What you know is only half of innovating. The other half is the new knowledge you can and must create.
After years of helping others generate ideas, I am now humbly aware that idea generation is less likely innovation’s critical path, despite what many assume. Ideas—particularly ones that deserve serious attention—are more likely outcomes, not the input, driving successful innovations. Peter Drucker alluded to this over 25 years ago when he declared in Innovation and Entrepreneurship that the bright idea is the least reliable source of innovation.
Innovation’s more likely critical path is knowledge-creation—what most of us call learning. Strong ideas come from knowledge-creation. Weak ones eventually reveal some form of knowledge omission or commission, especially about what is valuable to the customer. When R&D-based innovations break through an otherwise “me-too” crowd of look-alike products, it is not because someone made a lucky guess. It is because some new learning occurred, some new observation was made, some knowledge was created, and correspondingly, something new and valuable was conceived.
What most see in successful innovation are outcomes. However, the paths leading up to these outcomes are filled with:
• experience and experiments (physical, virtual, mathematical) that create data,
• interpretations of data that create information, and
• applications of the information, successful or not, that create understanding of what works and what doesn't, and why...and that, in turn, develops knowledge.
These knowledge-creation paths twist and turn and cycle back, far from the mythical straight line hindsight suggests—more like a “Slinky” than a taught string.
R&D organizations are filled with knowledgeable experts—those who understand not only what works, but why. However, it is not simply what experts know that creates value. It’s what experts do with what they know that leads to value creation—the critical path for innovating.
The irony of the expert’s knowledge is that it can be the very thing that blocks learning, especially about the customers’ value ecosystem. It has often been said, “all value is derived from context.” If true, then to create new knowledge the expert must interpret the context (the ecosystem) in which the customer lives. That is where new value emerges and where innovation is nourished.
Identifying where gaps are in our knowledge, especially of what’s meaningful and valuable to customers, may be where the critical path begins for the knowledge creators in R&D. Selecting and describing gaps in our knowledge may be one of the more reliable ways of targeting where we should focus our efforts. Successful innovations always seem to emerge from that kind of knowledge-creation.
What you know is only half of it.
This article was originally published in Innovating Perspectives in September 2011. For this and other back issues of our newsletter, please visit our website at innovationsthatwork.com or call (415) 387-1270.
Tuesday, December 27, 2011
Tuesday, December 20, 2011
Reducing “Waste” from Innovating
“We already know we need to keep innovating. What we don’t know is how to do it more cost effectively.”
A client and colleague said this in a recent phone conversation. He had just returned from giving some bad news to an outside patent counsel regarding the need to curtail their services. It was one of many similar conversations he has had in the past several months. Absorbing reactions was getting to be a real drag. His remark–understandable given the economic “reset” most are experiencing–caught my ear.
Just what are the costs, hidden and otherwise, of innovating? Certainly there is cash. Perhaps more “costly,” however, are the time and attention of our more experienced innovators and inventors. These costs are felt even more acutely with fierce internal competition for the time and attention of these veterans.
Innovating costs, of course, must be weighed against risks of not innovating. Opportunity costs. Most innovation portfolios are designed to hedge against opportunity costs through some form of diversification. Given the risks associated with innovating, the conventional wisdom goes, if several options are kept open, risk can be spread across multiple, simultaneous efforts.
There are implicit costs associated with spreading risk, however. When too few innovators are chasing too many opportunities, delays, interruptions, divided attention and diffused effort is the result. Innovating waste. These two words don’t normally appear so close together (not to be confused with innovations from waste, which is a well-mapped territory.)
Most conversations about innovation and its management concern themselves with effectiveness. Few address reducing waste, partly because waste from innovating may be more difficult to pinpoint. Identification and elimination of waste can and should be a part of the conversation about innovating.
Without a coherent diagnosis of innovating waste, managers are left with little alternative but to treat symptoms rather than causes. And if Ikujiro Nonaka and Hirotaka Takeuchi (The Knowledge Creating Company, Oxford, 1995) are correct–it is not merely what a company knows which creates wealth, but its ability to create new knowledge where it matters most–then relearning and reinvention may be the most costly form of innovating waste.
