Showing posts with label predictive analytics. Show all posts
Showing posts with label predictive analytics. Show all posts

Using Data and Deming in a Pandemic

Throughout history military leaders have wrestled with the “fog of war" - the desperation of not knowing critical information.  Information as basic as where are my forces and where are the forces of my opponents?  We face similar information needs today in our battle against the COVID-19 coronavirus.

“The ultimate purpose of data is to provide a basis for action or a recommendation for action,” wrote the revered quality improvement consultant W. Edwards Deming.  Today, in our battle against the COVID-19 virus, we are struggling to make informed decisions because of our own lack of data.  The absence of information both paralyzes decision-making and forces us to expend enormous amounts of time and energy defending against all kinds of scenarios that may not in fact be relevant.  We just don’t know.  Think about a scenario of being lost in a dark forest at night with all kinds of strange sounds and dangerous predators lurking about. How would you defend yourself? Which way would you turn? It would be difficult in the best of times, but the absence of data can make it even more excruciating!  We are struggling with this today.

Today the fog of war can largely be lifted with the combination of software systems, mobile phones, sensors and analytics.  With COVID-19, however, we have the necessary and important consideration of how to protect personal privacy.

Another relevant Deming adage, “The biggest problems are where people don’t realize they have one in the first place.” Not knowing the status of COVID-19 in our communities is a big problem.  In order to move forward and open the economy again we need to understand precisely our COVID-19 exposure and status.  We must quickly remove the blind spots by collecting as much data as possible, while at the same time protecting as much of our privacy as possible.

I look forward to quickly reaching a point where we replace conjecture with good data.  Removing the blind spots is our next best step for our physical, mental and financial health.

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Kevin Benedict
Partner | Futurist | Leadership Strategies at TCS
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***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I work with and have worked with many of the companies mentioned in my articles.

How Good is Your Mind at Predicting?

My friend, Peter Rogers, who lives in the UK was wrong at predicting Brexit, but right at predicting Donald Trump would win.  How did he get one wrong and the other right?  Read about his experiences here.

Guest Blogger - Peter Rogers
Peter Rogers Predicted Donald Trump

I always thought I was particularly good at prediction as a result of me working as a technologist most of my life, but my world was turned upside down after Brexit. It took a long time for me to work out why I got Brexit so wrong, but eventually I brushed myself off and started to read a lot of material on Super-Forecasters.

It learned I had been misleading myself for many years.  I thought I was good at non-technical decision-making. I recall looking at the Ladbrokes Swingometer for Brexit and being so sure of a "remain" vote, that I was going to place a large bet.  I was however, wrong. I made the classic mistake of polluting the decision-maker-mindset.

In order to forecast accurately I needed to consider a wide range of diverse opinions without being overly drawn to any one particular source. This of course, is where social media makes fools of us all. We are typically drawn to a small group of close friends for inspiration, and these friends typically share our opinions.  People rarely fact check on social media. We also read newspapers, which have an increasingly political bias, and a high percentage of us fail to fact check.

I decided if I was going to truly escape from newspapers and social media bias, then I was going to have to train myself to be able to forecast independently. As a first step, I built a website that enabled me to place forecasts and to track whether I was right or wrong. I added a scoring system so there was feedback for my predictions.  This was important as most people don't keep track of their predictions and the results.

Every day I made forecasts on politics, sports, weather, finances, entertainment, and just about anything else I could think of.  I thought anybody can make correct guesses in their own field of expertise, but how many people can make correct predictions outside of it?  Even that prediction was wrong!  In fact, it turns out that Subject Matter Experts (SMEs) are bad forecasters in their own field!

I learned there are two parts to being a good forecaster:

  1. A good gut feel
  2. Being able to show your "thought process."  Show how you worked through an "Outside Model" that is refined by an "Inside Model." 
I started out remarkably bad at forecasting. I soon learned to differentiate between the things I wasn’t so sure of, and mark these at a lesser percentage, from those that I was quite sure about, which I would place at a higher percentage. I also began regularly adjusting my prediction when new evidence became available. It actually started to feel a lot like betting, because I used a simple gamification hook with an avatar who gets weaker or stronger depending on my average score.

The bottom line, after 50 bets, I was actually able to predict with 95% accuracy that Donald Trump would be the next President many months before the actual election.

My goal now is to help other people improve their predictive powers as I did. The system still needs a lot of work.  Today it helps people improve their gut instinct, which is an improvement as I went from 25% accuracy to to 75% in just three months of time.  My plan now is to roll the system out to the general public as a beta.  You can register by simply emailing peterzrogers@hotmail.com, and I will send you the website address and a secure login token.

