Kevin Benedict Ponders the Future

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:

A good gut feel
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.

Digital Technologies Must Disappear in 2017

Almost a year ago, I wrote these words, "Technology has reached the tipping point for me, it moved from a help to a hindrance." The plethora of adrenaline and endorphin inducing mobile apps, 24x7 news, notifications, alerts and updates, drip fed my brain and hindered my "deep work and deep thoughts." In Cal Newport's new book titled, "Deep Work" he posits that most knowledge workers need concentration and substantial time, dedicated and uninterrupted, to produce their best work. He argues that a lot of technologies and open office layouts today inhibit creativity, "deep work" and "deep thoughts," and are the very things that are most highly valued, and one of the key differentiators between humans and robots.

Newport argues that we must understand and optimize the conditions that enable our brains to work best. To sum up his argument, constant drip feeding technologies serve to prevent deep thoughts and deep work, our most valuable assets. He recommends that we restructure our working environments, schedules, times, activities and technology uses to provide substantial "deep thought" times so we can maximize our brain's thinking.

A phrase I like to use is, "Just because technology can do it, doesn't make it useful." Don't get me wrong, I am a huge fan of technology and have dedicated my career to understanding, teaching and using it, but we must all realize that technology has not been designed to maximize our brain's potential. Often technology is designed to replace or degrade our brain's function, or to appeal to our addictive vulnerabilities. Have any of you, like me, lost themselves in a computer game, and then realized it was 4 AM? I did that when Doom first came out decades ago. I realized early on my brain was vulnerable to these games, and banished them from our home ever since, at least until Angry Birds came out on my iPhone and I welcomed back 4 AM.

In our professional life, it is so easy to let our email inbox and calendar invites become our boss and dictate our day's focus. Do any of us really believe this is the most productive behavior? Does our inbox recognize our priorities, goals, focus, deliverables and ambitions? I don't think so, so then why let it boss us around?

If we added up all of the mobile apps we have on our phones, then list all the possible alerts and notifications they each can provide, plus add in how many emails, messages and updates we see, and then add our social media and news feeds, it will literally be hundreds or even thousands of distractions daily. Do these distractions make us more productive or efficient? I don't think so.

In 2017, we need to reevaluate technology and take back our brains and purpose. We should be guiding our technologies, not the other way around. Technology needs to disappear into the background, while productivity and purpose should be our siren's call. We have approximately 700,000 hours between our birth and our death. About 350,000 of those hours are spent in our careers. How many of those hours do we want to waste on technology enabled distractions? I first published some of the following list nearly a year ago, but I needed the reminder, and perhaps it would be helpful for you as well. I propose the following:

Our schedules and activities must reflect our purpose and goals, not our inbox and social media feeds.
We must recognize what activities offer value, and what activities do not.
We shouldn’t have to read through hundreds of useless email messages to find the three necessary to complete our job. Communications need to change and email must disappear behind a veil of utility and productivity.
Someone emailing us, does not mean we need to respond.
We shouldn’t have to check dozens of different locations, apps and websites to communicate with our work colleagues and friends. All of these various collaboration and communication platforms need to disappear into a consolidated and efficient aggregated solution like Slack.
Communication technologies should disappear into the background, and the quality and utility of the message improved by technologies.
Email and meeting driven schedules must disappear, in favor of schedules that honor purpose and deliverables.
Prioritize thinking time and mental productivity, and dedicate the time they deserve.
Scientists agree that the creative parts of our minds work better at different times of the day. Those times need to be reserved, blocked and honored on schedules, to optimize productivity.
The requirement to develop, store and retrieve dozens of different passwords and user names must disappear. The ability to accurately authenticate a user must become more efficient and secure.
Trivial messages and alerts from hundreds of different sources arriving 24 hours a day must disappear. Trivial messages and an urge to immediately respond must not be allowed to intrude on our thinking, creating, planning, sleeping, loving, relationship building, driving and the handling of dangerous equipment.
On-premise IT solutions, hardware and apps that serve to distract from the business, and offer no additional business value, competitive advantages or market agility must disappear into the cloud.
The 200+ mobile applications on my iPhone must disappear into an artificial intelligence engine that will access their functionality and assist me even before I ask.
Mobile applications that are not personalized, and are not contextually relevant should disappear. I don’t care what you sell, if I am not interested, or it is not relevant to me, I don't want to see it.
The routine process work I do on my computer must go away. Intelligent process automation should be pushed down to individuals. An AMX mobile app should process my expenses without me. It should only alert me to exceptions, not the routine.
Technologies and the use of technologies that hinder creativity, productivity and innovation must disappear.

