What is Data Analytics? Everything You Need to Know in a Nutshell


The correct data can help businesses understand their client’s needs and improve their marketing campaigns. Companies can personalize their product as per the needs of their customers also. If you proceed in the right direction, the process brings fruitful results. But none of this would be possible without proper analysis of data. So, the question is, what is data analytics? Let’s know.

What is Data Analytics?

Data analytics means collecting and analyzing atomic data to conclude the target audience’s behavior. It is the act of examining and analyzing massive datasets to uncover hidden patterns, trends, and correlations and derive essential insights for business forecasting. It boosts the company’s speed and efficiency.

The methodologies used to analyze data differ depending on the organization’s needs. Whatever the company may be, the data analytics tools and techniques used should uncover as many secrets as possible and provide valuable insights to scale the business in the right direction.

Many data analytics techniques use specialized methods, systems, and software to get the desired results. The influence of data analytics is such that it can impact a company’s decisions. The predictions made from data analytics play a vital role at the managerial level. Managers and executives use these predictions to stay competitive in the market.

How to do Data Analytics?

1) Fix your objectives and questions

The very first thing a company needs to do to perform data analytics is to fix its goal. The right direction is essential in business, but the chances of getting onto the wrong path are also high without knowing the right objective.

A company needs to fix its goals and start creating questions around them. This is necessary to collect the data that data scientists will analyze later. For example, if a company sells mobile phones and is not satisfied with the results, the questions that should be asked would look like this:

  1. Are we targeting the right audience?
  2. If yes, then do our products provide the desired value for money?
  3. If not, then which platform does our target audience spend time on?
  4. Is our product a competitive one?

These are some questions the answers to which the company needs to know.

2) Data Collection

Once the company has laid the foundation of the right questions, the process of data collection starts. Data can be collected from various sources.

Talking to the employees and understanding what they feel is what most companies use. Another reliable data source can be the sales representatives, who speak with the company’s customers to know about the requirements of the customers.

Many companies use online surveys to collect data on a large scale. There are plenty of ways with which a company can collect data.

3) Data Cleaning

One minor but crucial step before the data analysis begins is data cleaning. It happens often that after collecting data from surveys, interviews, and other sources, some information might be missing and some of the values are inaccurately placed.

In step three of data analytics, we also clean the data and remove the unnecessary data. The data collected from errors is known as dirty data. The Data Warehouse Institute (TDWI) estimates that unclean data costs businesses $600 billion each year.

Companies must understand what generates dirty data and how to fix it best to address the issue entirely. For high-quality data, data clean(s)ing is not an option. Rather than wasting time in cleaning up data, it is critical to ensure that information is kept correctly.

4) Analyzing the Data

Once the data is cleaned, amended, and organized, the next step would be to analyze it using the best data analytics tools. The methods and techniques to be used here would be influenced by the type of outcome required. Data is generally of two types: quantitative and qualitative.

Quantitative data refers to numbers that can be added or subtracted or multiplied or divided and still give a helpful number. For example, suppose the revenue generated by a company in the last five years gets added and divided by five. In that case, it will tell us about the average sales revenue per year of the previous five years.

Qualitative data, however, is a little more theoretical and easy to interpret.  Qualitative data is theoretical data one can find from interviews, surveys, questionnaires, and so on. Exploring qualitative data helps us in finding new ideas.

5) Interpreting the Data

The final step in the data analysis process is about interpreting the data and checking the quality of results. Expectations are set during the first step of data analytics and verifying that our expectations meet reality is done in the final step.

Finding the behavior, and future trends helps in making decisions. This part also includes visualizing the data. Information can be shown in the form of pie charts, bar graphs, histograms, and so forth. These charts make it easier to understand the numbers. Histogram is highly regarded to represent quantitative data.

6) Summary

No analysis is complete without writing a summary and a conclusion. Summarizing what have been stated so far and concluding the same to obtain a final product of the process is a necessity. Conclusion is written based on the knowledge of facts analyzed above and it gives direction towards a new decision that is to be made for scaling the business.


Data analytics is an important part of the data science process. It is an important tool that helps organizations and individuals to draw out actionable insights from the collected data.

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