In operations and production environments what flows are standard units. In an assembly process, specified parts go in, and what comes out are multiples of a standard, assembled product. In a continuous process, specified ingredients or raw materials go in, and predictable material in an expected form comes out. It is appropriate to seek and possible to approximate, a repeatable process. This is the world where scale matters.
In innovating environments, however, what flows is not standard, nor fixed. What flows also develops, morphs and grows. When we fill our pipelines (or funnels) so full, we do not leave room for development, morphing or growth. We defeat the very thing we are trying to achieve, largely because we are hedging our bets and spreading our risk. This is the world where scope matters and repeatable process may be a misguided quest.
A client and colleague said this in a recent phone conversation. He had just returned from giving some bad news to an outside patent counsel regarding the need to curtail their services. It was one of many similar conversations he has had in the past several months. Absorbing reactions was getting to be a real drag. His remark–understandable given the economic “reset” most are experiencing–caught my ear.
Just what are the costs, hidden and otherwise, of innovating? Certainly there is cash. Perhaps more “costly,” however, are the time and attention of our more experienced innovators and inventors. These costs are felt even more acutely with fierce internal competition for the time and attention of these veterans.
Innovating costs, of course, must be weighed against risks of not innovating. Opportunity costs. Most innovation portfolios are designed to hedge against opportunity costs through some form of diversification. Given the risks associated with innovating, the conventional wisdom goes, if several options are kept open, risk can be spread across multiple, simultaneous efforts.
There are implicit costs associated with spreading risk, however. When too few innovators are chasing too many opportunities, delays, interruptions, divided attention and diffused effort is the result. Innovating waste. These two words don’t normally appear so close together (not to be confused with innovations from waste, which is a well-mapped territory.)
Most conversations about innovation and its management concern themselves with effectiveness. Few address reducing waste, partly because waste from innovating may be more difficult to pinpoint. Identification and elimination of waste can and should be a part of the conversation about innovating.
Without a coherent diagnosis of innovating waste, managers are left with little alternative but to treat symptoms rather than causes. And if Ikujiro Nonaka and Hirotaka Takeuchi (The Knowledge Creating Company, Oxford, 1995) are correct–it is not merely what a company knows which creates wealth, but its ability to create new knowledge where it matters most–then relearning and reinvention may be the most costly form of innovating waste.
In operations and production environments what flows are standard units. In an assembly process, specified parts go in, and what comes out are multiples of a standard, assembled product. In a continuous process, specified ingredients or raw materials go in, and predictable material in an expected form comes out. It is appropriate to seek and possible to approximate, a repeatable process. This is the world where scale matters.
In innovating environments, however, what flows is not standard, nor fixed. What flows also develops, morphs and grows. When we fill our pipelines (or funnels) so full, we do not leave room for development, morphing or growth. We defeat the very thing we are trying to achieve, largely because we are hedging our bets and spreading our risk. This is the world where scope matters and repeatable process may be a misguided quest.
Scope requires making difficult choices. Healthy choosing requires having viable options to choose from. Keeping options open may mean no choice has been made. Perhaps there is dissatisfaction with the option sets being presented. Perhaps there is insufficient clarity about where innovations are needed in the ecosystem, and why. Perhaps keeping options fresh is better than keeping them open.
Innovation Trustee
One way to reduce the cost and waste so often associated with innovating–real and perceived–is to have a mechanism that regularly creates better sets of options. This will improve the quality of the choices made. However, when these mechanisms on the front end start, stop and then start up again, delays, relearning and reinvention are the result.
Generating (or percolating) option sets is necessary, but not sufficient. Someone (or a few) must make a selection when a set is presented. You can hedge bets and spread risk. But as we all know, hedges and spreads don’t solve, resolve, or eliminate risk. Someone still has to make a selection. Waste is generated when we are not clear who the trustee is, when there are too many of them, and when the trustee lacks a clear understanding of the context (i.e., future ecosystem emerging).
One diagnostic principle of systems theory and systems engineering–that a system has a purpose that governs it–invites us to ask what and who is governing the company’s innovating system.