In the lifecycle of any technology, there is a time when we should be enamored and distracted by how it works, but these times must quickly pass and the technology should disappear into the background. I propose that digital technologies should improve and optimize our brain power, and make the human experience richer, deeper and more purposeful than ever before. This year, I am more committed than ever to making technology work for me, not against me, by being less intrusive and distracting. What do you think? Message me.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

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The Day Big Data Analytics Died

The Huffington Post gave Donald Trump a 2% chance of winning, The New York Times 15%. The best polls, prediction markets and analytics predicted a Hillary Clinton victory in the days before the election, yet they were all wrong. The national media’s predictive analytic systems failed catastrophically. Why?

Analytic systems require timely data on all the variables that impact a system and measure its performance. Analytics requires support from an optimized information logistics system (OILS), which describes the a system that manages the full lifecycle of data from collection, transmission, processing, analysis, reporting, data driven decision-making, action and archiving. An OILS is only as good as the data. It can only function correctly if it is collecting the necessary data inputs. For example the sensors in an Internet of Things (IoT) system must be attached to the right “things” that impact operations, to provide full system visibility and insight. The pre-election big data analytics systems used by pundits, media and prediction markets used incomplete data that resulted in operational blindness, a massive failure for those responsible.

A simple phone poll may not measure the degree of sentiment, neither does it measure those not on the phone. It appears from reports this morning that large numbers of folks whom rarely if ever voted - voted. This unmeasured, invisible group, that was an important data input, was not measured and analyzed in the OILS.

When I meet with business and IT strategy leaders and discuss data analytics and OILS, I always ask them, "What data are you NOT collecting that potentially could be important to your plans and operations?" Many have never considered this simple question. They look at their available data, but not their data gaps. Today in a world of hyper-connectivity, bots, real-time operational tempos and decision-making, having the right data at the right time is critical. What data are you not collecting?

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

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Merging Humans with Enterprise AI and Machine Learning Systems

Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them. Often this means massive amounts of learning can be accomplished every second without stop 24x7x365. Code doesn't need to take weekends off, holidays, or sick time. Code doesn't get tired. It can recognize complex patterns, areas of potential improvement and problems in real-time (aka digital-time). Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time.

I believe the first step is to admit we have a problem - the problem is a difference in the speed that computers can operate and the speeds us humans can operate. The second is to understand what a solution might look like - how humans and computers can best integrate and operate together for business success, and the third is to have a strategy and plan.

Imagine a scenario where you arrive at your desk on a Monday morning and find hundreds of recommendations for business process improvements from your AI and machine learning systems. Each of the recommendations might take weeks to socialize across the organization and implement. Now, imagine that happens every day of the year. We humans would be completely overwhelmed! In this scenario, humans are the weak links in business process optimization.

How are we then to best utilize AI and machine learning in a manner that benefits humans, and human powered organizations? I propose the answer lies in developing a powerful AI and machine learning platform that understands our goals and aspirations, can filter findings, run and test simulations, and then present a few select prioritized recommendations to human decision-makers. Simply feeding leaders with unlimited numbers of recommendations may actually paralyze, rather than empower them. What are needed are systems that won’t overwhelm humans, but rather augment them.

In order for any of these AI and machine learning systems to actually work, the data that fuels these systems must first be available, accurate, normalized and timely - for many that is the essence of digital transformation. A digital transformation initiative often includes upgrading IT systems to become “optimized information logistics system” (OILS). Systems that ensure the right data is collected, available, analyzed and its meaning and context understood and utilized.

