Data is one of the most abundant resources in nature as it revolves around almost anything. In analyzing data, we simply refer to a process of breaking a complex data set into smaller parts in order to gain a better understanding of it.

In other words, we can define Data Analysis as a process of inspecting, cleansing, modelling and transforming data with the aim of discovering useful information, informing conclusions and supporting decision making.

To decide which data analytic tool best suits your work we will have to consider some factors for which can be a determinant and also, we will talk briefly on the in-demand data analytic tools available in the world today.



  1. Consider your organization’s business needs and your job description.
  2. Consider if it will be used by non-technical users or by data analysts or the interface should suit both users.
  3. Find out if the tool provides visual context through maps or graphs relevant to your organization or work.
  4. Consider the ability of the tool to model data. Data modelling entails the process of analyzing data objects (data table) and their relationship to other objects.
  5. Consider price and licensing. Some are free, some are subscription based.


  1. Excel For Data Analysis: It is the most common tool used for manipulating spreadsheet. It can support almost any standard analytics workflow and is extendable through its native programming language (Visual Basic).Suitable for simple analysis NOT suited for analyzing big data. It is limited to 1million rows. Large companies should consider more modern cloud -based analytics platforms for large and collaborative analyses.
  2. Tableau For Data Analysis: Tableau majorly works to connect and extract data stored in various places such as Excel, PDF, Oracle or AWS. Data extracts can be connected live to the tableau data engine. Data analysts works with the data that was pulled from other sources and develops a pictorial representation of the data using maps, graphs, charts or other visuals. Tableau can run on the cloud and on premises. The drag and drop dashboard and visualization components translates into efficient back-end queries and minimizes end user performance optimization. However, it lacks support for advanced SQL queries.

  3. Sql For Data Analysis: Structure Query Language is a widely accepted language used for database maintenance by performing operations such as retrieval and manipulation of data stored in a database. Its statements are used to perform tasks such as retrieving and updating data from a database.

  4. Microsoft Power Bi For Data Analysis: It is a powerful business analytics tool that provides non-technical business users with tools for aggregating, analyzing, visualizing and sharing data. Interface works a lot seamlessly with users familiar with Excel. The platform supports dozens of data sources, allows users to share reports, visualizations and dashboards. End users can combine a group of dashboards and reports into a Power BI app for simple distribution. It allows users to build automated machine learning models and integrates with Azure machine learning.

  5. Python For Data Analysis: It is a multifunctional maximally interpreted programming language with several advantages. It is an open-source high level programming language. Its distinctive characteristics that make it best option for data analysis are: (a) Easy to learn: novices can easily use its relatively simple syntax to create effective solutions for complex scenarios with just few lines of codes. (b) Versatility: its flexibility helps for data models to be created. (c) Huge libraries collection: It has free libraries that are open to the public and such libraries keep expanding and provides robust solutions. (d) Graphics and Visualization: It provides users with an abundance of different visualization options which is a must have for all data science.

  6. R Programming For Data Analysis: It is an open-source programming language and computing environment with a focus on statistics and graphical data visualizations. Features like numerous graphical tools and over 15,000 open-source packages available including many for loading, manipulating, modelling and visualizing data. The environment allows technical analysts with programming skills to build almost any type of data table but users without those programming skills can’t cope with it.


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