A Simple Case for Analytics
Rumack: You'd better tell the Captain we've got to land as soon as we can. This woman has to be gotten to a hospital.
Elaine Dickinson: A hospital? What is it?
Rumack: It's a big building with patients, but that's not important right now
Apparently we frequent fliers weren't mistaken; US airlines WERE padding their flight times in the mid-2000s to game the system and demonstrate better on-time performance. In 2007, fuel was (relatively) cheap and fliers were plentiful. About the only threat to US airlines was the bad press that accompanied poor on-time performance statistics.
But a funny thing happened not long aftewards. In the immediate wake of the financial crisis, demand for air travel cratered, forcing a few more carriers into bankruptcy and heightening all carriers' emphasis on improving operational efficiency. Simply put, carriers had to put more rears in fewer plane seats and have those planes fly more hours. An obvious target for the efficiency improvement was the airlines' flight schedules themselves. Department of Transportation statistics be damned; the imperative became, "how can we generate more cash from the same assets (planes) with same labor costs?"
At that point, presumably, an extensive review of airline flight times and performance was undertaken by our largest air carriers. Flight times by route, by equipment, by season, etc, were undoubtedly trended and graphed in the operational war rooms of these airlines. In short, carriers were aggregating, transforming and analyzing time series data across multiple operational systems to identify the optimal flight schedules. The net result became, for example, American Airlines had their fleet in the air an average of 24 hours more per day last year versus 2007. It doesn't sound like much, but when you multiply the average fare paid in 2011 ($337) by the average capacity (149 people) of a US-based 'mainline' carrier by 6.17 (the average number of flights/segments a typical jet flies per 24 hour period) by 365, the result is a whopping $115 million in additional revenues/year by shaving just 1 minute off the average flight schedule. Incredible!
While your business may not struggle with how to optimize the flight schedule for 1,500+ flights per day, there are invariably ways you intuitively know you can remove friction and waste. But you don't employ 1,000’s in IT like the legacy airlines have, nor do you have an army of consultants at your disposal to sift through the mountains of data available to your company. However, the problems you're trying to solve are dimensional in nature; that is to say they are not linear and univariate--time, channel, region, supplier, seasonality, etc, all possibly influence the answer. Just as the airlines weren't able to identify that precious 1 minute per flight savings by loading a spreadsheet into a fancy visualization tool, your business questions likely can't be answered by a pretty picture that can't support the analysis of data - influenced by multiple variates - over time.
Few companies can afford the cost and complexity of the BI platforms offered by the legacy “Big BI” vendors, however, you still need a real BI platform than can recognize that, for example, a flight doesn’t just shuttle back and forth between two cities; shaving 5 minutes off of the Charlotte-Newark leg can only happen if the inbound equipment from Pittsburgh consistently arrives on time. An agile business analytics platform, like Birst, gives your organization the ability to perform the sophisticated analysis they really need--the kinds of analysis businesses need to perform daily to thrive.
Without Birst, you might be answering, "It's a big building...with patients", rather than answering the actual question being asked.