While there is often plenty of attention being paid to the management of innovating, often there is an absence of governance. Perhaps the innovating governors (sponsors) might take on the responsibility not only of selecting what to work on next, but also reducing the waste that comes from attempts to reduce risk.
Instead of filling development pipelines with many options, a less wasteful approach might be to see to it that options are continually generated and continually pruned. Keeping track of what is being learned–the knowledge created–is likely a key lever in avoiding innovating waste to begin with.
________________
This article was originally published in Innovating Perspectives in November 2011. For this and other back issues of our newsletter, please visit our website at innovationsthatwork.com or call (415) 387-1270.
Innovation Trustee
One way to reduce the cost and waste so often associated with innovating–real and perceived–is to have a mechanism that regularly creates better sets of options. This will improve the quality of the choices made. However, when these mechanisms on the front end start, stop and then start up again, delays, relearning and reinvention are the result.
Generating (or percolating) option sets is necessary, but not sufficient. Someone (or a few) must make a selection when a set is presented. You can hedge bets and spread risk. But as we all know, hedges and spreads don’t solve, resolve, or eliminate risk. Someone still has to make a selection. Waste is generated when we are not clear who the trustee is, when there are too many of them, and when the trustee lacks a clear understanding of the context (i.e., future ecosystem emerging).
One diagnostic principle of systems theory and systems engineering–that a system has a purpose that governs it–invites us to ask what and who is governing the company’s innovating system.
While there is often plenty of attention being paid to the management of innovating, often there is an absence of governance. Perhaps the innovating governors (sponsors) might take on the responsibility not only of selecting what to work on next, but also reducing the waste that comes from attempts to reduce risk.
Instead of filling development pipelines with many options, a less wasteful approach might be to see to it that options are continually generated and continually pruned. Keeping track of what is being learned–the knowledge created–is likely a key lever in avoiding innovating waste to begin with.
________________
This article was originally published in Innovating Perspectives in November 2011. For this and other back issues of our newsletter, please visit our website at innovationsthatwork.com or call (415) 387-1270.
Tuesday, December 13, 2011
Measuring Process or Making Progress?
I have a confession to make. An uneasy feeling rises up in me whenever I am in or near discussions to define measurements for use during the innovating process.
Measuring innovation after the fact (e.g., the classic X% of revenues or profit coming from products that the company was not making three years earlier, etc.) is certainly more reasonable than trying to measure innovating while you’re in the middle of it. Time-to-market, time-to-positive cash flow, or to time-to-break even, get close. But even these metrics are more hindsight than “now-sight.”
When you are in the midst of innovating—whether discovering a consumer pain, charting the topography surrounding a customer’s unmet need, inventing solutions, reducing a solution to practice, or iterating with inevitable adjustments—it is very difficult to find any objective metric that enables sponsors, managers or innovators themselves to avoid using their intuitive judgment.
It turns out, I am not alone in this uneasy feeling. One of my clients devoted considerable effort in developing just such a metric system for monitoring their in-process innovation portfolio. After several years, their enthusiasm for its usefulness has faded.
The need to monitor and measure is there for sure. But the ability to fill the need may be out of reach. Outputs can be measured. Inputs can certainly be measured as well. However, what may be impossible to measure is the “throughput” of content which is by definition “developing.” This content is forming and reforming into a prototypical instantiation, the ultimate value and success of which can only be determined when it enters the market. The more generative or creative the process, perhaps, the more difficult it is to measure and therefore manage—at least for those who ascribe to the management mantra of “you can only manage what you can measure.”
What happens in our desire to manage during the innovating process is that we measure what we can (the number of ideas, the nodes in our discovery networks, etc.). But just because something can be measured doesn’t mean that the resulting measure is important or useful. As Albert Einstein said, “Everything that can be counted does not necessarily count; and everything that counts cannot necessarily be counted.”
This difficulty reflects some misplaced expectations that we can actually manage innovation in a fashion similar to the way we manage operating or production systems—systems that have produced data and left tracks that can be measured. Innovation systems are different in that what they produce—innovations—have produced little if any data that can be tracked until they have entered the market. And there is much about innovating that happens before that entrance.
