We can evaluate a discipline’s progress by analyzing the advancement of its tools and the ease with which their use has spread within its community. As the domain advances, so do the tools deployed in the field. Data Analytics, or Business Analytics, is the newest and most important discipline revolutionizing modern-day businesses.
The various paid and free data analysis tools discussed below allow companies to gain tremendous insight from their raw unstructured data. You can work with any tool for analysis from many free and paid tools according to your need.
What is Data Analytics?
Data analytics involves the procedures of examining, transforming, cleaning, and modeling data using many paid and free data analysis tools. It helps in extracting meaningful insights, concluding, and supporting decision-making.
It involves using various tools, techniques, and methodologies to analyze data, uncover patterns, trends, and relationships within the data.
Data analytics is applied in diverse fields, including business, social sciences, finance, marketing, healthcare, sports, and more. It helps to gain valuable insights from data and make informed decisions.
Data Analytics allows companies to have-
- A better understanding of what has happened in the past
- What is happening exactly in the present
- What could happen in the future, given certain circumstances
This allows the leadership to come up with a much-informed decision. It was earlier impossible as many decisions were based on the leadership’s experience, intuition, and very little data. Major tactical and strategic decisions are sometimes solely based on analytical results.
With the acceptance of data analytics as a legitimate and essential part of a company’s operations, its use has reached companies where analytics was unheard of. The process of data analytics is reflected directly in the analytics tools. They are now much more advanced, sophisticated, and easy to use.
How Data Analysis Tools can make things easier?
The quantity of data available exceeds humans’ manual analysis and visualization capacities. Organizations take professional and technical help to increase efficiency. Data exploration tools are ideal solutions that combine augmented analytics, machine learning, and embedded AI systems.
These tools simplify, automate, and accelerate the data analysis process. Data analysts are employed in every sector, be it healthcare, finance, e-commerce, Social media, and technology.
They utilize the analysis tools to dig out the data and collect valuable insights for better decision-making. Following are the inevitable contribution of data exploration tools in businesses.
- Data analysis tools help democratize business analytics. The functionalities are designed to offer self-service, on-click or drag-and-drop library functions, and directed navigation.
- Analysis tools offer a user-friendly platform for applying analytical procedures over the data. The dashboards are easy to use and wrap up the complex processes under the library functions.
- They are integrated platforms offering analytics and data management facilities.
- The tools are compatible, accessible, interoperable, and scalable from personal systems to business servers. This provides solutions for future predictions and recommendations.
- Tools effectively guide the digital transformation of the industry and its culture. This shifts the decision-making to be more innovative and forward-thinking.
- Modern-day innovators, algorithm-driven organizations, and business leaders find strong support by discovering and analyzing hidden trends. These patterns are further applied to create algorithms that can increase efficiency.
- A proper data drill using the analysis tools enables crafting personalized consumer experiences, optimizing operations, assembling interconnected digital products, and boosting employee productivity.
- Collaborative data analytics also encourages communication from within and beyond the business to connect and collaborate for optimized knowledge development.
- An analysis tool impels businesses forward by inculcating algorithms ubiquitously and easily to optimize decisive business moments.
Free Data Analysis Tools online
Excel is a member of the Microsoft Office family, so its compatibility and ease of use make it widely accepted across the globe. It is a conventional spreadsheet-based data analysis tool that’s versatile.
Excel is a simple yet efficient tool for data acquisition and is among the free data analytics tools. The functionalities include manipulating rows and columns, creating pivot tables, conditional formatting, computing quantitative data to build complex equations, and data visualization using graphs and charts.
Excel works well with small quantities of data and basic statistical analysis but lacks collaboration and cloud-based analysis support. Every organization uses Excel in its data collection and analysis processes.
- Also read: Excel interview questions
Microsoft’s Power BI is one of the centralized and scalable free data analytics tools offering self-service business intelligence and analytics. The cloud-based analysis platform provides you with real-time and rapid insights.
Power BI assembles data from numerous sources to give an inclusive view of the information to create an analytical environment. The local and cloud-based data is drilled over the live dashboards to yield reports and illustrate metrics and performance indicators. It is a tool that allows clients to build automated ML models.
Power BI can be integrated with Azure and Office 365 suites. Power BI products include Desktop, Premium, Embedded, Pro, Mobile, and Report Server. Adobe, Heathrow, and GE Healthcare are companies using Power BI.
Python is an open-source high level programming language that is used for data analysis. It offers many libraries, packages, and functions with easy coding processes.
Python is used for implementing data analysis processes like data crawling, data cleaning, modeling, and creating algorithms for data analysis specific to enterprise scenarios.
