10 Essential Features of Big Data Analytics Tools


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Big data analytics is not a single process instead is a collection of many processes that are related to business and they may be related to data scientists, business management, and production teams too. Data analytics is just a part of this big data analytics. There are several tools that are being used for big data analytics model and they must have certain features and characteristics.

Today, we have brought this blog to throw the light on features of these tools. They can help you in reducing efforts that are required to improve the business results. Data scientists require these tools to make the process more efficient and quick.

10 ust-have Features of Big Data Tools

1). Easy Result Formats

Results are imperative parts of big data analytics model as they support in the decision-making process, that are made to decide future strategy and goals. Scientists prefer the results to get the result in the real-time so that they can take better and appropriate decisions, based on the analysis result.

The tools must be able to produce a result in such a way that it can provide insights into data analysis and decision-making platform. The platform should be able to provide the real-time streams that can help in making instant and quick decisions.

2). Raw data Processing

Here, the data processing means collecting and organizing data in a meaningful manner. Data modeling takes complex data sets and displays them in the visual form or diagram or chart. Here, data should be interpretable and digestible so that it can be used in making decisions. Tools of big data analytics must be able to import data from various data sources such as Microsoft Access, text files, Microsoft Excel and other files. Tools must be able to collect data from multiple data sources and in multiple formats. In this way need for data conversion will be reduced and overall process speed will be improved. Even the export quality and capability to visualize data sets and handling various formats like PDFs, Excel, or Word files can be used directly to collect and transfer the data. Below-listed features are essential for the data processing tools:

  • Data Mining
  • Data Modeling
  • File Exporting
  • Data File Sources

3). Prediction apps or Identity Management

Identity management is also a required and essential feature for any data analytics tool. The tool should be able to access any system and all related information that may be related to the computer hardware, software or any other individual computer. Here, the identity management system is also related to managing all issues related to the identity, data protection, and access so that it can support system, network passwords, and protocols. Here, it should be clear that whether a user can access the system or not and to which level the system access permission is granted? Identity management applications and system ensure that only authenticated users can access the system information and the tool or system must be able to organize a security plan and include fraud analytics and real-time security.

4). Reporting Feature

Businesses remain on top with the help of reporting features. Even time-to-time data should be fetched and represented in a well-organized manner. These way decision-makers can take timely decisions and handle the critical situations as well, especially in a society that is moving rapidly. Data tools use dashboards to present KPIs and metrics. The reports must be customizable and target data set oriented. The expected capabilities of reporting tools are Real-time reporting, dashboard management, and location-based insights.

5). Security Features

For any successful business, it is essential to save their data. The tools that are used for big data analytics should offer safety and security to the data. For this there should be SSO feature that is known as a single sign-on feature with the help of that there is no need for the user to sign-in multiple times during the same session, even with the help of single or same login user can log in multiple times and monitor user activities and accounts. Moreover, data encryption is also an imperative feature that should be provided by Big Data analytics tools. It means to change the form of data or to make it unreadable from a readable form by using several algorithms and codes. Sometimes automatic encryption is also offered by web browsers. Comprehensive encryption capabilities are also offered by data analytics tools. For this single sign-on and data encryption are two of the most used and popular features.

6). Fraud management

A variety of fraud detection functionalities remain involved in the fraud analytics. Mainly when it comes to the fraud detection activities then it involves various fraud analytics. Due to these activities, businesses mainly focus on the way with which they will deal with the fraud rather than preventing any fraud. Fraud detection can be performed by data analytics tools. The tools should be able to perform repeated tests on the data at any time just to ensure that there will be no amiss. In this way, threats can be identified quickly and efficiently. With effective fraud analytics and identity management capabilities.

7). Technologies Support

Your data analytics tool must support the latest tools and technologies, especially those that are important for your organization. Here, one most important one is the A/B testing that is also known as the bucket or split testing, in this testing two webpage versions are compared to determine the performance of a better page. Here both the versions are compared on the basis in which user interacts with the webpage and then the best one is considered. Moreover, as far as technical support is concerned then your tool must be able to integrate with Hadoop, that is a set of open-source programs that can work as the backbone of data-analytics activities. Hadoop mainly involves the following four modules with which integration is expected:

  • MapReduce: It can read data from a file system that can be interpreted in the visualized manner.
  • Hadoop Common: For this, Java tool collection may be required to read data stored in the user’s file system.
  • YARN: It is responsible to manage system resources so that data can be stored and analysis can be performed
  • Distributed File System: It allows data to be stored in an easy format. If the results of tools will be integrated with these Hadoop modules then the user can easily send the results to the user system. In this way flexibility, interoperability and both way communication can be ensured between organizations.

8). Version Control

Most of the data analytics tools are involved in adjusting data analytics model parameters. But it may cause problems when pushed into production. Version control feature of big analytics tools will surely improve the capabilities to track changes and it is able to release previous versions too whenever needed.

9). Scalability

Data will not the same all the times but it will grow as your organization is growing. With big data tools, this is always easy to scale-up as soon as new data is collected for the company and it can be analyzed well as expected. Also, the meaningful insights driven from data is pushed or integrated into the previous data successfully.

10). Quick Integrations

With integration capabilities, this is always easy to share data results with developers and data scientists. Big data tools always support the quick integration with cloud apps, data warehouses, other databases etc.


Hope it might be clear so far that what features should be included in data analytics tools and where your business should focus on? Just make sure that the tool that you select possesses all of these features along with other required ones to support organizational decision-making teams and business results too.

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