Two years ago Massive Open Online Courses (MOOCs) were all the rage. Elite universities were lining up to partner with MOOC providers, such as Coursera, and they were free to boot. MOOCs had something new, cool, and disruptive to how larger institutions provided curriculum. But they also had one major flaw: Few people took them seriously, including me.
I would sign up for dozens of Coursera MOOCs, download some material, watch a few videos and call it good. Very few did I actually complete and even then I just watched the videos and took some notes, never engaging in any of the forums, discussions, or the exams. There was little true incentive to stay 100 percent engaged in the course. In fact, most people don’t. According to recent analysis of MOOCs, only 10 percent of registered students actually complete the course.
Still, with enrollment reaching hundreds of thousands, MOOCs are providing access to higher education to the masses—just maybe not the masses they originally expected to reach. As The New York Times article “Demystifying the MOOC” shows, most people enrolled in MOOCs already have a degree.
So with high numbers of enrollment from educated users, but low completion rates, what now? Until recently, no one had cracked that code. Now Coursera is making a run at it with Specializations—a series of classes strung together to provide an elite university certificate for a minimal fee, especially compared to if you took the same series at a local institution. For example, the Data Scientist Specialization through Johns Hopkins costs $470 or Data Mining through Duke University is $294, a bargain compared to other certificate programs or universities that might charge 10 times that much. This lends itself to an important question: Are MOOCs, for the first time, developing a model that could disrupt education as we know it?
If MOOC specialization skills become as valuable as the skills obtained from a regular university, this model will have the ability to disrupt what higher education has to offer, especially the ones positioned as continuing education. MOOC specializations are also competing against other traditional online universities such as University of Phoenix and Walden University. These MOOCs offer similar advantages compared to other major online degree granting universities while keeping costs down even more. The MOOC platform started out by offering individual courses, but now is extending into specializations. If this platform can be scaled to provide online degrees, it will be able to disrupt both campus-based and online universities.
You might be thinking, “True, but how will these specializations hold up in the real world, especially compared to a Data Scientist degree from say the University of California Berkley?” And you would be right to have that question. But remember the true reason you educate yourself in institutions and universities—to make yourself more marketable in the real world, so businesses find you valuable. And remember who makes that decision; it is the HR reps, CEO, your manager, etc. These days, where and how you get your education is becoming less relevant as a differentiator than it used to be. The business decides whether you fit the bill. Clearly, Coursera is recognizing this trend with its users—that they are taking targeted courses as part of their professional development to build their value to the business. Udacity is also shifting its focus to concentrate on fee-based corporate and vocational training.
So here is the big question: When will a business treat someone with a Data Science degree from Berkley the same as one with a specialization in Data Science and Data Mining from Coursera? I understand there are other classes and activities from a university that comprise an education, but will the business care? Or do they just want the best person with practical experience, application, drive, and relentless passion and curiosity? Then ask this: When will the $50,000 degree matter compared to the $766 certificate of Coursera specialization?
In my experience, it is as much about the person as it is the education, which leads me to my last point about the business model for universities and educational institutions: It is broken. This isn’t a model that should be fixed, it should be buried, and a new model created around the value proposition for universities and institutions. Universities are trying to compete based on the education they offer instead of focusing on the real value they can provide.
I always said my degree in engineering, even though I never used it professionally after school, was a great investment for me. But it wasn’t the degree that mattered; it was the life lessons it taught me along the way—time management, stress management, focus, and attention to detail, among others. The truth is universities and institutions are not in trouble just because they are failing to reinvigorate their business models, but because they lost focus on a value proposition MOOCs simply cannot, and will never be able to, give you—an education in life itself.Section: Business Model Innovation
Innovation appears prominently as part of almost any company’s strategy. Why then is it so hard to make it repeatable, scalable and lasting success? Scholars name key elements that bring innovation in sync, such as leadership, strategy and governance. Often, though, it’s not what organizations aren’t doing that causes a problem, but what they are doing—they’re tripping themselves up.
While there are many ways to trip, see if you recognize one of these three common ways in your organization. Fixing them can turn into a fast win and create the momentum necessary to get all the other pieces in sync.We Don’t Have problems; We Have Challenges
“I don’t want to hear about problems, show me solutions.” Sound familiar? There are multiple reasons why different corporate cultures come up with different terms to beat around the fact that problems exist. Some cultures use “challenges,” “hiccups” or “issues,” for example. I’m sure you can think of others. Language both reflects and shapes thinking and behavior. What does this do to the overall culture?
