Content mining, machine learning, text and data mining (TDM) and data analytics all refer to the process of obtaining information through machine-read material. Faster than a human possibly could, machine-learning approaches can analyze data, metadata and text content; find structural similarities between research problems in unrelated fields; and synthesize content from thousands of articles to suggest directions for further research explorations. In consideration of the continually expanding volume of peer-reviewed literature, the value of TDM should not be underappreciated. Text and data mining is a useful tool for developing new scientific insights and new ways to understand the story told by the published literature. Continue reading “Unrestricted Text and Data Mining with allofPLOS”
Who are the people really making a difference in digital media this year? Who is flying under the radar but needs to be in the spotlight for innovation? Who is making a difference in their community, in the nation or the world? MediaShift wants to find out with your help. We are launching our first MediaShift20 list for 2017, and want to highlight people in the digital media world who are truly change-makers and who are leading the way.
We will have three lists: our overall MediaShift20 that will include everyone in digital media, from entrepreneurs to journalists to producers; the EducationShift20 that will focus on innovation by educators; and the MetricShift20 to highlight great work in media metrics. Our goal is to bring attention to those who have done great work in 2017 and deserve more notice.
Last month, the new PLOS Cholera Channel joined existing Veterans Disability & Rehabilitation Research, Tuberculosis and Open Source Toolkit Channels in providing distinct and cohesive scholarly homes for research communities. These innovative forums increase the visibility of curated, quality research and reliable news and commentary, bridging a gap in relevance that contributes to public misunderstanding of research.
The Channels Program launched with Veterans Disability & Rehabilitation Research (VDRR), and as Veterans Day in the US approaches it’s an opportunity to take a moment to relay the channels origin story, highlight the latest content and re-introduce the editors behind this program. Continue reading “PLOS Channels Provide Opportunity for Discovery, Exploration and Contextual Insights”
Major news conferences are now tackling shiny new tech topics like bots and AI, and mixed, augmented and virtual reality, for journalism. While these are important topics that have their place in the conversation, one essential topic is being dangerously overlooked: mobile. Continue reading “Why have we stopped talking about mobile?”
Your steps in digital transformation
Recently, I was invited to give a keynote speech on the impacts of digital disruption at a company’s Innovation Day event. The participants, consisting mainly of the company’s senior management team, were enthusiastic and motivated to develop innovation strategies for their business. During the course of the event, the team discussed digitisation of their business operations as the core of digital transformation.
Although in many cases, digitisation (converting something analogue to digital formats e.g., replacing paper based filing system for online document management) is a key element in organizational transformation for the digital age, I believe it is only one part of digital business transformation in its entirety. Incumbent organisations looking to gain competitive advantage in the digital economy will need to address several levels of change, improvements and development. I have developed a simple illustration of these levels to help companies understand the scope, impact and potential outcome of initiatives at each level.
Level 1 – OPTIMIZE the foundation
The ‘Optimize’ level focuses on initiatives that strengthen and improve the existing business elements to create strong foundation. As a business, basic operations need to function smoothly for the company to exist and sustain itself. These include the physical operations, day-to-day activities, financial management, among other things. The focus here is really to ensure these basic operations are optimized – reduce redundancy, high efficiency, low costs etc. Many ‘Optimize’ activities may involve digitization of the operations, for example process automation, implementation of ERP systems, launching a responsive website, or using social media channels for customer interactions.
‘Optimize’ initiatives are activities that companies should be implementing to improve operations and efficiency, whether ready for digital transformation journey or not. Companies that have not optimized will struggle to stay relevant in the digital economy.
Level 2 – EVOLVE the business
The ‘Evolve’ level focuses on transforming selected foundation elements to prepare for dynamic needs of digital disruption. The rapid development of disruptive technologies are enabling new ways of doing things in an organization. Companies now have the possibility to apply these technologies to enable, improve or transform their business. For example, the use of chatbots on Facebook messenger to improve customer interaction and increase sales conversion, or using machine learning or AI in data analytics to gather predictive insights on customer behaviour or preferences.
‘Evolve’ initiatives typically focus on digitalization of a specific function or business area to improve or transform by leveraging digital technologies. For example, I recently saw a smart factory demonstration where speed and efficiency of the assembly line was improved through the use of smart bins. The bins were programmed to order parts as soon as it hits a minimum threshold level to avoid delays.
Initiatives at this level can go a long way to creating competitive advantage for companies. However, it is not as simple as throwing new technology at a business problem. I have seen many examples of companies launching a mobile app in the name of digital transformation. Here, real benefits are gained only by leveraging technology in a strategic and targeted way to resolve or transform an existing business challenge.
Level 3 – INNOVATE for the future
The ‘Innovate’ level focuses on exploring innovative initiatives to transform the business and create a sustainable competitive edge in the digital economy. This level involves true organization wide digital business transformation. For example, disruptive business model innovation falls within the Innovate level.
I believe that digital disruption will hit every industry at some point over the next five years and change the global business landscape. However, some industries will be hit quicker than others, simply due to the nature of their business. For example, the first big wave of disruption will be technology, media, retail and financial services due to rapid developments in technology, evolving consumer behaviours and high number of digital entrants. On the other hand, oil and gas or utilities may be hit at a later stage.
Despite this, every company should be exploring Innovate initiatives in order to prepare for this disruption. As a result, new operational activities, products / services, or business models may surface, creating a need for organization wide transformation.
Evolution process, not one time change
The digital business transformation journey can be viewed as an evolution process, with change happening over a course of time. As a knee-jerk reaction to disruption, business leaders may be tempted to implement a new technology in an effort to ‘digitize’ the business. However, it is worth exploring initiatives across the various levels to determine where a company needs to focus for highest returns. Keep in mind that not all levels need to be implemented simultaneously. As a starting point, focus on implementing ‘Optimize’ and ‘Evolve’ initiatives. But leadership teams need to already be exploring the ‘Innovate’ level, particularly in the highly disruptive industries.
