Five Uses for Strategy

I have been a strategist for a long time and worked on many strategy teams. I have developed strategies, written strategic plans, executed strategies and strategic plans, and assessed strategic execution and engagement. I enjoy reading and learning about strategy, strategy development, strategies in execution, and strategic decision making. I am fascinated by the articulation and critique of strategy by politicians, pundits, business and community leaders, and the various Departments and agencies from defense to State to Homeland Security. Yet, every strategic endeavor I have led, participated in, observed or felt the effects of has been challenged by a common problem. That problem, which often surfaces in the very first strategy session of any team, is how to define strategy. Attempts to define strategy can bring all progress to a standstill, as each individual and organization seems to have their own definition of strategy, one that often is revered as dogma by those charged with the development or execution of strategy. For that reason I am thankful to Mintzberg for providing an articulation of a range of purposes and definitions for strategy. While most of the time we think of strategy in terms of a plan, Mintzberg provides four other purposes for strategy. One is to provide a strategic perspective, a way the organization sees its environment and the context in which it functions. Another is a strategic position, that of locating, and not just physically, an organization within its environment. A fourth purpose is to understand the pattern in the action and decisions of an organization. The fifth purpose Mintzberg suggests is as a ploy, a way of employing strategy to cause others to adjust their own strategy to your benefit. Certainly these ae not the only purposes, but taking this view has two immediate, significant effects when you are embarking on any strategic endeavor. It frees you personally to take a broad view of strategy and consider such strategy from many different viewpoints and second, and perhaps more important, this approach can avoid many hours of useless argument by the strategy team over the exact definition of strategy. If you would like to read Mintzberg’s articulation of this approach in his own words:

Mintzberg, H. (1987). The strategy concept I: Five Ps for strategy. California Management Review. 30(1), 11-24.

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Big Data, the Internet of Things (IoT), Planning and the Lure of Near-Perfect Knowledge

 

There are two related trends in information technologies that may have a significant effect on strategic, project and operational planning in the near future. These trends are “Big Data” and the “Internet of Things (IoT).” These initiatives promise near-perfect knowledge and with it near-certainty in planning and decision making. But perhaps a word of caution is advisable.

 Big Data

 Big data refers to the collection of extremely large data sets that are gathered largely via the participation of millions of people in social media and on-line marketplaces. The data sets are huge and currently require expensive collection, storage and processing capabilities to overcome the challenges of collection, storage, search, sharing, analysis, and visualization. The trend to larger data sets is due to the ability to identify and analyze market activity, buying and sales trends, conduct research of large population samples, track and treat diseases, and reduce crime.

The promise then of big data is near perfect knowledge that enables certainty in decision making, greater efficiency in operations, and mitigation of risk. SAS suggests that with big data it is possible to:

  •  Determine root causes of failures, issues and defects in near-real time, potentially saving billions of dollars annually.
  • Optimize routes for many thousands of package delivery vehicles while they are on the road.
  • Analyze millions of SKUs to determine prices that maximize profit and clear inventory.
  • Generate retail coupons at the point of sale based on the customer’s current and past purchases.
  • Send tailored recommendations to mobile devices while customers are in the right area to take advantage of offers.
  • Recalculate entire risk portfolios in minutes.
  • Quickly identify customers who matter the most.
  • Use clickstream analysis and data mining to detect fraudulent behavior.

 The Internet of Things

 The Internet of Things (or IoT for short) refers to the trend to connect virtually every object we touch or use to the internet in a way that enables an exchange of data. McKinsey suggests that equipping all objects in the world with minuscule identifying devices or machine-readable identifiers could be transformative of daily life. For instance, business may no longer run out of stock or generate waste products, as involved parties would know which products are required and consumed. One’s ability to interact with objects could be altered remotely based on immediate or present needs, in accordance with existing end-user agreements.

 In fact, the predictable pathways of information are changing: the physical world itself is becoming a type of information system. In what’s called the Internet of Things, sensors and actuators embedded in physical objects—from roadways to pacemakers—are linked through wired and wireless networks, often using the same Internet Protocol (IP) that connects the Internet. These networks churn out huge volumes of data that flow to computers for analysis. When objects can both sense the environment and communicate, they become tools for understanding complexity and responding to it swiftly. What’s revolutionary in all this is that these physical information systems are now beginning to be deployed, and some of them even work largely without human intervention.