Let me introduce you to Alexej. He has been hired from a startup-gone-bust into product development for a large German corporation. His first weekly report is greeted frostily. He has identified a problem, but merely naming in a report is considered unethical finger-pointing because of a silent consensus on whose fault it was. This bright young man learns this lesson fast. His reports turn into a list of “last week’s accomplishments.” He hides from others the challenges he is working on and stays away from sharing the opportunities for improvement he comes across. This already siloed organization not only loses the creativity and enthusiasm of a highly skilled individual, but also foregoes the enormous potential residing in an all-one-team approach to tackling problems.
Organizations should acknowledge: Human life is problem solving. For people, any level on Maslow’s hierarchy of needs can quickly turn into a problem. Processes and entire departments are there to solve problems: “I don’t know next quarter’s financial results.” Industries solve problems, too: “I can’t communicate with a far-away person.”
The Russian innovation thinker Genrich Altshuller, inventor of the Theory of Inventive Problem Solving (TRIZ), observed: What sets the inventor apart is his or her ability to spot problems where the rest of us have grown accustomed to living with the hassle. Indeed, at times we don’t even notice that hassle anymore, that is, until someone comes up with the solution. Did anyone have a problem before the wheel was invented?
So firstly, from an organizational perspective, learn to recognize and acknowledge problems at face value, and to value the individuals who spot and communicate these problems.
The other two common ways organizations trip when it comes to innovation are adhering to the equation “innovative = creative = good” and overdoing the “let’s form a team approach.”
Read more about them in my full article on Innovation Management.Section: Structure & Methods
It seems lately that everyone is talking about increasing access to data, data and more data. In fact, no longer is there an issue of sourcing data, but instead a problem of what to look at, when and how to interpret it.
All well and good, you say—data is the lifeblood of intelligence and innovation! Well, not exactly. In order to convert data into knowledge, you need to process it. You need to connect the dots and turn information into meaning. Then you need to act on that meaning. As technology progresses, we are becoming better, not just at collecting data, but also at assimilating it. The challenge is that very few of us are good at or qualified to turn this assimilated data into decisions and strategy.
We see this in our personal life, in business and also in society. This article on the investment in “sci-fi tech” for the UK police is one such example. At first glance such equipment—including a range of wearable computers—seems like a great innovation designed to protect our forces and give them more live information for policing on the streets. But dig a little deeper and you will quickly find problems with utilization. The recent BlackBerry investment has returned much less than the expected ROI and I expect the wearable glasses and smart watch to come up against the same problem.
Compare this technology on a personal level with sports wearable tech. How many of you have running watches or devices that collate everything from heart rate to pace and even elevation but, at the end of the day, simply plug this into a social platform, turning the data into little more than a boast or at best a GPS track of your activity? How many of you read and assess the data in order to truly improve your performance?
Reflecting the same challenge back to policing, are we seriously saying it is productive for police to spend their time monitoring social feeds through a watch, rather than talking to individuals and using their eyes in order to know what is happening in their area? And if they were to do this, what would they be looking for? How would they respond? Would it not be better to have an intelligence team in headquarters dedicated to this?
In a business context, I recently had a conversation with a big data executive leading a company that acts as a “curator” for the wealth of data streaming into businesses today. They sort through the data, connect the dots and provide this back in a more usable and value-add form. This concept of curation is essential to turning the mass of data we continue to have at our fingers into knowledge, decision and actions. Even in business this is a relatively new concept that leaders are still getting to grips with.
Sadly it seems that many of the innovations in data technology will remain untapped and perhaps even counterproductive until the skills and systems for curating this new world of data are advanced and widely used. Personally I’d like to see more innovation in this field. Until then, I’m afraid, technology and data will only be as good as its wearer.Section: Product Innovation
Have you seen this equation: innovative = creative? Novelty always comes from “outside the box,” right? It’s a land of confusion to many, who then conclude they are just not the creative type. As a result, organizations lose out because being innovative is but one of a myriad of ways to being creative. All people can be creative—in their own way.
