The business value of data visualization projects is growing by the day as more and more organizations begin to deploy big data systems in hopes of leveraging a rapidly growing pool of data resources. Previously, data visualization was more closely associated with self-service business intelligence and data discovery applications, which were used by business users to create basic charts of revenue, profit and other KPIs (key performance indicators). But now, more and more data visualization tools are being deployed in big data analytics environments to fuse multiple data sources from internal and external sources into actionable information.

The biggest challenge facing IT, business intelligence, and analytics teams on big data visualization projects is how to filter all the data that needs to be processed into a form that is easy to grasp, so that this form can positively impact the decision-making process. The approach that readily comes to mind is to use data visualization software to make a coarse selection of all kinds of data and then create refined tables and graphs. A more quantifiable and systematic approach can be used, which can give better results.

Not to add more work to the team by creating too many data visualizations, as this sometimes prevents the team from focusing on the real goals of the analytics application, such as enhancing business processes and better assisting in business decisions. It's important to simplify things that don't really make sense without making all those flashy visualizations for them.

Data visualization should also be used in moderation

A financial company uses the BI tools in its NexusWire CRM system to analyze customer data and help the company more accurately price its financial products to individual viewers and segments of customers. They also use Seeing Data, sometimes supplemented by the D3 open-source visualization library, to visualize data from company performance reports that are presented to the management team.

Most of the long-term drivers of business decisions can be extracted from a pivot table or a simple table, and there are many ways to work with performance data, but what we really want to know is how to better price based on (customer) segmentation.

Even heavy data science work like deploying and running predictive models to evaluate the credit worthiness of new customers doesn't require the creation of complex big data visualizations to represent the analytics. If the purpose of the modeling effort is simply to understand the connections between different data elements for certain customers, then creating further visualizations is a waste of time and effort.