Shipulski On Design

Archive for the ‘Seeing Things As They Are’ Category

Expectations result from mental models and wants. When you have a mental model of a system and you want the system to behave in a way that fits your mental model, that’s an expectation.  And when you want the system to behave differently than your mental model, that’s also an expectation.  When the system matches your wants, the world is good.  And when your wants are out of line with the system, the world is not so good.

Speculation is not expectation. Speculation happens when you propose, based on your mental model, how the system will behave. With speculation, there’s no attachment to the result, no wanting it to be one way or another. There’s just watching and learning.  If the system confirms your mental model, the applicability of the model is reinforced (within this narrow context.) And when the system tramples your mental model, you change your mental model.  No attachment, no stress, no whining, no self-judgement.

When doing work that’s new, system response is unknown. Whether the system will be exercised in a new way or it’s an altogether new system, metal models are young and untested.  When it’s the first time, speculation is the way to go. Come up with your best mental model, run the experiment and record the results.  After sitting in data, refine your mental model and repeat.   If your mental model doesn’t fit the system, don’t judge yourself negatively, don’t hold yourself back, don’t shy away.  Refine your mental model and build-test-learn as fast as you can.  And if your mental model fits the system, don’t judge yourself in a positive way.  This was your first test and you don’t understand the system fully.  Refine your model and test for a deeper understanding.  [Note – systems have been known to temporarily conform to mental models to obfuscate their true character.]

When doing work that’s new, expectation gets in the way. If you expect your models to be right and they’re not, you learning rate is slower than your expectations.  That’s not such a big deal on its own, but the rippling self-judgement can be crippling. Your emotional state becomes fragile and it’s difficult to keep pushing through the work. You doubt yourself and your abilities; you won’t put yourself out there; and you won’t propose radical mental models for fear of looking like you don’t know what you’re doing.  You won’t run the right experiments and you never the understand the fundamental character of the system. You block your own learning.  If you expect your models won’t to fit the system, you block your learning from the start.  Sometimes your lack of confidence blocks you from even trying. [Note – not trying is the only way to guarantee you won’t learn.]

Within the domain of experiments, mental models and generic systems, it’s relatively easy to see the wisdom of speculations and the perils of expectations, where wanting leads to judging and judging leads to self-blocking.  But it’s not so easy to see in the domain of life where experiments are replaced with personal interactions and generic systems are replaced with everyday situations and mental models are ever-present.  But in both domains the rules and consequences are the same.

Just as in the lab, in day-to-day life expectations are dangerous.

Image credit – Dermot  O’Halloran

If you think something can’t be done, it won’t get done.  And if you think it may be possible, or is possible, it may get done.  Those are the rules.

If an expert says it will work, it will work.  If they say it won’t work, it might.  Experts can tell you will work, but can’t tell you what won’t.

If your boss tells you it won’t work, it might. Give it a try.  It will be fun if it works.

If you can’t make it work, make it worse and then do the opposite.

If you can’t explain the problem to your young kids, you don’t understand the situation and you won’t make it work.

If something didn’t work ten years ago, it may work now. Technology is better and we’re smarter.  More likely it would have worked ten years ago if they ran more than one crude experiment before they gave up.

If you can’t draw a one page sketch of the problem, it may never work.

If you can’t make it work, put it down for three days. Your brain may make it work while you’re sleeping.

If you don’t know the problem, you can’t make it work.  Be sure you’re trying to solve the right problem.

If your boss tells you it will work, it might.  If they tell you how to make it work, let them do it.

If none of your attempts have been fruitful and you’re out of tricks, purposely make one performance attribute worse to free up design space. That may work.

If you don’t know when the problem occurs, you don’t know much. Your solutions won’t work.

If you tried everything and nothing worked, ask someone for help whose specialty in an unrelated area.  They may have made it work in a different domain.

If you think everyone in the group understands the problem the same way, they don’t.  There’s no way they’ll agree on the best way to make it work. Don’t wait for consensus.

If you don’t try, that’s the only way to guarantee it won’t work.

