Economics, Monetization and the “New Order” Automobile Industry

Economics, Monetization and the “New Order” Automobile Industry

Premise: How will the traditional car industry create and extract customer value in the future when the source of that value is no longer the vehicle itself?

An increasingly digital economy is overwhelming every industry, and no business, from the largest legacy institutions to plucky start-ups, is safe. To survive, businesses must embrace digital transformation. For those who haven’t, we have already seen what happens to them. Once long-standing giants in advertising, marketing, commerce, entertainment, transportation and hospitality industries have either shuttered their doors for good, or are on the fringes of the industries they once dominated (see Figure 1).

Figure 1: Digital Transformation Causing Industry Disruption and Disintermediation

 

These industries all have something in common.They have seen both new and established competitors leverage digital technologies to drive superior customer, product, service and operational insights that have disrupted business models and disintermediated customer relationships. For those businesses who feel they have not yet been impacted, it’s only a matter of time before digital transformation hits you (see Figure 2).

Figure 2: More Industries Under Attack

 

Which is probably why the former vice chairman of GM, Bob Lutz, stated “the automobile industry has no future.”

To quote Mr. Lutz:

“Our daily travel will migrate to standardized passenger modules as the demolition of the traditional auto industry accelerates. Within five years, people will start selling their cars for scrap or trade them in for autonomous passenger modules as self-driving cars take over transportation. Within 20 years, human-driven vehicles will be legislated off highways. Companies like Lyft, Uber, Google, and other technology companies will take charge of an industry now centered in Detroit, Germany, and Japan.”

And market projections support Mr. Lutz’s position (see Figure 3).

Figure 3: Automobile Industry Transformation from “Cars as Product” to “Cars as Service”

 

However, I’m not quite as negative as Lutz. The automobile industry can maintain its relevance by embracing digital transformation to find new sources of customer, product, service and operational value. And the economics of these “superior customer, product service and operational insights” will be the key to creating new monetization opportunities.

The New #Economics of #Transportation

As usual, economics will ultimately be the basis for digital transformation and uncovering new monetization opportunities. This affords us the opportunity to revisit an important concept from our college economics classes: Economic Utility

Utility” refers to the total satisfaction a customer receives from consuming a good or service. The economic utility of a good or service will directly influence the demand, and therefore price, of that good or service. The standard unit of measurement that microeconomics uses to measure economic utility is called the “util.

For example, I may go to the supermarket with $100 to spend, along with a phantom 100 utils representing 100% of the value I expect to receive from my purchases. Let’s say that $67 of my $100 is spent on necessities (meat, bread, milk, produce). However, although 67% of the $100 is spent on necessities, the number of utils assigned to those purchases may only be 40 for me. The remaining 33% of the money is spent on Snicker bars, Halo Tops, Cap’n Crunch, and other unnecessary but delightful goodies. But the utils or value that I receive from these purchases totals 60.

Utils provide a rough numerical measure of consumer value for a product or service.  However, there are problems with the utility concept in execution:

  • Utils are hard to measure; that is, they must be measured or quantified in an indirect manner
  • The value of utils are different for every individual consumer

But, that’s where Big Data and Data Science jump in!

Using Big Data and Data Science to Determine Consumer Utils

So how can industry leaders use the economic utility concept and utils to guide their industry’s digital transformation? It starts by understanding and quantifying each individual consumer’s utils or value associated with travel.

Step 1:  Understanding the Individual – the Power of One

What we learned from the economic utility concept is that utils differ by individual consumer; that each individual consumer has different preferences, propensities, tendencies, inclinations, biases, interests, passions, associations, and affiliations (see Figure 4).

Figure 4: Big Data Is about Monetizing the Power of One

 

Understanding and quantifying these individual behaviors and tendencies is critical to driving the organization’s monetization efforts. From the blog “Becoming Netflix Intelligent: Something Every Company Can Do!” we get this guidance about how a leading analytics company like Netflix leverages individual behaviors and tendencies to guide their monetization efforts:

The secret to Netflix’s success comes from the power of combining detailed viewer behavioral data and detailed show/program characteristics data with machine learning to make predictions about what shows what viewers might want to watch. This is a recipe that every company can and should follow!

