Data visualization and mining add an incredibly valuable dimension to enterprise information analysis and reporting activity. Because organizations and users have to wade through so much data in order to produce relevant insights and make meaningful decisions based on them, it's of utmost importance that information be communicated in an approachable, engaging way. It can be hard to produce a visualization ideal for the task of data mining, but software development tools like DataGrid can help take analytics applications to the next level.

Data mining has emerged over the past few years as an essential aspect of information analysis. In short, it's the process of looking for patterns and trends in raw data. The most efficient mining can take place when data is simply presented, aesthetically pleasing and encourages multiple viewing perspectives. Data mining should ultimately drive efficiency, wrote BusinessNewsDaily contributor Chad Brooks.

"The software gives businesses the ability to speed up discovery with semi-automated analyses, break up customers into groups based in similar activities and demographics and predict future trends," Brooks observed.

How data visualization drives mining practices

The combination of speed and depth that data mining can cause depends on the quality of data visualizations. Charts and graphs have to be dynamic and easy to work with. Users ought to be able to perform queries and manipulations to push their analysis in a desired direction, but in time, their movements should happen organically and take them in unexpected directions. After all, a user won't know all the answers before he or she gazes into a graph - a good data visualization can offer surprises. Users should be able to discern information almost without thinking about it - almost by magic, according to data analysis expert Jean-Francois Belisle.

"[A] data miner is like a Criss Angel (You can pick any other magician here!) that will make appear from your messy ocean of data, insights that will be valuable to your company and may give you a competitive advantage compared to your competitors," he wrote.

Understanding how users experience data visualization

So can a software developer turn employees into Criss Angel-level information mindfreaks? Using programming tools designed to improve the quality of data visualizations is a good first step. As anyone who has spent time any time at all exploring visual design - even something so simple as choosing a font - knows how hard it can be to strike the exact balance between engaging and useful. This is particularly relevant given the rise of non-data scientist end users who will be wielding these tools.

As citizens of the Internet, it's likely that they've spent a lot of time with data visualizations - probably more than they realize. After one has so many encounters with visuals and design, some aspects begin to "seem" or "feel" right. Even if the user isn't able to articulate what exactly it is that makes a visualization work or not, they likely have some compass that knows one when they see it - and is aware something is missing when a visual doesn't "work." 

Not every visual has to have the exquisite detail of cutting-edge maps. What it does have to do is present information in an accessible way. DataGrid for WPF, for example, provides advanced data visualization features for WPF applications. It is comprehensively interactive, offering users the ability to reorder, edit, sort, filter and group information for optimal analytical opportunity. It lets users handle larger datasets than they would be able to in applications not specifically geared for optimized data mining. DataGrid for WPF also provides easy export to Excel, so users can wield another familiar tool in their effort to mine information for next-level insights.