Big Data Analytics Tool: 10 Essential Attributes you Must Look For

You must look for these 10 essential features in big data analytics tools to revolutionise business in 2021.

Traditional big data analytics is a time-consuming process in this fast-paced world. The business world is revolving around the continuous flow of real-time data from various parts of the environment. In order to adjust with the current scenario, companies must invest in big data analytics tools as a part of Artificial Intelligence. A company needs a simple data architecture to conduct effective strategies. Some essential attributes must be present in these AI-based tools to transform the decision-making process and generate business insights according to market trends.

Big data analytics is a successful trend in all companies around the globe. It has become a new part of all types of business ranging from start-ups to hi-tech heavy industries. Big data analytics deals with a vast flow of both structured and unstructured data, out of which it extracts important useful real-time data to understand consumer buying behaviours, loopholes, critical problems and solutions and market trends. This helps to spend less time on calculating raw data and more time in other departments of the business. Big data analytics assist in effective sales and marketing of the company, leading to brand loyalty and brand image against the competitors.

Let's look at 10 essential attributes you must have in your big data analytics tool

High volume raw data processing: The tool is equipped with high power to process high volumes of raw data efficiently and effectively. The main steps to be present in this feature are data mining, data modelling, file exporting and data file sources. Thus, the tool can properly collect real-time data and organise those in an effective manner to help in rapid decision-making processes. It transforms data with data visualisation software to graphical representations for easy understanding.

Flexible identity management: The big data analytics tool should be flexible and fully compatible with the existing systems in your company. If the tool can be in sync with the already installed system, you will have to spend much less time and trouble to provide business insights within a short period of time. It should have smooth access to all types of information in hardware and software. Identity management helps to manage all kinds of issues regarding identity and protection of network passwords and protocols to avoid fraud data analytics.

Dashboard templates: Big data analytics can be used by non-IT employees as well as just the beginners in the industry. Dashboard templates should have access to real-time data with interactive dashboards. Various widgets and filters need to be present with just one click in order to choose the personalised data visualisation to detect market trends and business insights for effective comparisons.

Effective reporting: A Big data analytics tool should have effective reporting features for better analysis and to attract potential customers with after-sale service. Updated real-time data should be extracted and generated into interactive personalised reports for rapid decision-making process while managing critical situations. Effective reporting features should have dashboard management and location-based business insights. It should also report about customer experiences on a regular basis. This transparent reporting attribute can be used to train agents for specific calls and better performance.

Data governance: To support the company to stay under full security, data governance features must be present in the big data analytics tool. It has the capability to monitor data sources and secure the real-time data employed by the users. This tool supports very sensitive data and keeps those under strict security protocols. With the control of privacy regulations, it should have SSO (Single sign-on) feature for effective data governance. There should also be the presence of comprehensive encryption capabilities for fraud detection security purposes.

Scalable analytics: The future of real-time data is dynamic which means a company cannot get access to the same format of data for a long period of time. The flow of data keeps changing according to the market scenario. The big data analytics tool should assist the company to scale up instantly as fresh data is collected to analyse appropriately. The company can integrate customised new data analytics with the past data to receive meaningful business insights as per the market trends.

Smart device access: Big data analytics tool should be in sync with any device in any environment. It should not have exclusive access to in-office systems but also in-personal devices. It should allow users to get access to updated real-time data and solve critical issues anywhere in the world while having a connection with the office. Users should not experience any loss with the help of smart device access.

Drag-and-drop: Companies must have this effective feature known as drag-and-drop for a user-friendly interface. It enables the user to work efficiently without any complicated coding. The companies can drag and drop various statistics on an empty canvas to generate interactive reports. This attribute helps the users with filtering, segmenting, and analysis instantly without any complications. Through drag-and-drop, any user can produce colourful dashboards for future business insights.

Embedded result: Additional values of information can be gained from the business insights and important decisions. The embedded feature involves the ability to create business insights in such a format where it can be easily embeddable into decision-making platforms. These embedded results can be applied in a real-time stream of event data for instant decisions.

Varieties of graphical representations: Traditionally there are three basic graphical representations— bar graph, pie-charts and line chart. But due to advancements in technology and updated applications, there is an introduction of vast types of graphical representations such as scatter plots, histogram, bubble, treemap, stock charts, spatial charts and many more. The companies must ensure to have these different varieties of graphical representations under data visualisation tools.

Big data analytics is not just a style anymore. It has successfully created a permanent space in the systems of various kinds of companies. The effective use of real-time data to generate business insights is the main attribute of any big data analytics tool.