 So, if we connect virtually everything to the internet and are continuously and ubiquitously collecting data that we now have the capability to quantitatively analyze it would appear that we are approaching near-perfect knowledge. That would suggest significant impacts on planning:

  • Less need for detailed planning since we will be able to make decisions near-real time off near-perfect data; allowing more effort to be spent on concepts and problem solving/problem management.
  • Greater ability to see and anticipate changes in environment and market and perform adaptive planning
  • Near-perfect situational awareness, especially on status of our own organizations and capabilities.
  • Planning that is produced, distributed, acknowledged and implemented in near-real time.
  • Planning in packets that automatically update.
  • Seamless planning and execution
  • Immediate execution feedback enabling plan adjustment

 All of this is exciting and many of these potential improvements to planning may come to fruition. But, a word of caution. All of this new knowledge is quantitative, not qualitative. The political, economic/market, and security environments are systems that react as much if not more to subjective decision making as to objective decision making; and past performance is not necessarily a predictor of future performance, particularly with regard to innovation or disruption. Take for example the colossal failure of “New Coke.” Every aspect of market analysis told the Coca Cola Company that New Coke would be a hit, yet consumers refused to buy it. Why? Because they didn’t like the taste, a fact that could not be predicted through past buying habits or even early testing. Moreover, the dislike of New Coke “went viral” in today’s terms and the reaction rapidly spread nationwide and worldwide, so that New Coke quickly disappeared and Classic Coke (the old formula) replaced it. Another example is the Vietnam war. Every measurable data point about the war pointed to an overwhelming US/South Vietnamese victory, except for the entirely subjective will to win of the Viet Cong and North Vietnamese. Clausewitz suggested that the more we know, the more information we have, the more uncertain we become because each new piece of information suggests new questions. We must therefore be careful of our decision making system locking-up as it is presented with more information, much of which often conflicts, than we can absorb, assess and make decisions on.

 References

http://www.mckinsey.com/insights/high_tech_telecoms_internet/the_internet_of_things

 

http://www.sas.com/big-data/

 

Abductive Logic: what it is and why you should care

Most of us baby-boomers, Gen X and Gen Y folks are familiar with deductive logic and inductive logic. It’s what we were taught in school and how we solve problems much of the time. Deductive logic is basically if a then b reasoning. Deductive logic enables us to conclude that something is true. For example, if all dentists use toothpaste and this man is a dentist, then we can conclude this man uses toothpaste. Inductive logic is basically if a then probably b reasoning. Inductive logic enables us to infer that something is true. For example, if every dentist we have ever met used toothpaste and we meet a dentist we can infer that this dentist uses toothpaste. But, that result is not guaranteed. This may be the one dentist who doesn’t follow the guidance of his own profession (akin to doctors who smoke – but that’s a separate discussion).

There is, however, a third type of logic and reasoning. It’s rarely taught formally in school and yet it is the reasoning we use most often…abductive logic. Abductive logic is basically if a then maybe b reasoning. Abductive logic enables us to guess that something is true. In abductive logic we form a hypothesis based on our observation. For example, we observe a man buying several cases of toothpaste and guess, hypothesize, that he is a dentist. So, why is abductive logic important to us…because unlike those of us from Gen Y or older, abductive reasoning is how the Net Generation thinks. Those of us roughly thirty years old or younger, who grew up connected to the web, largely learn and make decisions through abductive logic.

If I ask you right now what is the primary crop of India, few of you (to include myself) would know. So, how do you find out? Simple, you pick up your smart phone or go to your laptop and google (search for) “primary crop of India.” The first link that pops up on this day is “more on crops in India.” Your guess is that this won’t answer your question, so you go to the next without even opening the first. The second is “the primary cash crop of India.” That sounds like to might work, so you open it. It suggests tea, but you are not sure, so you go to the next on “agriculture in India.” That site suggests rice is the primary crop. You go to another site, “Major crops of India,” which also suggests that rice is the major cash crop, so you conclude that rice is the major crop.

