As organizations grow and collect more data, the role of data analytics reporting is gaining importance: by accumulating vast amounts of data in terms of petabytes, it is becoming important to track information and compile it into valuable insights for use. The outcome of data analytics provides insights which are reported to make sound business decisions. Data analysts use software to analyze data from across the enterprise and present it to stakeholders. Selecting one or more data visualization and predictive modeling tools requires an understanding of the scope of the project: what data will be needed and the business problem we are looking to solve.
In my experience as a data analyst of the many software tools available, the three noteworthy ones are:
- Tableau — According to Gartner, Tableau is a leader in BI Magic Quadrant for the fourth straight year. Tableau comes with a great visualization power. Users can connect to any data source generating extracts, which then they can simply drag-and-drop to generate valuable insights. Tableau provides features where multiple users can work and share it simultaneously. Visualization has the power to convey complex data in the form of simple stories. Key features include data extract and data blending. Tableau does not require coding skills, making it easy to use for non-technocrats.
- R — This awesome, open-source platform is on the rise as a business analytics tool. I started with R for my graduate work and found it very effective. It includes every statistical data manipulation chart that the modern data analyst could ever need. R programming comes with a set of packages to perform almost all data mining algorithms. There are many statistical methods and predicting models available as a reference for new learners. R has the potential to process millions of records with many attributes in just a few minutes. For example, Facebook uses a technique called “power analysis” to track user interaction with its new features. This was developed in R. Organizations that really want to stay ahead of their rivals by making use of analytics should consider exploring R.
- Rapid Miner — Provides a platform for predictive analytics for business and commercial applications. It can be used for prototyping and building first-level models before moving on to actual application development. I personally found the Graphical User Interface (GUI) very friendly, with easy drag-and-drop capabilities. It has over 1000 operators, which include data access, statistical algorithms, looping functions, thresholds, etc., for transformation and analysis, making predictive model development faster. It has capabilities for all steps of the data mining process, including preparation of the data, prediction, visualization and validation.
There are a number of tools available in the market place. These three tools may be worth considering to get you started with your business analytics efforts and to bring value to your business. So just go out and explore!
Exploring different software tools will allow you to select the right ones to optimize your business. Build your analytical skills with AMA's resources and seminars.