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Combining today's analytical solutions with the vast performance improvements of modern enterprise platforms,  translates into a powerful computational force to extract far greater information from your data.  What we used to call "number crunching" is now the work of sophisticated analytical tools that can sift through massive amounts of data in search of trends and distinct patterns that can provide great insight into the behavior of your market, or target of interest.  Often, these tools are used to discover past trends and behaviors.  Increasingly though, they are becoming more capable of predicting, with some certainty, the current and future movements of the environment, events that are likely to occur in the environment, and  changes to the populations that inhabit those environments.  
             DATA Analytics
Our experience in bringing tools like these to bear on real world problems, allows us to identify the right tool, or combination of hardware and analytics to solve your problem, or extract the maximum value from your data.  By understanding these tools and working directly with their provides, we can best deliver solutions that meet your schedules, and within your budget.  Seems like every day there is a new company popping up with an innovative approach to big data analytics.  While many of them offer costly proprietary systems, our approach is to leverage, to the greatest extent possible, open source or less costly tools.  Usually, these proprietary systems can trace their roots to these same open source tools; but repackage them with an expensive service and maintenance license in order to create a business model.  However, with the advent of cloud computing, there is now a rich set of open tools, along with remote computing power, available to meet most business level needs.  Let us find the solution for you.  


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Due to the chaotic nature of non-linear dynamic systems, weather patterns for instance, prediction is limited by the propogation effects of small, sometimes miniscule, variances in the data from one time slice to the next. For these class of systems, constructing an accurate dynamic model is required to understand these variances.  Fortunately,  most of the real world problems that business and agencies need solutions to fall into the category of linear systems, which lend themselves to being more predictable [citation]