Data analysis tools consist of software and programs. The main function of the tools is to collect and analyse data about a company, its business, its customers, and its competition.
The tools help improve processes and make it easy to make data-driven decisions.
Analysis of data is required for organisations to get quality data to help them understand the market, customers, competitors, and activities performed by their own company.
In modern business, data analysis plays an important role. The Data science helps industries analyse researched data on current requirements and their competition.
Furthermore, the data collected by the data analysts and data scientists is analysed using tools to boost business growth.
Demo or free trial
A good Data analysis tool should have a trial version. The trial version of the tool offered to the organisation usually lasts for 30 days.
This helps organisations analyse their compatibility with the tool and plan their work accordingly before paying for it.
A demo of the tool slightly differs from a trial, since the tools providers demonstrates only the tool's features and functionality.
Data security is another factor that plays a major role in the selection of a Data analysis tool. A company’s database and data base management system are often integrated with the tool.
Without proper security, sensitive data and company information can be accessed by anyone in the organization.
Data security with respect to data access must include measures, permissions, and level access controls that provide protection against data breaches. The analysis of data in a secured analytical tool makes it good tool for the business.
Before deciding on any Data analysis tool, data integration must be considered. It is a critical feature that any data-analytics tool must possess.
A company's data is extended to different locations, which includes databases and other storage formats. Also, data import and integration should be possible with only a few clicks.
Most of the data analysis process's final step is data visualization. A good Data analysis tool must be able to share and communicate the findings of data scientists and analysts to other members of the company.
It must have different data visualisation techniques to make it very easy to understand and share data, such as graphs and charts.
Dashboards and user-friendly
An interactive dashboard design and user-friendliness are essential for an effective and efficient Data analysis tool.
Even if the user is not familiar with programming languages, it should be easy to use. It must provide ease of doing analysis of data, integration, and sharing of the data.
Lastly, the compatibility of the tool with mobile apps is an added advantage for viewing reports.
Below mentioned are the best 10 data analytics tools of 2023.
The above mentioned key features are helpful in making a decision to select tools as per the business's requirements. Now let’s jump into the list of Data analysis tools.
SAP BusinessObjects comes with a set of business intelligence applications for data discovery, analysis, and reporting.
The Data analysis tool is designed for business users who require less technical data analysis, but complex analysis can also be performed.
SAP BusinessObjects and Microsoft Office products enables business analysts to quickly switch between applications like Excel and BusinessObjects reports. The SAP BusinessObjects also allows for self-service predictive analytics.
This type of application is known for its self-service analytics. It comes with built-in collaboration features that include the facility to push reports to mobile applications.
SAS Business Intelligence is flexible and provides a wide range of applications. It is expensive as compared to other tools. Due to its versatility, larger enterprises may prefer it.
The TIBCO Spotfire Data Analytics tool provides natural language search and AI-powered data insights.
It offers a wide range of visualisation tools that can show reports on desktop and mobile applications. For building predictive analytics models, Spotfire provides a point-and-click tool that helps in exploring data.
The Qlik Data analysis tool offers a self-service data analytics and business intelligence platform.
This tool supports cloud and on-premises deployments. The data analytics tool also claims strong support for data exploration and discovery in a similar way for both technical and nontechnical users.
Qlik comes with embedded SQL and drag-and-drop modules that have many types of charts to help users customise their data.
The data visualisation and analytics platform is the feature of Tableau that helps users make reports and share them through desktop and mobile platforms within a browser or embedded in an application.
Tableau Data analysis tool can run in the cloud or on-premises. Most of the Tableau platform is built on top of its core query language, VizQL. The drag-and-drop dashboard and visualisation components are translated into efficient back-end queries, and the requirement for end-user performance optimizations is reduced.
However, the downside of the Tableau doesn’t have support for advanced SQL queries.
The advantage of the Google Data Studio Data analysis tool is that it automatically integrates with most other Google applications. It is a free dashboarding and visualisation tool.
Google Data Studio is mostly used to analyse Google data. Google Analytics, Google Ads, and Google BigQuery are the Google services that can be integrated with Data Studio.
Data Studio can work with data from various other sources. To get data on Google Data Studio, it is first replicated to BigQuery with a data pipeline using Stitch.
The Redash Data analysis tool is used for querying data sources and building visualizations. It is a lightweight and cost-effective tool.
Redash code is open source, and it offers organisations an affordable hosted version for fast initiation.
The query editor is the core of Redash and comes with a simple interface for writing queries, exploring schemas, and managing integrations.
Within Redash, the query results are cached, and scheduled updates by the user can be run automatically.
The Thoughtspot Data analysis tool is an analytics platform where users can explore different types of data through natural language searches and reports.
SpotlQ is the AI system of Thoughtspot that finds patterns automatically to help uncover those that are outside of the user's understanding.
This Data analysis tool permits users to join tables from different data sources automatically.
The Python for data analysis is an open source. It’s a high-level programming language that is often used by data scientists and technical analysts.
Python is capable of handling different types of analysis, and for machine learning and data visualization, integration with third-party packages is possible. It has more than 200,000 packages available.
It includes data visualization packages like Matplotlib, Plotly, and Seaborn. This Data analysis tool is also used for other analytics systems as a programming interface.
The Microsoft Power BI Data analysis tool is a preferred business intelligence platform that provides support for many data sources.
To create and share reports, visualizations, and dashboards, the Microsoft Power BI is of great use.
Microsoft Power BI permits users to build automated machine learning models and integrates with Azure Machine Learning.
The article covers features and different tools for data analysis that are popular in 2023. The key factors mentioned here for the selection of Data analysis tools help in making decisions before finalizing tools for data analysis.
There is a wide range of options for Data analysis tools available in the market for businesses. An organization can quickly find the best Data analysis tool based on their requirements.
The different analytical tool providers for data analysis offer a free trial plan or demo, which further makes it easy for the organizations to make a decision before opting for the subscription.
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