6/10/2019 ☼ Strategy ☼ Design ☼ Goals
Defines strategy; describes the conventional idea of good strategy; explains some problems with the conventional idea of good strategy; defines design; explains how design helps fix problems with conventional strategy.
At its most fundamental, strategy consists of intentionally choosing to take actions in order to achieve goals. This definition of strategy is both simple and generally applicable. Though strategy is often talked about in the context of businesses, any person or organization taking action to achieve a defined goal already has a strategy. An undergraduate has a strategy if she decides to work hard at her summer consulting internship (action) so she will be hired full-time at the company (goal). A basketball team trying to beat an opposing team has a strategy. The National Health Service has a strategy for delivering healthcare effectively given changing demographics in the UK.
Strategies range in quality: They can good or bad or, more accurately, strategies for achieving the same goal can be relatively better or relatively worse compared to each other.
Organizations that are operationally effective do the same things as their competitors but they do them better—they’re more efficient or effective at doing the same things. Though the most operationally effective businesses perform better than their competitors in the short term, that performance difference erodes away in the long term. This is because less operationally effective organizations tend to die by going out of business, and more operationally effective businesses tend to survive. The level of operational effectiveness in an industry increases over time—supernormal profitability derived from operational effectiveness gets competed away. Being operationally effective is not the same thing as good strategy.
Strategy is simply taking actions to achieve goals. Good strategy implies three things:
A strategy can be different in many ways. Three important ways are:
Tradeoffs mean not doing everything—i.e., not taking every action that can be taken. Tradeoffs are always necessary because actions consume resources, and resources are always limited. If a scoop of ice cream costs £2 and you have only £2, you can only have one scoop of ice cream even if there are hundreds of liters of ice cream in the shop.
Good strategy requires choosing the correct tradeoffs when taking action. If you have £2 and want to buy a scoop of ice cream, and you enjoy chocolate and dislike strawberry, buying a scoop of strawberry ice cream represents choosing an incorrect tradeoff—you should have chosen a scoop of chocolate ice cream instead. If an organization’s goal is to be innovative and develop new products, but it allocates most of its operating budget to sales and marketing instead of R&D, it has probably chosen the wrong tradeoffs too.
In the ice cream example above, the action of buying a £2 scoop fits with the resource of having £2 to spare. If there was no ice cream shop nearby, or if you had no cash on hand, you would not have been able to take that action—it would not fit because you would either lack the necessary resources (not enough cash) or you would be constrained (by the absence of the ice cream shop).
An organization’s actions can fit its resources and constraints in an analogous way. For instance, choosing to open expensive new robotic manufacturing facilities would be a good fit if the organization is flush with cash, while saving money by dumping production waste in a nearby river would be a bad fit if there are laws which impose severe fines for pollution.
Single actions are relatively easy to copy, while sets of actions are harder to copy. Strategy is harder to copy if it consists of sets of actions. Thinking about sets of actions rather than just individual actions reveals a different way of thinking about uniqueness, tradeoffs, and fit.
Strategies can be unique if they consist of a set of actions which no one else takes—even if none of the individual actions in the set are not unique.
Actions in a set always affect each other. This interaction between actions can be detrimental (i.e., not good strategy) if the actions are:
A set of actions is good (i.e., is good strategy) if the actions are coherent with each other—they are not inconsistent, incompatible, or confusing.
The more coherent the set of actions is, the better the fit. There are three progressively greater levels of fit or coherence:
Conventionally, good strategy means intentionally choosing a unique and difficult to imitate set of actions that both fit with each other and with the available resources and constraints—this set of actions allows the organization to achieve its goals.
So far, this picture of conventional strategic thinking implies that good strategy requires a lot of planning in advance. Advance planning is needed to think through options, do market research, and then decide which tradeoffs to make to achieve unique fit. The strategies which result from this intensive planning process are often highly comprehensive and carefully thought out—their success often seems nearly inevitable.
The future is never known for certain. This can be deeply scary, especially for managers and leaders whose fates are closely tied to the future performance of their respective organizations. The planning and research needed for conventional strategy-making can be seen as a reaction to this fear of an unknown future. Though it is seldom emphasized, conventional strategic planning requires making a set of strong assumptions about what the future will be, then making a complex, interconnected set of choices based on those assumptions. This long, intensive process often leads planners to forget that assumptions aren’t necessarily true. Just like that, the unknown future becomes “knowable” and can be planned for. You can see how conventional strategic planning allows people (and organizations) to cope with their fear of the unknown future by turning it into an apparently solvable problem. But what happens if the future that results doesn’t match the assumptions of the future used in the strategy planning process?
The second problem is simple and fundamental, yet counter-intuitive. Conventional strategic thinking assumes a lot about what goals to pursue and how to measure success. Nearly all conventional strategy (including the profusion of strategic frameworks) assumes that profit, sales, growth, or innovation are the goals of strategy. But people and organizations can have different goals. Can a non-profit organization have profit as a goal? Having different goals generally leads to taking different strategic actions. For instance, a clothing manufacturer with the goal of maximizing its profits will probably take strategic actions that are different from one with the goal of reducing its impact on the environment. If assumptions about the goals of strategy are incorrect or incomplete, the strategy itself will be ineffective.