Once an organization has an OILS in place and the data available, then leaders must ensure their organizational structures and business processes are capable of responding at an operational tempo sufficient to capture the benefits and competitive advantages available from the insights derived from AI and machine learning. That is no small feat. It does no good, as we pointed out, to have recommendations that cannot be implemented.

Massive volumes of new data from sensors, mobile devices, embedded computers and online activities means there are enormous opportunities to learn and gain competitive advantages as a result of it, but only if our leaders, IT and business systems, and our organizations are capable of responding fast enough to capture the value. Understanding how to create OILS, how to utilize AI and machine learning to effectively augment our leaders, and then knowing how to create an organization able to respond fast enough to capture the competitive advantages in the data are the monumental tasks before us today.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

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Join the Linkedin Group Strategic Enterprise Mobility

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Mobile Expert Interviews: PowWow Mobile's CEO Kia Behnia

The enterprise mobility software vendor space has been around for more than a decade, but new start-ups continue to enter the market with ambitions to address unsolved problems and challenges. In this interview, I ask PowWow Mobile CEO Kia Behnia, why an enterprise mobility start-up now?

Kevin Benedict's latest video on mobile commerce trends and strategies: ************************************************************************

Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Subscribe to Kevin'sYouTube Channel

Join the Linkedin Group Strategic Enterprise Mobility

Join the Google+ Community Mobile Enterprise Strategies

Profits that Kill in the Age of Digital Transformation

Presenting 40 Months of Hyper-Digital Transformation in Lisbon, PT

Digital transformation initiatives both increase revenue and decrease expenses according to our upcoming study titled Work AHEAD 2016 by Cognizant’s Center for the Future of Work. For this study we surveyed over 2,000 executives across 18 countries. The expected net business impact over the next 3 years of rapid digital transformation, i.e. hyper-transformation, is an increase in revenue of 11.4% (Revenue = 10.3%, Costs = -1.3%) amongst the companies in our study. These numbers represent the difference between success and failure in many industries.

To get the predicted revenue impact, companies expect to increase their spending on digital technologies from 11.3% of their revenue today, to 16.6% of revenue by 2020, while decreasing their non-digital investments.

In addition, our study reveals 64% of the futurists in our study, experts paid to study digital technology trends and to develop future strategies, believe digital transformation will boost revenues by 2020, 76% believe it will strengthen their competitive positioning, and 88% believe it will accelerate their speed to market. If you accept these predictions, then your organization should quickly transform itself and jettison the heavy baggage that accumulates as a result of decades of institutionalized methodologies developed and codified in an analog era - an era operating at a far slower operational tempo. The good news is that ROIs (return on investments) from digital transformation initiatives are compelling. Nearly 30% of the surveyed firms are receiving an ROI greater than 100%.

These revenue numbers have investors interested and asking about the digital transformation plans of companies they invest in, or are considering investing in today. We have all read about the list of companies that have failed this year due in part to their slow and/or inadequate response to digital technologies and changing competitive marketplaces. As a result investors today want to understand the speed and scale of their portfolio’s digital transformation initiatives, as these initiatives can be predictors of future value.

For executives, transforming an enterprise is difficult at any time, but when an enterprise is highly profitable - digital transformation is even harder. Why? The temptation to follow the maxim, “Don’t fix what isn’t broken,” is just too compelling. The challenge enterprises are faced with today, however, is that digital technologies are changing the competitive landscape faster and in different ways than executives have ever seen before.

Recently, we presented a workshop on digital transformation strategies to a highly profitable automotive parts and systems manufacturer. The company is filled with smart people using efficient long standing manufacturing systems and processes. The company is innovative in R&D, and forward thinking in the adoption of digital technologies and strategies for their products and services.

The challenge we all faced in the room is how do we justify digital transformation investments, when today the old systems and ways of thinking pay out in record profits? How do we articulate that sensors, artificial intelligence, digital platforms and real-time data are taking on a whole new level of importance? In fact, executives believe big data and business analytics will have the biggest business impact of all technologies on businesses from years 2020-2025.