At a recent gathering of veteran innovators in Denver in October, one of the participants, Andrew Zander, a veteran director of engineering of many instrument innovation efforts, said something simple, but very profound. My paraphrase, with apologies to Andy if I got it wrong, is as follows:
“We shouldn’t confuse process with progress when it comes to innovating. We can’t measure process (in any meaningful way), which may not be that important to measure in the end anyway. What is important, however, is to make progress, which we will not be able to determine until, at various points in time, we stop and assess what we have learned, determine where we are, and clarify what our next steps should be. When we compare these points, then we can determine whether we are making progress or not. Only when we have determined whether progress is being made or not, are we really in any position to make decisions.”
Perhaps the key point for innovators, sponsors and innovation midwives is to let go of our quixotic quests for innovation metrics and get down to the business of making and assessing progress, not process. Then we will know better what decisions we need to make; and then, of course, we need to make them.
For measuring during the innovating process, perhaps Italian physicist Enrico Fermi said it best: “There are two possible outcomes: if the result confirms the hypothesis, then you’ve made a measurement. If the result is contrary to the hypothesis, then you’ve made a discovery.”
Measuring innovation after the fact (e.g., the classic X% of revenues or profit coming from products that the company was not making three years earlier, etc.) is certainly more reasonable than trying to measure innovating while you’re in the middle of it. Time-to-market, time-to-positive cash flow, or to time-to-break even, get close. But even these metrics are more hindsight than “now-sight.”
When you are in the midst of innovating—whether discovering a consumer pain, charting the topography surrounding a customer’s unmet need, inventing solutions, reducing a solution to practice, or iterating with inevitable adjustments—it is very difficult to find any objective metric that enables sponsors, managers or innovators themselves to avoid using their intuitive judgment.
It turns out, I am not alone in this uneasy feeling. One of my clients devoted considerable effort in developing just such a metric system for monitoring their in-process innovation portfolio. After several years, their enthusiasm for its usefulness has faded.
The need to monitor and measure is there for sure. But the ability to fill the need may be out of reach. Outputs can be measured. Inputs can certainly be measured as well. However, what may be impossible to measure is the “throughput” of content which is by definition “developing.” This content is forming and reforming into a prototypical instantiation, the ultimate value and success of which can only be determined when it enters the market. The more generative or creative the process, perhaps, the more difficult it is to measure and therefore manage—at least for those who ascribe to the management mantra of “you can only manage what you can measure.”
What happens in our desire to manage during the innovating process is that we measure what we can (the number of ideas, the nodes in our discovery networks, etc.). But just because something can be measured doesn’t mean that the resulting measure is important or useful. As Albert Einstein said, “Everything that can be counted does not necessarily count; and everything that counts cannot necessarily be counted.”
This difficulty reflects some misplaced expectations that we can actually manage innovation in a fashion similar to the way we manage operating or production systems—systems that have produced data and left tracks that can be measured. Innovation systems are different in that what they produce—innovations—have produced little if any data that can be tracked until they have entered the market. And there is much about innovating that happens before that entrance.
At a recent gathering of veteran innovators in Denver in October, one of the participants, Andrew Zander, a veteran director of engineering of many instrument innovation efforts, said something simple, but very profound. My paraphrase, with apologies to Andy if I got it wrong, is as follows:
“We shouldn’t confuse process with progress when it comes to innovating. We can’t measure process (in any meaningful way), which may not be that important to measure in the end anyway. What is important, however, is to make progress, which we will not be able to determine until, at various points in time, we stop and assess what we have learned, determine where we are, and clarify what our next steps should be. When we compare these points, then we can determine whether we are making progress or not. Only when we have determined whether progress is being made or not, are we really in any position to make decisions.”
Perhaps the key point for innovators, sponsors and innovation midwives is to let go of our quixotic quests for innovation metrics and get down to the business of making and assessing progress, not process. Then we will know better what decisions we need to make; and then, of course, we need to make them.
For measuring during the innovating process, perhaps Italian physicist Enrico Fermi said it best: “There are two possible outcomes: if the result confirms the hypothesis, then you’ve made a measurement. If the result is contrary to the hypothesis, then you’ve made a discovery.”