Applying the “Netflix Analytics Recipe” to the automobile industry, we could leverage each individual consumer’s behavioral insights, tendencies, and preferences to place value or utils the following types of questions:

  • What in-car services are most valued by what customers based upon their usage patterns?
  • How do I get more customers to try new services based upon their individual propensities?
  • Which vehicle types (e.g., sedan, SUV, station wagon) are preferred in what situations?
  • When is transportation demand the highest and what are the likely destinations based upon day of the week and holidays?
  • How much of a premium is a customer likely to pay to get to their destination faster, and in what situations?
  • How much of a premium is a customer likely to pay for a more luxurious ride, and in what situations?
  • How long is a customer willing to wait for their transportation given their situation?

Being able to assign economic utility or value to each consumer in each of the above situations (use cases) is key to creating new monetization opportunities. However, answering these questions requires organizations to start capturing data AND building out analytic insights at the level of the individual consumer.

But, how can you hope to monetize each individual’s economic utility if you cannot capture the data and create the analytic insights at the level of the individual consumer?

Step 2:  Understanding (Envisioning) the Role of Data Science (AI | ML | DL)

Data science is the organization’s data monetization engine. It is through data science that we will derive the economic utility or value for each individual consumer within each unique transportation scenario, or use case. It is also through data science that we will drive new sources of customer, product, service, and operational value.

Artificial Intelligence, Machine Learning and Deep Learning (AI | ML | DL) is critical to helping organizations to predict outcomes and prescribe actions for each individual consumer in each of the key monetization scenarios (use case). Business executives need to invest the time to understand and envision what AI | ML | DL can do in each of the different consumer scenarios (use cases) which includes:

  • AI (Artificial Intelligence) is the theory and development of computer systems able to perform tasks normally requiring human intelligence (e.g. visual perception, speech recognition, translation between languages, etc.).
  • ML (Machine Learning) is a sub-field of AI that provides systems the ability to learn and improve by itself from experience without being explicitly programmed.
  • DL (Deep Learning) is a type of ML built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network.” A DL framework is SW that accelerates the development and deployment of these models.

Understanding how AI | ML | DL help organizations envision how to leverage their data is critical to deriving and driving new sources of customer, product, service, and operational value.

Economics Driving Business Disruption and Customer Disintermediation

When looking at the big picture, let’s be sure not to overcomplicate the data monetization and digital transformation processes. The real digital transformation process starts by identifying, validating, and prioritizing the consumer’s key business and operational scenarios.

Once the data has been captured, the incumbent automobile manufacturers must combine their discoveries with their foundation of institutional knowledge. Then starts the creative work. Companies must think “outside the vehicle box” to combine that institutional knowledge with new sources of consumer behavioral and engagement data to create a more holistic view and understanding of each individual consumer.

With that holistic view captured, the organization can then apply modern data science concepts with AI | ML | DL to derive and drive new customer, product, service and operational monetization opportunities. If they don’t, a competitor may perfect the analytics necessary to extract more economic utility out of the market and seize market share.

The automobile industry should not be intimidated by digital transformation. The recipe is there. It just requires a new mindset and approach for understanding and quantifying the economic utility (value) for each individual consumer and key transportation scenario of use case.


Economics, Monetization and the “New Order” Automobile Industry

Understanding Behavioral Economics to Change Behaviors with Big Data

Understanding Behavioral Economics to Change Behaviors with Big Data

My good friend Vinnie participates in an automobile insurance program that rewards him for good driving behaviors; the better driving behaviors he exhibits, the more money he saves on insurance. You stick a device into the vehicle’s diagnostic port (usually under the steering wheel in most vehicles manufactured after 1996), and the automobile insurance company tracks your driving behaviors and offers you automobile insurance discounts based upon the quality of your driving behaviors. The program actually “grades” driving behaviors including acceleration, turning, speed and braking, and once a month sends a report card on the past month’s driving performance (see Report Card in Figure 1).

And for my friend Vinnie, as a result of sharing his detailed driving data, he saved $1.49 over the past 6 months. That’s right! Vinnie is saving $2.98 per year by sharing his detailed driving data with his auto insurance company. That’s not even enough for a Starbucks’ Venti Chai Latte!

