CBoeing has announced that they are providing advanced analytic solutions to several airline customers including All Nippon Airways (ANA), who signed a renewal contract for Airplane Health Management (AHM) on its entire future fleet of Boeing 787 aircraft, British Airways for Wind Updates, currently installed on 88 airplanes with plans for additional fleet integration, Delta Air Lines, who signed an agreement to use Airplane Health Management (AHM) on its Boeing 737, 747, 767 and 777 fleets, GOL, who signed an agreement to use the Engine Fleet Planning and Costing (EFPAC) tool, which significantly reduces operating costs by determining specific engine management practices over the life span and enabling better decision making and Pobeda Airlines, who signed a contract to deploy Fuel Dashboard services across its fleet of Boeing 737s.
This is a smart move by Boeing to create new services (and new sources of revenue) to help its airline customers get more value out of their investments in Boeing aircraft. It even sets the stage for Boeing to expand beyond just servicing and supporting Boeing aircraft to servicing other aircraft (Airbus, Bombardier, Embraer Lockheed, McDonnell Douglas, etc.) in order to create even more monetization opportunities for Boeing.
However, I’m a bit distressed by organizations that are so quick to give up their data for a short-term win. It won’t be long until all the airlines have the same analytic services being provided by Boeing or GE or Pratt & Whitney. And if everyone has the same analytics, what’s the long-term source of competitive advantage? In fact, I think it boils down to a very important organizational and cultural mentality:
Does your organization see big data as an opportunity to “Save Me More Money”, or does your organization see big data as an opportunity to “Make Me More Money”?
This question sets the tone for your big data and analytics efforts and investments, and how committed your organization is to leveraging data and analytics to power the business.
It’s a corporate cultural and management issue and I see it all the time in my big data travels. Some companies are focused on the “save me more money” aspects of big data (which it then makes sense to outsource) but others are focused on the “make me more money” aspects of big data where they see data and the associated insights as a means for uncovering new monetization opportunities. This corresponds to Phase IV: “Insights Monetization” phase of the Big Data Business Model Maturity Index which guides organizations to focus on capturing, refining and re-using the analytic insights, to identify “white spaces” in the markets to create new monetization opportunities such as new products, services, markets, channels, audiences and new partners.
So what insights might these airlines be forfeiting – insights that might lead to new monetization opportunities – by outsourcing some of their analytics to Boeing? In order to answer this question, we first need to identify the airlines’ key business entities; that is, what are the business entities around which the airline would want to gather behavioural insights such as tendencies, inclinations, propensities, usage patterns, interests, passions, associations and affiliations? Well, my starter list of key business entities for an airline would include the airplanes, routes, hubs, pilots and mechanics.
The next step would be to identify the types of [predictive] insights that one might want to capture on each key business entity, such as:
- Airplanes: Which airplanes are most efficient from an operational as well as performance perspective? Which airplanes are most efficient with which routes and under what weather conditions? Which airplanes are “easiest” to maintain? Which airplane configurations are most fuel efficient? Which airplane configurations get the highest passenger satisfaction and referral ratings from the airlines’ “most valuable” passengers? Which airplanes are easiest to re-configure?
- Routes: Which routes are most efficient from an operational as well as performance perspective? Which routes are most efficient under weather conditions (seasonality)? Which routes to the same destinations get the highest satisfaction, Net Promoter Scores (NPS) and referral ratings from the airline’s “most valuable” passengers? Which routes have the lowest percentage of weather-induced delays?
- Pilots: Which pilots are most efficient from an operational as well as performance perspective? What are the background characteristics (tenure, experience, certification, training, demographics, behaviors) of the “best” pilots”? Which pilots are most effective on which routes and under what weather conditions?
If you look at analytics just as a way to drive out costs, then you probably should outsource as much analytics as possible. However, if you believe that the exhaust from your product and service usage might be more valuable than the product and/or service itself, then you need to embrace big data analytics as a source of competitive differentiation.
