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10 features to look for in visualization tools for big data Posted on : Nov 21 - 2018

Big data is meaningless if users don't understand it. Experts and users explain why organizations need data visualization tools that offer embeddability, actionability and more.

Visualization tools for big data show promise in unlocking the value of data collected by enterprises. Getting the best results requires building out an infrastructure for aggregating data from across the enterprise and simplifying the process of discovering and sharing insights.

Why? Because data is only as valuable as someone's ability to understand it. Humans are primarily visual creatures who understand relationships and trends in data swiftly and intuitively through images, charts and graphs.

"In the big data era, good visualization helps cut through the noise in your data to pick out significant values or patterns quickly and accurately," said Zachary Jarvinen, senior analytics product marketing manager at OpenText, a supply chain software provider.

The following are a variety of features and capabilities that experts recommend organizations consider when adopting visualization tools for big data:

1. Embeddability: Big data is used to drive real business insights. Those insights then need to be embedded into operational business systems to properly guide users as to what has happened, why it happened, what will happen and the steps they can take to alter that outcome, said David Marko, managing director of on-demand analytics solutions and information management at Acumen Solutions Inc., an IT consultancy.

Visualizations can provide more value to business users when embedded via dashboards into the interfaces or tools that end users love and live in. This includes portals and applications already in use, because visual analytics don't require users to acquire a new skill set. Good APIs are important to help extend visualizations into other applications, said Saurabh Abhyankar, senior vice president of product management at MicroStrategy Inc.

2. Actionability: Tools for visualization must deliver practical value via useful predictions and prescriptions. "A visualization on its own delivers insight, but when coupled with transactions or write-back features makes it immediately actionable, and these instruments elevate the end user's role by making them more responsible," Abhyankar said. Features that support actionability include support for trend lines, one-click metrics and guided advanced analytics workflows.

3. Performance: If visualization tools for big data distract workers from the flow of their work, they're less likely to be used. A couple seconds' delay may not be significant for some use cases but may discourage users tasked with evaluating hundreds of decisions throughout the day. Features that help improve performance include prompts, data optimization settings and dynamic loading options. View More