Some support a semantic layer or can perform data modeling themselves. The tool should also provide support for visualizations relevant to your enterprise.Ĭonsider a tool's data modeling capabilities. Will it be used by sophisticated data analysts and data scientists, by nontechnical users who need an intuitive interface, or should it suit both kinds of users? Some platforms provide an interactive experience for iterating on code development - typically using SQL - while others focus more on point-and-click analysis for less technical users. How do you find one that's a good fit for your company? Start by considering your organization's business needs and learning who will be using your analytics tool. Once you have data ready, you can try analyzing it using different tools. If data is shared between departments, for example, there should be access control and permission systems to protect sensitive information. You'll want to assess data security and data governance as well. In addition, before you can begin analyzing data, you'll need to select data sources and the tables and columns within them, and replicate them to a data warehouse to create a single source of truth for analytics. You should first understand the types of data your enterprise wants to analyze, and, by extension, your data integration requirements. There are a few things to take care of before evaluating the available tools. To help you determine which data analysis tool best fits your organization, let's examine the important factors for choosing between them and then look at some of the most popular options on the market today. Choosing the right data analytics tool is challenging, as no tool fits every need.

Top 24 tools for data analysis and how to decide between themĭata analysis is a core practice of modern businesses.

Statistical tools for data analysis