Now maybe if Vinnie had a better report card he might get enough of a discount to afford two Venti Chai Lattes every 12 months (he’s got about a C+ average…)

The objective of this blog is to highlight how combining big data with behavioral economics to provide timely, detailed and actionable recommendations can influence behaviors and drive desired outcomes.

Behavioral Economics

The purpose behind the insurance company’s program and the resulting report card is to make drivers better insurance risks by improving their policyholders’ driving behaviors – to get their policyholders to conform to whatever this particular insurance company has determined as the best practices for turns, acceleration, braking, speeds and time of day driving. And incenting people to change their behaviors through monetary and other rewards is the heart of Behavioral Economics.

Behavioral economics seeks to quantify the effects of psychological, social, cognitive, and emotional factors on the economic decisions of both individuals and institutions including purchase, pricing, consumption, financial and resource allocation decisions[1].

Influencing behaviors and habits requires an awareness and feedback with respect to one’s performance as compared to industry norms, benchmarks and/or segments of similar others. However, in many situations, the timeliness of this feedback greatly influences the degree to which we can influence the behaviors; that is, the most effective outcomes are achieved when the latency of the performance feedback is minimized.

For example, imagine that you are a baseball player whose batting average has slowly been declining over the past several months. Getting a “Batting” report card once a month or even daily does little to help the player understand specifically and in detail what behaviors to fix and what behaviors to continue.

Without nearly instantaneous feedback on the batter’s behaviors and performance, there is no learning.

In order to fix the batting problem, the player would want feedback on each pitch regarding hitting variables such as hitting approach, pitch selection, stance, swing mechanics, balance in the batter’s box, weight transfer thru the swing, etc. The key point is that if one wants to change behaviors then the feedback on that behavior (e.g., batting, driving, smoking, diet, exercise, performing arts, finishing cement, installing electrical) must be specific, detailed and nearly instantaneous (see Figure 2).

Figure 2: “The Science of Hitting” 

With the Internet of Things, new devices and sensors are providing new metrics that can be used to change behaviors. Continuing our baseball example, there are two new metrics[2] that leading baseball organizations are exploiting to try to improve batting behaviors and outcomes:

  • Launch Angle measures the vertical direction of the ball coming off the bat; a launch angle of zero degrees would be a flat line, with positive numbers indicating an upward ball flight and negative ones indicating a ball driven into the ground. Hitters with high launch angles tend to be sluggers who produce lots of fly balls.
  • Exit Velocity represents the speed at which a ball leaves the bat; a detailed measure of how hard each ball was hit. Unsurprisingly, at the top of last season’s exit velocity leaderboards you’ll find the game’s greatest sluggers — such as Giancarlo Stanton (99.1 mph), Miguel Cabrera (95.1) and Jose Bautista (94.3) — with average exit velocities greater than 90 miles per hour.

If the hitter knows these two metrics – plus other traditional hitting metrics – immediately after each swing, then the batter will be able to constantly tweak or refine their batting mechanics in order to achieve a desirable result (like a higher batting average and slugging percentage).

Supervised Machine Learning Learnings

The similarities between these Behavioral Economics concepts and Supervised Machine Learning are striking. Let me explain.

Supervised Machine Learning draws inferences from labeled outcomes or responses such as fraud, customer attrition, purchase transaction, part failure, social media engagement, or web click (versus Unsupervised Machine Learning which draws inferences from data sets without labeled responses).

If a baseball batter knows the desired results (e.g., exit velocity, launch angle) after every swing (labeled outcome), then the human mind and the hitting coach (like a supervised machine learning algorithm) can process the different hitting mechanic variables to determine which of those variables – hitting stance, swing mechanics, bat grip, weight transfer, hitting approach – might have impacted the outcome.  And over a large enough detailed data set of swings, the batter – and their hitting coach – would be able to detect patterns or behaviors that are indicative of a good (versus bad) swing mechanics (see Figure 3).