Image credit – Simon Greig

Urgency is important, but it’s not everything. It creates focus, but washes out the radical fringe. It’s easy to measure, but easy to measure doesn’t mean it’s the best thing to work on.

In the heat of the moment urgency is king. Frantic project managers take shortcuts to meet a deadline defined fourteen months ago; Lean Startup-ers ready-fire-aim their way from pivot to pivot; And resources flow to projects that are scheduled to finish soonest.

Urgency is attractive because it’s so clear cut, so objective, so easy to measure.

Due Date – Today’s Date = Urgency.

There’s always consensus on today’s date, everyone knows the due date and subtraction come easily.  There you go. No debate, no discussion.  This project has more urgency than that one.  Just do the math. But where did the due date come from? Did the work content define the due date?  If so, projects with the least work content, with their immanent due date, are the most urgent and resources should flow to the shortest projects.  Did the annual trade show set the due date?  If so, projects with earliest trade shows should get priority.  Did the CEO define the due date for reasons unknown to mere mortals?  If so, projects that finish before the declared date should get priority and projects that finish after the due date should get put on the back burner.

Project scope defines work content and start date plus work content equals due date.  For two projects with equal work content, the project that starts first has more urgency. Should projects start sooner to increase urgency? Should project plans pile on resources to pull in the completion date to increase urgency? Should project managers strip the sizzle out of projects so they finish sooner?

Urgency isn’t important. Importance is important.

The problem with importance is its subjective nature. Because there is no objective measure of importance, judgement is required.  The cold scoring systems to rank projects don’t work.  There are no scoring rubrics, no algorithms, no customized weighting factors that can objectively quantify importance.  It’s either important or it isn’t.  It’s important in the chest, or it’s not. It’s all about judgement.

The context defines what’s important. Market share has dropped five years in a row, some projects are more important than others. Market share has increased five years in a row, a different set of projects is important. Can’t make payroll, urgency-based project selection is best. Technology is long in the tooth, it’s important to fund projects that buy or build new technology. Which projects are most important? It depends.

The best way to sort projects by importance is to ask “Is this project important?” and have a discussion. Some projects will have more upside and others will have more certainty.  Some could create new markets and other will proved two percent growth in a guaranteed way. Which are most important? It depends.

Importance is relative. Use the “Is this project important?” methodology to force rank your projects by importance.  Once complete, take a step back and ask if the ranked list makes sense.  Reshuffle if needed.  Starting from the top, fully staff the most important project. For the next most important project, allocate the remaining resources and repeat the process project-by-project until the resources are gone.  This process ensures the most important projects on the list get the resources. But there’s a hole in the methodology.

What if our innate urgency bias keeps the most important projects off the list?

Image credit – Stephen Depolo

The past has past, never to come again.  But if you tell yourself old stories the past is still with you.  If you hold onto your past it colors what you see, shapes what you think and silently governs what you do.  Not skillful, not helpful.  Old stories are old because things have changed.  The old plays won’t work. The rules are different, the players are different, the situation is different.  And you are different, unless you hold onto the past.

As a tactic we hold onto the past because of aversion to what’s going on around us. Like an ostrich we bury our head in the sands of the past to protect ourselves from unpleasant weather buffeting us in the now.  But there’s no protection. Grasping tightly to the past does nothing more than stop us in our tracks.

If you grasp too tightly to tired technology it’s game over.  And it’s the same with your tired business model – grasp too tightly and get run through by an upstart.  But for someone who wants to make a meaningful difference, what are the two things that are sacred? The successful technology and successful business model.

It’s difficult for an organization to decide if the successful technology should be reused or replaced.  The easy decision is to reuse it.  New products come faster, fewer resources are needed because the hard engineering work has been done and the technical and execution risks are lower.  The difficult decision is to scrap the old and develop the new.  The smart decision is to do both.  Launch products with the old technology while working feverishly to obsolete it.  These days the half-life of technology is short.  It’s always the right time to develop new technology.

The business model is even more difficult to scrap. It cuts across every team and every function.  It’s how the company did its work.  It’s how the company made its name. It’s how the company made its money.  It’s how families paid their mortgages.  It’s grasping to the past success of the business model that makes it almost impossible to obsolete.