I am also very interested in talking to people who would like to take the system forward because I strongly believe that digital systems to enhance forecasting are in demand.

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Kevin Benedict
Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler
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***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I am a mobility and digital transformation analyst, consultant and writer. I work with and have worked with many of the companies mentioned in my articles.

Code Halos - Tracking the Mobile Workforce, Equipment and Other Variables for Optimal Performance

I write and speak often on the need to have a thoughtful Code Halo strategy in addition to your mobile and digital strategies.  Code Halos is the term for the information that surrounds people, organizations, and devices.  Many companies consider Code Halo strategies only for marketing, sales and customer service, but a well thought out Code Halo strategy for work done in the field like maintenance, repairs, asset management, construction and engineering is also important.  Let me try to make the case here.

There are many different objects and variables that can impact the performance of a mobile workforce, especially in the services industry.  In my enterprise mobility workshops I call these things PIOs (performance impact objects), and PIVs (performance impact variables).

Examples of PIOs:
  • People
  • Parts/Supplies/Materials
  • Tools
  • Job locations
  • Equipment (and availability)
  • Transportation (and availability)
  • Vendor (and availability)
  • Subcontractor (and availability)
  • Jobsite access
  • Permits/Approvals
Examples of PIVs:
  • Schedules (dependencies)
  • Qualifications
  • Skills
  • Experience
  • Weather
  • Traffic
  • Condition of equipment repair/maintenance
  • Sickness/Health
  • Funding
Each of these items must come together at the right time and right place to optimize the performance of a field service technician.  I think of PIOs and PIVs in the context of building the first transcontinental railroad in 1869.  In order to be completed and functioning, all the PIOs/PIVs had to come together at the right physical place and time.  If pieces were missing, or misaligned the entire system was delayed or fails.

In an ideal world, we would have full situational awareness.  All of the data from each PIO and PIV would be instantly available to our management system so predictive analytics and artificial intelligence could align all the variables for optimized service delivery.  Full situational awareness does not happen by accident.  It requires a great deal of strategy, planning and execution.

All of the PIOs and PIVs need to be tracked and monitored.  Sensors (IoT), GPS vehicle tracking and smartphones all play an important role here.  The data that is needed to make right decisions, either by a human decision maker or an artificial intelligence system needs to be collected, and as data has a shelf-life, it needs to be timely.  Those on the Titanic knew they were in trouble, but only when it was too late to prevent the trouble.  They would have appreciated good information a few minutes earlier.

Let me provide a scenario for consideration.  A customer calls in and requires repairs to a specialized, expensive piece of equipment.  The repair requires specialized training and skills, certifications, special parts, special tools and experience.  Knowing just the schedules and locations of your field service technicians is not good enough.  You need to know information concerning each PIO and PIV.  In order to optimally provide service to your customer, you need to know and monitor all relevant information, and since most field services teams are mobile, that means mobile technology and wireless sensors must be integrated with as many PIOs and PIVs systems as possible in order to provide the necessary data and visibility to maximize productivity.

When PIOs and PIVs are all connected via a shared network that provides visibility to network members it is called a Network Centric Operation.  A full network centric operational environment may not be economically feasible for 25 service technicians, but for 2,5000 service technicians yes.

If you have an available field service technician without the right experience or qualifications, then that doesn't help.  If you have a qualified, experienced and available field services technician, but without the right tools, equipment, parts or their location is too distant to be of service, then that also doesn't help.

PIOs/PIVs are most often not in one location for easy management.  They are located in many different locations and accessed via many different systems.  Enterprise mobility, sensors, connectivity, integration, dashboards, dynamic scheduling, HCM (human capital management), GPS tracking and event/project management, predictive analytics and artificial intelligence are all required to bring all of these pieces, data and variables together to provide optimal productivity.  Ideally these would be brought together under a considered Code Halo strategy for collecting, analyzing and using data to optimize productivity.



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Kevin Benedict
Writer, Speaker, Editor
Senior Analyst, Digital Transformation, EBA, Center for the Future of Work Cognizant
View my profile on LinkedIn
Learn about mobile strategies at MobileEnterpriseStrategies.com
Follow me on Twitter @krbenedict
Join the Linkedin Group Strategic Enterprise Mobility
Join the Google+ Community Mobile Enterprise Strategies
Recommended Strategy Book Code Halos
Recommended iPad App Code Halos for iPads

***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I am a mobility and digital transformation analyst, consultant and writer. I work with and have worked with many of the companies mentioned in my articles.

Interviews with Kevin Benedict