Figure 3: “Swing the Bat”

Insurance Company Economic Benefits

One last point before wrapping up another overly long blog, this insurance “driver data” program enables the insurance company to collect detailed driving data that can be mined for new insights that can ultimately be used to create new monetization opportunities across a number of dimensions including:

  • Traffic conditions by geo-locations, time of day, day of week, holidays, etc. that could be valuable to logistics and delivery companies
  • Performance benchmarking of cars that could be valuable to car manufacturers, car dealers, finance companies and consumers
  • Road conditions (potholes, bumpy road sections) that could be valuable to the Department of Transportation, construction companies as well as logistics and delivery companies
  • Age and condition of cars which could be valuable to automobile manufacturers, car dealers and service stations
  • Driver behavioral segments which could be valuable to marketing companies and maybe even law enforcement (watch out Vinnie!)

I’m not sure how customers are being rewarded for sharing their detailed driving data that enables these new monetization opportunities, but I suspect it’s worth more than an annual free Venti Chai Latte!

Summary

So like how a good hitting coach can make a batter aware of good and bad hitting behaviors with specific, detailed and nearly instantaneous feedback  (the heart of effective behavioral economics), organizations that want to incentivize their customers, partners and employees (and other humans) to “improve” their behaviors, need to provide specific, detailed and nearly instantaneous feedback.  That’s just like what an effective supervised machine learning algorithm would do.

Sources:

[1] Behavior Economics

[2] “The New Science of Hitting

Figure 3: “Swing the Bat


Understanding Behavioral Economics to Change Behaviors with Big Data

Data Monetization? Cue the Chief Data Monetization Officer

Data Monetization? Cue the Chief Data Monetization Officer

Data Monetization! Data Monetization! Data Monetization!

 It’s the new mantra of many organizations. But what does “data monetization” really mean, how do you do it, and more importantly, who in the organization owns the job of “data monetization”?

The role of Chief Data Officer (CDO) would seem to be a godsend to answer the data monetization challenge. They should be the catalyst in helping organizations to become more effective at leveraging data and analytics to power the digital transformation.

However, all is not well in the world of the CDO. Many organizations appoint a CDO with an Information Technology (IT) background – the same background and experience as the Chief Information Officer (CIO). The organization then ends up splitting the existing CIO role between the current CIO and the CDO; giving the CDO the tasks associated with data collection, governance, protection and access.

Splitting the existing CIO role isn’t sufficient. Instead, the CDO needs a totally different charter than the CIO, and a key aspect of that charter must be around data monetization.

A recent article titled  “The CDO and the CIO: Is it a Love or Hate Relationship?” highlighted some of the challenges that the CDO faces in getting the support they need to be successful:

  • Only 47 percent of CDOs are given a clear remit or objective when they join an organization.
  • Less than half are given the appropriate staffing for their office.
  • Only a quarter are given authority over data across departments.
  • CDOs are given budget and applicable technology just over half the time.

My personal experience is consistent with these findings. In a blog titled “Chief Data Officer: The True Dean of Big Data?” I stated:

“The CDO doesn’t need an IT background (that’s the CIO’s job). I recommend an economics education because economists have been trained to assign value to abstract concepts and assets. An economist is “an expert who studies the relationship between an organization’s resources and its production or output (value).” And in today’s world, assigning value to complex data sets can be extremely abstract.

 A more accurate title for this role might be CDMO – Chief Data Monetization Officer – as their role needs to be focused on deriving value from, or monetizing, the organization’s data assets. This also needs to include determining how much to invest to acquire additional data sources that would complement the organization’s existing data sources and enhance their analytic results.”

That’s right. Much of the confusion between the roles, responsibilities and expectations between the CIO and the CDO could be clarified with a simple title change: Chief Data Monetization Officer. The CDO, or CDMO, would have responsibility for monetizing the organization’s data and analytics; for managing, refining, sharing and monetizing the organization’s data and analytic digital assets.

Enter the Chief Data Monetization Officer

The title says it all; the role of the Chief Data Monetization Officer is to lead the organization’s efforts to monetize the organizations data (and resulting analytics). To accomplish that, the CDMO’s key responsibilities need to include:

Document Business Use Cases for Data Assets. Implement a methodology that identifies, validates, prioritizes and documents the organization’s key business and operational use cases. The use case documentation should call out the use case financial drivers as well as the implementation risks.

Figure 1: Document Business or Operational Use Cases

 

Capture and Re-use Data Assets. Create a methodology that facilitates the capture, refinement, enhancement and sharing of the organization’s data assets.

