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Analytics > Brainpower versus Software power

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Sep 30 2009

I have been asked my opinion many times by clients on whether to hire strong analytic talent or spend that money on GUI-based semi-automated analytic software that the business users may use.  Both are costly so the answer should be based on ROI.
   
In the majority of environments, analytic brainpower will outperform semi-automated analytic software horse power – outperform in terms of long term ROI.   I’m not saying that you do not need analytic software; you do.  In fact, many of the analytic software packages are great.  The point to be made is that a strong analytic resource will usually utilize a much cheaper version of the software packages, with all the same capabilities, and deliver much more value as an analytics consultant does not need the expensive gui-based analytic solutions.  Further, a strong analytic consultant will know how to properly streamline analytic processes and seamlessly integrate to other components (database, marketing systems, output channels,..), which is just not possible with expensive analytic solutions alone, thus freeing up more time for additional analyses in the future.   

In fact, if you hire enough analytic brainpower you will minimize the cost of your analytic software as analytic resources are evolving to open-sourced analytic software and databases in production environments.  Take R for example (http://www.r-project.org/), an open sourced analytic solution.  R is widely considered one of the most robust analytic software packages available but it requires significant database, programming and statistical skills to install, operate and integrate.  There are a few aspects R is not good at presently, like big data processing, so you will most likely need another analytic or data processing solution for the time being but at the very least R will allow you to minimize the price of your analytics software as you only must buy to compliment R’s strong statistical and integration capabilities.      

There are exceptions to this rule however.  For example, in some very large analytic environments it may make sense to have both a strong analytic team and analytic software with all the bells-and-whistles to maximize ROI.  But, when starting out, in most cases hiring a strong analytic resource is a good first step if you have the funds then let that expert choose the analytic software which best fits your environment.
 
Regardless, a business case should be developed so you make the proper long-term decision as all environments are different.  Bottom line: People solve problems and drive insight … analytic software is only one component in the process of driving ROI through analytics.

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