Kamales Lardi is a digital business transformation strategist and dynamic keynote speaker. She helps companies leverage digital disruption to create new opportunities for business and generate revenue. Kamales is also a published author, lecturer, mentor to entrepreneurs and member of the MBA Advisory Board at Durham University, UK.
Connecting the classical & colloquial
It won’t have escaped your notice that ‘disruption’ has been the de rigueur term for many people in business, technology and innovation for a while now. The danger of an increased popularity and visibility of such a term is its dilution and misappropriation. As a person seemingly genetically opposed to business-speak and buzzwords, and as Managing Editor of D/SRUPTION, it is especially important to me that we keep a close eye on it.
Talk of disruption and disruptive technology first appeared in a 1995 article by Clayton M. Christensen called, ‘Disruptive Technologies: Catching the wave’. He developed these ideas further in his now well known book, published in 1997, ‘The Innovator’s Dilemma’. Christensen’s theory started the revolution in business thinking that we can call ‘classical’ disruption.
What is disruption?
Disruption is the process that happens when an upstart company with few resources is able to successfully take on an incumbent company, and win. . . It goes a little something like this:
– The incumbent tends to focus its efforts on improving products and services for its most profitable customers. Due to this, it pays less attention to its less profitable customers (or ignores them and other potential new markets entirely).
– As the incumbent business has spent all its effort focusing on its higher value customers, it has ignored the lower value segments, these present an opportunity for the potential upstart disruptor.
– The upstart can become the disruptor of the large company by successfully catering for these ignored or untended groups, providing them with similar products or services to the incumbent, or by answering similar needs and often at a cheaper price.
– The incumbent may not acknowledge or even know about the threat because initially the upstart is just meddling in a small section of its least profitable market. So the upstart may be operating under the radar or written off as piffling, and it often will be – not all upstart businesses go on to succeed let alone be disruptors.
– Notably, the disruptor will often find a way to take people from these less tended segments who aren’t much of a customer for the incumbent (or indeed a customer at all) and turn them into one for themselves.
– Once the upstart has established itself by fulfilling the needs of the people ignored or untended by the incumbent, it will be working hard to grow and expand upwards.
– As a result of this growth, its products and services begin to improve and will start to include more of the qualities and features that the more high value customers of the incumbent company demand or expect.
Ultimately this results in some of those customers shifting from the incumbent to the upstart. Once that number is significant – that is disruption.
Disruption in Christensen’s model has the clear and specific definition that I’ve outlined above. Many uses of ‘disruption’ today are not in keeping with this model at all. It is perhaps due to the availability of the word in everyday speech that there is a significant gap between Christensen’s definition, and the more colloquial uses of the word.
Understandably, like anyone with a good theory, Christensen is keen to preserve his own, and to make sure the terminology used stays tight to his definitions to ensure his theory remains as specific as it is. He has himself expressed regret about choosing the term ‘disruption’ to denote this precise meaning because of this potential confusion – between the specific and the colloquial. Many baulk at putting even a toe outside of Christensen’s definition – but I’ve encountered many more that use the term ‘disruption’ seemingly without any awareness of the theory, perhaps other than in name, at all.
Although it may prickle some, language and terminology will always evolve. Despite the inevitable frustrations, the term has come to mean other things to other people – or more accurately, it continues to mean what it had always meant long before Christensen arrived with his dilemma.
Colloquially, we tend to use ‘disruption’ to describe when an event, system, or process, is interrupted and prevented from continuing or operating in its usual way. It is this definition that many have in mind when they say they, or their businesses, are ‘disruptive’, and there is certainly nothing wrong with that provided we keep in mind the clear distinction between this and Christensen’s model.
Classical vs colloquial – Airbnb vs Uber
You can’t go far into a conversation about disruption these days without someone mentioning Airbnb or Uber. What’s interesting is that by Christensen’s ‘classical’ definition – Uber’s taxi business is not actually disruptive at all (although other aspects of Uber’s wider business may prove to be). Christensen has pointed this out. In terms of his theory, we can see that Uber did not create a new market nor did it begin at the low end of an existing market. Airbnb on the other hand does indeed fulfil the ‘classical’ definition of disruption.
However, in terms of colloquial disruption, both Uber and Airbnb are widely, and rightly, regarded as disruptive for a combination of sheer impact, speed of growth, and the use of technology to radically change the way the world works.
A significant marker for both ‘classical’ and colloquial disruption is that these businesses often appear to come out of nowhere – although this is an illusion – no business or innovation whether disruptive or not comes from nowhere – but often the shock impact and exponential growth will indicate something major has happened, and quickly – and that is usually disruptive in one sense or the other.
Symptom & Cause
If we understand the model as intended, we can also be free to explore other concepts; even if unfortunately they are forced to share the same name. Christensen points out that broadening the definition of disruption in relation to his theory undermines it, and in this he is correct. But, so long as we understand that we are holding two different things in mind that share a name – but not a definition, we are capable of gaining a lot from each.
While acknowledging the theory and definition put forward by Christensen, there are some wider uses of disruption that are also worthy of consideration. Outside of what I’ve named, ‘classical’ disruption, the term has come to signify a range of approaches to business and innovation and often indicates a radical change to traditional ways of working, thinking and doing.
Disruption in a ‘classical’ sense is a symptom of a serious problem that incumbent businesses have experienced due to an upstart, it is retrospective and an indicator that a major shift has occurred from them to the upstart. Disruption in the colloquial sense, that of ‘disruptive thinking’ and ‘disruptive approaches’ is a cause.
Colloquial disruption is an approach, both a fire starter and a rallying cry to create new futures and explore the possibilities, opportunities and hazards. By considering these possibilities we are able to inform strategies and mitigate risks. The exact nature of the future is unknown, but that doesn’t mean we can’t know anything about it.
If we use foresight in an informed and intelligent way, we can identify possible threats and opportunities and use these to our advantage in our businesses. An awareness of the potential threat of disruption in Christensen’s sense, and a disruptive approach to innovation in the colloquial sense can both be essential parts of business planning. While they are certainly not strategies in their own right, the potential threat posed by Christensen’s disruption, and the opportunities opened by colloquailly disruptive approaches can tangibly and helpfully influence how we plan for our future when faced, (as we always are), by a complex world of constant change.