Figure 2: Capture, Catalog, Refine and Share Data Assets

 

Capture and Re-use Analytic Assets. Embrace a methodology and tools that facilitate the capture, version control, regression testing and sharing of the organization’s analytic assets.

Figure 3: Capture, Catalog, Refine and Share Analytic Assets

 

Create Collaborative Value Creation Platform. Develop and leverage a data lake that becomes the ultimate repository for the organization’s key digital assets – data and analytics.

Figure 4: Data Lake: The Collaborative Value Creation Platform

 

Analytic Tools and Methodology Management. Deploy and run analytic and data management tools including the evaluation, selection, management and retirement of the organization’s data management and data science tools. This role also owns the development and adoption of a data science exploration and testing process (see Figure 5).

Figure 5: Data Exploration Process

 

Cultivate Data Science Team. Develop the organization’s data engineering and data science capabilities. This includes the hiring, training, growth, management and retention of the data engineering and data science teams, as well as any partnering strategies.  It is the data science team that ultimately powers the organization’s “data monetization” efforts.

Chief Data Monetization Officer: Digital Transformation Catalyst

A small number of organizations are starting to understand the subtle yet critical differences between a Chief Data Officer (who is chartered with managing the organization’s data) and a Chief Data Monetization Officer (who is chartered with monetizing the organization’s data). And there are examples from which we can learn more about the nature of the monetization role. For example, most digital media organizations have a Chief Revenue Officer. The Chief Revenue Officer is responsible for driving better integration and alignment between all revenue-related functions, including marketing, sales, customer support, pricing, and revenue management.

Ultimately the CDO needs to own the organization’s “Digital Transformation” process, which includes addressing:

  1. How effective is your organization at leveraging data and analytics to power your business model?
  2. Do you understand your organizations key business initiatives and how they benefit from big data?
  3. Do you have business stakeholder active participation in setting your use case roadmap?
  4. Do you understand the economic value of your data and how that affects your technology and business investments?
  5. Do you understand how to create a platform that exploits the economic value
    of your data?

Data Monetization Call to Action

It’s time to arm the CDO with the tools necessary to drive the organization’s data monetization efforts. This includes:

  • The Vision Workshop as a means to identify, validate and prioritize the organization’s data monetization efforts. 
  • The data lake as the organization’s “collaborative value creation platform.” 


Data Monetization? Cue the Chief Data Monetization Officer

CPG Industry Levels Playing Field with Power of One

CPG Industry Levels Playing Field with Power of One

Special thanks to Brandon Kaier (@bkaier) for his research and thoughts on the Digital Twins concept.

Unilever, one of the Consumer Package Goods (CPG) industry’s titans with over 400 brands and annual sales greater than $60B, recently bought Dollar Shave Club for $1B. Now normally I would not think twice about such an acquisition, peanuts in the world of mergers and acquisitions.

However, this one feels different.

Two billion people use Unilever products every day according to Unilever’s 2015 annual report. Dollar Shave Club only has around two million members; the vast majority of who are likely already Unilever customers. So I don’t think Unilever bought Dollar Shave Club for their customer base.

The Harvard Business Review speculates  that “Unilever has acquired Dollar Shave Club, a young startup, for $1 billion in a move to introduce a new model of subscription sales.”

It seems that Unilever could have easily created their own subscription model without having to pay $1B for customers with whom they already have a relationship. So I don’t believe that Unilever just bought a subscription model. Instead, I think Unilever bought a capability; a capability to capture and mine individual customer product purchase behaviors – the frequency, recency, intensity, magnitude and monetary value of purchase behaviors at the level of the individual consumer – and to eventually apply this analytic capability across more of their brands.

Think about the purchase behavior details Dollar Shave Club has on each of its individual subscribers. Unilever has no similar behavioral knowledge or insights at the level of the individual consumer; they only know how much product they push through retailers and distributors like Walmart, Kroger and Target.

To be actionable, Big Data must get down to the level of the individual – the “Power of One.” Big Data enables capturing customers’ individual tendencies, propensities, behaviors, patterns, associations, and relationships in order to monetize the resulting customer, product and operational insights (see Figure 1).

