Running an analytics practice can be highly variable in terms of human and capital resource demands. This is often compounded by the scarcity of highly skilled resources in the space (Statisticians, Data Architects, BI Architects, Data Modelers, Data Scientists, etc.).  The challenge escalates as data volumes grow into the “Big” range.  Traditionally the demand for such variable analytics skills and resources is acquired on a project-by-project basis according to capital spending and all of the budgeting and process surrounding capital.  The unfortunate truth of capital spending is that while it may save on taxes it greatly slows the pace of innovation and in the case of analytics an organization's access to information.

The cloud has taught us that subscribing to software and infrastructure as a variable expense can dramatically speed IT turnaround times to near real-time proportions.  The same too is possible with scarce skillsets in analytics.  Imagine an operational paradigm where analytics, data integration and system architecture expertise was available together with the technology those experts require on a part-time and scalable subscription basis: Analytics-as-a-Service (AaaS).  While you lose the tax savings from capital funded projects you may gain months, even years, in your access to information from closing the gaps created from capital budgeting and recruiting skillsets.

One of the great breakthroughs with AaaS comes from empowering data experts to be agile and solve problems without encumbering them with IT infrastructure project demands.  Consider the scenario of an analytic question that requires a new data source and thus an unknown amount of additional disk space and hours of computing time to answer.  A good AaaS subscription would provide you real-time access to the expertise to identify the exact capacity and technology gap to answer the question.  By leveraging the cloud your analytics provider would stand up the extra capacity and any firewall rules needed for answering that question that same day.  No special project or prolonged wait for expertise and infrastructure would be necessary.

Analytics as a Service  can take on many flavors.  Read our subsequent posts for a few examples.