People grasp onto the past for protection and companies are nothing more than a loosely connected network of people systems.  And these people systems have a shared past and a good memory.  It’s no wonder why old technologies and business models stick around longer than they should.

To let go of the past people must see things as they are.  That’s a slow process that starts with a clear-eyed assessment today’s landscapes. Make maps of the worldwide competitive landscape, intellectual property, worldwide regulatory legislation, emergent technologies (search YouTube) and the sea of crazy business models enabled by the cloud.

The best time to start the landscape analyses was two years ago, but the next best time to start is right now.  Don’t wait.

Image credit – John Fife

If the work is new, there is no best practice.

When you read the best books you’ll understand what worked in situations that are different than yours.  When you read the case studies you’ll understand how one company succeeded in a way that won’t work in yours.  The best practices in the literature worked in a different situation, in a different time and a under different cultural framework.  They won’t work best for you.

Just because a practice worked last time doesn’t mean it’s a best practice this time.  More strongly, just because it worked last time doesn’t mean it was best last time. There may have been a better way.

When a problem has high urgency it should be solved in a fast way, but if urgency is low, the problem should be solved in an efficient way. Which way is best? If the consequences of getting it wrong are severe, analyses and parallel solutions are skillful, but if it’s not terribly important to get it right, a lower cost way is better.  But is either the best way?

The best practices found in books are usually described a high level of abstraction using action words, block diagrams and arrows.  And when described at such a high level, they’re not actionable.  You may know all the major steps, but you won’t know how each step should be done.  And if the detail is provided, the context of your situation is different and the prescriptive steps don’t apply.

Instead of best practices, think effective practices.  Effective because the people doing the work can do it effectively.  Effective because it fits with the capability and capacity of the people doing the work.  Effective because it meshes with existing processes and projects.  Effective because it fits with your budget, timeline and risk profile.  Effective because it fits with your company values.

Because all our systems are people systems, there are no best practices.

image credit — johnwayne2006

One of the biggest mistakes I know is to get too little done by trying to do too much.

In high school we got too comfortable with partial credit. Start the problem the right way, make a few little mistakes and don’t actually finish the problem – 50% credit.  With product development, and other real life projects, there’s no partial credit.  A project that’s 90% done is worth nothing.  All the expense with none of the benefit.  Don’t launch, don’t sell. No finish, no credit.

But our ill-informed focus on productivity has hobbled us.  Because we think running projects in parallel is highly efficient, we start too many projects.  This glut does nothing more than slow down all the other projects in the pipeline.  It’s like we think queuing theory isn’t real because we don’t understand it.  But to be fair to queuing and our stockholders, queuing theory is real.

Queues are nothing more than a collection of wayward travelers waiting in line for a shared resource.  Wait in line for fast food, you’re part of a queue.  Wait in line for a bank teller (a resource,) you’re queued up.  Wait in line to board a plane, you’re waiting in a queue.  But the name isn’t important.  Line or queue, what matters is how long you wait.

Lines are queues and queues are lines, but the math behind them is funky.  From firsthand experience we know longer queues mean longer wait times. And if the cashier isn’t all that busy (in queuing language – the utilization of the resource is low) the wait time isn’t all that bad and it increases linearly with the number of people (or jobs) in the queue.  When the shared resource (cashier) isn’t highly utilized (not all that busy), add a few more shoppers per hour and wait times increase proportionately. But, and this is a big but, if the resource busy more than 80% of the time, increasing the number of shoppers increases the wait time astronomically (or exponentially.)  When shoppers arrive in front of the cashier just a bit more often, wait times can double or triple or more.

For wait times, the math of queueing theory says one plus one equals two and one plus one plus one equals seven.  Wait times increase linearly right up until they explode.  And when wait times explode, projects screech to a halt.  And because there’s no partial credit, it’s a parking lot of projects without any of the profit.  And what’s the worst thing to do when projects aren’t finishing quickly enough?  Start more projects.  And what do we do when projects aren’t launching quickly enough?  Start more projects.