Figure 1: “Power of One” to Understand and Monetize Individual Customer Insights

The Power of Digital Twins 

Digital Twins is a concept that exploits the “Power of One.” Picked by Gartner ( “Gartner Top 10 Strategic Technology Trends for 2018”)as one of the top 10 strategic technology trends in 2018, Digital Twins couples virtual and physical worlds to facilitate analysis of data and monitoring of systems in order to avert problems, prevent downtime, develop new opportunities and support planning via simulations[1].

But the Digital Twin concept isn’t new. The concept of a digital twin was originally developed by NASA in order to help manage unexpected “situations” that might occur during space travel (remember Gary Sinise in the movie “Apollo 13”).

NASA grappled with the challenge of designing things that travel so far away, beyond the ability to immediately see, monitor or modify. NASA’s innovation was a Digital Twin of the physical system, a complete digital model that can be used to operate, simulate and analyze an underlying system governed by physics[2].

This Digital Twin concept is being embraced throughout the Industrial Internet of Things (IIOT) world. GE may be the most famous of those IIOT companies in adopting this concept, “Digital Twin at Work: The Technology That’s Changing Industry.”

To quote GE:

Digital twin eliminates guesswork from determining the best course of action to service critical physical assets, from engines to power turbines. Moving forward, easy access to this unique combination of deep knowledge and intelligence about your assets paves the road to optimization and business transformation.

But Digital Twins isn’t just relevant to IOT. The Digital Twins concept, when instantiated via Analytic Profiles, plays a major role in understanding and monetizing human behaviors as well.

Analytic Profiles: Simplifying Digital Twins Concept

I blog and teach frequently on how organizations can embrace Analytic Profiles as a mechanism to help organizations capture, refine and share analytic insights at the level of individual humans and things.

Analytic Profiles provide a storage model (think key-value store) for capturing the organization’s analytic assets in a way that facilities the refinement and sharing of those analytic assets across multiple business use cases. An Analytic Profile consists of metrics, predictive indicators, segments, scores, and business rules that codify the behaviors, preferences, propensities, inclinations, tendencies, interests, associations and affiliations for the organization’s key business entities such as customers, patients, students, athletes, jet engines, cars, locomotives, medical devices, and wind turbines (see Figure 2).

Figure 2: Customer Analytic Profile

Figure 2: Customer Analytic Profile

 

Analytic Profiles provide an operational framework for capturing, refining and sharing the organization’s analytic assets. For example, Analytic Profiles provide the foundation for clustering customers into similar behavioral segments, creating detailed behavioral and usage profiles based upon purchase behaviors, and calculating the current and predicted customer lifetime value (see Figure ).

Figure 3: Leveraging Analytic Profiles to Determine Predicted Customer LTV

 

Without the analytic insights captured, refined and shared within Analytic Profiles, you lack the customer, product, service, operational and market insights that are powering new trends, such as those in Figure 4 below.

Figure 4: Gartner’s Top 10 Strategic Technology Trends for 2018

 

CPG Firms: Leveling the Playing Field

A company called MoviePass is promoting what appears to be a totally unsustainable subscription business model – pay $9.95 per month to see any movie in a movie theater that you want, AND the movie theater is reimbursed full price for the ticket. On the surface, that doesn’t seem to make any financial sense. However, the customer and movie insights that MoviePass is gaining about the behaviors, tendencies and inclinations of movie goers and the movies that they watch is likely to open all sorts of new monetization opportunities for MoviePass that can help filmmakers, producers, and studios turn a profit in areas such as movie planning, budgeting, development, customer profiling, customer targeting, promotion, advertising, merchandising, foreign sales, and DVD/TV/Video on Demand streaming rights.

This shift towards subscription business models could give CPGs an opportunity to level the playing field with retailers who have detailed customer transactional data (courtesy of their Point of Sales system and customer loyalty program).  These subscription business model, coupled with analytic profiles, provides an opportunity for CPG firms to gain rich insight into the behaviors of individual customers that can drive research, product development, marketing, advertising, sales and customer service.

