5 Best Data Analytics Tools for 2023

8 Dec · 5 min read

5 Best Data Analytics Tools for 2023

Data is one of the most valuable assets in the world at the moment. Due to this, the necessity of turning data into a form that is easy for human consumption has become necessary. The collected data comes in a raw form that looks like gibberish to most. It needs to be presented in an understandable format to extract information and valuable insights from it. Data analytics has single-handedly proven to improve business performance exponentially over the past few years.

What is Data Analytics?

Data analytics is an umbrella term used for different data analysis techniques. Businesses have started utilizing these data analytics techniques to improve the efficiencies of operations and business processes. This is due to the fact that the tools that perform these tasks not only help understand the pain points but also point towards actionable insights for improvement. Using data analytics, business owners can now understand and improve on the issues that exist within their funnels, marketing plans, user interfaces, and supply chains. Data analytics techniques are being used by businesses for the following purposes:

  • Increase engagement
  • Improve user experience
  • Making informed decisions
  • Streamlining tasks
  • Prioritizing business needs
  • Eliminate bottlenecks, etc.

The aforementioned implementations have allowed businesses to reduce overhead and increase profitability across industries. With that being said, several tools can help do predictive data analysis or create actionable insights with the given data.

Top 5 tools for Data Analytics

The following tools are considered industry standards when it comes to data analysis and the extraction of valuable insights:

  • Excel
  • Power BI
  • Python and R
  • Tableau
  • Apache Spark

1) Microsoft Excel

Excel is one of the most powerful spreadsheet programs that can be used for data collection and analysis. The data can easily be cleaned and arranged using filters and functions. Any kind of quantitative data can be organized, calculated, and evaluated using the software.

Excel has a built-in pivot table feature that can be used to sort data. Apart from that, by using the right functions on the given data, data analysts can easily extract meaningful information.

Excel also offers add-ons like "Data Analysis ToolPak" to perform statistical analysis and visualisations of your data. Similarly, other add-ons can be added to cater to the shortcomings of Excel.

2) Power BI

Power BI offers visualization, dashboarding, and reporting of data. It is one of the most popular tools developed by Microsoft for data analysis and has won multiple awards over the years.

Business Intelligence (BI) is the act of using software to turn raw data into meaningful insights. Power BI achieves the purpose through its custom visualization tools. The software has multiple versions for different use cases, desktop, pro, and premium. Due to its popularity and utility, it is used by organizations like Adobe, Heathrow, Nestle, etc. Power BI has a very minimal learning curve and has a lot of free training videos available online.

3) Python and R

Unlike others on the list, Python and R are programming languages. Both of these are used as the top programming languages in the Data Analytics field. Python and R are both open-source languages, and a lot of code is available for use online. Both of these languages are free to use and are used by developers globally.

Python is an easy-to-learn programming language due to its readable format. It has a lot of libraries available to perform data analysis tasks. Python’s library, Pandas, is extremely powerful and is used by data analysts, data scientists, and data engineers globally. The Matplotlib function helps with data visualizations that can be performed on any sort of data imported through code. There are other libraries available for statistical modeling, including Scipy, Scikit-learn, and StatsModels.

R has a steeper learning curve as it is not as high-level as Python. It is mainly used for statistical modelling, big data, machine learning, and visualizations. The main use case for R is to perform heavy statistical computing tasks. R has a very active community of developers who post a lot of open-source material, making its usage easy.

4) Tableau

Tableau is a solution for creating dashboards and data visualisations without coding expertise. The easy and interactive UI makes it very easy to use and navigate. Most operations can be performed through simple drag and drop. It is used to handle large amounts of data and create meaningful results in seconds. The software can be used for data manipulation as well, but since it lacks the scripting element, there are limitations. However, the visualisations are very advanced, due to which they are considered the industry standard by organisations worldwide.

5) Apache Spark

Apache Spark is a big data processing framework. It is used by data scientists to process vast data sets. Since it has a defined use case for large data sets, it can be used to access data across different platforms. It uses RAM instead of local memory, which makes it an extremely fast alternative to the competitors. Apache can easily be used to process unstructured big data. The software also has libraries for machine learning, which can be used to create value out of the data. Due to its speed and ability to swiftly handle huge amounts of data, Apache Spark is trusted by companies like Uber, Slack, and Spotify for their data analytics tasks.


A lot of businesses go through recurring issues in their funnels, supply chains, and operations. The wave of awareness related to data has allowed these businesses to tackle and mitigate these issues using the tools mentioned above. For example, Excel, Python, and Tableau can be used to collect data, clean data, manipulate it, and create meaningful visualizations. However, these tools are powerful enough that they can be used as standalone solutions as well.

As for individuals, the tools mentioned above are in high demand. Organizations are collecting this data and trying to make use of it. Mastering the tools and data analysis techniques not only helps organizations improve and grow, but it also helps people get employable skills. A data analyst roughly makes $70k-$100k in the US. All in all, data is exploding and creating opportunities for businesses and individuals. Putting this massive amount of data into a meaningful visual form enables businesses to make critical decisions for their businesses. Tools like Excel, Python, Tableau, R, and Apache Spark are creating immense value in this regard.

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