Big Data Dilemma: Save Me Money Versus Make Me Money

My friend Dan sent me this press release (since he knows that I like all things “Data Analytics” related). In the press release, “Boeing Announces Data Analytics Agreements with Six Airlines,” Boeing announces that they are providing advanced analytic solutions to several airline customers including:

• All Nippon Airways (ANA) signed a renewal contract for Airplane Health Management (AHM) on its entire future fleet of Boeing 787 aircraft. ANA uses AHM tools to monitor their aircraft in real time and proactively manage maintenance operations more efficiently.
• British Airways signed a contract for Wind Updates, currently installed on 88 airplanes with plans for additional fleet integration. Customers of Wind Updates typically average a savings of 200-400 lbs. of fuel per flight by leveraging real-time information about atmospheric conditions to improve performance.
• Delta Air Lines signed an agreement to use Airplane Health Management (AHM) on its Boeing 737, 747, 767 and 777 fleets. Delta uses AHM’s analytics-based predictive alerts to reduce delays and cancellations by scheduling maintenance in a controlled manner, to avoid schedule disruptions.
• GOL 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.
• Pobeda Airlines signed a contract to deploy Fuel Dashboard services across its fleet of Boeing 737s. Fuel Dashboard helps airlines reduce fuel consumption 2-7 percent annually.

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.  I love it!

However, I’m always just 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 is not an insignificant question, because it 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 (see Figure 1).

Figure 1: Big Data Business Model Maturity Index

The “Insights Monetization” phase of the Big Data Business Model Maturity Index guides organizations to focus on capturing, refining and re-using the analytic insights (captured in Analytic Profiles – see “Orphaned Analytics” blog), to identify “white spaces[1]” in the markets to create new monetization opportunities such as:

• New products
• New services
• New markets
• New channels
• New audiences
• 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 behavioral 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 following:

• Airplanes
• Routes
• Hubs
• Pilots
• Mechanics

The next step would be to identify (brainstorm) 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 (seasonality)? 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?

I think you can start to see the realm of what’s possible (if not, you may want to sign up for one of our Vision Workshops) with respect to the types and levels of insights that can be gathered about the organization’s key business entities even from activities that start out to “save me more money” perspective.

Transportation Industry at Phase IV

Let’s see another example of Phase IV in action: transportation.  Let’s say that you operate a fleet of vehicles (company cars, rental cars, taxis, limos, delivery trucks, shipping trucks, etc.).  While the car and truck manufacturers could (and probably will) offer analytics to help fleet operators to reduce their operating and maintenance costs, the operators of those vehicles should care about the analytics because those vehicles are emitting tons of potentially valuable data about customer preferences and usage patterns, travel congestion, destination preferences and product performance.  For example:

• What features are most used on the car (could guide the development, pricing and packaging of vehicle features)?
• What radio stations are most used on the car (could create digital marketing and cross-sell opportunities)?
• What routes do passengers take most often (could create data that might be valuable to city and road planners, and could also create marketing and promotional opportunities for nearby services)?
• Where are the most common destinations by what times of the year (could create marketing and promotional opportunities)?
• Where do most accidents take place (could be used by insurance companies to set rates and by city management for road maintenance planning)?
• What driving patterns are most unusual (could be used to identify potentially drunk drivers, texting drivers or drivers playing Pokemon Go)
• Etc.

In fact, the “exhaust” from the operation of these vehicles could be more valuable than the vehicle itself!!

Summary

It’s very seductive to chase after the “low hanging fruit” by choosing to outsource your big data “save me more money” efforts. And there is nothing wrong with those short-term wins.

However, organizations should not make short-term cost-saving decisions that sacrifice the longer-term monetization and competitive differentiation opportunities. If everyone is using the same analysis to run their businesses, then where are the sources of competitive differentiation?

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.

Yes Dan, our Big Data challenge is up-hill both directions!

[1] “White space” is defined as unmet and unarticulated needs in the market. It is where products and services don’t exist based on the present understanding of values, customer needs or existing competencies.

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Thanks for taking the time to read my post. I’m fortunate that I spend most of my time with very interesting clients which fuel many of my topics. I hope that you are able to leave a comment or some thoughts about the blog. If you would like to read my regular blogs, please follow me on LinkedIn and/or Twitter.

In case you are interested, here are some of my favorite posts:

• Determining the Economic Value of Data
• The Big Data Intellectual Capital Rubik’s Cube
• How to Avoid “Orphaned Analytics”
• To Achieve Big Data’s Potential, Get It Into The Boardroom
• Vision Workshop
• Big Data Business Model Maturity Index (animation)
• How I’ve Learned To Stop Worrying And Love The Data Lake

I am the author of two Big Data books: “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”.  I also teach the "Big Data MBA" at the University of San Francisco (USF) School of Management, where I was named the School of Management’s first Executive Fellow. The opportunity to teach at USF gives me the perfect petri dish to test new ideas and concepts both in the classroom and in the field with clients.