The point is that in abductive reasoning we successively “try out’ answers until we find one that fits. This is how the younger, connected generation thinks. It is also how the rest of us think when presented with too few facts to employ deductive or inductive reasoning. An example most can relate to comes from the post-9/11 war in Afghanistan. Many American Soldiers went into a country far different from their own in culture, religion, government, society, education and values. Those Soldiers were often charged with bringing peace to villages and trying to get the local people to support the government and not support the Taliban insurgency. So, the Soldiers would try to get the local Imam’s (religious leader) support to persuade the people. If that didn’t work they might appeal to the local governing council, called a shura in Afghan. If that didn’t work they might try to influence the local population by supporting schools. The point is they were using abductive reasoning, trying to find the hypothesis that actually provides the answers to what they observed.

Many of you may be familiar with Recognition Prime Decision Making (RPM) (Gary Klein). Klein’s classic example of Recognition Prime Decision Making is the fire chief who arrives at a fire after the alarm goes off. He (or she) has seen hundreds of fires and immediately can recognize through experience how the fire is acting and what needs to be done. This works well for situations that you have the experience to be able to recognize through repeated observation of similar instances what to do. But, in the case of our soldiers in Afghanistan they were seeing new instances and environments for the first time. So they needed abductive logic to help them think through what needed to be done. Thus, in today’s globalized, time-compressed, interconnected world we need to understand abductive logic for two reasons: first, most all of our younger generation thinks that way and we need to know how they learn and think; and second, we can use abductive reasoning to think our way through unfamiliar problem situations in which we don’t have enough information to employ traditional deductive or inductive logic.

In his book Tempo, Venkatesh Rao introduces us to the concept of narrative decision making. His observation, a correct one I think, is that decisions are not made at singular “decision points,” but are instead made over time as part of a narrative. In describing narrative decision making Rao employs a “Double Freytag Triangle,” which is a model of narratives intended as a prototypical example of a deep story, and is depicted visually by the following diagram.Double Freytag Triangle

Leaders and staff alike must understand narrative decision making. We must understand the flow of conceptual and practical learning and thinking that leads to a decision. In examining the Double Freytag Triangle as a model of narrative decision making we find that the first triangle is aimed at the conceptual side of the learning and thinking. In exploration we start to explore, learn about and understand the specific environment, including research, use of metrics and mapping out the systems that affect our own organization and ultimately success.

Armed with that understanding Rao suggests that we will have “aha moments” or employ “cheap tricks” as answers to the challenges we face. We might also call those concepts or courses of action. Armed with those we place them in context and mentally or physically examine the ideas in a sense-making effort. This signals the value and applicability of the ideas we have drawn from our explorations. The US military calls this left-side, conceptual triangle operational design. Many civilian organizations, both public and private, would call the left-side triangle strategy development or visioning.

The valley in the diagram separates the conceptual portion of the narrative, or in a very real sense planning effort, from the practical application portion of the narrative. The leader and staff then engage in what Rao terms a heavy lift. The heavy lift is the detailed planning and coordination that is going to be necessary for a correct decision and effective execution. The separation event is the actual execution of the action that is being decided upon. It starts with the task or order that comes from the leader and ends when the task, action, or event is complete. And, finally, the retrospective is the assessment of the decision or plan. This could be an after action review, hotwash, evaluation, assessment, test, or some other means of providing feedback to the narrative decision making effort.

Using the mental model of narrative decision making has helped me to better understand the relationship between learning, critical and creative thinking, planning, decision-making and execution. In military terms, it links together the conceptual effort of operational design with the practical or detailed planning of the military decision making process. Understanding that all decision making is narrative decision making and that decisions are made within the context of our individual or organizational life narrative as opposed to singular decision points will enable us to take decisions that are more relevant, more informed and more effective.

For greater understanding read: Rao, V. (2011). Tempo: Timing, tactics and strategy in narrative-driven decision making. Las Vegas, NV: Ribbonfarm. Note that this is a print on demand work.

 

 

Disruptive thinking and Instructional Design

 

In a blog on Disruptive Thinking and Education at http://cagelessthinking.com/disruptive-thinking-in-education/  it has been suggested that Disruptive Thinking is a concept that is based upon doing the opposite of what is expected/what convention tells you will be successful. 

