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Traditional data reporting just isn’t up to snuff when it comes to generating meaningful data for business leaders to make decisions, a fact solidified by Gartner Inc.’s decision to rename its annual BI quadrant rankings report from “Business Intelligence Platforms” to “Business Intelligence and Analytics Platforms”.
“The dominant theme of the market in 2012 was that data discovery became a mainstream BI and analytic architecture,” Gartner’s report stated.
So what does that mean for scientific companies looking to harness the most powerful data analysis software in 2013? It means looking beyond data reporting to find tools that not only are capable of extracting relevant data but also present that data in a scientifically-meaningful way.
As the era of “big data” continues to generate more and more information, the ability to capture and understand meaningful data goes a long way. A recent story published by NPR called big data the equivalent of the steam engine in terms of technological impact. Whereas the collection of big data from digital activity can offer insight into human behaviors, likewise the collection of data points from scientific research can offer valuable insights into experimental patterns and materials behaviors.
And the future of harnessing all the information contained within that data? That’s where data visualization comes in: Visualization provides an intuitive method for researchers to sift through and expose relationships between data sets. By replacing rows and columns of data with pictures and charts to graphically represent information, users can absorb information in real-time and also locate information much faster using visual discovery tools.
Critically, data visualization allows for optimization of personnel resources. Whereas traditional enterprise-level data reports often necessitate the efforts of an IT support group to write code allowing end-users to use query language to find data, data visualization allows end-users to both enter and retrieve data from enterprise systems.
Not only does data visualization allow more personnel to be dedicated to discovering patterns within data, it allows those people to make better and more informed discoveries. A 2013 scholarly article found that when doctors and patients processed and discussed diagnosis findings, visual aids increased comprehension of probabilities and numerical data.
This finding isn’t just meaningful for data relevancy in the medical field – it demonstrates that humans innately are better at understanding data when it is presented graphically. Not surprisingly, this natural proclivity for humans to draw meaningful information from visual representations is what makes data visualization tools so powerful when used in data analysis.