Satisfaction is stagnation
Applying decent futures thinking to a business is a smart move. A business doing well would doubtless like to stabilise and maintain its position but digging ourselves in and holding hard isn’t an option when faced with unavoidable change. It may work for a time but it is not sustainable in the longer term.
Recognising the constant state of flux, exercising foresight, and understanding and incorporating what disruption means in all senses are all prerequisites to putting businesses in the best position to be adaptable in the face of change, and to give the best chance of survival and success.
Merely stating, ‘we are disruptive’ is just not good enough. This thinking must influence planning, strategy, culture and brand as part of an ongoing process if it is to mean anything useful at all; and those that understand disruption both colloquially – (as an approach to be used), and classically – (as an indicator of a threat to be avoided), by far stand to gain the most.
By Clay Christensen (these highlights provided for you by Annotote)
“ [Publishers] remain mired in the innovator’s dilemma: A false choice between today’s revenues and tomorrow’s digital promise.
“ it shouldn’t be a surprise when new entrants like The Huffington Post and BuzzFeed, which began life as news aggregators, begin their march up the value network … They are classic disruptors.
“ The problem is that too many newsrooms’ strategies are based around exactly this assumption — that their businesses can best be explained in terms of key demographics, price points, or distribution platforms. Instead, a better way of thinking about the business you’re in is through the lens of a theory that we call jobs-to-be-done. The basic idea is that people don’t go around looking for products to buy. Instead, they take life as it comes and when they encounter a problem, they look for a solution — and at that point, they’ll hire a product or service.
“ a huge job in the media market [is] “I have 10 minutes of downtime. Help me fill it with something interesting or entertaining.”
“ too often, consumers are unable to articulate exactly what it is they are looking for, their thinking constrained by the solutions that already exist in the market.
“ The jobs are consistent — it’s the products that change
“ journalism’s “middle ground” has eroded as new products have appeared at either end of the market for news and information. At the low end, products and services like Metro and Twitter are serving consumers whose need is simply “Help me fill this 10 minutes right now” … At the other end of the spectrum, for the job of “[I have] four hours, and I want to be intellectually stimulated,” sites like Longreads and tools like Instapaper and Pocket
“ content must be so compelling that users will pay for it. This requires targeting the right jobs.
“ News organizations used to control the gathering, packaging, distribution and sale of the news product. Today, journalism is a disintegrated and open process.
“ Most traditional news organizations operate a value chain that is made up of three distinct parts. First, there is the newsgathering; this comprises all the resources and processes required to collect, write, shoot, edit, produce and package news and information. Second, there is the distribution of the product; this encompasses all the ways that news organizations get their content into the hands of the audience. Third, there is the selling of the news; this part includes not only sales and subscriptions but also advertising and marketing.
“ General interest and breaking news reporting comprised of answering the “who, what, when and where” has become commoditized … The value for news organizations now increasingly lies in providing context and verification — reporting the “how, why and what it means” — and facilitating communities around that news and information.
“ What can sales and marketing teams do to create additional value? Consulting services, event marketing, and long-tail repurposing are three possibilities.
“ There are three factors that affect what an organization can and cannot do: its resources, its processes, and its priorities … these factors might affect their organization’s capacity to change.
“ There are several possible ways to [produce change]:
- Creating new capabilities internally …
- Spinning out an independent organization [#skunk works] …
- Acquiring a different organization [#M&A]
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For decades, managers have been focused on efficiency. From Frederick Winslow Taylor and his Principles of Scientific Management early in the 20th century to more modern practices like Six Sigma, executives continually honed their operations to achieve maximum productivity at minimal cost.
For the most part, this type of approach can be amazingly effective. Even a relatively small improvement of a few percentage points, if repeated annually, can produce amazing results over the long haul. Multiply that process in multiple areas across your business and you can build a significant competitive advantage.
Yet the single-minded pursuit of efficiency can also backfire. If you’re not careful, you’ll end up getting better and better at things that matter less and less. This is especially true with innovation, because anything that’s truly new and different can’t be graded by conventional metrics. We need learn how to manage the tradeoff between efficiency and innovation.
Unleashing The Unexpected
By 2006, we knew we had a problem. Afisha, our entertainment magazine that had previously been the cash cow whose profits powered our company’s growth, was now in a steady decline. The Ukrainian ad market had been growing by leaps and bounds, which had attracted stiff competition from international publishers like Hearst and Hachette, and also significantly increased salary costs.
Over the previous two years, I had been revamping the company’s operations. From sales and marketing practices to reporting structures and the way we conducted meeting, our operations had been completely overhauled. That made us vastly more efficient and our other divisions were now printing money.
But Afisha was a different kind of problem. The product still led its category and remained a hit with readers, but because of the increased competition from related categories, as well as the increase in operating costs, we were beginning to have trouble earning a profit even though operations had improved just as significantly as in our other businesses.
In the end, what saved us was not a plan, but something unexpected. We launched a number of initiatives, most of which had limited impact. However, one unlikely idea — an events calendar tied to our loyalty card — ended up growing into a significant new revenue stream. If we had stuck to “high percentage moves” or “best practices,” all would have been lost.
Sometimes, improving your business model isn’t the answer and you have to create an entirely new model.
The Efficiency Paradox
When General Stanley McChrystal took over US military operations in Iraq, he had a problem that was very similar to Afisha’s. Although he led some of the most capable teams in the world and was winning every battle, his forces were losing the war. It didn’t matter how many terrorists his troops killed or captured, more would pop up somewhere else.
As he described in his book, Team of Teams, the problem was that his forces were organized for efficiency, not interoperability. Commando teams would capture valuable intelligence, but it would often sit for weeks in a closet before an analyst would take a look at it. Or an intelligence officer would locate a terrorist, but by the time the information got through the chain of command, he would be long gone.