When there’s no partial credit, instead of efficiency it’s better to focus on effectiveness.  Instead of counting the number of projects running in parallel (efficiency,) count the number of projects that have finished (effectiveness.)  To keep wait times reasonable, fiercely limit the amount of projects in the system.  And there’s a simple way to do that.  Figure out the sweet spot for your system, say, three projects in parallel, and create three project “tickets.” Give one ticket to the three active projects and when the project finishes, the project ticket gets assigned to the next project so it can start.  No project can start without a ticket.  No ticket, no project.

This simple ticket system caps the projects, or work in process (WIP,) so shared resources are utilized below 80% and wait times are low. Projects will sprint through their milestones and finish faster than ever.

By starting fewer projects you’ll finish more.  Stop starting and start finishing.

Image credit – Fred Moore

Whether it’s strategic planning or personal planning, work always outstrips capacity.  And whether it’s corporate growth or personal improvement, there’s always a desire to do more.  But the more-with-less and it’s-never-good-enough paradigms have overfilled everyone’s plates, and there’s no room for more. There is no more time to double-book and there are no more resources to double-dip.  Though the growth-on-all-fronts will not stop, more is not the answer.

Growth objectives and BHAGs are everywhere and there are more than too many good ideas to try.  And with salary increases and incentive compensation tied to performance and the accountability movement liberally slathered over the organization, there’s immense pressure to do more. There’s so much pressure to do more and so little tolerance for a resource-constrained “No, we can’t do that.” the people that do the work no longer no longer respond truthfully to the growth edict.  They are tired of fighting for timelines driven by work content and project pipelines based on resources.  Instead, they say yes to more, knowing full well that no will come later in the form of slipped timelines, missed specifications and disgruntled teams.

Starting is easy, but starting requires resources.  And with all resources over-booked for the next three years, starting must start with stopping.  Here’s a rule for our environment of fixed resources: no new projects without stopping an existing one. Finishing is the best form of stopping, but mid-project cancellation is next best.  Stopping is much more difficult than starting because stopping breaks commitments, changes compensation and changes who has power and control.  But in the age of growth and accountability, stopping before starting is the only way.

Stopping doesn’t come easy, so it’s best to start small.  The best place to start stopping is your calendar.  Look out three weeks and add up the hours of your standing meetings.  Write that number down and divide by two.  That’s your stopping target.

For meetings you own, cancel all the status meetings.  Instead of the status meeting write short status updates.  For your non-status meetings, reduce their duration by half.  Write down the hours of meetings you stopped. For meetings you attend, stop attending all status meetings. (If there’s no decision to be made at the meeting, it’s a status meeting.)  Read the status updates sent out by the meeting owner.  Write down the hours of meetings you stopped attending and add it to the previous number.

If you run meetings 3 hours a week and attend others meetings 5 hours per week, that’s 8 hours of meetings, leaving 32 for work. If you hit your stopping target you free up 4 hours per week.  It doesn’t sound meaningful, but it is.  It’s actually a 12% increase in work time. [(4÷32) x 100% = 12.5%]

The next step is counter intuitive – for every hour you free up set up an hour of recurring meetings with yourself. (4 hours stopped, 4 hours started.)  And because these new meetings with yourself must be used for new work, 12% of your time must be spent doing new work

The stopping mindset doesn’t stop at meetings.  Allocate 30 minutes a week in one of your new meetings (you set the agenda for them) to figure how to stop more work.  Continue this process until you’ve freed up 20% of your time for new work.

More isn’t the answer.  Stopping is.

Image credit – Craig Sefton

For established companies, when times are good, it’s not the right time to try something new – the resources are there but the motivation is not; and when times are tough it’s also the wrong time to try something new – the motivation is there but the breathing room is not.  There are an infinite number of scenarios, but for the established company it’s never a good time to try something new.

For startup companies, when times are good, it’s the right time to try something new – the resources are there and so is the motivation; and when times are tough it’s also the right time to try something new – the motivation is there and breathing room is a sign of weakness.  Again, the scenarios are infinite, but for the startup is always a good time to try something new.

