Artist’s rendering of Bitcoin. THERE ARE NO ACTUAL COINS THAT LOOK LIKE THIS. Don’t ever let anyone sell you one.
Today, many cryptocurrencies lost ~35-50% of their value. Reddit even posted contact information for the National Suicide Prevention Hotline in /r/cryptocurrency, knowing how emotional investors were bound to be today. Bitcoin, which was nearly $20K in mid-December and has been hovering near $14K this past week, dropped nearly $4K and almost sunk below the $10K milestone. I usually track the price of Bitcoin at http://bitcointicker.co, which can show the posted prices from several exchanges (web locations where people go to buy and sell, like Ebay). There are hundreds of cryptocurrencies and many of them dropped in value today.
Why did the prices drop so much on Tuesday? Here are some likely influences:
The government of South Korea announced its plans to prepare a bill banning cryptocurrency trading (specifically Bitcoin, Ethereum, Ripple); trading volume has been high in South Korea this past year, and the transactions have propped up global cryptocurrency prices.
Market prices are usually driven by supply and demand — for example, if there aren’t that many lobsters available in a particular area at a particular time, and you go to a restaurant hoping to order one — you’ll pay a premium. But that price is also influenced by the quality of the product, the image of the product, which influences your perception of its value. Quality reflects how well something satisfies stated and implied needs or expectations.
Value, however, is quality relative to price, and influenced by image. And people are not always rational: they’ll pay a premium for image, even if the value of a product isn’t particularly high. Just think of all the Macs on display at schools, coffee shops, and airports. Price is related to value… usually, price goes up as value goes up.
Where’s the value of cryptocurrency? A Bitcoin does not, on its own, have any inherent value — just like a dollar or a Euro (a “fiat currency”). But the prospect of an asset that will increase in perceived value — where you can buy low, hold (sometimes just for a few days), and sell high because there are lots of people willing to buy it from you — will have perceived value. Hundreds of early adopters — or “Bitcoin millionaires” — are getting people excited about the prospect of making small investments and reaping huge rewards. That this has happened so recently lends a mystique to ownership of cryptocurrencies and Altcoins (or “alternatives to Bitcoin,” like Ether) in addition to the novelty.
Value is attributed to things by people, and cryptocurrencies are no exception. The quality of the currency itself, and the technical solidity of the platform upon which one is based, isn’t really tied to the cryptocurrency price right now — although this will probably change as knowledge and awareness increases.
Is this the end of Bitcoin? That’s doubtful — there are too many innovators who insist on exploring the technological landscape of cryptocurrencies and blockchain technology, and lots of investors willing to fund them. In the meantime, there are unlikely benefits: because cryptocurrencies are not yet mainstream, a “crypto crash” is not as likely to ripple through the whole economy (no pun intended) like the subprime mortgage crisis of 2008. But if you do decide to buy cryptocurrency, don’t invest any more than you can afford to lose.
Image Credit: Doug Buckley of http://hyperactive.to
[This post is in response to ASQ’s February topic for the Influential Voices group, which asks: Where do you plan to take your career in 2016? What’s your view of careers in quality today—what challenges is this field facing? How can someone starting out in quality succeed?]
We are about to experience a paradigm shift in production, operations, and service: a shift that will have direct consequences on the principles and practice of design, development, and quality management. This “fourth industrial revolution” of cyber-physical systems will require more people in the workforce to understand quality principles associated with co-creation of value, and to develop novel business models. New technical skills will become critical for a greater segment of workers, including embedded software, artificial intelligence, data science, analytics, Big Data (and data quality), and even systems integration.
Over the past 20 years, we moved many aspects of our work and our lives online. And in the next 20 years, the boundaries between the physical world and the online world will blur — to a point where the distinction may become unnecessary.
Here is a vignette to illustrate the kinds of changes we can anticipate. Imagine the next generation FitBit, the personalized exercise assistant that keeps track of the number of steps you walk each day. As early as 2020, this device will not only automatically track your exercise patterns, but will also automatically integrate that information with your personal health records. Because diet strategies have recently been shown to be predominantly unfounded, and now researchers like Kevin Hall, Eran Elinav, and Eran Siegal know that the only truly effective diets are the ones that are customized to your body’s nutritional preferences , your FitBit and your health records will be able to talk to your food manager application to design the perfect diet for you (given your targets and objectives). Furthermore, to make it easy for you, your applications will also autonomously communicate with your refrigerator and pantry (to monitor how much food you have available), your local grocery store, and your calendar app so that food deliveries will show up when and only when you need to be restocked. You’re amazed that you’re spending less on food, less of it is going to waste, and you never have to wonder what you’re going to make for dinner. Your local grocery store is also greatly rewarded, not only for your loyalty, but because it can anticipate the demand from you and everyone else in your community – and create specials, promotions, and service strategies that are targeted to your needs (rather than just what the store guesses you need).