McChrystal realized that in order to defeat a network, his forces had to become a network. So he took a number of steps that actually decreased the efficiency of individual teams, like embedding top special forces operators in intelligence units and vice versa. Liaison officer positions — previously neglected — were now only given to top performers.
At first, these moves inspired resistance in the ranks — nobody wants their team impaired — but as the plan took shape, it became clear that it was working. The individual teams might have slowed down slightly, but the increased interoperability allowed the army as a whole to move much faster, attacking targets almost as soon as they were identified.
Moving Innovation Off The P&L
When Eric Haller returned to Experian after seven years working in startups, he saw his former company with new eyes. Although the data giants was incredibly efficient, it seemed to him that the data giant was missing out on important opportunities simply because there wasn’t enough historical data to create reliable predictions.
So he convinced the board to create a new unit, called DataLabs, that would be specifically geared to seeking out new opportunities. Stocked with data scientists, it would be focused on solving customers’ problems, with little regard for whether there was a quantifiable business opportunity.
“When we set up DataLabs,” Haller told me, “we didn’t want to create a P&L for it, because we wanted to take more risks. We knew going in many projects would fail, but we didn’t want that to hinder our ability to swing for the fences. It also allows us to explore more options and try more approaches, even things that have never been tried before.”
Today, six years later, DataLabs has become a growth engine for Experian. It has created about a dozen product lines that are generating at least a million dollars of revenue or an average of about two per year. In a $4 billion company, that may not seem like much, but these are completely new opportunities that will grow for years to come.
Innovation Is Exploration
In researching my book, Mapping Innovation, I found that the most important thing that great innovators do differently is that they are actively seeking out new problems. In other words, they not only continue to hone their existing processes and practices, they go actively look for areas where they can make an impact.
“Optimization is about working within an existing framework, while innovation is focused on developing new frames of reference,” says Experian’s Eric Haller. That’s the tradeoff between optimization and innovation. It’s not enough to continually get better at what you already do well, you also need to charge boldly into the unknown.
The truth is that innovation needs exploration. If we had stuck with the “high percentage moves” at Afisha, we would have never developed our events business. If General McChrystal had stuck to existing military doctrine, he would never have prevailed in Iraq. DataLabs doesn’t rely on marketing data to create new businesses, because it wants to do things that are completely new.
So if you want to innovate, forget the metrics and focus on your mission. Investigate an unknown area. Go out and find a problem that needs solving. Take a chance on an unproven approach. You will, of course, fail more frequently, but as long as the risks you take are manageable, you open up far more potential for success.
When most people hear the word innovation, they think about Uber, Airbnb and Amazon — disruptive companies that upended entire industries with a radical new way to do business. But Wharton operations, information and decisions practice professor David Robertson argues that this view is too narrow a definition of innovation, and one that is not useful to most companies.
In 1980, an obscure professor at Harvard Business School named Michael Porter published Competitive Strategy, which called for managers to drive efficiency by optimizing their firm’s value chain, maximize bargaining bargaining power with buyers and suppliers, while at the same time minimizing threats from new market entrants and substitute goods.
These concepts launched Porter into the top rank of strategic thinkers and profoundly influenced how businesses were run. Much like chess grandmasters, CEO’s worked to develop the right sequence of strategic moves that would position their firms to exert power and dominate their respective industries.
Yet much has changed in since then. Rather than an orderly marketplace defined by clear boundaries of industry and geography, we operate in a semantic economy where everything is connected. The most important assets are no longer the ones we control, but those that reside in ecosystems that we access through platforms. That changes the game entirely.
Going From Research & Develop To Connect & Develop
Procter & Gamble is exactly the type of company that Porter had in mind when he formulated his theory of competition. Through its efficient supply chain and world class marketing operation, it created massive bargaining power with buyers and suppliers, while its century long commitment to R&D helped it fend off potential competitors and grow revenues.
However, in the late 90’s, when Nabil Sakkab took over as Senior Vice President of Research and Development at the Fabric Care division, he saw that even the resources of a giant like P&G were no longer enough to keep growth going. He quickly realized that he had to do something radically different.
So rather than looking to increase his bargaining power with suppliers, he did something that had the potential to undercut it. He began working closely with suppliers to solve key research challenges. It worked so well that the process was expanded into P&G’s Connect & Develop program, which has made open innovation a top priority.
Today, Connect & Develop is clearly an unqualified success. Sakkab was able to meet the goals he set out to achieve for his division, helping to grow the business by 50% while reducing R&D resources by 25% by the time he moved to a corporate role in 2005. It also led to the led to development of Swiffer and to the expansion of Febreze, both are now billion dollar brands.
Why Open Beats Closed
Around the time that Sakkab began thinking about partnering with suppliers to solve problems, executives at Eli Lilly were thinking about how they could leverage the Internet. Alph Bingham, who was a responsible for managing the pharmaceutical giant’s portfolio of research, was invited to a brainstorming session to stimulate new ideas.
Bingham was already fascinated with Linux, the open source operating system that Linus Torvalds had released in 1991, and how an ecosystem of thousands of volunteers were able to create and advance complex software that could compete with the best proprietary products. He thought that there could be great potential for a “Linux with a bounty” that could solve some of the tough problems that Eli Lilly hadn’t been able to find an answer for.
The Innocentive platform went live in June 2001 with 21 problems, many of which the company had been working on for years. Although the bounties were small in the context of the pharmaceutical industry — $20,000 — $25,000 — by the end of the year a third of them were solved. It was an astounding success!
It soon became clear that more challenges on the site would attract more solvers, so they started recruiting other companies to the platform. When results improved, they even began inviting competitors to post challenges as well. Today, Innocentive has over 100,000 solvers that work out hundreds of problems so tough that even the smartest companies can’t crack them.
In 2005, Eli Lilly spun out InnoCentive as a fully independent platform. It only attracted about $30 million — not a material event for a company that counts its revenues in the billions. Being able to access a fully open platform was worth more to it than being able to own one it could control.
