Must Have Data Analysis Tools in 2022

  • Datameer, Inc.
  • June 23, 2022
Must Have Data Analysis Tools in 2022

Data analysis tools help to wrangle data – arrange data of different types and in different formats, clean data, and represent aspects of that data to produce actionable information that enhances the profitability of your business model. Let’s take a look at the must have data analysis tools in 2022.

What to do with all that data?

Organizations have to collect data frequently; this ensures that insights needed to make informed decisions that lead to outstanding outcomes are generated, analyzed, and factored.

Data is a term that was used even before computers went mainstream and have become an essential part of how we use computers, especially in the business world today.

The increased amount of available data has brought with it the challenge of finding more efficient ways to process, analyze and represent that data to generate meaningful insights.

With massive data available to arrange, data analysts generally need to sort through data to determine what is essential, generate precise business insights, and finally use that data to shape their decision-making process. 

To make this article simple and informative, we’ll look at the best data analysis tools in the context of the existing tools and their examples, according to top-rated data consultant Jonathan Ng.

Types of Data Analysis Tools:

1. Spreadsheets 

Data is often stored in many forms, one of the most common being spreadsheets. A spreadsheet is a valuable tool that lets you capture and display data in a tabular format.

It provides a simple interface for collecting and displaying data in rows and columns, which is helpful in displaying data of different categories.

The best software of this type includes;

– Microsoft Excel 

Microsoft Excel is a spreadsheet software by Microsoft and is considered the leading spreadsheet software by all data enthusiasts. 

Key Features

  • Simple and easy interface for organizing and displaying sets of data.
  • Includes features for applying formulas and performing calculations on multiple cells of data.
  • Provides tools and features for visualizing data on charts and graphs. 

Google Sheets

Google Sheets is an online spreadsheet software by Google used to create tables that store related sets of data. 

All data captured and the manipulations done on them are stored on Google Cloud, presenting more advantages such as easy accessibility, better collaboration, and fault tolerance, which means supporting uninterrupted access to your data despite the failure of your hardware components.

 Key Features

  • Simple and easy interface for organizing and displaying sets of data.
  • Includes features for applying formulas and performing calculations on multiple cells of data.
  • Provides tools and features for visualizing data on charts and graphs.

 2. ETL Tools

In computing, extract, transform, the load is a three-step process where data is extracted, then transformed, and finally loaded into a single data container. 

The data is often gotten from different sources and is usually of different types, so it must first be converted into a unified format.

A special software of this type is:

– Talend

Talend is an ETL tool for Data Integration. It provides software solutions for data preparation and data quality, amongst other things.

 It allows for working with data from different sources.

 Key Features

  • Free to use.
  • Makes use of connectors to add data from different sources.
  • The large community of support.
  • Offers training and certification exams.

3. Databases 

A database provides a structure for storing data that will usually have to be used by third-party software.

It proves to be a helpful tool when you are working on data that has been collected from sources like web and mobile applications.

A good example is:


This is a relational database. A relational database is a database where each table has a relationship with another. 

Oracle developed it and is one of the most commonly used databases for websites today.

Key Features

  • Free to use 
  • The open-source software that can be downloaded and used by all.
  • Supports much larger sets of data and databases.
  • Can store data of different types and record each type of stored data.

Must Have Data Analysis Tools in 2022 2

4. Self-Service Data Visualization 

Data visualization is one of the most critical skills for a data analyst because it provides a visual form to represent data, making reading the data more accessible and generating insights quicker.

The following software represents the industry standard in data visualization:

– Tableau

Tableau is a leading data analytics software specializing in the visualization of data sets and has an interface that is easy to use by analysts of all skill levels. 

Key Features

  • Offers an analysis of data in real-time.
  • Has a large community for support.
  • Allows querying of data with no code.

– Power BI

Power Bl is a data visualization and business intelligence software by Microsoft. It turns your unrelated data sets into visually comprehensive and interactive insights.

Key Features

  • Offers a range of data visualization options.
  • The “Get Data” feature allows an analyst to join data from different sources.
  • A variety of DAX (|Data Analysis Expressions) functions give the analyst a list of predefined commands to perform data analysis functionalities.

5. Programming Languages

Using a programming language for analysis is useful when complex or unique computations need to be performed on data to arrive at insights more suited to the organization’s needs.

The languages listed here are selected because they are the most efficient in handling data in a more computational capacity. They include:

– Python

Python is a high-level, general-purpose programming language that has a simple syntax. It is the most widely used programming language.

Key Features

  • Easy to understand and get started using.
  • A robust library of functionality, especially for data analysis (such as Pandas, Matplotlib, and many more).
  • Additional framework (Anaconda) with integrated tools for predictive analytics of data. 

– R

    R is a popular open-source programming language. It is commonly used to create statistical software. Although it has a syntax more complex than python, it was built primarily to deal with heavy data sets and is very popular for visualizing data.

    Key Features

    • Strong graphical capabilities for visualization.
    • ” Wide variety of packages and modules.
    • Active community for support on projects.
    • Has distributed computing capabilities making for faster processing.

    – No Code

    There has been a surge in no-code data analysis tools in the past few years. While these tools may seem like they are for the those who don’t know how to code, they are also a great alternative for those who can code but want to get their analysis done faster.

    Key Features

    • Faster turnaround on data projects
    • More people in the team can be involved in data analysis

    6. Big Data Tools 

    Big Data is data that is considered too large to be processed sequentially. This presents the need for systems that can work on different parts of a dataset at the same time.

    Therefore the tools stated below are the industry standard for distributed computing.  

    – Hadoop

    Apache Hadoop is a suite of open-source software that uses a network of many computers to solve problems involving large amounts of data and computation. This is done using a model of programming called MapReduce.

    Key Features

    • Gives faster results in terms of data processing.
    • Affordable and cost-effective to work with.
    • Reliable fault tolerance mechanism which ensures all systems are running smoothly.

    However, the data world is moving away from Hadoop to cloud technology. This is because of its complexity, cost, and limited scalability.

    – Snowflake 

    Snowflake is a robust cloud-based data warehouse. 

    It helps organizations eliminate data silos by integrating all data sources in one platform and provides a wide range of tools to process data sets and workloads in real-time. 

    Key Features

    • It has an easy-to-use interface 
    • Full technical and business support on data migration, security, and Snowflake best practices 
    • Wide range of tools to process data sets across over 15 different industries 
    • Proven cloud architecture that reduces the time taken to process terabytes of data sets in minutes  
    • Integrated with Datameer 

    7. Bonus Tool: Datameer  

    Datameer is the only collaborative multi-person data transformation tool in Snowflake. It uses a spreadsheet-like interface.

    It employs both the cluster architecture of a Hadoop system and the client-server architecture of cloud platforms to transform data into insights in minutes.

    A key feature is that it caters to the requirements of data analysts who are averse to programming and desire GUI applications that use SQL, or No Code at all, or both.

    Conclusion: Keep it in the cloud with Snowflake + Datameer

    Using the suitable data analysis tool bridges a sea of overwhelming data and clear, actionable information you need to move your business to the next level. 

    Now you have an idea of some of the analytics tools and the industry-standard software for each category.

    Understanding where each tool is most effective would help you transform your data better. And if you are an organization that deals with big data, then a cloud-based big data tool like Snowflake and integrations like Datameer would be your best bet. 

    Learn more about Datameer on Snowflake.