With this detailed consumer insights, CPG companies could now start operating more like Netflix in their ability to monetize their customers’ purchase and behavioral insights 

[1] “What Is Digital Twin Technology – And Why Is It So Important?” by Bernard Marr

[2] “The Rise of Digital Twins”


CPG Industry Levels Playing Field with Power of One

Hacking the Autonomous Vehicle

Hacking the Autonomous Vehicle

I love it when I get feedback from a blog that I’ve written. I appreciate the different perspectives and insights that others bring to a topic of interest. 

The section of the blog that fueled the most comments stem from a scene in the movie I, Robot where Detective Spooner (played by Will Smith) is explaining to Doctor Calvin (who is responsible for giving robots human-like behaviors) why he distrusts and hates robots. He is describing an incident where his police car crashed into another car and both cars were thrown into a cold and deep river – certain death for all occupants. However, a robot jumps into the water and decides to save Detective Spooner over a 10-year old girl (Sarah) who was in the other car. Here is the dialogue between Detective Spooner and Doctor Calvin about the robot’s “decision” to save Detective Spooner instead of the girl:

Doctor Calvin: “The robot’s brain is a difference engine. It’s reading vital signs and it must have calculated that…”

Spooner: “It did…I was the logical choice to save. It calculated that I had 45% chance of survival. Sarah had only an 11% chance. She was somebody’s baby. 11% is more than enough. A human being would have known that.”

One of the readers, Warren, shared an MIT site (http://moralmachine.mit.edu/) that allows one to compare their answers to others around various autonomous vehicle life-and-death situations. Some of the scenarios are fairly straightforward…unless you’re a cat lover (see Figure 1):

Figure 1: Kills Cats or Little Old Ladies?

 

However, the scenarios get increasingly more complex (see Figure 2).

Figure 2: Kill 5 Passengers in the Car or 5 Pedestrians?

 

Another reader, Swen, provided an interesting perspective about the potential insurance ramifications related to the “life-and-death” analytic models pre-programmed into the autonomous vehicle:

“But, there is another, very important party involved which has not been mentioned before. It is the very powerful insurance companies. Based on a general “zero law” they will have a very decisive impact on what will be and what not. They will only insure you  and the damage you make  if you have driving software version “XYZ” that complies with their regulations. Else you will not get insured.”

Hacking the Autonomous Vehicle

Maybe my favorite perspective came from Patrick Henz , who shared with me the article “Compliance Tasks Related to Self Driving Technology.” The article poses another challenge facing the autonomous vehicle industry  hacking of the “life-and-death” analytic models:

“Today chip-tuning is already used to change the management of the engine and find additional horsepower. This is in most cases legal, but liberates the car manufacturer from its guarantee. When self-driving cars are a relevant market, it is a question of time, when programmers will offer software to ensure a higher safety for their owners, programmed preference for the passenger against the pedestrians.”

In the same way that there are after-markets for computer chips that override the engine performance settings that come with the automobile out of the factory, will there evolve an after-market for technicians who can “hack” the life-and-death settings that are pre-programmed into an autonomous vehicle?

We are already seeing situations where customers are resorting to “hacking” their vehicles. Farmers are hacking their John Deere tractor’s firmwarein order to perform their own maintenance repairs on their John Deere tractors. Farmers are struggling with the John Deere software license that only allows Deere dealers and “authorized” shops to perform maintenance repairs on tractors.

According to some farmers, John Deere “charges out the wazoo” for repairs. Plus “authorized” mechanics might not arrive to fix a broken tractor in a timely manner, which can affect a farmer’s operations and eventually, their finances.

Summary

Will smart mechanics hack the life-and-death decisions pre-programmed into an autonomous vehicle? Or maybe there’ll be a “Death Selector” user setting in the autonomous vehicle preferences (see Figure 3).

Figure 3: Autonomous Vehicle User Settings

 

On September 6th, the United States House of Representatives voted to speed the introduction of self-driving cars  by giving the federal government authority to exempt automakers from safety standards not applicable to the technology.

I’m not sure how this will end, but I’m certain that this is not an issue that should be decided by technology companies. And now I have concerns about the federal government’s ability to address this issue, given how quick they were to obfuscate the automakers from any safety liabilities associated with an autonomous vehicle.

However, I also know that I don’t want “machines” making these decisions themselves. Machines don’t fear death, and I’m not certain how to program an autonomous vehicle operating system that fully appreciates the moral consequences and ramifications of death.

