Paralysis By Analysis: data overload and what revenue managers can do about it

Too much data analysis and not enough decision making can cause the very real problem of paralysis by analysis: a poisonous situation that creates slow decision-making—or brings it to a halt—due to redundant analytical processes and sheer data overload.

Published: 18 Aug 2011

Too much data analysis and not enough decision making can cause the very real problem of paralysis by analysis: a poisonous situation that creates slow decision-making—or brings it to a halt—due to redundant analytical processes and sheer data overload.

I was reminded of paralysis by analysis in a recent posting by David Carr at “Don’t Just Analyze Your Business, Optimize It".

While this is a topic that may not be “top of mind” to revenue managers or the hotel industry at large, we are part of an industry that is awash with data and can be rendered paralysed by attempting to analyse and act upon it.

Of course, in business, the most commonly understood example of paralysis by analysis is when a project involves so much computer-generated analytical data that employees have no idea where to begin and where to end. Furthering the “paralysis” is the authorisation process that is usually required to act on the data. Such processes delay decisions by requiring slower human “re-analysis,” which usually comes in the form of meetings and/or committees. In describing paralysis by analysis via a bureaucracy or committee, renowned businessman Ross Perot, years after selling controlling interest of EDS to General Motors (GM), famously said this about problem solving at GM:

I come from an environment where, if you see a snake, you kill it. At GM, if you see a snake, the first thing you do is go hire a consultant on snakes. Then you get a committee on snakes, and then you discuss it for a couple of years. The most likely course of action is -- nothing.

Perot’s example highlights the inevitable results of paralysis by analysis: inaction, or the failure to respond to the situation in a timely manner.

For the hotel revenue manager, much like in the above example, paralysis by analysis can be attributed to the vast amount of rate-determining data coming in from various sources (market data, online travel agency channels, inventory data, historical pricing information, etc.). And contrary to popular belief, the addition of computers to the RM’s business environment can actually enhance paralysis by analysis because of the overwhelming amount of data that is instantly available. This is where David Carr’s blog relates to paralysis by analysis: Carr emphasises that, unless data is optimized—where a system initiates a decision process based upon the data—overwhelming amounts of data can paralyse the process of making important, timely decisions. Such paralysis is especially frustrating for us in the revenue management environment, where over-analysis can be a colossal waste of time given the capabilities of today’s revenue management software.

In his post, Carr relies heavily on the research of Steve Sashihara, one of the leading experts in optimised data and author of “The Optimisation Edge: Reinventing Decision Making to Maximise All Your Company’s Assets.” Carr says:

One of his [Sashihara’s] most interesting arguments is that a great deal of the effort spent on information gathering and analysis is wasted — or, at least, used sub-optimally — when it’s used to feed business intelligence systems that produce reports that ultimately wind up with being fed into spreadsheets and PowerPoint slides. Managers then sit around in a conference room listening to presentations and debating what the data means and what decisions should be made about it — when, in many cases, good software could make the decision itself.

This is a perfect way of describing revenue management software: software assists and recommends actions for the RM to take based on an optimisation of rate-determining data. The software analyses the situation and initiates a decision model, leaving the revenue manager to deem the best course of action.
I recently discussed this issue with a number of hotel clients and all of them mentioned paralysis by analysis in one form or another due to overwhelming amounts of disparate data. One RM told me that, “every time I initiate a strategy based on the latest and greatest channel strategy, another channel pops up, adding to the different data we need to add to our system—every year it seems that channel management is thrown on its head.” Another RM added, “Our system is overwhelmed. There’s so much data today, and not enough people or time to put it to good use. A lot of times, we just have to ‘surrender’ to the overwhelming amounts of data and simply make our pricing decisions based on historical data and inventory.”

Such is the conundrum in revenue management: we have all of this powerful knowledge instantly available to us, yet we fail to simply kill the snake, as Ross Perot would say. Adding data that is not optimised for instant decision making results in a bloated system that leads to inertia, or worse—decisions that ignore the data altogether.

Much like business industries in general, data optimisation is the solution to paralysis by analysis in the hotel revenue management business. Software that instantly queries and analyses current market data to form optimised rate recommendations allows the RM to inoculate himself from paralysis. Rate optimisation and pricing automation can directly interface with a hotel’s booking engine to combine the revenue manager’s expertise (the RM can customise the software’s decision-making parameters based on his strategy and his experience) with that of the optimised data in order to achieve the best possible outcome. The result is a powerful synergy between human and machine that not only enables the revenue manager to make decisions quicker, but also allows him or her to make them confidently, based on current market conditions.

Put simply, an optimised software solution is the antidote for revenue managers to the poison of paralysis by analysis.

This article has been contributed by Jean Francois Mourier, RevPar Guru.

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