But this is not a binary world. To create new markets and new customers, established companies must be a little bit startup, and to scale, startups must ultimately be a little bit established. This ambidextrous company is good on paper, but in the trenches it gets challenging. (Read Ralph Ohr for an expert treatment.)  The establishment regime never wants to do anything new and the startup regime always wants to.  There’s no middle ground – both factions judge each other through jaded lenses of ROI and learning rate and mutual misunderstanding carries the day.  Trouble is, all companies need both – established companies need new markets and startups need to scale. But it’s more complicated than that.

As a company matures the balance of power should move from startup to established.  But this tricky because the one thing power doesn’t like to do is move from one camp to another. This is the reason for the “perpetual startup” and this is why it’s difficult to scale.  As the established company gets long in the tooth the balance of power should move from the establishment to the startup.  But, again, power doesn’t like to change teams, and established companies squelch their fledgling startup work. But it’s more complicated, still.

The competition is ever-improving, the economy is ever-changing and the planet is ever-warming.  New technologies come on-line, and new business models test the waters. Some work, some don’t.  Huge companies buy startups just to snuff them out and established companies go away.  The environment is ever-changing on all fronts.  And the impermanence pushes and pulls on the pendulum of power dynamics.

All companies want predictability, but they’ll never have it.  All growth models are built on rearward-looking fundamentals and forward-looking conjecture.  Companies will always have the comfort of their invalid models, but will never the predictability they so desperately want.  Instead of predictability, companies would be better served by a strong sense of how it wants to go about its business and overpowering genetics of adaptability.

For a strong definition of how to go about business, a simple declaration does nicely. “We want to spend 80% of our resources on established-company work and 20% on startup-company work.” (Or 90-10, or 95-5.)  And each quarter, the company measures itself against its charter, and small changes are made to keep things on track.  Unless, of course, if the environment changes or the business model runs out of gas.  And then the company adapts.  It changes its approach and it’s projects to achieve its declared 80-20 charter, or, changes the charter altogether.

A strong charter and adaptability don’t seem like good partners, but they are.  The charter brings focus and adaptability brings the change necessary to survive in an every-changing environment.  It’s not easy, but it’s effective.  As long as you have the right leaders.

Image credit – Rick Abraham1

Anything that happens happens because of people, and anything that doesn’t happen doesn’t happen because of people.  Technology doesn’t create itself, products don’t launch themselves, companies don’t build themselves and trust doesn’t grow on its own.  Any kind of work, any kind of service, any kind of organizing – it’s all done by people.

The productivity/quality movement has been good for factories – parts move in a repeatable flow and they’re processed in repeatable ways by machines that chunk out repeatable output.  Design the process, control the inputs and turn the crank. Invest in the best machines and to do the preventative maintenance to keep them in tip-top shape.  Just follow the preventive maintenance (PM) schedule and you’ll be fine.  But when the productivity/quality movement over-extended into the people domain, things don’t go as well.

People aren’t machines, and their work product is not cookie-cutter parts.  And, there’s no standard PM schedule for people. We all know this, but we behave like people are machines – we design their work process, train them on it and measure their output. But machines are iron-based entities that don’t have consciousness and people are carbon-based beings with full consciousness.  The best machines do what their told, but the best people tell you what to do.  Machines and people are fundamentally different, but how we run them is markedly similar.

Where machines need oil, people need empathy. And for empathy you need vulnerability and for that you need trust. But there’s no standard PM schedule for trust.  There’s no flowchart or troubleshooting protocol for helping people.  What work do you give them? It depends. When do you touch base and when do you leave them alone? It depends. How much responsibility do you give them? It depends.  With machines it’s follow the PM schedule and with people – it depends.

Where machines wear out, people develop and grow. And to grow people you need to see them as they are and meet them where they are. And to do that you’ve got to see yourself as you are.  You can’t give people what they need if you add to the drama with your reactivity and you can’t discern their suffering from your projections if you’re not grounded. How much time do you spend each day to learn to dampen your reactivity?  How much time do you spend to slow your monkey mind so you can see your projections?