This article was originally published in Innovating Perspectives in November 2010. For this and other back issues of our newsletter, please visit our website at innovationsthatwork.com or call (415) 387-1270.
Tuesday, December 6, 2011
Earning Follows Learning
Just recently I heard several conversations suggesting a potentially troubling misperception about innovating and learning. One senior executive at a major manufacturer said to a subordinate about an innovation effort underway, “We pay you for what you know, not for what you can learn.” Another executive said, “We don’t train people anymore. We hire those with the know-how and expect them to bring it to work everyday.” A third executive, a veteran software architect, was bemoaning several incidences of places on product development road maps where it says “acquire” the sub-system, ignoring the fact that acquisition does not eliminate integration, something for which learning is required.
In these three snapshots, each from a different context, the intent of the company’s senior leadership may have been to emphasize urgency and speed of execution. However, the way the messages were expressed reveals a troubling perception: that productive work derives more from what is already known than from the ability to learn. Just the opposite is closer to the truth.
In the late 1990s, Ikujiro Nonaka and Hirotaka Takeuchi’s observed that it’s not what a company knows that creates wealth, but its ability to create new knowledge, which was introduced in their book The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Shortly after, Al Ward—one of the more insightful commentators on Toyota’s Development System—defined innovation itself as “learning applied to creating value.” Then Arie de Geus, completing his 38 year career in Royal Dutch/Shell’s celebrated scenario planning group, coined the phrase “the learning organization,” where he said learning capability is the primary differentiating factor in a company’s longevity in his book, The Living Company: Habits for Survival in a Turbulent Business Environment (1997). One of the more serious “learning disabilities” companies suffer from is learning what learning itself is and how basic it is for effective innovating.
Having facilitated a variety of brainstorming sessions for more that 30 years, I am convinced of the direct correlation between the quality of the ideas flowing and the presence of learning going on, at the same time. When there is learning going on among the people generating the ideas, the ideas are more original, fresh and interesting to those generating them. When there is scant learning going on among those generating ideas, the ideas are more conventional. While the quantity and quality of ideas typically receive the most attention in brainstorming exercises, the flow of learning going on among and between the diverse and relevant experts may be as, if not more, important. This tends to be one of the dissatisfying aspects of internet-mediated idea generation exchanges: the ideas are flowing, but interactive learning is missing.
Not all learning is of the same type.
Learning by searching (enabled by Google, Wikipedia and networking) is likely a necessary first step not only to determine what has been thought of or tried already, but also to discover who the experts are and what they are saying. Nonaka and Takeuchi refer to this as knowledge-creation that happens by connecting.
Another type is called learning by expressing. When one expresses what has impressed, the learning that occurs is similar to what is learned when we are put in a position to teach. Writing as a form of expression is a healthy and rigorous discipline that forces learning. Nonaka and Takeuchi refer to this as knowledge-creation that happens by articulating.
There is also learning by doing–gaining first hand knowledge through direct experience, as in lab or field experiments. Learning by doing requires both time and a safe, insulated space to gain this kind of experience, given that learning is greater from mistakes made and corrected than successes enjoyed. Nonaka and Takeuchi refer to this as knowledge-creation that happens by embodying or reducing something to practice.
Then there is learning by collaborative problem solving. Learning in this manner with diverse and relevant experts is an effective and efficient way of reflecting on experience and exploring possibilities that would not otherwise be imagined. Thinking things through—in what Einstein referred to as thought experiments—is an especially productive form of learning by problem solving. Nonaka and Takeuchi refer to this as knowledge-creation that happens by empathizing.
Innovating requires the presence and mix of all four types of learning. The first lends itself to exchanges of information and knowledge and is enabled by a network social architecture for connecting to others. The other three types of learning happen where face-to-face dialogue occurs. Thinking out loud together breeds understanding. The Institute for Research on Learning taught us that learning occurs in community (not to be confused with networks). In these kinds of “learning spaces”—what Nonaka calls “ba” or a trusted, safe space between diverse experts—trust and collaboration can build sufficiently to carry the creative tension required for original and inventive thinking.
