Knowing the main tools of Data Analytics serves to obtain the best results and that these are useful for decision making. For this, it uses advanced collection systems of large collections of structured and unstructured data to produce valuable information for companies.
When companies understand the importance of using data to make decisions, they look for tools that allow them to do better. As a result, companies need to obtain and secure raw data, to turn it into useful information and obtain better insights that allow them to make informed decisions.
For this reason, it is necessary that you consider Make a consultation to integrate this process into your company's decision-making. But, before doing it, here we will tell you a little more about the main Data Analytics tools.
How do the main Data Analytics tools work?
As we mentioned, data analysis (Data Analytics) is important because it allows companies to make informed decisions based on real and objective information, instead of based on assumptions or hunches. By analyzing data, companies can identify patterns, trends, and relationships that might not otherwise be visible, allowing them to spot problems, opportunities, and areas for improvement.
In addition, the main tools of Data Analytics can also help companies improve efficiency, reduce costs, increase sales and improve customer satisfaction. For example, if a company detects a recurring problem in customer satisfaction through data analysis, it can take steps to fix the problem and improve the customer experience. For all of the above, here we mention the main Data Analytics tools for you to take advantage of:
A free tool called Tableau Public offers the ability to connect multiple data sources, such as Excel, Data Warehouse, and web-based data, and creates real-time maps and data visualizations that can be shared with clients or via social media. For best results, a good data source is required.
Even, the tool can handle large amounts of data and its analysis and visualization capacity surpasses other data visualization programs, which makes it very valuable. It also offers the option to download different file formats for the created visualizations.
Of the main data analytics tools, this one integrates data science, is highly efficient and has been developed by a company specialized in predictive analysis, data mining, text analysis and machine learning, among others. It is capable of connecting to different data sources, including Oracle, Excel, Dbase, IBM SPSS, Teradata, Access, Sybase, and Microsoft SQL.
Best of all, it requires no programming skills to use. In addition, you can process analytics based on data transformation settings in real time, enabling greater control of your data and the creation of high-quality predictive analytics.
In 2009, the AMP Lab at the University of California, Berkeley developed a tool called Apache, which is a large-scale data processing engine. This tool can run applications on Hadoop clusters faster, up to 10 times faster on disk and up to 100 times faster running in memory.
This is one of the best Data Analytics tools, it is very useful in the field of data science, since it makes it much easier to manipulate large data sets. It is widely used to develop machine learning models and data pipelines. Apache also has a library called MLib, where you can find techniques for iterative data science, such as regression, classification, and collaborative filtering, which are based on an advanced set of algorithms.
Microsoft Power BI
Power BI is a very popular data analysis tool from Microsoft that allows you to create interactive data visualizations and connect with more than 60 different data sources. One of the great advantages of Power BI is that it can be easily integrated with other Microsoft tools and can also be integrated into third-party applications.
In addition, it is very easy to use and no technical knowledge is required to handle it. Power BI also enables secure sharing of reports and data analysis.
This is one of the best Data Analytics tools available today for data modeling and statistics. It is easy to use and allows you to manipulate and present data in a variety of ways. In terms of performance, data capacity, and results, it outperforms many other tools.
Furthermore, it is compatible with various platforms such as MacOS, Windows, and UNIX. With over 11,000 packages sorted by category and automatically installed based on requirements, this tool is highly versatile and can adapt to a variety of needs.
SAS is one of the Data Analytics tools that has been in development since the 1960s. It is one of the leaders in data analytics and is known for its ability to manipulate and analyze data in easy and accessible ways. It can work with data from multiple sources and is used in various fields, such as marketing and social media analytics.
In 2011, SAS launched a suite of products that allow users to profile customers and prospects, as well as predict behavior and optimize communication with them.
Python is an open source programming language that was developed in the late 1980s by Guido van Rossum. It is an object-oriented scripting language that is used in multiple applications and industries. It is very easy to read, write, and maintain, making it popular with both novice and experienced programmers.
One of the great advantages of Python is its wide variety of libraries and modules, which allow users to perform different tasks such as data analysis, machine learning, web development, task automation, among others.
The main tools of Data Analytics can allow you to find important market opportunities from your data and important information. Remember that you can request a consultancy with Codster to solve your doubts about it.