The scope of data analytics in the business world is growing all the time, reflecting the proliferation of analytics technologies that aim to promote the use of data by companies. However, choosing the right analytics tool that can meet our business needs is extremely important - here are the 9 best data analytics tools of 2022!
Nowadays, data analysis plays a leading role in companies. Increasingly, organizations require quality data (data quality) to help them understand the market, customers, competitors and their own activity. Data analytics and data science have become the big bet of the leading companies in the market and, although ten years ago only a few organizations used data analysts, the demand is now in the majority.
Business trends are progressively moving towards a data-driven culture, data-driven decisions and the deployment of data-related processes such as data collection, data consolidation and data governance.
Analytical software vendors have been able to respond to the growing demands of companies, which has resulted in more and more data analysis tools available on the market. The growth of the offer, however, makes it difficult to choose the right tool according to the business logic, our objectives and what we want to achieve with the data.
The 9 best data analysis tools of 2023
1. Power BI
Power BI is Microsoft's data analysis and visualization tool and one of the most popular in the market due to its business logic, multiple capabilities and a level of usability superior to other more complex analysis technologies.
More than a tool, Power BI is a set of business intelligence services that helps organizations organize their data, analyze it and present it in the most effective way to facilitate intelligent decision making.
Power BI has many advantages, including its ability to integrate with virtually all corporate systems, its powerful visuals and the ease of creating reports and dashboards with this tool. Power BI also promotes data integration, as it connects to more than 60 data sources and, through Power BI Embedded, facilitates the distribution of data, dashboards and reports.
At Bismart, as a Microsoft Power BI partner, we develop many of our dashboards and solutions with this set of services.
2. Excel
Excel is another of Microsoft's most popular analytics tools. Virtually all data analysts use this tool, which includes analysis capabilities through a spreadsheet-like interface that makes it easy to work with data, perform calculations and apply formulas quickly and easily.
While Excel also includes visualization capabilities, they are much less comprehensive than those of Power BI. Microsoft Excel is a tool more oriented to mathematical analysis and the creation of tabular reports, while Power BI enables the creation of reports in multiple formats. In addition, Excel is not a platform designed to work with Big Data and can process a limited amount of data. On the other hand, its data processing capabilities are more extensive than those of Power BI.
Finally, Excel is a great platform to work on an individual level, but it makes it difficult to work collaboratively and simultaneously with other team members. Nevertheless, Excel is undoubtedly one of the analysis platforms par excellence and has been a must-have for any organization for many years.
3. Tableau
Tableau is a very complete data analysis software that, like Power BI, allows the creation of dashboards, maps and visualizations from data. Its free version, Tableau Public, allows non-experts in data analytics to play with their data and transform it into more or less complex visualizations.
However, in order to enjoy all the advantages of Tableau, you will need to have a certain level of coding knowledge and a powerful data source, as it is a more difficult platform to use than Power BI.
Finally, Tableau connects to various sources and databases such as Microsoft Excel or corporate data warehouses.
4. Qlik
Qlik is a set of analytics, data integration and programming platforms that includes different tools. Among them, QlikSense and QlikView, dedicated to analytics and business intelligence. Qlik is deployed in a hybrid cloud that has a fairly complete offering of integration and analytics processes, including functions for data warehouse automation.
With Qlik, we can integrate and transform data, create visualizations and dashboards from analytics, and venture into augmented analytics. Like many of the other technologies listed, this is a good option for transforming data into intelligence and facilitating business decision making. Qlik is a complete business intelligence solution that allows companies to analyze multiple data sets stored in different sources of origin, thus providing a global view of the information available to the company.
It also allows statistical analysis of the data, as well as the creation of dynamic graphs, presentations and visualizations.
5. Python
Python is one of the most widely used open source software for programming and development through code. More than a platform, Python is a programming language that works through object-oriented scripting. It is very similar to other programming languages such as JavaScript or PHP, but its particularity is that it relies on a simple, readable and easy-to-code language. In other words, compared to other languages, Python is one of the easiest to learn.
The tool includes machine learning libraries and integrates with other programming platforms such as JSON, SQL or MongoDB.
