The 9 best data analysis tools for data management 2023

The importance of data analysis has continued to grow in recent years, resulting in a significant opening of the global market. Thus, data analytics tools have also taken center stage and there are now such a large number of them that it becomes difficult to choose the most suitable one. There are many factors to consider before choosing a data analysis tool, such as whether it is performance-oriented and easy to use. In addition, data analysis can no longer be understood as a single process and must be related to data integration, data consolidation and data quality. Below are some data analysis tools that can be used for seamless data management.

Before discovering the best data analysis tools for a perfect data management you should know that we have updated the list with the best data analysis tools of 2022. Don't miss it!

1. Microsoft Power BI

Power BI is Microsoft's analytics tool and one of the most popular, as it offers interactive data visualizations as well as easy integration with other corporate tools. In addition, Power BI can be integrated with third-party applications through Power BI Embedded and allows for secure report sharing and data analysis. It can connect to more than 60 data sources, such as Spark, Hadoop or SAP, and is easy to use even for non-technical users.

Bismart is a Microsoft Power BI partner and we have been working with this tool for years to provide the best solutions to our customers.

2. R Programming

This is one of the best analytical tools available in the industry today. It is mainly used for data modeling and statistics. This tool is very easy to use and makes it possible and easy to manipulate and present data in many ways. It outperforms many other tools in terms of performance, data capacity and results. It works on multiple platforms, including MacOS, Windows, UNIX, etc. Depending on requirements, it has more than 11,000 packages and they are categorized and installed automatically.

3. SAS

This tool was initially developed in 1966 at the SAS Institute, and then improved in the 1980s and 1990s, and is also one of the leading tools in data analysis. It is a programming language that allows easy manipulation of data. It is very manageable, accessible and has the ability to analyze a data regardless of the data source. In 2011, SAS launched a suite of products, for its multiple SAS modules including social media, web, marketing analytics, etc. It is now widely used to profile both customers and prospects and to predict their behaviors and optimize communication with them.

4. Python

It is an open source tool and an object-oriented scripting language that is very easy to maintain, read and write. Python was developed in the late 1980s by Guido van Rossum to support both structured and functional programming methods. Learning to use this tool is very easy and has many similarities to other languages such as Ruby, JavaScript and PHP. It has libraries for machine learning and can be used on platforms such as JSON, MongoDB database, SQL server.

5. Excel

Excel is a very popular and almost fundamental analytical tool. It is a fairly basic, yet very versatile tool and is used in virtually every industry. It doesn't matter if you are an expert in SAS, Tableau or R programming, or even if you are just in the business of writing custom essays, you need to learn how to use Excel as well. This tool plays a big role when a client's internal data needs to be analyzed. Although that it is a basic tool, it also has an advanced business analytics option for modeling capabilities with pre-built options such as time clustering, DAX functions and automatic ratio.

6. Tableau Public

Tableau Public is a free software that connects different data sources, such as Microsoft Excel, Data Warehouse, web-based data, etc. and creates dashboards, maps and data visualizations that have real-time updates from the web. You can also share them with a customer or via social media. Once you have access, you can download different formats for an archive. To typically enjoy this tool and see its power, your data source must be very good. The Big Data capability of this tool makes it important and the fact that you can analyze and visualize the data much better than other data visualization programs gives them an advantage.

7. Rapid Miner

This is a powerful tool for integrated data science. It was developed by a company that also performs predictive analytics and some more advanced analytics such as machine learning, text analytics, visual analytics and data mining without programming. This tool can be incorporated into any type of data source, including Excel, Oracle, IBM SPSS, Teradata, Access, Microsoft SQL, Dbase, Sybase, etc. This tool is capable of generating analytics based on real-life data transformation settings, which means it can control data sets and formats for predictive analytics.

8. Apache Spark

Apache was developed in 2009 at the Berkeley MPA Lab at the University of California. This tool is a data processing engine that works at large scale and can run applications that are already in Hadoop clusters faster (up to 10 times) on disk and up to 100 times faster if in memory. The concept of this tool makes data science (on which it is based) very easy. It is a very popular tool for developing machine learning models and data pipelines. It also has a library called MLib, from where you can get techniques for data sciencerepetitive such as collaborative filtering, regression, classification, etc. from an advanced set of algorithms.

9. Qlik View

This tool has a number of unique features, some of which are in-memory data processing and patented technology to help you run your result quickly and store all the data that is present in the report. This tool automatically maintains the data association and can compress the data to only 10% of the initial size. It visualizes the relationship of data with colors, with one color given to related data and another to unrelated data.

All of these tools greatly facilitate data analysis and any one of them will optimize data management. They all work differently, with different capabilities and offer different features. We just need to know our needs well to be able to choose the one that best suits them.