It executes advanced analysis procedures using machine learning and text-mining features. Portability, scalability, and data integrity make Python is a preferred choice for developers across industries. The companies like Spotify, Reddit, Netflix, Google, and Dropbox use Python in their analytical operations.
- Also read Python libraries for machine learning
A widely used statistical analysis tool, R is an open-source language developed by statisticians. With an ecosystem of above 15000 extensions and packages, it runs successfully to undergo statistical analysis, data manipulation, modeling, visualization, and hypothesis testing.
The functionalities include data cleaning, reduction, analysis, and reporting for data analysis. R is used for performing complex mathematical operations and crafting quality visualizations.
R can be downloaded from its official website for free. Actively monitored by statisticians and researchers, it upgrades the platform to the required changes. Apart from having a strong foot in the academic sector, R is used in large organizations like Meta, Facebook, ANZ, and Airbnb.
- Also read: Benefits of learning R
Mode is an analysis platform recognized for providing rapid and deep analytics solutions. A self-service advanced analysis platform that flawlessly toggles between Python & R notebooks, cloud-based SQL Editor, and interactive visualizations.
Mode offers quick codeless calculations, transformations, and easily understandable insights with creative visual elements. The powerful Helix data engine helps switch between data-rich visualizations easily. Helix permits server-side memory ingestion of up to 10Gb for faster data processing.
Konstanz Information Miner- KNIME is an easy, open-source, and instinctive environment that allows the interactive participation of stakeholders in the data analysis process.
KNIME Analytics Platform collects data from multiple sources to get faster insights, well documented and reusable workflows to execute repetitive tasks effectively.
High-quality results, shorter feedback cycles, fast prototyping, an easy drag-and-drop dashboard, seamless data integration, transformation, and reusable and comprehensive workflows are some of the appreciated features of KNIME.
Talend is a globally used data integration platform. It is a Java-based ETL tool used for undergoing data management procedures, enterprise application integration, cloud storage, and providing data quality. The unified cloud platform of Talend offers big data integration, cloud pipeline designing, and data governance solutions.
Google Data Studio
Small businesses and individuals can take a step up by using their Google data in a proper manner. Data Studio by Google combines all your data in the same place to find solutions to important business questions.
It handles all data-related issues like invoking access rights, data authentication, structure selection, conversions, and data visualizations. It incorporates data connectors to import data from Google tools like Google Ads, BigQuery, MySQL, and Analytics.
With Data Studio, one can work on creating meaningful visuals for insights, colorful illustrations, shareable charts, dynamic controls, interactive logos, and much more. The interactive and real-time dashboard facilitates communication with the team for better decision-making.
Jupyter Notebook is a free, open-source, web-based application developed by Fernando Pérez and Brian Granger. It is a computational and analytical tool used for drafting codes, rendering complex equations, creating visualizations, and statistical modeling.
The operations like numerical simulations and complicated scientific expressions are possible jobs done with the Jupyter Notebook. Effective integration with big data tools like Apache Spark is good enough for large dataset analysis.
It offers the freedom to select from over 40 programming languages like Julia, R, Python, Scala, etc. The outputs of Jupyter Notebook are interactive and high quality consisting of images, LaTeX, videos, and MIME.
Premium and Paid Data Analysis Tools
Tableau platform is a business intelligence tool collaborating three functional tools to construct a unified data flow, from raw information to precise insights. It is known in mid-sized organizations for its fast analytics and capabilities to handle various data.
The USPs include allowing automated cleansing, data exploration through interactive dashboards, data visualization, analysis, and collaboration. The tool’s cohesive data integration and visualization techniques help analysts discover and communicate the insights to the business executives, stakeholders, and team.
Tableau lets analysts work on live data such that it minimizes data-wrangling efforts.
SAS Forecast Studio
The full form of SAS is Statistical Analysis System. SAS Forecast Studio is a part of SAS Forecast Server. It is a desktop-based software suite with a simple graphical user interface to speed up forecasting procedures through automation.
It uses various methodologies like event modeling, what-if analysis, hierarchical reconciliation, and scenario planning for forecasting business trends. Automation, scalability, data preparation, and infinite model repositories are the highlights of the SAS Forecast Studio.
It leverages data visualization by generating reports to communicate forecast results with teams.
Qlik Sense is a part of the associative analytics solutions provided by Qlik. It offers data preparation and integration to combine and work on data from multiple sources effortlessly.
Features like AI-powered analytics, application automation, well-organized data catalog, proactive data monitoring, creative visualization, mobility, and fully customized analytics solutions for businesses have made it a preferred choice for 38000+ customers and analysts.
Qlik Sense users can interactively investigate computations and predictions at the engine level and in real time. The analysis process yields instant answers to the questions. Integration of Qlik Sense with Qlik Cloud delivers unrivaled analysis performance and flexibility.