Obviously, to use this program we must have knowledge of code. However, it is also a good option to get started in the world of programming due to the readability of its code.
6. Looker
Looker is Google's data analysis and BI platform.
From this platform, organizations can analyze and integrate data from multiple data sources, as well as create interactive reports and dashboards. The tool also offers the possibility of working with workflows and creating customized applications.
7. Rapid Miner
More than an analytics platform, Rapid Miner is an integrated data science tool that facilitates control over data sets and predictive analytics.
The advantage of this tool is that it enables several advanced analytics models such as machine learning, data mining or text analytics without resorting to programming. Moreover, it integrates with virtually any database: IBM, Oracle, Excel, Acces, SQL, SPSS, etc.
8. Apache Spark
Created in a laboratory at the University of California in 2009, Apache Spark is one of the leading data science platforms on the market because it is open source and simplifies the processing of both structured and unstructured data.
Apache Spark is a big data solution because it can handle large datasets that are too large or complex for other databases. It has an engine with a large processing capacity and greatly facilitates the creation of pipelines, cluster management, machine learning and deep learning.
On the other hand, the tool has its own machine learning library, MLib, where users can download algorithms for classification, regression, collaborative filtering, etc.
Finally, Apache Spark is compatible with several programming languages such as Python, R, Scala, Java, SQL and .NET.
9. QuickSight
QuickSight is Amazon's BI and analytics software. It is a cloud service that integrates to cloud data sources such as AWS, SaaS, Excel, etc.
The goal of QuickSight is for decision makers to be able to explore their data and interpret information in a simple and visual way. However, it also has advanced capabilities and, for example, can be used for machine learning. Just like Power BI, it allows sharing analyses and reports and facilitates collaborative analysis.
Conclusion
Data analysis is the order of the day in the business world. Few companies do not yet have some of the analytics technologies listed here and, according to the predictions of major international technology consultancies, the importance of data analytics in the business world will continue to grow.
In this article we have presented 9 types of analytics, data science and business intelligence tools whose functions tell us where the latest trends in analytics and data are heading.
If you want more information on how you can get more out of your data assets and generate strategic value from analytics, don't hesitate to contact us! One of our data analytics and BI experts will be happy to assist you.
Nowadays, data analysis plays a leading role in companies. Increasingly, organizations require quality data (data quality) to help them understand the market, customers, competitors and their own activity. Data analytics and data science have become the big bet of the leading companies in the market and, although ten years ago only a few organizations used data analysts, the demand is now in the majority.
Business trends are progressively moving towards a data-driven culture, data-driven decisions and the deployment of data-related processes such as data collection, data consolidation and data governance.
Analytical software vendors have been able to respond to the growing demands of companies, which has resulted in more and more data analysis tools available on the market. The growth of the offer, however, makes it difficult to choose the right tool according to the business logic, our objectives and what we want to achieve with the data.
The 9 best data analysis tools of 2023
1. Power BI
Power BI is Microsoft's data analysis and visualization tool and one of the most popular in the market due to its business logic, multiple capabilities and a level of usability superior to other more complex analysis technologies.
More than a tool, Power BI is a set of business intelligence services that helps organizations organize their data, analyze it and present it in the most effective way to facilitate intelligent decision making.
Power BI has many advantages, including its ability to integrate with virtually all corporate systems, its powerful visuals and the ease of creating reports and dashboards with this tool. Power BI also promotes data integration, as it connects to more than 60 data sources and, through Power BI Embedded, facilitates the distribution of data, dashboards and reports.
At Bismart, as a Microsoft Power BI partner, we develop many of our dashboards and solutions with this set of services.
2. Excel
Excel is another of Microsoft's most popular analytics tools. Virtually all data analysts use this tool, which includes analysis capabilities through a spreadsheet-like interface that makes it easy to work with data, perform calculations and apply formulas quickly and easily.
While Excel also includes visualization capabilities, they are much less comprehensive than those of Power BI. Microsoft Excel is a tool more oriented to mathematical analysis and the creation of tabular reports, while Power BI enables the creation of reports in multiple formats. In addition, Excel is not a platform designed to work with Big Data and can process a limited amount of data. On the other hand, its data processing capabilities are more extensive than those of Power BI.