Making Common Cause With The Barbarians At The Gate
Yet things had changed by 2010. When Microsoft launched Kinect for the Xbox in 2010 it quickly became the hottest consumer device ever, selling 8 million units in just the first two months. Almost as soon as it was launched, hackers started fiddling with it, altering its capabilities to do things that Microsoft never intended.
Historically, Microsoft would have had its lawyers crank out cease and desist orders. But it didn’t. In fact, the tech giant embraced the hackers, altering the USB cable to allow for more developmental flexibility, releasing a software development kit (SDK) in order to make modification easier and even creating an incubator to offer financing for the best hacks.
As for Linux, apparently Microsoft now loves it and has built its new Azure cloud platform around the open source operating system. It seems now that even in Redmond it is clear that power has shifted from corporations to platforms.
Collaboration Is The New Competitive Advantage
Clearly, much has changed since Porter wrote his book nearly a half century ago. Today, we live in a networked world and competitive advantage is no longer the sum of all efficiencies, but the sum of all connections. Strategy, therefore, must be focused on widening and deepening links to resources outside the firm.
So we increasingly need to use platforms to access ecosystems of talent, technology and information. Even the internal capabilities of corporate giants like Procter & Gamble, Eli Lilly and Microsoft pale in comparison to that which can be found outside the boundaries of an organization. As Bill Joy put it, “no matter who you are, most of the smartest people work for someone else.”
Power, therefore, no longer resides at the top of the value chain, but at the center of networks. That’s why collaboration is becoming a new source of competitive advantage. Today, the best way to become a dominant player is to become an indispensable partner. Nobody, no matter what assets they control, can afford to go it alone anymore.
So this new era of platforms offers great opportunities, but also great challenges. We now need to design our organizations for agility, empathy and interconnectedness, rather than for scale, dominance and efficiency.
This article has been adapted from my book, Mapping Innovation
There’s a story told of the theoretical physicist Wolfgang Pauli that a friend showed him the paper of a young physicist that he suspected was not very good but on which he wanted Pauli’s views. Pauli remarked sadly “It is not even wrong”. For a theory even to be wrong, it must be predictive and testable and falsifiable. If it cannot be falsified – if it does not make some prediction that could in theory be tested and proven false – then it does not count as science.
I’ve always liked this quote in its own right, but it’s also very relevant to talking about new technology and the way that people tend to dismiss and defend it. For as long as people have been creating technology, people have been saying it’ll never amount to anything. As we create more and more – as ‘software eats the world’, the urge to dismiss seems only to get stronger, and so does the urge to defend. However, these conversations tend to follow a fairly predictable sequence, and quickly become unhelpful:
- That’s just a toy
- Successful things often started out looking like toys
- That’s just survivor bias – this one really is a toy
- You can’t know that
- So tech is just a lottery?
The problem with both of these lines of argument is that they have no predictive value. It is unquestionably true that many of the most important technology advances looked like toys at first – the web, mobile phones, PCs, aircraft, cars and even hot and cold running water at one stage looked like faddish toys for the rich or the young. Even video games, which literally are toys, are also largely responsible for the GPUs that now power the take-off of machine learning. But it’s also unquestionably true that there were always lots of things that looked like toys and never did become anything more. So how do we tell? Is it that ‘toys’ occasionally turn into something else through some unpredictable chance? Do we throw up our hands and shrug? William Goldman famously said of Hollywood “Nobody knows anything”, but that feels like an abdication of reason and judgement. We should try to do better.
So, what do we mean when we say that some new piece of technology is a toy? It seems to me that there are two parts to this: either it doesn’t work, or it won’t matter even if it does work. On the one hand, it cannot do what it is supposed to do because it is incomplete, impractical or expensive, and on the other, even if it does work no-one will want it, or, perhaps, even if they do it won’t matter. These are all effectively assertions that nothing will change: the product won’t change, or people’s behaviour won’t change, or the things that are important won’t change.
How can we predict whether something will change?
Let’s start with whether it can work. Imagine if you had seen the Wright Brothers’ Flyer in 1903. It was small and flimsy, and it could only carry a single person a few hundred meters. But it was a theoretical breakthrough, and it was entirely clear that it could be expanded upon to get to something that could carry several people several hundred miles, and perhaps more. Blériot flew across the English Channel just 6 years later. Move from wood and fabric to aluminum, and make more and bigger engines, and there was a clear roadmap to the Dakota and the Lancaster. You could have plotted the next couple of decades, and indeed people did. It was plenty of work to get from the Flyer to the Constellation, but there was no barrrier of principle to cross.
There did come a point at which piston engines could be taken no further and we needed something else – you could not use them to build a 707, let alone a Concorde. That something else turned out to be jets, and you might not have predicted jets in 1903 (though ships already had turbines). Jets delivered the power and efficiency to create mass air transport, and make flight truly cheap as well as practical. But the breakthough of 1903 was enough to take us forward for decades.
The first car phones appeared in the late 1940s, and were deployed across many cities in many countries. Here, though, there were two fundamental problems: there was no roadmap to get them from something that filled the trunk of a car to something that you could carry, and there was no roadmap to use spectrum in ways that allowed millions of simultaneous calls in a city instead of dozens of calls. These combined also meant that the product was extremely expensive. Solving the first of these, to minaturise the device, needed the entire computer revolution to happen (and this had barely started), and solving the second needed the theoretical breakthrough of cellular itself and successive theoretical breakthroughs around multiplexing (TDMA and CDMA*).
Unlike flight in 1903, ‘mobile’ phones in 1947 had no path to improvement that anyone could start working on in 1948. There were fundamental barriers that could not then be overcome, and no amount of iteration on what you already had could get you from there to a world of $5 phones and 5bn mobile users. In 1947, mobile phones were a toy. It was only thirty years later that we had enough of the necessary breakthroughs: we had the concepts of spectrum use and we had the computer industry that could implement those concepts in silicon. It was in the 1970s, no the 1940s, that mobile phones reached the Flyer stage, and the history of mobile since then looks much more like the history of flight since 1903. In 1947 there was no roadmap to make mobile phones more than toys – in 1977 there was a roadmap.
