As analytics determines the success of any business, it is one of the most widely discussed themes nowadays. Data analytics consultants DataArt underline the importance of analytical tools for conducting high-quality data analysis. Currently there are 10 most popular instruments for deep data assessment. This article will give you insight into them and make you understand all the complexity of analytical work.
10 most popular data analytics instruments
- Microsoft Power BI
This tool enables the process of importing data from a range of sources, both external and internal ones. The transmitted information may be presented in different formats and size. If you lack a data warehouse, this tool may serve as an alternative information visualisation instrument.
Microsoft Power BI is an affordable tool for data mining although not for challenging issues. For more complicated tasks the knowledge of such instruments as Power Query or Power Pilot is essential!
- SolveXia
It is an information automation instrument for analytics purposes. It is suitable for companies of all sizes and specializations. Within a few seconds this tool is capable of carrying deep data analysis with the help of AI and RPA.
Thanks to drag-and-drop and pre-existing library features this instrument is easy to use. Moreover, to its functionality adds the function of data sources and legacy systems integration.
- SAS
This tool is considered to be the most suitable ones for large products or entities. It is capable of what-if assessment, strategy planning, event modeling and hierarchical matching.
Thanks to a comfortable graphical user interface customers can easily automate all the processes. The SAS system provides deep and high-quality analysis of information from any sources starting from marketing analytics and ending up with social media assessment.
- RapidMiner
This tool is frequently used in the interests of organisations, the performance of which depends on analytics. Most data scientists apply exactly to this tool. RapidMiner has at least 1500 different algorithms and both prescriptive and descriptive advanced analytics. Moreover you can integrate this tool with R and Python without any difficulties.
- Tableau
The main advantage of this tool is that it is free. Tableau serves as a connector for different data sources like Microsoft Excel or warehouse. It is capable of creating dashboards, maps and even information visualisation for sharing and presenting data through the web. However, due to being free, Tableau has some limitations concerning customisation and SQL via coding.
- Google Data Studio
It is another beneficial and free tool for information visualisation. Analytics frequently opt for using exactly this instrument since it offers a transparent way for data reviewing and analysis. The main reason for such a comfortable usage is that Google Data Studio is a cloud-based tool. However, it can’t be directly integrated with Excel.
- Excel
Microsoft Excel must be the most familiar tool for you. Despite being relatively easy, it provides table solutions for all kinds of purposes. It helps automate a number of processes and speed up the work of the analysts.
Thanks to tables you can sort out all the information, group it and then filter. In such a way it will be much more comfortable to analyze everything you have and make appropriate decisions. However, it is not the best instrument for collaborative projects.
- KNIME
This free and open analytical platform is essential for high-quality information visualisation, integration, reporting and processing. It doesn’t require strong coding knowledge and skills. This tool can be integrated with Python and R information sources which enables multi-platform interoperability. Among disadvantages is the fact that KNIME works slowlier than some other tools.
- Python
Python is one of the most popular open-source programming languages. It is capable of conducting information assessment and software development. Python can be used on any available platform.
- QlikView
This analytical tool can deliver the results of information analysis in a twinkle of an eye. Thanks to the transparent interface it is easy to use. Moreover, all the information is stored and compressed 10 times. However, the users are required to build the UI themselves which may be a real challenge for many people.