Although parts of this example may seem futuristic, the technologies are already in place. What is missing is our ability to link the technologies together using development processes that are effective and efficient – and in particular, coordinating and engaging the people who will help make it happen. This is a job for quality managers and others who study production and operations management
As the Internet of Things (IoT) and pervasive information become commonplace, the fundamental nature and character of how quality management principles are applied in practice will be forced to change. As Eric Schmidt, former Chairman of Google, explains: “the new age of artificial intelligence is beginning, and it’s a big deal.”  Here are some ways that this shift will impact researchers and practitioners interested in quality:
Strategic deployment of IoT technologies will help us simultaneously improve our use of enterprise assets, reduce waste, promote sustainability, and coordinate people and machines to more effectively meet strategic goals and operational targets.
Smart materials, embedded in our production and service ecosystems, will change our views of objects from inert and passive to embedded and engaged. For example, MIT has developed a “smart band-aid” that communicates with a wound, provides visual indicators of the healing process, and delivers medication as needed.  Software developers will need to know how to make this communication seamless and reliable in a variety of operations contexts.
Our technologies will be able to proactively anticipate the Voice of the Customer, enabling us to meet not only their stated and implied needs, but also their emergent needs and hard-to-express desires. Similarly, will the nature of customer satisfaction change as IoT becomes more pervasive?
Cloud and IoT-driven Analytics will make more information available for powerful decision-making (e.g. real-time weather analytics), but comes with its own set of challenges: how to find the data, how to assess data quality, and how to select and store data with likely future value to decision makers. This will be particularly challenging since analytics has not been a historical focus among quality managers. 
Smart, demand-driven supply chains (and supply networks) will leverage Big Data, and engage in automated planning, automatic adjustment to changing conditions or supply chain disruptions like war or extreme weather events, and self-regulation.
Smart manufacturing systems will implement real time communication between people, machines, materials, factories and warehouses, supply chain partners, and logistics partners using cloud computing. Production systems will adapt to demand as well as environmental factors, like the availability of resources and components. Sustainability will be a required core capability of all organizations that produce goods.
Cognitive manufacturing will implement manufacturing and service systems capable of perception, judgment, and improving quality autonomously – without the delays associated with human decision-making or the detection of issues.
Cybersecurity will be recognized as a critical component of all of the above. For most (if not all) of these next generation products and production systems, quality will not be possible without addressing information security.
The nature of quality assurance will also change, since products will continue to learn (and not necessarily meet their own quality requirements) after purchase or acquisition, until the consumer has used them for a while. In a December 2015 article I wrote for Software Quality Professional, I ask “How long is the learning process for this technology, and have [product engineers] designed test cases to accommodate that process after the product has been released? The testing process cannot find closure until the end of the ‘burn-in’ period when systems have fully learned about their surroundings.” 
We will need new theories for software quality practice in an era where embedded artificial intelligence and technological panpsychism (autonomous objects with awareness, perception, and judgment) are the norm.
How do we design quality into a broad, adaptive, dynamically evolving ecosystem of people, materials, objects, and processes? This is the extraordinarily complex and multifaceted question that we, as a community of academics and practitioners, must together address.
Just starting out in quality? My advice is to get a technical degree (science, math, or engineering) which will provide you with a solid foundation for understanding the new modes of production that are on the horizon. Industrial engineering, operations research, industrial design, and mechanical engineering are great fits for someone who wants a career in quality, as are statistics, data science, manufacturing engineering, and telecommunications. Cybersecurity and intelligence will become increasingly more central to quality management, so these are also good directions to take. Or, consider applying for an interdisciplinary program like JMU’s Integrated Science and Technology where I teach. We’re developing a new 21-credit sector right now where you can study EVERYTHING in the list above! Also, certifications are a plus, but in addition to completing training programs be sure to get formally certified by a professional organization to make sure that your credentials are widely recognized (e.g. through ASQ and ATMAE).