Mobile phones in the 1940s may have been toys, but there was at least some sense that at some point in several decades’ time it might be possible to make something useful with the same basic principles – radio, plus a microphone and speaker, plus a phone number. That isn’t always true. In 1960, rocket packs looked just as limited and impractical as the Wright Flyer in 1903. Indeed, just like the Flyer, they could carry one person a few hundred meters and nothing more. The crucial difference was that the Wright Flyer was a breakthough of principle that could then be expanded upon and the rocket pack was not: it could not be expanded. It flew for only 21 seconds because that was how much fuel you could carry, and there was no roadmap of iteration and improvement to change that (well, not much: the subsequent 60 years have improved this to 30 seconds). To get more range you need more fuel, but then you weigh more and so need more fuel again, and there is no amount of iteration that can solve that – you need some new and discontinuous technology. Rocket packs look more like hot air balloons in 1783 than they do the Wright Flyer – they’re not the first step on a journey.
The question, then, is not whether something works now but whether it could work – whether you know how to change it. Saying ‘it doesn’t work, today’ has no value, but saying ‘yes, but everything didn’t work once’ also has no value. Rather, do you have a roadmap? Do you know what to do next?
- The Wright Flyer looked like a toy but was in fact a breakthrough in flight with a clear roadmap that was easy to follow for it to become something huge almost immediately. Then we needed another breakthrough, around jets, to get to cheap mass air travel in the second half of the century.
- Mobile phones in 1947 had no roadmap to become a mass-market product, but mobile phones in 1975 or 1980 absolutely had such a roadmap, with a path to make them cheap and universal.
- Rocket packs have remained a toy and we have never had any roadmap for making them anything more.
Bringing this back to 2017, I’ve suggested elsewhere that voice interfaces do not have a roadmap to become universal computer interfaces or platforms. Machine learning now means that speech recognition can accurately transcribe the sound of someone speaking into text and that natural language processing can turn that text into a structured query – that’s one breakthrough. But you still need somewhere to send the query, and it is not clear that we have any roadmap to a system that can give a structured answer to any query that any person can pose, rather than just dumping you out to a keyword search of the web. To even start making voice interfaces useful for general purpose computing rather than for niches, I would suggest that we would need general AI, which is (at best) a few decades away.
I don’t think that anyone believes that if we had general AI, it would be a toy – indeed it’s more likely that it would think that we were a toy. But there are plenty of other important technologies that were dismissed on the grounds that even if they did work, they’d be useless. If you can analyse whether a technology has a way to become something that works, can you also analyse whether it has a way to become something anyone would want?
First of all, it’s quite common, especially in enterprise technology, for something to propose a new way to solve an existing problem. It can’t be used to solve the problem in the old way, so ‘it doesn’t work’, and proposes a new way, and so ‘no-one will want that’. This is how generational shifts work – first you try to force the new tool to fit the old workflow, and then the new tool creates a new workflow. Both parts are painful and full of denial, but the new model is ultimately much better than the old. The example I often give here is of a VP of Something or Other in a big company who every month downloads data from an internal system into a CSV, imports that into Excel and makes charts, pastes the charts into PowerPoint and makes slides and bullets, and then emails the PPT to 20 people. Tell this person that they could switch to Google Docs and they’ll laugh at you; tell them that they could do it on an iPad and they’ll fall off their chair laughing. But really, that monthly PowerPoint status report should be a live SaaS dashboard that’s always up-to-date, machine learning should trigger alerts for any unexpected and important changes, and the 10 meg email should be a Slack channel. Now ask them again if they want an iPad.
In the enterprise, new technology tends to solve existing problems in new ways (or of course solve the new problems created by the new tech). In consumer products, it’s more common to seem to be proposing a change in human behaviour, and so in human desires. You may in some underlying way ‘really’ be replacing an existing behavior in a different way, as Word replaced typewriters and email replaced Word, but that line of reasoning can easily lead you to unfalsifiable assertions when you move up Maslow’s Hierarchy. ‘Millennials care less about driving because smartphones give them their freedom now’ certainly sounds good, but I have no idea how you could tell if it’s true, far less predict it. This is not a falsifiable analysis. All that you can hold in your hands is that you’re proposing a new human desire, and that’s a subjective view, not the objective analysis one could do of the roadmap for flight in 1903 – worse, it requires a change in your subjective view. You don’t think that you want to listen to music walking down the street, and you don’t think that you want to be able to call anyone from anywhere you might be. The argument for progress here is effectively false consciousness – ‘you think you don’t want this, but you are wrong, and one day you will realise the truth of your own feelings’. But you can’t ever know this – again, you can’t falsify it.
One way to solve this problem is to try to separate the fundamental capability that’s being proposed from the specific uses. Edison thought that sound recording would be good for sermons, not music, and it’s hard, and perhaps impossible, to tell what people will use the new thing for. But sound recording and one-to-one and one-to-many sound transmission were much more fundamental changes than the ability to listen to a sermon on demand. What mattered was seeing the value of the capability, not predicting any particular applications. The mistake to make in looking at Edison’s recording technology would have been to argue about whether people wanted sermons – the mistake is to look only at the application that this technology is proposed to provide, and not the actual capability that has been created. Sermons might not work**, but sound is a big deal.
You can see a more recent example of this mistake in the video below: “mobile phones are better payphones that are useful for people who travel a lot”. If you focused on the application rather than the capability in this way, you’d have thought that the mobile opportunity was, say, 25% of the population of rich countries and that no-one else would want one, whereas in fact 99% of the adult population of Earth will have a mobile phone in the next couple of years.
Where Cellnet missed it, Orange got much closer to the actual capability: the future is wire-free. Why is your phone tied to the wall of a particular room with a piece of wire? Cellnet was guessing about applications while Orange talked about the breakthrough.
