Growing amount of data available to the organization has led to the development of many analytical tools. As the big data has no significance to the company until and unless it can be converted into valuable insights.
There are many open source and paid version of data analytical tools are available. Choosing the data analytical tools to mine your data, visualize data and create valuable insights depends upon the purpose of the organization and features of tools.
Based on popularity, user friendly and performance oriented, below are some powerful analytical tools an organization can use:
It is the most powerful and widely used tool to analyze the data. It does not require any programming language. It has significant contribution in analyzing internal data. It helps in filtering of data, scrutinize the data using Pivot table, it also facilitates model building using Solver and other tool that helps in decision making. Excel has many inbuilt business model tools that helps in data analysis and data mining and predict the possible impact of decision to some extent.
It is one of the most powerful tool for statistical analysis and create data models. R is a programming language that is used in data mining. It helps in linear and non-linear model building. It facilitates various statistical tests and time series analysis. R programming provides some graphical techniques as well.
It is the most powerful data visualization software. It requires huge data source and accepts data from any source viz. corporate data warehouse, web-based data, data on excel spreadsheet. It creates data visualization map and dashboards and facilitated real time updates on the web. It is an open source analytical tool that facilitate downloading of files in various different formats.
It is one of the leading analytical platform based on data driven innovations. It is integrated and open source to analyze data, discover hidden patterns, visualize fresh data and predict the potential future.it is very powerful analytical platform which provide more than 1000 modules, range of integrated tools and advanced algorithms.
It is also one of the most powerful analytical tool that support data from any source be it Excel, Access, My SQL, Oracle, IBM SPSS etc. it is an integrated tool that facilitate building of analytical workflows, machine learning, model validation and model deployment. This improves the efficiency of data scientist by providing integrated approach in a single environment. It helps in predictive analytics and advance analysis such as text analytics, visual analytics, data mining etc.
Apart from these some other tools such as Quill, Python, Apache Spark and many more. Some tools are freely available and some are paid. Data analyst can choose any of these analytical tools based on the requirement of the organization and their feasibility.