to make complex data into eye-catching compelling narrations, that even get the attention of a non-technical person. With the help of charts, graphs, geospatial techniques, infographics, and dashboard data analysts convert complex and boring information into bursts of colors and images which not only attract the attention of the board members, but it gets into the minds of the members too. However, these methods come with their set of obstacles too, like misinterpretation of the information or misrepresentation of data. 

With continuous new developments happening the world of data analytics is also growing leaps and bounds. Aspiring candidates and professionals have to constantly learn and keep themselves updated to get the leadership edge. 

As for businesses, in this data-driven revolution, they must know how to harness the power of the data and explore means to take more informed decisions.

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Q. What are Data Analytics Concepts?

Data Analytics Concepts encompass the steps of converting raw data into comprehensible information based on which informed decisions are taken. The steps involve collecting raw data through methods like surveys, interviews, polls, time series analysis, barometric methods, and others, and categorizing it. The second step is cleaning the data to ensure the analysis results are not miscalculated with the help of binning methods, clustering, and regression techniques. Once the data is cleaned, then with the help of analytic techniques like descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis the data is evaluated, and trends and patterns are obtained.  The results then with the help of Data Visualization methods are presented.

Q. What is the difference between traditional data processing methods and Data Analytics Concepts?

In traditional data processing methods, the data collected is for the purpose of having information at hand, generating reports, and managing transactions. Data Analytics involves not only collecting data but inferring trends and patterns from them to make informed decisions regarding future actions. With the growth in business volumes and going global, with the traditional data processing method, collecting data becomes difficult on the other hand data analytics is designed to handle large volumes of data. 

Q. How can businesses employ Data Analytics Concepts to get a competitive edge?

With the help of Data Analytics, a business can find out the relation between its past data to different variables to predict trends and patterns, on the basis of which they can make informed decisions. These decisions are taken to increase the revenue flow, optimize operational efficiency, optimize cost on advertising campaigns and avoid unfavorable events. 

Geetanjali Pantvaidya is a Post Graduate in MBA Marketing from Army Institue of Management Kolkatta. A Y2k batch pass out , She started her career with Caltiger.com which the country’s first free ISP. She has over 12 years experience in marketing working in the telecom industry, banking , insurance and the education industry. Hailing from an army family background, the love for travelling was deeply rooted in her veins since childhood, thus, her stint as a travel manager with Thomas Cook. She embarked on her journey as a content writer with a travel company.

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