An organization’s ability to bring to best use the individual, team and collective creativity of its people is an important differentiator. That being widely acknowledged, organizations strive for diversity: diversity in gender, age, education, culture and so forth. The argument here is that they can’t overlook another type of diversity: that of being creative.The under-appreciated obvious
There is nothing new about the call for creative diversity. In December 2007, Coyne, Clifford and Dye published an article in Harvard Business Review entitled “Breakthrough Thinking from Inside the Box.” They stated the obvious that nobody seemed to have noticed: People who like to explore “inside the box” should not be under-appreciated.
This point is backed up by robust research in cognitive psychology. Since the 1970s, Dr. Michael Kirton has been exploring how people solve problems. Seen in the light of cognitive science, solving problems and being creative is one and the same thing. As Genrikh Saulovich Altshuller, inventor himself of the Theory of Inventive Problem Solving (TRIZ), observed: Inventors are able to see problems where the rest of us have grown used to living with the hassle.
Acknowledging these key elements, Dr. Kirton investigated how people actually solve problems. Complex problems are solved in teams, which comes at a price. If you want to solve a technical “Problem A” with your team, then you have to face the additional “Problem B” of managing that same team: finding a place and time to meet, going through the stages of team development and tackling the team’s diversity.
In his research, Dr. Kirton found that people tend to confuse two things: level and style of creativity. If someone’s style of being creative is different from yours, then you might conclude that their level of creativity doesn’t match yours. Examples abound: For some time, Tesla worked for Edison. Both are recognized to have been highly creative people, but creative with very different styles (see this study for more). To solve difficult problems, we need creative diversity in our problem solving teams, but then we struggle to deal with it.
Insights gained with the Kirton Adaption-Innovation Theory help individuals understand their own preferred problem solving style, appreciate their colleagues’ styles and manage diverse teams so that complex problems can be solved.
Read more, including three case study examples, in my full article on Innovation Management.Section: Culture & Teams
When we play, we create fresh and new meanings expressing our creativity. The deep connection between free playing, learning and creativity has been studied by researchers such as Csikszentmihalyi (2008) and Bruce (2005) and also by the LEGO Learning Institute (2010). As Bruce has noted, the nature of play is an active process that is intrinsically motivated.
During play, players explore alternative worlds through imagination which can lift their creativity to their highest levels of functioning. Creative activities enable players to demonstrate mastery or competence they have developed from previous experiences which may include struggles, manipulations and exploration. It is during these activities that they get a chance to reflect and become aware of what they know. The significance of play is that it provides a powerful mechanism to bring together what we have learned and to generate insights for future.
Both children and adults play in solitary or partnership mode. They often play to break from established methods and conventions, in order to experiment inside a rich world of imagination and to push themselves to find new frontiers. More and more companies are using playful learning as a desirable approach to simulate alternative realities and future scenarios in order to accelerate their product, process and business model innovations.
For example, many companies use the Lego Serious Play method to develop new business strategies and guiding principles. It is used as a structured and voluntary activity that involves and stimulates the imagination of problem solvers. With time and space as constraints, and structured by rules and conventions, participants draw on elements of fantasy and creative imagination to explore guiding principles of strategy development [See: The Science of Lego Serious Play (2006)]. Other examples of games often used by corporations include the Beer Distribution Game from MIT Sloan School of Management [Sterman (1989)] that simulates the supply chain optimization and the Manufacturing Game by Ledet and Paich (1994) that simulates the plant operations, as well as a range of other commercial and noncommercial business simulations and games.
Csikszentmihalyi (2008) introduced the idea of the link between creativity and a state of “flow.” According to Csikszentmihalyi, flow is the experience of being enjoyably immersed in a challenging task. These moments occur when a person’s body or mind is stretched to its limits in a voluntary effort to accomplish something both difficult and worthwhile. This is the essence of playful learning.
Read more in our full article on BMGI.com.
By Dr. Phil Samuel and Dr. Michael OhlerSection: Culture & Teams
Countless articles argue: To remain competitive, companies need to consistently build their innovation portfolio. Value-oriented improvement and new developments must permeate the business. This article discusses a structured approach, known as a Rapid Innovation Cycle, which brings a repeatable process to innovation, empowering individuals to contribute more and organizations to look beyond themselves—all leading to a higher success rate.
Whether your innovation challenge is product, process or business model oriented, business problems all benefit from a methodological analysis to separate experiential bias from business need. Continuous improvement methodologies such as Lean and Six Sigma (many more exist) enable practitioners to refine their existing solutions, but do not offer an effective conduit for management of novel and unconventional thinking.
