Analytics is inherently exploratory, speculative, and adaptive. Itís fundamentally a creative and human activity, and it involves making mistakes. Itís enabled by software, but many of its processes are not well defined and are barely repeatable. These characteristics are most pronounced for organizations that value analytics highly and use it strategically.
Strategic analytics elevates the value and impact of executive decisions, creating entirely new business processes and structures. At the other end of the scale, operational analytics adds marginal value to existing processes and automates low-level decisions. The sustained extraction of strategic value from data is not amenable to commercial off-the-shelf software "solutions," turnkey approaches, and "best-practices." Systems built on these belong in well defined, routine, compliance-driven, noncompetitive, noncore areas of business. The closer the relationship between an organization's sources of unique competitive advantage and its leverage of analytics, the more customized and original its analytics will be.
Budget spent on commercial analytics software is money not invested in other capabilities, such as analyst education and talent recruitment. But the up-front capital expenditure requirement of commercial software applications also reduces flexibility on a range of key fronts. Initiating business cases -- typically high-budget, high-profile, and technology-centric -- invariably attract the conflicting agendas and priorities of a multitude of internal stakeholders at the point at which they are least informed. Analytics sponsors and teams are forced into premature commitment to certain business applications, outcomes, and benefits. These in turn drive specific tool and technology choices that proscribe alternatives. In a well defined process environment, these forms of lockin are not problematic. However, analytics is different. It's a data-driven, results-contingent, uncertainty-bounded activity.
These dependencies prevent many businesses from getting analytics initiatives off the ground, and they make it harder for those that do to learn and adapt. Specific pre-commitments, especially those that are operational (such as "more accurate forecasts that will reduce inventory costs" or "better customer targeting that will increase retention") frequently become shackles for analytics teams. Analytics uncovers insights, but it canít know what these are going to mean ahead of time or guarantee that they will be actionable to positive effect. Up-front capital expenditure on commercial software has the unfortunate effect of tethering the political capital of analytics teams and their executive sponsors to prematurely determined outcomes. The more complex, committed, and compromised these outcomes, the higher the risk of perceived or actual failure for analytics and those affiliated with it.
Organizations can either avoid these risks altogether or greatly reduce them and make them more manageable through employing commodity and open-source software for analytics. By using the commodity software already on their desktops (e.g., Microsoft Excel and Access) and freely available via open-source licensing (e.g., R, RapidMiner), analytics teams avoid technical lockin, maintain the ability to adapt to changing business priorities, and can devote more resources toward education and talent (via training, experimentation, learning from mistakes, and recruitment).
Importantly, they can do all of this without risking their political capital. In this way, commodity and open-source tools matter by not mattering. Analysts retain the freedom to pursue insights of unique and strategic value to their businesses, and the flexibility to instantiate these in the form of novel and customized business processes. Some of these may turn out to be well suited to the analytics software applications on offer from commercial vendors, in which case the organization is now an educated buyer.