Citing Deming and Drucker, and noting how so many organizations rely on a “carrots and sticks” approach to performance management, he converges on the following recommendation: “The way to create a high performance culture is to seek out poor performance, embrace it and fix it, not punish it.” I think, though, that this is not a new approach… rather than improving upon poor performance, why don’t we seek out truly amazing performance and then just make more of it? These three steps will help you do it:
Eliminate power relationships. Power is poison! It creates and cultivates fear (which, according to Deming, we need to drive out). Unfortunately, our educational system and our economy are firmly steeped in power relationships… so we’re not accustomed to truly cooperative relationships. (In fact, being reliant on the income from our jobs shoehorns us into power relationships before we even begin working.) Holacracy is one approach that some organizations are trying out, but there are many possibilities for shifting from organizational structures that are designed around power and control, versus those that are designed to stimulate interest, creativity, and true collaboration.
Create systems to help everyone find (and share) their unique skills, talents, and gifts. This is the key to both engagement and high performance — and this isn’t a one-shot deal. These skills, talents, and gifts are extremely dependent on the organizational context, the external environment, and a person’s current interests… and all of these change over time!
Create systems to help people become stewards of their own performance.Accenture and Google have both recently given up performance reviews… and Deming has always warned about them! Unless we’re managing our own performance, and the process and outcomes are meaningful to us individually, we’ll just be dragged down by another power relationship.
Quality professionals are great at designing and setting up systems to achieve performance goals! Now, we have an innovation challenge: adopt the new philosophy, design quality systems that substitute community in place of power and control, and use our sophisticated and capable information systems to give people agency over their own performance.
“Creative teamwork utterly depends on true communication and is thus very seriously hindered by the presence of power relationships. The open-source community, effectively free of such power relationships, is teaching us by contrast how dreadfully much they cost in bugs, in lowered productivity, and in lost opportunities.” — E. S. Raymond inThe Cathedral and the Bazaar
This post was motivated by two recent tweets by Dr. Diego Kuonen, Principal of Statoo Consulting in Switzerland (who you should definitely follow if you don’t already – he’s one of the only other people in the world who thinks about data science and quality). First, he shared a slide show fromCIO Insightwith this clickbaity title, bound to capture the attention of any manager who cares about their bottom line (yeah, they’re unicorns):
I’m so happy this message is starting to enter corporate consciousness, because I lived it throughout the decade of the 2000’s — working on data management for the National Radio Astronomy Observatory (NRAO). I published several papers during that time that present the following position on this theme (links to the full text articles are at the bottom of this post):
First, storing data means you’ve saved it to physical media; archiving data implies that you are storing data over a longer (and possibly very long) time horizon.
Even though storage is cheap, don’t store (or archive) everything. Inventories have holding costs, and data warehouses are no different (even though those electrons are so, so tiny).
Archiving data that is of dubious quality is never advised. (It’s like piling your garage full of all those early drafts of every paper you’ve ever written… and having done this, I strongly recommend against it.)
Sometimes it can be hard to tell whether the raw data we’re collecting is fundamentally good or bad — but we have to try.
Data science provides fantastic techniques for learning what is meant by data quality, and then automating the classification process.
The intent of whoever collects the data is bound to be different than whoever uses the data in the future.
If we do not capture intent, we are significantly suppressing the potential that the data asset will have in the future.
Although I hadn’t seen this when I was deeply enmeshed in the problem long ago, it totally warmed my heart when Diego followed up with this quote from Deming in 1942:
In my opinion, the need for a dedicated focus onunderstanding what we mean by data quality (for our particular contexts) and then working to make sure we don’t load up our Big Data opportunities with Bad Data liabilities will be the difference between competitive and combustiblein the future. Mind your data quality before your data science. It will also positively impact the sustainability of your data archive.
Papers where I talked about why NOT to archive all your data are here:
“The totality of characteristics of an entity that bear upon its ability to satisfy stated and implied needs.” — ISO 8402 (deprecated)
Even though they do not specifically teach about quality, I’d like to share two of my sources of inspiration: philosopher and activist Charles Eisenstein, and psychologist Barbara Fredrickson.
In Sacred Economics and The More Beautiful World Our Hearts Know is Possible, Charles Eisenstein encourages us to look beyond the subtle assumptions and limitations imposed upon us by being embedded in a market economy. What is quality in the absence of a commercial environment to exchange products and services?? How can we more effectively relate to ourselves and to one another, so that we can better satisfy our stated and implied needs? Eisenstein’s work inspires me because it encourages me to reflect on the unspoken assumptions of the quality profession, and how those assumptions might be holding us back from evolving our skill sets to meet the changing needs of society. (Sacred Economics is also available in print from Amazon.)
In Positivity, Barbara Fredrickson provides a simple, data-driven path (the “positivity ratio”) for improving our psychological health; in Love 2.0, she helps uncover ways for us to create substantive, authentic connections with one another. Her work can help us cultivate greater quality consciousness – because we are best able to satisfy others’ stated and implied needs when 1) we understand them, and 2) we are mentally and emotionally equipped to help deliver them! Although aspects of the positivity ratio have been criticized by researchers studying dynamical systems, I still find the concept (and measurement tool) very useful for raising the awareness of individuals and teams.