To give one more example, in 2000, it seemed as though the only question any telecoms investor ever asked was ‘what’s the killer app for 3G?’. It turned out that the killer app for having the internet in your pocket was having the internet in your pocket: a general technology breakthrough matters not because of a particular application that it enables but because of all and any of them. I had little idea of the specific ways you’d use your phone to access all the world’s information and share stuff with your friends, but it was a safe bet you’d want to do it somehow.
So, the use cases are subjective, but the capability is objective, and it’s the capability that matters. Really, the new technologies that matter give us superpowers. Is that what we’ve made this time? Electricity is a superpower, and so are cars, and flight, and mobile. I can rub my watch and tell the djinn that lives inside to summon a car, and there’ll be one waiting at the door. We can hear, or see, or travel, in ways we could not do before. Where we go and what we listen to are secondary questions. You can’t necessarily predict the applications, but you can predict that people will like having a new superpower. What you do with your superpower is up to you.
Returning to Pauli, the test throughout this post is falsifiability and predictive power. “That is a toy’, ‘everything looks like a toy’, ‘no-one will want that’ and ‘no-one wanted phones either’, paradoxically, are statements that are both completely true and ‘not even wrong’: you cannot use them as a test for anything. They have no predictive power. Of course, asking whether there is a technology roadmap, or whether this is a superpower, are analytic projects that might get you to the wrong answer. But they do give you a roadmap to understanding what might happen.
* Though Hedy Lamarr proposed some of the technology in CDMA during WW2
** Though see the importance of cassette tapes of sermons in pre-revolutionary Iran
Two-thirds (64 percent) of organizations are adapting their technology strategies in the midst of unprecedented global political and economic uncertainty, the survey found.
More than half of the respondents (52 percent) said they are investing in more nimble technology platforms. It is clear digital strategies have infiltrated businesses across the globe at an entirely new level. The proportion of organizations surveyed that now have enterprise-wide digital strategies increased 52 percent in just two years, and organizations with a chief digital officer have increased 39 percent over last year.
“From an organizational and cultural perspective, the CIO is now faced with a full transformation to digital, enterprise-wide,” said Harvey Nash president and CEO Bob Miano in a statement. “Digital is without question the CIO’s priority, but especially for legacy organizations, leading this change to a complete, unified digital strategy is top of mind. CIOs are responding by tackling this head-on with innovation and agility.”
To deal with that change, companies are increasing their demand for enterprise architects — the fastest growing technology skill this year, up 26 percent compared to 2016.
Cybersecurity vulnerability — as demonstrated by the latest ransomware case — is at an all-time high, with a third of IT leaders (32 percent) reporting their organization had been subject to a major cyberattack in the past 24 months — a 45 percent increase from 2013. Only one in five (21 percent) say they are “very well” prepared to respond to these attacks, down from 29 percent in 2014.
Despite recent headline-grabbing cyberattacks, the biggest jump in threats comes from insider attacks, increasing from 40 percent to 47 percent over last year.
“In order to stay ahead of the unprecedented levels of disruption and change facing today’s CIOs, they have needed to become more strategic and functionally integrated,” said Denis Berry, KPMG principal and U.S. CIO advisory leader, in a statement. “Today’s technology executives need to understand how business models impact their organization’s infrastructure — not only from a technology standpoint but from an economic, social, political, and regulatory one as well, especially in order to stay nimble, adapt to an uncertain climate, and truly discover where the opportunities are.”
The survey is in its 19th year, and the company says it is the largest IT leadership survey in the world. The survey was conducted from December 19, 2016, to April 3, 2017, across 86 countries; 4,498 CIOs and tech leaders responded.
Other findings: Digital leadership has changed. Almost one in five CIOs (18 percent) report their organizations have “very effective” digital strategies.
CIOs at these digitally enabled organizations are almost twice as likely to be leading innovation across the business (41 percent versus 23 percent), and are investing at four times the rate of non-leaders in cognitive automation (25 percent versus 7 percent).
Overall, the survey found almost two-thirds (61 percent) of CIOs from larger organizations are already investing or planning to invest in digital labor.
CIOs love their jobs, and are more likely to be involved at the board level. The number of CIOs who are “very fulfilled” in their role is at a three-year high — rising from 33 percent in 2015 to 39 percent this year. For the first time in a decade, more than seven in 10 CIOs (71 percent) believe the CIO role is becoming more strategic.
Ninety-two percent of CIOs joined a board meeting in the past 12 months. However, the average CIO lifespan is just five years or less (59 percent), although many want to stay longer.
Marketing innovation and creative thinking often come from the most unexpected places. When I worked in management consulting, my colleagues took on some of the world’s most challenging economic problems. What avenues are open to boutique hotel owners in Jamaica trying to compete with international chains? How can animation studios as far afield as Macedonia acquire international business? How would a fast-growing internet industry change the playing field for entrepreneurs in East Africa?
The answers to these challenges were not, as I initially suspected, buried in tomes about these specific disciplines. Instead, marketing breakthroughs occurred when we looked at how change happened in other domains and experimented with those ideas in a new context. It was here that I first learned to see that real innovation and big ideas happened when you brought together inspiration from different fields. Where does this nexus of innovation happen in content strategy, creativity, and marketing? And how can today’s talent push beyond the boundaries of what they already know to develop more interesting, engaging campaigns that challenge the obvious approach and generate real results?
Image attribution: Breather
Catalytic Questioning Through the Lens of Other Domains
Sometimes the process starts with asking the right questions. Hal Gregersen is a thought leader on the intersection of leadership and innovation. One of his approaches for stimulating innovation is the idea of catalytic questioning. He writes that leaders are often shielded from the information they most need in order to innovate, and by systematically engaging in catalytic questioning (an alternative to brainstorming), you can break through to big ideas: “The process simply allows one to concentrate on a problem—no matter how big or small—and examine alternative vantage points so they can arrive at a new and innovative solution.” He encourages leaders to find a writing surface, gather their team, center on a problem, and then start asking questions. Aim for 50, at least.
The key, of course, is how to ask good questions. One way to approach the process is to break your questions into three sections:
- Think through the lens of your field. How would people with your specific background or expertise approach the problem? What questions would they ask? Follow this train of thought as far as possible to see what unique advantages your perspective could give you.