3 Theorems on the Economic Value of Data

3 Theorems on the Economic Value of Data

Since releasing the University of San Francisco research paper on “How to Determine the Economic Value of Your Data” (EvD), I have had numerous conversations with senior executives about the business and technology ramifications of EvD. Now with the release of Doug Laney’s “Infonomics” book that builds upon Doug’s EvD work at Gartner, I expect these conversations to intensify. In fact, I just traveled to Switzerland to discuss the potential business and technology ramifications of EvD with the management team of a leading European Telecommunications company.
From these conversations, I am starting to form some “theorems” to guide organizations regarding how EvD could impact their business and technology investments. A theorem is defined as “a general proposition not self-evident but proved by a chain of reasoning; a truth established by means of accepted truths.” Well, it might be a stretch to call these “theorems” at this point, but I hope over the next several months to turn these “observations” into “theorems.”
Also, I fully expect the number of theorems to grow as the EvD concepts mature, especially as organizations look for data and analytics to the fuel their digital transformation initiatives.

Economic Value of Data Theorem #1:

It isn’t the data that’s valuable; it’s the relationships and patterns (insights) gleaned from the data that are valuable.

We highlight the difference between monetizing data versus monetizing insights when we discuss the Big Data Business Model Maturity Index (see Figure 1).

Figure 1: Insights Monetization Phase of Big Data Business Model Maturity Index

Phase 4 of the Big Data Business Model Maturity Index is the “Monetization” phase. However, organizations should not focus on the monetization of their data, especially selling their data. Selling data is a business model decision, not a business transaction. And there are significant liabilities that await an organization that moves into the business of selling data .

Instead, organizations should focus upon the monetization of the insights derived from the data. The monetization value isn’t in the data; the monetization value is in the unique customer, product, service, operational and market insights that are gleaned from the data. It is from these insights that organizations will be able to identify new services, new products, new customers, new markets, new audiences, new channels and new partnerships.

Economic Value of Data Theorem #2:

It is from the quantification of the relationships and patterns that we can make predictions about what is likely to happen.

It is the quantification of the relationships and patterns around customers, products, services, operations and markets that drive operational, management and strategic predictions. And it is the value of these predictions (in support of business use cases) that ultimately determines the economic value of your data. We want to quantify the relationships, patterns, propensities, tendencies, biases, preferences, associations and affiliations at the level of the individual customer, product, service, operational process and markets (see Figure 2).

Figure 2: Uncovering Relationships and Patterns in the Data

From these detailed insights, organizations can make predictions about their customers, products, services, operations and markets: what products and services customers are likely to buy, when customers will likely have a life stage change, what products are likely in need of servicing or retirement, what operations are likely candidates for operational optimization, what markets are likely ripe for new products or services, etc.

These predictions, though never 100% accurate, give organizations an “edge” in their operations, management and strategic decisions and use cases. For example, having better predictions about which customers are likely to attrite and the predicted lifetime value of those customers gives you an edge over the competition. It may not be much, but sometimes it is the smallest of edges that can separate the winners from the losers.

Economic Value of Data Theorem #3

Predictions drive monetization opportunities through improved (optimized) strategic and operational use cases.

It is application of predictions against business use cases (i.e., clusters of decisions) that determines the economic value of the data. For example, it is neither sufficient nor actionable to know that there is an increase in head injuries, lacerations and broken bones during and immediately after a local professional football game. That’s interesting, but not actionable.

However, if you can predict a 37% increase in head injuries, lacerations and broken bones during and immediately after the professional football game, then that is actionable! With that prediction, I can now make recommendations (prescriptive analytics) about extra nurses, doctors and supplies one might need at the hospitals nearest the stadium.

The Future of Economic Value of Data Theorems

I can see the potential for more theorems as the EVD discussions mature. I can see, for example, a theorem on “variable predictability” and its importance in attributing financial value to the appropriate data sources.

We will continue to explore, test, fail and learn as we seek to perfect the methodology and formulas that can help organizations determine economic value of their data sources. I believe that this will become a business mandate as organizations look for a management framework to help them optimize the business and technology investments that are driving digital transformations.


3 Theorems on the Economic Value of Data