Close Enough is Good Enough

In a perfect world, there would be perfect information on which you could base your business decisions.

In the real business world, decision-making data is often in short supply.  With a bit of work, you can almost always gather more – but doing so costs you in time and/or money.  Knowing when “close enough is good enough” is an important element in the art of managing.  Too little data for the circumstances leads to errors and mistakes.  Too much puts you in the realm of perpetual decision-making delay.

Predicting is difficult, especially when it comes to the future

Wouldn’t it be nice to have a better forecast of how a competitor will react to your new product?  How about a more accurate estimate of who in management will stay and who will leave when you announce that merger?  And what about a clearer picture of where that product you’re trying to license is in its lifecycle?

It’s often tempting to send every recommendation and proposal back for more digging, and sometimes this reaction is justified.

But in my experience, it is easy to slip into “analysis paralysis.”

When you find yourself frittering away time focusing on second order effects or highly unlikely scenarios, you’re probably going too far.

After looking at dozens of decisions over the years, most of the ones that went off the tracks have been a result of events that seemed so unlikely at the time as to have never even entered into consideration.  An unpredictable recession.  War.  Death of a key player.  A competitor’s new technology that leapfrogs your own.  Political actions like embargos, abusive tariffs or outright bans.  If you thought any of these things was going to happen, you wouldn’t have even considered the project in the first place.

In a major factory expansion project we failed to anticipate the ability of a competitor to successfully petition the government to have an obstructive importation tariff put in place.  The tariff was completely in opposition to international trade agreements, and my post-event assessment was that no amount of data would have convinced us this was a possibility.

This is the cover of my most recent novel, EMPOWERED. 

This is the cover of my most recent novel, EMPOWERED

Was it discoverable?  Maybe.  We could have tried teasing the information out of the competitor’s former employees – if they’d even known the plan.  Maybe we could have somehow discovered it from government sources.  Of course, doing so would have delayed the project for weeks.  Possibly months.  The risk was one of hundreds of variables involved in executing the project.  If I’d insisted on gathering better data on all of these risks, the project would have taken years to formulate.

So what did we do?  Scaled back the project, looked for other markets to serve, and petitioned the government to roll back the tariff.  Was it a disaster?  No.  But the project was never the success I’d hoped for.

Experience teaches that the issue that is likely to derail your decision is something that will come out of right field.  Often it won’t even be a part of your analysis.

Separate the significant few from the trivial many

Most managerial decisions don’t require and can’t justify extensive data gathering.  Do you conduct a survey of your subordinates to find the optimal time to have a staff meeting?  Do you perform extensive testing of your messages before having an all-employee meeting?  Do you conduct conjoint analysis before a small price increase that is driven by cost increases that impact all of your competitors?

In most cases, the answer these questions will be “no.”  Instead, you rely on reasoning, experience, past practice, and estimates.  You might discuss your thoughts with others, but normally you don’t gather data.

Why?  Because the importance of the decision doesn’t justify the effort.  Knowing when to “go with your gut,” when to get other opinions, and when to dig in with data-gathering is a key job of a manager.

An easy way to think about this is to break your decision making into three buckets.

  1.  Everyday decisions.  These are calls where getting things wrong have relatively small consequences.  Or they could be bigger decisions, but ones where you have the ability to quickly and painlessly respond to errors in the initial direction.
  2. Big decisions.  These usually have visibility to those higher in management, or they have a significant impact on the performance of the company/business unit/department.  Smaller decisions can fit into this category when their impacts are irreversible once made (for example, firing an employee with unique skills/abilities).
  3. Career make-or-break decisions.  Acquisitions/mergers/license agreements.  A major new product line.  A new factory.  A substantial change in strategy.  These are the calls that build or destroy your career.

Not surprisingly, you need to gather more data as you move down through the various buckets.

In my experience, there are normally only a handful of “Class 3” decisions that most managers will be faced with each year.  You should devote plenty of effort to running through all kinds of “what-ifs” and “scenarios” for these decisions.  The “Class 1” decisions should be dispensed with as quickly and efficiently as possible.

Time sensitivity

Many academics suggest conducting sensitivity analysis as a means of dealing with uncertainty.  This is useful to a point, but hardly a panacea.  I recall numerous “Class 3” projects where the actual results fell outside of the “best case” or “worst case” scenarios within a year or two.  Predicting anything that goes out more than a couple of years into the future becomes an exercise in managing multiple assumptions.  That’s why so many long-term projects that require financial analysis to justify them are manipulated to get the desired result.  There are often dozens of critical assumptions, all of which by themselves can seem reasonable and can be justified, but ultimately produce an irrational result.

I wish there was an easy way to call BS on this type of fluffy financial analysis.  The best way I’ve found is to step away from the numbers and think about how many things need to “go right” for the results to come out as projected.  If the proposal only requires a few assumptions to be wrong in order for the project to fail, you’re not on solid ground.  Utilize your experience and instincts to make a holistic judgment, and don’t be persuaded by numerical analysis alone.

I always had a tough time dealing with this conundrum.  There was rarely a deal or project I didn’t like.  I usually found myself dwelling on all the enticing possibilities in a future that included the project, and tended to ignore or downplay the risks.

I found the best way to deal with my sunny predisposition was to have someone with a “cup is half-empty” mentality working with me.  Sometimes that was my boss.  Other times, the role was filled by an accountant or other financial person.  Even with the advice, I still needed to discipline myself to listen, rather than just brushing aside objections.


Managers can’t permit themselves to become the victims of “analysis paralysis.”  A part of the task of management is applying investigative effort where it is warranted, and dispensing quickly with smaller decisions.

The ability to discriminate between various decision-making levels is something that comes from both experience and careful reflection.

With your most important decisions, take the time to dig into the risks behind the assumptions, but don’t become a slave to the numbers.  Instead, step back and think about the project on a holistic basis.  And don’t ignore your gut.  If you recognize your own predisposition for examining such projects, seek balancing opinions and make sure to listen to them.  2.2

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To the right is the cover for DELIVERABLES.  This novel features a senior manager approached by government officials to spy on his employer, concerned about how a "deal" the company is negotiating might put critical technical secrets into the hands of enemies of the United States.  Of course, things are not exactly as it seems....

My novels are based on extensions of 27 years of personal experience as a senior manager in public corporations.