 

If disruptive thinking is the opposite of conventional thinking let’s apply that to instructional design. Imagine a course that is free, that has no prerequisites, that has unlimited enrollment, that has no set curriculum, that has no tests, that has no textbooks, that the student can start or stop or even participate when and if they wish, and that has no teachers. Sounds like a worthless course doesn’t it.

 

Yet, the description above describes exactly the Massive Open On-line Course “Change 2011.” Sponsored by George Siemens, Stephen Downes, and Dave Cormier the course ran for 35 weeks, had tens of thousands of participants and was focused on how education was changing with a connectivist approach. Connectivism is an approach to networked learning in which individuals connect to other learners and sources of information in order to learn themselves and contribute to the learning of others.

 

As a part time participant in the Change 2011 MOOC I learned a great deal about learning and education, in particular leveraging 21st Century technologies. The course was for me a disruptive learning experience. At the same time I was learning formal, traditional educational approaches in my PhD in education program I was being exposed to arguments and evidence that suggested that there were new ways to structure student-centered education and therefore new approaches to instructional design.

 

Where I personally ended up was somewhere in the middle. I still believe that there are significant advantages to institution-based education, but I also recognize the advantages of the connectivist-based approaches. My intent is to take an instructional design approach that seeks to combine the advantages of both, one that is student-centered, using a connectivist approach, but still leverages the expertise, dedication and focus that the instructor brings to the effort, while having enough structure that broad organizationally desired learning outcomes are achieved.

Most of us spend much of our day learning…and often in very different venues. For adults, many of us learn often at work, either formally in workforce development classes or informally in our teams or on our own. Many of us are also taking classes outside work, whether short one or two-day or weekend seminars, or night classes, or on-line programs (as I write this as a requirement for an on-line PhD program in Education). Some of us also learn for volunteer work with non-profits, or learn in support of hobbies that we enjoy. The point is we are almost constantly learning about different topics or disciplines as we move through our environment.

Moreover, all of that learning is connected in several ways. The primary connection of course is our own interest and motivation to learn these things. The second connection is that because these are our interests often in our mind they are connected and blur, so we seek to make connections between learning that occurs in these many different venues. And, the third connection is our desire to share what we have learned with our friends and others.

The challenge of course is that our current learning venues are largely compartmentalized. We leave our work learning behind as we head off to night classes or a non-profit or a hobby. The blackboard or other academic knowledge management system we use in academic classes is different than the knowledge management system and workforce development system our work uses. Our informal learning is often not even captured at all.

What we need is portability of learning. We need a single learning and knowledge management system that we can take with and access wherever we go, contribute to whenever we want and adapt for new formal or informal learning. Many of us turn to our smart phones to take notes, add in some apps and connect to others. We are informally building our own personal learning network. Unfortunately though our personal learning network may not connect to our academic or workplace environments.

We need to correct that. First, we need to deliberately structure our own personal learning network, so that it meets the needs of our learning in each of our learning environments. We can do that through adding apps, RSS feeds, blogs, websites and tweet followings to acquire the knowledge we need for each purpose. We can also adapt as necessary to support new learning, by adding or subtracting from our personal learning network. Second, we need to seek formal institutions and public/private organizations to open their systems to enable the connectivity to our personal learning network. In that way we can truly make our learning portable and be able to draw from, add to, or share our learning at any time in any venue.

Triple Loop Learning and Bloom’s Taxonomy

Triple Loop Learning has been explained by Chris Argyris as progressive learning. In the first loop we ask ourselves – are we doing things right. In the second loop we ask ourselves – are we doing the right things. In the third loop we ask ourselves – how do we know they are the right things. There are of course learning requirements associated with each of these loops. When we fold in Bloom’s Taxonomy, a pattern begins to emerge. Single loop learning is associated primarily with learning in the psycho-motor domain – how do we behave? Double loop learning is associated primarily with the cognitive domain – how do we decide? Triple loop learning is associated primarily with the affective domain – how do we perceive?

The goal would appear to be to get to triple loop learning, as that implies that the individual is learning in all three of Bloom’s domains.