What Are The Core Capabilities Of Analytics Tools?

Analytics tools refer to the application of techniques to discover, interpret, retrieve and communicate data. Business analytics tools are types of application software that retrieve data from business systems and integrate it into a repository so that it can be reviewed and analyzed. In other words, data analytics is the process of parsing data with the motive of drawing conclusions for future analysis and reference.

Take a look at some of the core capabilities of these tools:

  • Usability: Each and every organization, depending upon the nature of its business, has different requirements. In general, three types of users require high-level analytics capabilities: data scientists, knowledge workers, and business users. Everyone has their own specific requirements with respect to data analytics tools and, in this regard, companies like Provalis Research offer software that is designed specifically for each individual group.
  • Scalability: Tools must be able to accommodate large amounts of data and help prepare predictive models. Ingesting heaps of data and turning it into the required content is one of the important capabilities of a good analytic tool.
  • Large Content: It is important that the analytics tool is able to work on both structured and unstructured data and is able to integrate with different ERP systems. Along with the management of large content, software should also include data mining and data extraction features.
  • Data Preparation: An effective analytics software is able to join, expand, and integrate data in an easy-to-use, visual, and drag-and-drop environment. The software should be able to identify and extract all types of data from data repositories both inside and outside the organization. The software should enable the user to quickly retrieve any relevant information from the pool of data with ease.
  • Algorithm and Model: The best software will include a large variety of the most common algorithms such as regression, classification, decision trees and Bayesian algorithms, which enable data scientists to construct predictive and decision-making models for the business.
  • Open Source: This means that the source code of the software is freely available for the public and they have access to view and modify the data. This helps data scientists make modifications and changes to models quickly and cost-effectively.

Having all these features in an analytic tool helps streamline processes, optimize customer engagement, improve decision-making, and automate operations.