RapidMiner is one of the best data analysis tools. It is an enterprise-ready data science platform that infuses data engineering focusing to accelerate the speed of your business transformation. RapidMiner incorporates AI innovation throughout the data science lifecycle to accelerate business analytics.
The innovative platform engages everyone (Domain experts, IT teams, Leaders, And Data science experts) involved in the data analysis workflows. RapidMiner’s data mining and machine learning operations include statistical modeling, predictive analytics, evaluation, and deployment.
The predictive analytics using rapidMiner will help find solutions like risk modeling, churn prevention, customer segmentation, and text mining. Moreover, the interactive GUI creates and executes business-specific analytical workflows.
ThoughtSpot is a revolutionary analytics tool that questions data in natural language. It uses search-based insights to visualize the data and deliver unique insights. It is a modern Analytics cloud that everyone in the business can use.
The tool uncovers even the granular insights of the data by automatically analyzing billions of rows of data to promptly discover inconsistencies, patterns, and novel opportunities to enhance business requirements. The developers claim the popularity of the tool among startups as well as enterprises.
ThoughtSpot provides a secure, open-source, developer-friendly, simple platform for true self-service analytics. The tool applies real-time insights to business use cases to improve operations, make smarter business decisions and raise productivity.
SIEMENS, Ericsson, Vodafone, workato, DAIMLER, and Hilton are some of the innovative businesses that use ThoughtSpot to empower their business processes.
erwin Data Modeler
erwin Data Modeler has been a trusted data modeling tool for over 30 years. It endows the users with logical and physical modeling of structured or unstructured data for schema engineering in a NoSQL or relational database.
It considers the data from warehouses, the cloud, and on-site as a single interface. The automatic data model generation and database design help increase efficiency and error reduction.
Increased data quality, application agility, improved data intelligence, stakeholder collaboration, and high data trust make it an effective choice for data modeling.
Basics to learn before using data analysis tools
The selection of data analysis tools is a critical business decision. If it fits to the business requirements, it guides decision-making. There are numerous tools for effective data analysis.
Selecting from the wide array depends on users’ technical backgrounds, data literacy, domain requirements, business objectives, etc. This section introduces prerequisites and factors for learning and choosing data analysis tools.
Even the easiest GUI-based analytical dashboards expect their users to have basic data analysis knowledge. They must have the fundamental Data Analyst skills listed below to grasp the analytics process fully.
- Mathematical and analytical skills
- Statistical programming using languages like R, Python
- Creative and critical thinking ability
- Interaction with SQL and Excel
- Data visualization and communication
How to choose the right data analytics tool?
It requires effort to select an ideal analytics solution for your business. Considering the below-mentioned factors, one can select appropriate tools that guide strategic decision-making and uncover new revenue streams for business proliferation.
Find your business objectives: It is important to identify business objectives to find a tool that satisfies existing and future business outcomes.
Industry-specific tool: Search for a platform that is industry-specific and has the capabilities to solve domain-related issues to create better opportunities.
Democratizing access to data: Data should be available to everyone using the data. Irrespective of the technical skills, all the managers, business executives, and developers should be able to use and question the data for insight building. Thus, a tool with an interactive dashboard, seamless data integration, and creative visualization can be chosen for easy usage and data access.
Pricing: Although there are various free analytics platforms available. Other premium and paid versions of the analytics platforms have subscriptions, hidden fees, and different costs associated with them. Businesses should learn pricing policies before investing to select from the tools.
Security, Agility, and Scalability: The analytics platforms should abide by the security norms for building trusted solutions. The tools should be agile for the business and should be scalable according to the business needs.
Data analysis tools are an integral part of modern-day data-driven businesses. These tools are unified solutions offering data integration, security, visualization, and advanced analytics.
Optimized use of analysis platforms leverages businesses with strategic decision-making, business intelligence, error reduction, and product development. The agility and robustness of the tools let businesses find answers to the hardest business questions.
With a wide variety of online data analysis tools in the market, one can choose an optimal analytics solution for the business.
What are some best free data analysis software?
Google Analytics, Zoho Analytics, Qlik Sense, and MS Excel are among the best free data analysis software tools available for online analysis of data.
How can I analyze data for free?
Many good free online data analysis tools to analyze data for free. Tools like Python, Microsoft Excel, R, Apache Stark and Hadoop, and more. You can work with anyone of them according to your requirements for online data analysis.
What is the best tool for data analytics?
The best tool for analysis varies based on various aspects of your specific use case, requirements, and expertise. Tableau, Power BI, Jupyter Notebook, etc., are some of the best online analysis of data tools. You can also avail of their paid versions for more features.