Learning to learn may be the secret so often missed by those who are quick to pursue a clever idea or too easily seduced by a seemingly bright idea—something that Peter Drucker referred to as the least reliable source of innovation. When the four types of learning are present and balanced, the quality of ideas flowing in and through our innovating efforts can improve significantly.
In these three snapshots, each from a different context, the intent of the company’s senior leadership may have been to emphasize urgency and speed of execution. However, the way the messages were expressed reveals a troubling perception: that productive work derives more from what is already known than from the ability to learn. Just the opposite is closer to the truth.
In the late 1990s, Ikujiro Nonaka and Hirotaka Takeuchi’s observed that it’s not what a company knows that creates wealth, but its ability to create new knowledge, which was introduced in their book The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Shortly after, Al Ward—one of the more insightful commentators on Toyota’s Development System—defined innovation itself as “learning applied to creating value.” Then Arie de Geus, completing his 38 year career in Royal Dutch/Shell’s celebrated scenario planning group, coined the phrase “the learning organization,” where he said learning capability is the primary differentiating factor in a company’s longevity in his book, The Living Company: Habits for Survival in a Turbulent Business Environment (1997). One of the more serious “learning disabilities” companies suffer from is learning what learning itself is and how basic it is for effective innovating.
Having facilitated a variety of brainstorming sessions for more that 30 years, I am convinced of the direct correlation between the quality of the ideas flowing and the presence of learning going on, at the same time. When there is learning going on among the people generating the ideas, the ideas are more original, fresh and interesting to those generating them. When there is scant learning going on among those generating ideas, the ideas are more conventional. While the quantity and quality of ideas typically receive the most attention in brainstorming exercises, the flow of learning going on among and between the diverse and relevant experts may be as, if not more, important. This tends to be one of the dissatisfying aspects of internet-mediated idea generation exchanges: the ideas are flowing, but interactive learning is missing.
Not all learning is of the same type.
Learning by searching (enabled by Google, Wikipedia and networking) is likely a necessary first step not only to determine what has been thought of or tried already, but also to discover who the experts are and what they are saying. Nonaka and Takeuchi refer to this as knowledge-creation that happens by connecting.
Another type is called learning by expressing. When one expresses what has impressed, the learning that occurs is similar to what is learned when we are put in a position to teach. Writing as a form of expression is a healthy and rigorous discipline that forces learning. Nonaka and Takeuchi refer to this as knowledge-creation that happens by articulating.
There is also learning by doing–gaining first hand knowledge through direct experience, as in lab or field experiments. Learning by doing requires both time and a safe, insulated space to gain this kind of experience, given that learning is greater from mistakes made and corrected than successes enjoyed. Nonaka and Takeuchi refer to this as knowledge-creation that happens by embodying or reducing something to practice.
Then there is learning by collaborative problem solving. Learning in this manner with diverse and relevant experts is an effective and efficient way of reflecting on experience and exploring possibilities that would not otherwise be imagined. Thinking things through—in what Einstein referred to as thought experiments—is an especially productive form of learning by problem solving. Nonaka and Takeuchi refer to this as knowledge-creation that happens by empathizing.
Innovating requires the presence and mix of all four types of learning. The first lends itself to exchanges of information and knowledge and is enabled by a network social architecture for connecting to others. The other three types of learning happen where face-to-face dialogue occurs. Thinking out loud together breeds understanding. The Institute for Research on Learning taught us that learning occurs in community (not to be confused with networks). In these kinds of “learning spaces”—what Nonaka calls “ba” or a trusted, safe space between diverse experts—trust and collaboration can build sufficiently to carry the creative tension required for original and inventive thinking.
Learning to learn may be the secret so often missed by those who are quick to pursue a clever idea or too easily seduced by a seemingly bright idea—something that Peter Drucker referred to as the least reliable source of innovation. When the four types of learning are present and balanced, the quality of ideas flowing in and through our innovating efforts can improve significantly.
This article was originally published in Innovating Perspectives in March 2011. For this and other back issues of our newsletter, please visit our website at innovationsthatwork.com or call (415) 387-1270.
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