Analytics is inherently exploratory, speculative, and adaptive. It’s fundamentally a creative and human activity, and it involves making mistakes. It’s 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 can’t 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.
I like what Ajay raises, the idea for understanding more than one platform going ahead. There is still not one "all-mighty" platform, though vendors will certainly try. There are training and certification programs avaialble, such as the Web Analytics Association certification course and exam. But being comfortable with various platforms will probably be with use for a while despite recent mergers and acquisitions.
@Stephen Very nice argument in favor of Open Source Analytical packages. I agree that commercial packages do lock one in to a certain methodology and outcome. And that the money would be better spent on training which will have to happen with either package.
Some due diligence must be used to make sure the open source package chosen will be around for the long haul, but all and all - I think open source presents a strong solution to the budgetary needs of most businesses today.
Great points, Stephen. Considering the great potential of analytics for future business uses, and our relatively rudimentary understanding still of what it can do for us (as I wrote about in August when discussing linguistic sentiment analysis), open-source definitely seems the way to go for some things in analytics. It's like traditional game theory. If everyone cooperates, everyone wins.
Well IMO it can be taken either way. Mostly young people do not like training stuff since its boring because they are the early adopters but when you consider the late adopters they need training and they value it more than anything else. Thats where training comes handy but all in all I also feel that if the training can be conducted in a much more interesting manner it will be very useful for both early and late adopters.
That's all true but no one wants to go through training. That's why everyone ignores it. Most people like to get their hands on it and just mess around with things. I think if training was a more interactive and engaging process, it would be alot more appealing to the target groups.
Ajay: Completely agree on the training. Vendors have a real asset there which buyers don't use to their advantage as much as they could. I argue that any organisation intending to license software should send its analysts on the vendor's training courses as a part of the evaluation process. Training provides knowledge which the other evaluation tools don't, i.e. sales and pre-sales consultation, written responses, and product demos.
I do see a trend for analysts to learn more than one platform in analytics in the future, some of them may be commercial some of them open source. As of now, there is a shortage of analysts trained across multiple tools, part of the reason is analytics training even by mainstream analytics vendors is considered a secondary and niche revenue stream. If analytics vendors took training solutions as important as product marketing , both commercial and open source vendors would benefit.
I guess on second thought we might say that lack of expertise in analytics is no better served by commercial applications and perhaps less so. One option, if you lack the expertise in the field yourself, might be to partner with someone with the necessary skill set. So, for example, I start a small manufacturing or retail business and want to use analytics to manage everything from supply line to marketing to customer loyalty management. One option, if I can't afford to hire someone and am too busy with other parts of the business to learn the skills myself, is to look for a partner with the requisite experience.
Beth: Putting aside any specific tool comparisons for the sake of the argument, I don't believe that intuitiveness, GUI-richness, common task automation, or other ease-of-use software characteristics can make a mediocre analyst into a good one. The upside of these features is that they augment already effective analysts and make them more efficient. There are downsides, however. I discussed financial and political capital risk in the post, but there's also the risk that 'easy to drive' software can mask incompetence, making it easier for bad analysis to be produced by poor analysts.
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
The Current Discussion
Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
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