Design helps solve some of the problems of conventional strategic thinking.
Design is taking action to convert the current situation into a more desirable one. Design is not only about making things pretty, nor is it only about problem-solving (though some people think so). Design is problem-finding first, and only then problem-solving.
If design is problem-finding first, then “designer” isn’t necessarily a specific role played by specific people on a team. Design thinking (see below) is a way to approach the world that every constituency and individual should possess, so that they can figure out the right problems to solve.
Sometimes, finding the right problem requires lots of search and research. This isn’t just reading papers and essays. Research is also (maybe more about) being in the world, exploring it, and understanding it. Most importantly, if design thinking is deciding what the right problems are to solve, then design at base is an issue of figuring out what values we have, what makes a problem the right one to put effort behind. Values aren’t objectively evaluatable, they cannot be commensurated. All you can do with values is state what values you have and be open to changing your mind.
Though it’s tempting to think of design in terms of final output, say a chair or piece of software, it is more useful to think of design as a process that leads to an outcome intended to solve a problem. The kinds of problems to be solved can be in nearly any domain—physical objects, processes, interactions, foods, intangible systems, data representations, institutions, organizations, public policy all can be developed using a design process, though the concrete details of the design process will vary depending on the domain.
Four main processes take place in the course of designing things (or services, or systems, or interactions): Problem-finding and definition, search/research, prototyping, testing and evaluation.
For me problem-finding and definition is foundational, yet often completely ignored. Defining the need (or problem-finding) often involves identifying problems to solve, problems to not solve, and criteria for judging solutions. Problems are another way to think about what goals are.
Problems can be defined at many levels of abstraction and frequently interact with each other; the outcome of a design process reflects the constellation of problems driving the process. For example, Gmail (the webmail service) resulted from a process driven by numerous problems ranging from relatively abstract ones like “How can email service be improved?” to concrete ones like “How can webpage programming languages like HTML and Javascript be modified to make a web application feel like a client application to the user?” or “Folders are silly. What do we replace them with?” Often, research, prototyping, and testing will indicate either that that the problem needs to be redefined, or that solutions to the original problems posed generate new problems.
For the designer trying to solve a given set of problems, the universe of possible solutions to a given set of problems is infinitely large and insufficiently understood. Search and research helps begin the process of increasing knowledge of possible solutions while also eliminating impossible, unfeasible, inelegant, or problematic solutions. Conducting research takes different forms depending on the kinds of problems faced and the degree of design iteration that has already occurred. Some examples of research include market studies, patent searches, product testing, conference attendance, and field studies. The objective of research in the design process is to leave the designer with a smaller, more probably viable, set of possible solutions to dissect, recombine, and evaluate.
Often, evaluation is easier and more effective when possible solutions are given a more concrete form that designers and testers can interact with—a prototype. Prototyping can range from developing a policy proposal, to making wireframes (mockups of how webpages or software applications will look and the order in which users will encounter them), to 3D and physical models of varying degrees of precision. Prototypes made early in the iteration process tend to be simpler, faster, and cheaper than those made later in the process. Building prototypes used to be expensive and slow, and required extensive equipment and/or specialized knowledge (hence the proliferation of proof printers, model shops, one-off precision milling shops, and the like). Fortunately, fast, inexpensive prototyping software, hardware, and facilities are becoming increasingly available. Fast, inexpensive prototyping accelerates the design iteration process significantly, since prototypes are frequently the source of rich insights for designers, particularly in fine-tuning elements of the product and in recognizing new problems that must be resolved.
Design prototypes are evaluated against the solution criteria established in the design process’s problem-definition stage. Designers collect information about the design’s performance and use this to evaluate the design; in other words, they try to understand if and how the design should be modified in the next iteration of the design process. Particularly in the case of software applications, it is becoming increasingly clear that designs cannot be considered complete or effective if they do not include methods for collecting information needed to evaluate the application’s effectiveness in solving the design problem. As with prototyping, gathering information about design performance has become easier, less expensive, faster, and more comprehensive for many categories of products.
While it is possible for, say, the design of a more efficient dialysis pump or a better system for mobilizing non-voters during an election year to materialize fully-formed from a designer’s head, this is a rare occurrence. More often, what we’re talking about is design as an approach which iterates through processes. You can begin this process at any one of the stages (and go in any direction). The process ends when the designer is satisfied with the outcome (or loses patience).
The problems with conventional strategic thinking highlighted above were about assumptions. Design thinking helps fix conventional strategic thinking because it forces questioning of assumptions. The three most important questions design poses are:
Conventional strategy emphasizes choosing the right sets of actions to take (given resources and constraints) in order to achieve goals. Design changes the emphasis. Strategy by design emphasizes first identifying goals and developing understanding of them, before moving on to understanding what actions to take (given resources and constraints) to achieve those goals.
Strategy by design has three parts:
Written for the UCL Management Science undergraduate lecture course in strategy by design. This approach to strategy by design builds on and extends ideas from classic works of conventional management theory and design theory. For a fuller bibliography, see the syllabus for my PhD seminar on Strategy and Design.