Postscript: Bill’s post made me think about another related question: “Who ARE the quality gurus?” I mean, everyone in the quality profession can call on Deming, Juran, or Crosby, but I’d toss luminaries like Csikszentmihalyi and Prahalad (plus others) in the mix as well. I searched online and found a nice “List of Gurus” that someone put together that includes my extra picks!
But!! There’s a problem with it.
WHERE ARE THE WOMEN? The one woman in this list is someone I’ve never heard of, which is odd, since I’ve read papers by (or about!) all of the other people referenced in the list. Which brings me back to my original point:WHERE ARE THE WOMEN QUALITY GURUS?It’s time to start celebrating their emerging legacy. If you are a woman who has made significant contributions to our understanding and/or practice of quality and improvement, PLEASE CONTACT ME. I’d like to write an article soon.
In his January post, ASQ CEO Paul Borawski discusses the results from ASQ’s 2013manufacturing outlook survey. Although the majority of manufacturers reported revenue growth in 2013, many are still very concerned about the state of the economy. Paul was inquiring whether this “optimistic but guarded” perspective was a good assessment, and asked for examples from manufacturers that might shed some more light on the situation.
I’ve alluded over Twitter that my relationship with quality – as a concept – has been changing over the past few months. That’s the main reason that I haven’t been posting as frequently on this blog… I’m trying to sort out my feelings. (It’s almost like what happens when you’ve been in a relationship for years, but then gradually discover that you’ve changed, and the relationship is no longer meeting your deepest needs.)
Paul’s January post has helped me clarify some of these feelings.
I was reminded ofClayton Christensen’s landmark 1997 book, The Innovator’s Dilemma.By successfully satisfying current needs, we are potentially blinded to the ability to satisfy future needs. We are so accustomed to the model of manufacturing working so well, over so many decades, that we may fail to recognize when the centralized approach is losing ground.
How are manufacturers addressing these shifts? Are they re-examining the core assumptions upon which their industries are based? I think this should be the focus, rather than continued revenue growth and “concern” about the state of the economy.
I just got done reading Jean Russell’s new book, Thrivability, from Triarchy Press. In my opinion, this is perhaps the most compelling book about innovation that’s been written in the past few years – and it’s not even expressly about innovation. But it can help you think about all the assumptions you make about society and the environment in which you’re embedded – assumptions that, when relaxed, can open up new ways of thinking that will help you more effectively innovate.
Here’s the review that I’ll be publishing in the January 2014 issue of the Quality Management Journal. In the meantime, I encourage you to read Jean’s book — and please share your comments below! I want to know what you think about it.
“Thrivability,” or the “ability to thrive,” suggests strength, grace, health, growth, and sustainable value creation – all in one word. In this book, Jean Russell articulates over 20 years of knowledge and insights she’s gleaned from delving into this one concept from the perspective of multiple disciplines. The end result is a book that is unique, richly textured, and achieves its stated goal: “to equip you with tools to see and act in ways that enrich your life, your community, your business, and our world.” As a result, this book contributes indirectly (yet profoundly) to the expanding body of knowledge on innovation.
The book is structured in three Parts: Perceiving, Understanding, and Doing. The first chapters encourage the reader to critically examine his or her external environment, the assumptions that are inherent to the economic and political systems within which we are embedded, and the individual stories that we use to construct our expectations about ourselves, our capabilities, and others around us. It does this by emphasizing the importance of storytelling and narrative – to imagine ourselves in the context of a story that inspires us about our world, rather than fills us with fear. To be successful at this, we must first learn how to look at our world and the people around us with compassion and acceptance. This, according to the author, will help us generate new perspectives on existing situations, and open us to new possibilities for improvement.
Part II, on Understanding, explores how we can shift our beliefs to help create more positive, productive, connected environments and organizations. A large part of this section reflects on the psychological influences of social media and how this is changing the ways we identify opportunities and even the definition of “success” itself. For example, in education, grades are losing their significance as society recognizes that complex creations are more effective measures of accomplishment than passing tests. Part III, on Doing, focuses on tools and techniques to enliven creativity, enhance trust, and break through limiting beliefs and blocking situations.
This book has essential insights for both academics and practitioners in quality-related fields. Most significantly, Russell’s work can help us envision the new world in which we might soon find ourselves, where the search for meaning and compassion for others (and our environment) take precedence over profit and capturing or creating new markets.