8 Most Popular Data Analytics Tools.
What is data analytics: Capella University defined data analytics as the science of collecting, organizing, and analyzing very large sets of data in order to identify patterns and draw conclusions. In other words, business users use quantitative and qualitative tools to enhance their ability to make precise business decisions and improve productivity.
Why you need to use data analytics tools:
- business analytics tools will enhance analyzing big data.
- Simplifying data and convert it into actionable information that can help organizations to achieve their goals such as growing revenue and increasing profitability.
Data analytics tools have been around to meet the expanding needs of businesses of all sizes and industries.
Here are the most common data analytics tools:
1. Tableau Public: It is one of the most popular free tools to analyze data. It allows you visualize data and share it with others in a way they can understand it. Tableau Public offers two services for free which are Tableau Public and Tableau Reader.
2. OpenRefine: OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another, and extending it with web services and external data.
3. KNIME: one of the data tools that allow you fast and easy access to your data and help your organization drive innovations. “Our KNIME Analytics Platform is the leading open solution for data-driven innovation, designed for discovering the potential hidden in data, mining for fresh insights, or predicting new futures”.
4. Rapidminer: is also one of the data tools that will make your data analysts more productive through a unified platform for machine learning procedures and data mining including data visualization, processing, statistical modeling, deployment, evaluation, and predictive analytics.
5. Google fusion tables is a popular free data tool that works more with merging data from multiple tables and conduct details discussions about the data and also it works with visualizing data on maps.
6. Nodexl: NodeXL Basic is a free, open-source template for Microsoft® Excel® 2007, 2010, 2013 and 2016 that makes it easy to explore network graphs. With NodeXL, you can enter a network edge list in a worksheet.
7. SAS: Is one of the most powerful data analytics tools. SAS will help visualize your data and make them in a format that is easy to understand and will enable you to tell a story with your data.
8. Dataiku DSS is a collaborative data science software that allows multiple users work on the same project together. Dataiku thinks of collaboration in two ways Synchronous collaboration and Asynchronous collaboration. Synchronous collaboration multiple people work on the same project at the same time. Asynchronous collaboration multiple people work together but not on the same project and at the same time. This video will explain more how Synchronous collaboration and Asynchronous collaboration are used:
All these data analytics tools will help you analyze data and provide answers to questions regarding your company’s performance and improvement. You also need to know the basic data analytics technique as a data scientist beside those data analytics tools. Data analytics techniques require a high understanding of statistical techniques such as understanding the correlation between two variables in a large database. You need to understand the data analytics techniques so you become able to analyze data and predict actionable information that can be used for achieving companies goals.
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