- Ask questions through a general business lens. What questions would people ask if they were coming at this challenge through the perspective of finance, sales, or customer service, for example?
- Shift your perspective even further. What questions would people ask about this if their specialty was in a completely unrelated field, like biology, computer science, or economics? An easy way to do this is to find someone with that expertise and ask her what kinds of questions she’d ask. Another is to find research frameworks and general methodologies for different disciplines and adapt these questions to the challenge you’re trying to solve.
When you ask the right question, unexpected insights and inspiration can emerge.
Image attribution: Redd Angelo
Marketing Innovation and the Collective Brain
Bringing in insights from different domains is one of the easiest ways to tap into what researchers have called “the collective brain.” Research shows that innovation is rarely the product of a single individual or idea. Instead, it’s the confluence of the activities, thoughts, and efforts of multiple people. As Katie Dowbiggin and Michael Muthukrishna said, “Ideas flow in these collective brains, much like neurons fire in our individual brains. We see multiple ‘inventors’ of the same idea, because if the historical, cultural, and conceptual conditions exist in the collective brain for an invention to emerge, inevitably there will be multiple individuals at the nexus of these conditions. Or to put it another way, innovations don’t rely on a particular innovator any more than your thoughts rely on a particular neuron.”
One of the most important insights the authors offer is how to stimulate innovation: connect previously unconnected ideas. They use the example of Darwin decoding evolution as he traveled the world and studied a variety of inputs. As marketers, writers, and content strategists, it’s our job to tap into the collective brain and stimulate innovation. We have to push beyond the bubbles of our disciplines, read more widely than our industry, and understand the best practices in problem solving and connecting with audiences from different industries. For example:
- What can screenwriters teach you about writing a compelling story audiences will love?
- Could psychology help you more effectively connect with the emotions of your audience?
- Does an understanding of evolutionary biology give you perspective on your audience’s fears and desires?
- What would a deep dive into data analysis show in the hidden patterns that fuel your creativity?
- If you studied the techniques of an industry that’s had success in digital marketing, like B2B technology, could you apply them to a different industry like agribusiness or manufacturing?
Here are practical ways to find inspiration in other fields to push your creative thinking and help you innovate in unexpected ways.
Approach a Problem With a Tactic From Another Field
Participatory observation is a technique I learned studying anthropology and archeology. One way we made sense of what we saw in the field was by using a technique called card sorting. You write down every impression, detail, insight, and observation that seems important. Then you sit down with a fresh eye and “bucket” similar things together. It helps researchers understand how individuals cognitively understand different categories.
The same technique has been adapted to user experience (UX design). In a similar way, explore how an expert in a different field might attack a problem you’re facing. For example, could thinking like a medical researcher and starting your content strategy with a comprehensive literature review give you a different perspective than diving right in?
Learn to Listen and Read for Unexpected Insights by Widening Your Pool
As we get more established and go deeper into our field, our focus often narrows and our expertise gets deeper. In the marketing field, it’s easy to spend your spare time reading the latest business books, keeping up with marketing blogs, and scanning issues of the Harvard Business Review. While you can glean great ideas this way—and keeping up with your field will put you ahead of many of your peers—there are two weak spots:
- The top thinkers in your field are drawing from the same pool of ideas and intellectual predecessors.
- Your competition is most likely reading (and being inspired by) the same pool of limited material.
Instead, breakthrough ideas are more likely to come when you push the borders of your reading. For example, if you love books about marketing, explore adjacent ideas by reading books on innovation or entrepreneurship. Books that integrate ideas from multiple domains to solve a problem can inspire you to think differently.
Payoff: The Hidden Logic That Shapes Our Motivations by Dan Ariely takes a broad look at the topic of human motivation. Adam Grant and Sheryl Sandburg teamed up to write Option B: Facing Adversity, Building Resilience. What these, and many other great books, have in common is that they draw from sociology, history, business, economics, pop culture, and many other places to make a strong argument about a particular topic. For marketers, you can achieve the same by reading popular books outside your field, exploring the blogs and articles that have leaders in different fields buzzing right now, and taking a deep dive into how different disciplines talk about a specific issue. For example, you can find everyone from wildlife biologists to hospital administrators talking about the challenges of leadership, offering wildly different examples and perspectives, often unified by common themes.
Cast a Wider Net for Your Team
If you’re working on a team with others, consider bringing in people with different expertise. For example, many companies have learned that pairing a writer, a designer, and a data expert can result in a much stronger story than a writer working alone. Could a content strategy or marketing campaign be richer if it incorporated perspectives from around the company or looked at completely different underlying factors such as psychology, digital marketing technique, copywriting, and systems thinking? Be bold about imagining how different types of expertise could lead to more creative thinking and, by extension, marketing innovation and transformation.
Fostering real innovation is a challenge; as marketers, our goal is often just to improve by incremental percentages and keep the momentum going forward. But when you’re aiming for real marketing transformation and breakthrough creative thinking, you can’t let yourself get trapped in the box of your own domain. Break down the walls by seeking out the ways that other disciplines ask questions, solve problems, and look at issues. Read widely, find diverse thought partners, and think about the ways you can bring insights from one domain into another. The end result may be a campaign that takes your work in completely unexpected directions.
Featured image attribution: Shannon Kelley
The post Marketing Innovation Happens at the Nexus of Different Domains appeared first on The Content Standard by Skyword.
Jessica Methot, an associate professor of human resource management at Rutgers University, studies these relationships, which most of other researchers overlook. She has found that this large, bland body of colleagues we probably can’t name (and who can’t name us) in fact plays a crucial role in innovation and productivity.
“Asking acquaintances for input is likely to elicit a broader and more diverse set of perspectives” than talking to friends alone, Methot says. This makes sense: the people you’ve chosen as your friends are more likely to think the way you do, and less likely to spot what you’ve overlooked. We also may be more inclined to experiment with new ideas around people we don’t know that well.”