With machines it’s control the inputs and get what you got last time.  With people it’s maybe; it depends; don’t worry about how it will go; and why don’t you try? Growing people is much more difficult than keeping machines running smoothly.  But, there is nothing more fulfilling than helping people grow into something they couldn’t imagine.

Image credit – Benjamin Balazs

Hep

If you don’t have a problem, there’s no problem. There are no resources without a problem and certainly no focus or momentum.  If you don’t know your problem, stop.  Take time to define your problem using a single page.  Make a sketch or make a block diagram but make it clear.  Make it so the problem description stands on its own. After you’ve defined your problem and someone calls it an “opportunity”, walk away because they can’t help you.  Taking advantage of opportunities is optional, but solving problems is mission critical.  No one worth their salt works on opportunities.  Rock stars solve problems.

After you’ve gnawed on a problem for a month and it hasn’t given in, what do you do?  When you’ve thrown everything at a problem and it still stands tall, what do you do?  When you’ve tried all your tricks and the intractable problem is still blocking an already overdue product launch, what do you do?  What you do is find someone who is unafraid trade an intractable problem for a solvable one, someone who will courageously give ground with the hope of opening up new design space, someone who will unabashedly take an anti-conventional (and hopefully controversial) approach.  What you do is find a rock star.

Intractable problems are not usually intractable; rather, intractable problems are either poorly-defined problems or are the wrong problem altogether.  Either way, it takes someone with courage, usually an outsider, to redefine the problem or see it differently.  But because of pride, an outsider can be brought in only after the team has exhausted all other possibilities.  Unless there’s a problem with the problem solving team (they can’t solve the problem), there’s no problem.  And without a problem, the team won’t accept help from an outsider.

At the rodeo when the cowboy is bucked off the raging bull, the cowboy runs away from the bull but the rodeo clown runs toward the bull to distract it.  Like the rodeo clown, the problem solving rock star runs toward raging problems at full tilt.  The rock star puts it all on the line as she grabs the problem by the scruff of the neck, wrestles it to the ground and hog ties it.  There’s no shyness, just well-practiced technique wrapped in implicit knowledge.  With courage and a cloud of dust, it’s no-holds-barred problem solving until the problem gives it up. Nothing is sacred, no assumptions go unchallenged, and no details are too small to ignore.  Like rodeo clowns, rock stars know their work looks funny from the outside, but they don’t care.  All they care about is solving the problem at hand. Right here, right now.

Before your next intractable problem, take a minute to scan your organization for the special people who have the courage to run toward even the most difficult problems.  Don’t be fooled by titles, positional power or how they dress.  Look deeply because like rodeo clowns, your magical problem solvers may not look the part on the outside.

Image credit – Ed Schipul

Doing new work isn’t difficult, thinking about is difficult.  Stop thinking and start starting; there’s no other way.

If you’re a scientist, everything has a half-life.  If you’re Buddhist, everything is impermanent.  If you’re a CEO, your business model is out of gas.  It’s scary to admit everything goes away, but it’s far scarier to deny it.

Just because an idea is threatening doesn’t mean it’s threatening.  It probably means it’s one hell of a good idea.

If it’s not different, it can’t be innovation.

Projects take too long because they’re poorly defined.  On a single page, define the novel usefulness the project will deliver, make a crude prototype and show it to potential customers. Refine, learn and repeat. Then launch it.  (This is the essence of Lean Startup without all the waste.)

If I could choose my competition, I’d choose to compete with no one.

Failure is never the right word.  Don’t use it.  Ever.  (Even failing forward or forward failing should not be used.)  No one wants to fail.  No one will ever want to fail.  Replace of the word “failure” with “learning” and learn quickly.

If you’re not scared, you’re not doing innovation.

Companies offer more-with-less for as long as they can; and when there’s nothing left they offer more-with-more. It would be better to offer less-with-far-less.

For Franklin D. Roosevelt, the only thing to fear was fear itself.  For business, the only thing to fear is the cow path of success.

Image credit – JasonParis