While many still feel that a systematic approach to innovation is impossible, innovation practitioners know how repeatable processes can be applied to achieve innovation objectives. A Rapid Innovation Cycle provides a process for leading teams through the front end of the innovation journey.
Read more in our full article on Innovation Management.
By Dr. Phil Samuel & Riaan BritsSection: Structure & Methods
Can you answer this riddle?
What human trait is genetically determined, is readily apparent in young children, can be reliably measured in teenagers and does not change with age or experience?
If you answered “problem-solving style,” give yourself a gold star!
Believe it or not, all of us have an innate problem solving, or cognitive, style that we use to express creativity, solve problems and make decisions. And, according to the theory, our cognitive styles vary across a continuum ranging from high adaption to high innovation. When our styles do not match the strategy required to problem solve, our success becomes limited. In other times, when working on a project with team members who have dissimilar styles, it can create issues with managing each other’s diversity. This leads to challenges in terms of communication, trust and ability to work together. If the team members have similar styles, it too can limit the complexity of problems that the team can solve.
The “Adaption-Innovation” theory was developed by Dr. Michael Kirton, a noted British psychologist. Kirton’s theory is the subject of more than 100 dissertations and over 400 research papers. KAI, or the Kirton Adaption-Innovation Inventory, is considered a highly reliable psychometric tool for measuring in what manner people tend to solve problems, make decisions and express themselves creatively.
Why has the KAI gained so much popularity? The KAI is a thoroughly researched and validated instrument that focuses on cognitive style, which explains why we sometimes have difficulty working with people who are not like us. It focuses on preference for structure and strategy for problem solving apart from motive and level.
There seems to be much confusion between cognitive level and cognitive style. Cognitive level refers to an individual’s inherent potential capacity, such as intelligence or talent, as well as their manifest capacity, which includes their existing knowledge, experience and learned skills—with all of these varying in terms of both type (e.g., engineering, nursing, economics) and amount/degree (e.g., novice to expert). Cognitive style, on the other hand, is defined as “the stable, characteristic and preferred manner in which an individual responds to and seeks to bring about change,” including the solution of problems. In simple terms, then, cognitive level indicates “by and with how much” a designer solves problems, while cognitive style indicates “in what way” he or she prefers to solve them.
The key distinction to differences between more adaptive and more innovative individuals is related to their preferred way of managing structure in problem solving. Individuals who are more adaptive prefer to operate with more structure, and with more of this structure consensually agreed. In contrast, individuals who are more innovative prefer to operate using less structure and are less concerned about achieving consensus around that structure as they proceed; indeed, they are more likely to want to change consensus than to conform to it fully. One way of summarizing these basic differences is to say that the more adaptive prefer to solve problems using the rules, while the more innovative prefer to solve problems despite the rules, however such “rules” might be defined and implemented.
So does this make “innovators” more innovative? Not necessarily. Neither style is more creative, or better at problem-solving or decision-making. Depending on the situation, one style may be more adept at solving the problem at hand. In many cases, however, a team composed of both adaptors and innovators is the most effective—as long as they understand how to work together and respect each other’s differences.
Style differences of 20 points or more on the KAI scale can lead to communication and trust issues, often affecting a team's ability to work together. For example, the more adaptive person on the team will try to solve the problem meticulously, aided by many details, while the more innovative person will prefer to stay high level and manage the big picture. The innovative person will also be eager to solve the problem by looking at it from unsuspected angles, whilst the more adaptive person will want to apply tried and tested solutions.
It’s not uncommon for team members to mistake these differences in cognitive style (“cognitive gaps”) for differences in capacity or ability. This can lead to generalizations (“She’s an accountant, she doesn’t care about the big picture”) and misperceptions (“This idea is just the boss’s latest pet project”). Such misunderstandings can lead to interpersonal conflict that seriously damages a team’s collaborative ability. And when team members don’t work together, we rarely see them develop effective solutions.
As discussed earlier, cognitive diversity within a team is necessary to provide the diversity of levels and styles required to solve the complex problems faced by the team. However, this same cognitive diversity is likely (indeed, is certain!) to lead to differences in outlook, ideation, evaluation, and approach during the design effort. These differences must be managed well to keep the team on track. This situation is an excellent example of the Paradox of Structure, i.e., the fact that every structure both enables and limits at the same time. This paradox applies to any kind of structure, whether it be physical, social, economic, conceptual and so on.