We were able to demonstrate, through an intensive brainstorming workshop, how a sensor enabled digital platform connected to their physical parts and equipment, and integrated with external databases could provide enormous customer and business value. We were able to show them how their current list of physical products and services, could be expanded by adding a whole new list of new digital products and services on top of their current offerings. The data they had access to in their physical products and solutions was unique and could offer significant value and competitive advantages if they made it available on a digital platform as a service. Our mission was accomplished.

Today an enterprise’s system for collecting, analyzing, reporting and sharing data is mission critical, especially given the increasing importance of artificial intelligence, robotic process automation and machine learning to companies’ future success. The end-to-end system for managing this data we call the “optimized information logistics systems” or by its acronym, OILS. Having effective OILS is mandatory in order to become a digitally transformed enterprise (DTE). Digitally transformed enterprises place a premium on data collecting, analysis, situational awareness and real-time action and reaction. Executives see the value in OILS, as 63% believe analytics will have a very strong impact on work by 2020, and 62% predict OILS will significantly enhance decision-making over the next 5 years.

DTEs see where their resources are located, where they are needed and how best to manage them at all times to successfully and efficiently accomplish their mission. They see how customers are using their products and how they are holding up. Real-time connectivity combined with OILS enables organizations to think, act and compete in real-time, a capability never before possible. It enables products in the field to be intimately tied to R&D and the manufacturing floor. It is a whole new world, which in spite of profits today, must be anticipated and invested in for tomorrow, or the profits of today might lead to the death of them tomorrow.

Authors: Tim Hillison, Senior Director, Cognizant Digital Works, and Kevin Benedict, Senior Analyst, Cognizant's Center for the Future of Work

*Source The Work Ahead, 2016, Cognizant Center for the Future of Work

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Subscribe to Kevin'sYouTube Channel

Join the Linkedin Group Strategic Enterprise Mobility

Join the Google+ Community Mobile Enterprise Strategies

In Defense of the Human Experience in Digital Transformation

Discussing Digital Strategies in Lisbon, PT

Last week, I was a keynote speaker at the Mobile Edge '16 conference in Lisbon, Portugal. Following the conference I met individually with many different companies. These companies represented a variety of industries, sizes and digital maturity levels. I was sharing my latest digital strategy report findings, and then answering questions and brainstorming about what the data meant to each of them.

At the hotel, following a long day of meetings, it occurred to me that the biggest value I was bringing to these businesses was the breadth of my experience - the kind of experience that has turned my hair gray.

The report I was sharing was titled, “40-Months of Hyper-Digital Transformation.” This report was the result of surveying 2,000 global executives across 18 countries on the current impact of digital technologies on their business, and their expectations for future impact. In this report I wrote a lot about artificial intelligence and software robots. I wrote about how robotic process automation will change the role of processes, employees and managers.

While pondering my day over a hot drink, it occurred to me that the report and the data I shared could have been presented by a junior employee with good communication skills. The value my gray hair brought to the table was the ability to understand the back-story, the history, how today is different, and analyze what needs to be done today to prepare for tomorrow. It was perspective, experience and wisdom gained from many years of accumulated successes and failures. It was my human baggage that was so valuable. It allowed me to listen to many disparate situations and challenges, understand them, and then see a unique path to success for each. It allowed me to see their challenges in abstraction and define steps to follow. I chuckled and sipped my coffee. Just try to do that, I challenged all the bots and artificial intelligence engines I had been writing and speaking about. Just try to do that.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

Kevin Benedict Ponders the Future

How Good is Your Mind at Predicting?

Digital Technologies Must Disappear in 2017

The Day Big Data Analytics Died

Merging Humans with Enterprise AI and Machine Learning Systems

Mobile Expert Interviews: PowWow Mobile's CEO Kia Behnia

Profits that Kill in the Age of Digital Transformation

In Defense of the Human Experience in Digital Transformation

Interviews with Kevin Benedict

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