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Why A Data Analyst? A Blog About Why Data Analysts Are Important And How They Deliver Insights

Data plays a crucial role in any business. In today's data-driven world, businesses are collecting vast amounts of data every day. It helps them study market trends and make predictions for future demands.

This data can come from various sources such as social media, customer interactions, sales, and much more. However, this data is meaningless if it cannot be analyzed and turned into actionable insights. This is where data analytics comes in.

In this blog, we will discuss what data analytics is and how it is used./p>

What is Data Analytics?

Data analytics is the process of collecting, processing, and analyzing data to gain insights and make informed decisions. In simple words, it is the process of organizing raw data into a meaningful form by using various tools and techniques.

This process involves the use of statistical and computational techniques to extract useful information from data. Data analysis can be used to identify patterns, trends, and relationships in data. It can also be used to predict future outcomes based on historical data. This helps businesses better optimize the processes for the upcoming future demand and also enables them to improve their existing products or services.

Data analytics is typically used for business intelligence and decision-making purposes. It focuses on answering specific questions related to business operations and helps organizations make data-driven decisions. The goal of data analytics is to optimize business performance by identifying opportunities for improvement and making data-driven decisions.

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Types of Data Analysis

There are three main types of data analytics:

  • Descriptive Analysis: It is the most common type of data analysis. This type of analytics is used to describe what has happened in the past. It involves summarizing and describing data using statistical measures such as mean, median, mode, and standard deviation.
    Descriptive analysis is used to understand the characteristics of the data, such as its distribution, central tendency, and variability. This analysis is further useful for generating insights and identifying patterns in data.
  • Diagnostic Analysis: This type of analysis is used to identify the cause of a problem or issue. It answers the question of why it happened in the past. It involves examining data to identify patterns and relationships that may be responsible for the problem.
    The data analyst tracks such data to look for the reason for such anomalies. The diagnostic analysis is useful for identifying factors that contribute to a problem and for developing solutions to address them.
  • Predictive Analysis: Predictive analysis is used to make predictions about future events or trends based on historical data. This type of analysis is used to predict what might happen in the future. It involves using statistical models and machine learning algorithms to identify patterns and relationships in data and make predictions.
    Predictive analysis is useful for forecasting future trends and identifying opportunities and risks.
  • Prescriptive Analysis: Prescriptive analysis is used to determine the best course of action to achieve a desired outcome. This type of analysis is used to prescribe what actions should be taken to achieve a particular outcome. It involves using optimization algorithms to identify the best course of action based on historical data.
    Performing prescriptive analysis involves analyzing data to identify potential solutions and then selecting the best one based on the available data. This analysis is useful for optimizing business operations and making data-driven decisions.

Data Analytics Process

The data analytics process typically involves the following steps:

  • Data Collection: This step involves collecting data from various sources such as databases, social media, and customer interactions.
  • Data Preparation: This step involves cleaning and preparing the data for analysis. This may involve removing missing values, dealing with outliers, and transforming the data.
  • Data Analysis: This step involves using statistical and computational techniques to analyze the data. This may involve identifying patterns, trends, and relationships in the data.
  • Data Visualization: This step involves creating visual representations of the data to help understand the insights gained from the analysis.
  • Reporting and Decision Making: This step involves presenting the insights gained from the analysis to stakeholders and making informed decisions based on those insights.

Also read about 'Creating a basic data visualization with Amazon QuickSight'

Data Analytics Tools

Here is a list of common data analysis tools that are accepted and used by various organizations. The list of most popular data analysis tools includes:

  • Microsoft Excel: Excel is a powerful tool for data analysis, especially for small to medium-sized datasets.
  • Amazon QuickSight: Amazon QuickSight is a data visualization tool that allows users to create interactive dashboards and visualizations.
  • Python: Python is a popular programming language for data analysis and machine learning.
  • R: R is a statistical programming language that is widely used for data analysis and statistical modeling.

Other data analysis tools include Power BI, ThoughtSpot, Qlik, SAS Business Intelligence, Google Data Studio, Redash, Metabase, etc.

What is the scope of data analysis in the future?

The scope of data analysis in the future is vast and exciting, as the amount of data being generated continues to increase at an unprecedented rate. With the advent of big data technologies, machine learning algorithms, and artificial intelligence, data analysts will have access to more sophisticated tools and techniques to analyze and interpret data.

The use of data analytics will continue to play a critical role in helping businesses make informed decisions, optimize processes, and identify new growth opportunities. As new sources of data emerge, such as the Internet of Things and social media, the scope of data analysis will expand to encompass a wider range of data types and formats.

In addition, data analytics will become more accessible to non-technical users, as tools for data visualization and dashboarding become more user-friendly. Overall, the scope of data analysis in the future is vast, and those with the skills to harness its power will have a bright future ahead of them.


Data analytics is a powerful tool for businesses. This helps them gain insights from their data and make informed decisions. Performing analysis of the data involves collecting, processing, and analyzing data to identify patterns, trends, and relationships. There are four main types of data analysis includes descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. The data analytics process typically involves data collection, preparation, analysis, visualization, and reporting, using tools such as Excel, Amazon QuickSight, Python, R, etc.

ITTStar has been working with various companies by engaging with their data and providing them with data-driven solutions through actionable insights. Our team of experienced data engineers, analysts, and data scientists can help you prepare business dashboards for your products and services.

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A. Data analysts help companies collect and analyze data. This helps in predicting future demands thereby helping them be prepared for upcoming challenges.

A. Although all the analytics tools included in this blog are widely used by businesses, each tool carries an importance of its own. For example, Amazon QuickSight is an excellent tool for creating dashboards while Python is used for the analysis of big data.

A. data analyst is responsible for thoroughly interpreting and analyzing the data. They also need to determine how to visually present data to the management and the stakeholders. They also must ensure data visualization covers business KPIs so that data-driven decisions are taken effectively.