Finally, Excel is a great platform to work on an individual level, but it makes it difficult to work collaboratively and simultaneously with other team members. Nevertheless, Excel is undoubtedly one of the analysis platforms par excellence and has been a must-have for any organization for many years.
3. Tableau
Tableau is a very complete data analysis software that, like Power BI, allows the creation of dashboards, maps and visualizations from data. Its free version, Tableau Public, allows non-experts in data analytics to play with their data and transform it into more or less complex visualizations.
However, in order to enjoy all the advantages of Tableau, you will need to have a certain level of coding knowledge and a powerful data source, as it is a more difficult platform to use than Power BI.
Finally, Tableau connects to various sources and databases such as Microsoft Excel or corporate data warehouses.
4. Qlik
Qlik is a set of analytics, data integration and programming platforms that includes different tools. Among them, QlikSense and QlikView, dedicated to analytics and business intelligence. Qlik is deployed in a hybrid cloud that has a fairly complete offering of integration and analytics processes, including functions for data warehouse automation.
With Qlik, we can integrate and transform data, create visualizations and dashboards from analytics, and venture into augmented analytics. Like many of the other technologies listed, this is a good option for transforming data into intelligence and facilitating business decision making. Qlik is a complete business intelligence solution that allows companies to analyze multiple data sets stored in different sources of origin, thus providing a global view of the information available to the company.
It also allows statistical analysis of the data, as well as the creation of dynamic graphs, presentations and visualizations.
5. Python
Python is one of the most widely used open source software for programming and development through code. More than a platform, Python is a programming language that works through object-oriented scripting. It is very similar to other programming languages such as JavaScript or PHP, but its particularity is that it relies on a simple, readable and easy-to-code language. In other words, compared to other languages, Python is one of the easiest to learn.
The tool includes machine learning libraries and integrates with other programming platforms such as JSON, SQL or MongoDB.
Obviously, to use this program we must have knowledge of code. However, it is also a good option to get started in the world of programming due to the readability of its code.
6. Looker
Looker is Google's data analysis and BI platform.
From this platform, organizations can analyze and integrate data from multiple data sources, as well as create interactive reports and dashboards. The tool also offers the possibility of working with workflows and creating customized applications.
7. Rapid Miner
More than an analytics platform, Rapid Miner is an integrated data science tool that facilitates control over data sets and predictive analytics.
The advantage of this tool is that it enables several advanced analytics models such as machine learning, data mining or text analytics without resorting to programming. Moreover, it integrates with virtually any database: IBM, Oracle, Excel, Acces, SQL, SPSS, etc.
8. Apache Spark
Created in a laboratory at the University of California in 2009, Apache Spark is one of the leading data science platforms on the market because it is open source and simplifies the processing of both structured and unstructured data.
Apache Spark is a big data solution because it can handle large datasets that are too large or complex for other databases. It has an engine with a large processing capacity and greatly facilitates the creation of pipelines, cluster management, machine learning and deep learning.
On the other hand, the tool has its own machine learning library, MLib, where users can download algorithms for classification, regression, collaborative filtering, etc.
Finally, Apache Spark is compatible with several programming languages such as Python, R, Scala, Java, SQL and .NET.
9. QuickSight
QuickSight is Amazon's BI and analytics software. It is a cloud service that integrates to cloud data sources such as AWS, SaaS, Excel, etc.
The goal of QuickSight is for decision makers to be able to explore their data and interpret information in a simple and visual way. However, it also has advanced capabilities and, for example, can be used for machine learning. Just like Power BI, it allows sharing analyses and reports and facilitates collaborative analysis.
Conclusion
Data analysis is the order of the day in the business world. Few companies do not yet have some of the analytics technologies listed here and, according to the predictions of major international technology consultancies, the importance of data analytics in the business world will continue to grow.
In this article we have presented 9 types of analytics, data science and business intelligence tools whose functions tell us where the latest trends in analytics and data are heading.
If you want more information on how you can get more out of your data assets and generate strategic value from analytics, don't hesitate to contact us! One of our data analytics and BI experts will be happy to assist you.