Five Crucial Features Your Big Data Visualization Tool Should Possess

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Click to learn more about author Vaibhav Shah.

Images communicate better than words. This isn’t new for any of us on earth – the reason being that they represent complex information in a meaningful manner that hundreds of lines of documents do not.

Similar is the case with a Big Data Visualization tool. It helps enterprises, organizations and companies to display data in the structured and ordered format, which is not only easy to interpret but meaningful and receptive to making decisions.

The processes like data gathering and data aggregation are not enough. Data becomes valuable only when it is interpreted well. For that, we have Big Data Visualization tools that are beyond the pictorial representation of numbers, corporate charts and graphs. They enable decision making by identifying correlations and patterns through advanced studies like heat maps, fever charts and more.

Big Data Visualization Tool

What basically a Big Data Visualization tool does is that it identifies patterns, limits noise and insignificant values from the data to produce actionable insights. This helps in making quick and effective decisions.

So, in order to make most out of the Big Data Visualization, you need to have the right tool. Here we list down top features that your Big Data Visualization tool should have.

1.    Decision-making Ability

A visualization tool provides data in a meaningful format by analyzing previously-recorded data and predicting future patterns. But this information only makes sense if the end user is able to make necessary decisions based on the revealed patterns and information. For that to happen, the tool should have support for trend lines, Advanced Analytics and other metric assessment features.

2.    Highly-effective Infrastructure

The other important factor that contributes to the success of Big Data Visualization tool is the infrastructure. The more dynamic infrastructure is, the better it is for the team to extract insights. The quantity of data stored, managed and analyzed is immense and maintaining an infrastructure for the same is huge.

Hence, in cases like these, Cloud Computing or Serverless Computing comes in the picture. For extended workloads, the infrastructure can be managed and configured easily without a hefty addition of expenses. Not only money, it grants agility to the process, saving time and effort of the workforce.

3.    Integration Capability

The Big Data insights only help users if they are integrated seamlessly into operational business systems directly on the dashboard. This is where they can easily explore the reasons of failure/success, view metrics associated for each, predict forthcoming and infer methods or procedures to produce a desired result. A Data Visualization tool must come with an API support to get insights on the tool or interface the users already use or love using.

4.    Prompt Discovery of Results/Insights

The inferences from data analysis are desired in real-time. A slight delay of things can ruin the entire workflow and experience of the users. If the Data Visualization tool lacks promptness, then it will be dumped by the users. There are hundreds of decisions to be made based on the insights and this could only be possible if the tool carries features like dynamic loading, dynamic edit/update/delete and other data optimization controls. The lack of direct memory access can also be one of reasons why your data tool does not respond quickly. So make sure that your tool has direct access to memory for prompt results and acceleration of complex data analysis.

5.    Real-time Team Collaboration

There are multiple instances when team members need to connect with one another about their insights or for having a meaningful discussion about current data. The real-time interaction is a much better way to concluding things rather than forwarding the gathered data as static files/documents and screenshots. Look out for this important feature in your data visualization tool for quick and productive results.

Final Words

Data Visualization is a valuable technique to accelerate business in the right direction and with the right resources. It cuts down the noise in your data and yields only the useful patterns and values that can impact business.

In today’s highly competitive world, there are no limits to how many options you can get for a particular thing. Same is the case with data visualization tools. The options are plenty but what makes the difference is how well you analyze your requirements and select an appropriate tool based on your needs.

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