This principle should be easily understood by teams from a technical standpoint—every material or machine has its advantages (is enabling) and its disadvantages (is limiting), and these need to be known and managed. So, just as every team (a social structure) is influenced by the paradox of its collective cognitive diversity, so every solution is affected by the Paradox of Structure as well. That is, the same technical features that enable the performance of a system also limit that system’s performance, as designers recognize when they consider design trade-offs, for example.
At BMGI, we incorporate the KAI evaluation into our engagements as a method of helping clients develop effective problem-solving teams. In addition to helping participants understand the significance of their own as well as other team members’ cognitive styles, we teach them how to manage the cognitive gaps between different styles. Team members learn how to manage diversity and even make it work to their advantage.
After all, effective innovation is usually the result of managing paradoxes and merging opposites—dynamic “innovative” thinking balanced by risk-minimizing “adaptive” behavior. In that sense, while it might be easier if “everyone were like me,” our ability to solve complex problems generated by the ever changing environment will become harder and harder. Instead of tolerating “everyone unlike me,” we should be embracing “people unlike me” to extract the power of collaboration through diversity of level and style.Section: Culture & TeamsStructure & Methods
When Amazon released its bold vision to introduce delivery drones, it was met with skepticism. What if they crashed in our front yards or worse? How would we distinguish safe flying objects from malicious ones? Amazon, and also Google X which is working on its own drone project, anticipated long regulatory approval cycles and debates in society—hence their roadmap of four to five years. You may not have noticed, but drone technology is ready. For a few hundred bucks, you can order your personal quadcopter today and the postman will deliver it tomorrow.
Skepticism with regards to innovation is perhaps more common in my home country of Germany than, say, Silicon Valley. When in 1835 the first train rolled at a speed of 18 miles per hour between Fürth and Nürnberg, some travelers readily concluded the speed was too high for our souls to stay healthy. While my compatriots are now traveling comfortably in trains at speeds beyond 180 miles per hour, people for many years happily stuck canes into the spokes of the German maglev train. It has taken more than 60 years and an ambitious city like Shanghai to set up the first commercial use of the technology developed in the 1970s.
As many innovators know, the adoption of bold innovations is more often a question of change leadership than of engineering and technology. Techies and large corporations alike tend to underappreciate that at times.
So who would think that Germany could be fertile ground for the first semi-commercial test-flights for delivery drones? From a change management perspective, what logistics company DHL has done is very clever indeed. The company is testing with flights to an island off the coast of Germany (so they are not flying over people’s homes) that deliver something nobody can object—medicine.
Let’s think ahead: What if that pilot program worked out and was well-publicized? What if then mayors of other islands demanded a similar service? Maybe some mountain huts and other remote places also? Could DHL create demand for this new service—not with an eye on turning that into a profitable business immediately but more to get people used to a measured use of drones, just like we got used to riding high-speed trains?
It is well-known that transformative innovations require the creation of an ecosystem around them for their success. If commercial applications inspire a somewhat fuzzy fear (like traveling at high speed might compromise people’s spiritual health), how could fear be reduced so that the debate can be elevated to a more rational conversation around risks and abatement plans?
From your own experience as facilitator, you may know the solution already. Decision making processes in organizations can indeed involve fear, which is certainly not an enabler of creativity, so we must use a playful approach to unlock a team’s full potential. What if DHL looked for playful applications of drones?
Intel’s “make it wearable challenge,” which is in itself a nice initiative, has brought attention to such innovations as a wearable drone developed by Nixie. The start-up is embracing a playful aspect by developing a drone that you can wear on your wrist and let it fly to shoot your personal pictures and videos.
You may still prefer a headcam for your next wingsuit dive off a cliff, but for slower speeds, headcams could soon find themselves “old hat,” replaced by the really cool thing to have: a flying wristband.
What if DHL launched a challenge of its own: Really cool and playful applications of drones? These days the coolest gadgets are the smallest ones, and there’s an entire institute for micro-technology, located right in the heart of Germany. I would say we have come a long way from 18-mile-per-hour trains.
Of course, these innovations have implications for logistics and delivery worldwide. The story of drones is just about to unfold. It will be fascinating to follow and contribute to it in the near future.
Illustration by Brian Miller.Section: Culture & TeamsService Innovation