An Everyday Data Strategy in 4 Simple Steps

“We have it all figured out!”

“Our product is exactly what you need!”

“This is clearly the solution!”

Whether it is the latest angry cable news people, vendors at a conference, or even professional services – certainty sells.

It seems like everyone with a point of view is so sure of themselves that people searching for answers have no idea who or what to believe.  How can we filter out the noise of this universal certainty and arrive at real possibilities worth considering?

By applying a data strategy to our everyday lives!

The steps are simple:

  1. Hypothesize – develop questions related to a perceived need
  2. Listen – gather data
  3. Develop Insights – make connections from the data
  4. Conclude or Repeat

This may seem obvious, but consider an example:

Let’s suppose you are shopping for a snow thrower.  What led to your decision to buy a snow thrower?  How did you logically connect your need to have passable driveways and walkways with your need for a snow thrower?  Why not get a shovel instead?

Much of the time we implicitly make the connections between needs (questions) and products (answers).  Recognize that in some cases the product is a service, but for simplicity let’s just call them products for now. 

These products are simply prepackaged answers in search of the right questions.  If you jump immediately to “snow thrower” then you have implicitly solved the problem without even knowing the questions!  It is simply left up to sales people to lead you from your general answer to a specific answer via the questions that give them their highest commission.

It may go something like this when people are answer-driven:

“I need to buy a snow thrower.”

“We have great snow throwers.  With the heavy snow we’ve been getting, you’ll want a dual-stage with a quick-throw clutch.  We’re running a great special on our best model.”

“Boy, I sure do need all of that!  Sign me up!”

The 4-step process outlined above makes analysis more explicit – starting by first identifying questions related to needs.  These questions should lead to data acquisition, which will then allow for unbiased analysis and development of more refined questions.  This process repeats until a clear solution emerges.

In this scenario, the same conversation could go like this:

“Can you help me keep my driveway clear of snow?”

“We have several tools and services available.  How big is your driveway?”

“Really big.”

“How much effort do you want to put into clearing your driveway?”

“As little as possible.”

“What is your budget?”

“I’d prefer to spend less, but I’m willing to pay for the best solution.”

“It sounds like you should either go with a snow thrower or a snow-removal service.”

“I never even considered a service before – that is a great idea!  How much is that?”

It may continue from there, but the flow of the conversation is clear: both parties asked questions, gathered data, analyzed the data, and then either asked more questions or reached a conclusion.  They let their questions lead them to possible solutions, but resisted the temptation to jump ahead and decide impulsively.

This approach is appropriate for both “buyers” and “sellers.”  In fact, the roles of buyers and sellers begin to fade away, and you are left with “information gatherers” and “information distributors” going back and forth in a problem-solving dance.  Great answers naturally evolve from these conversations! 

Incidentally, this is the kind of interaction that people should expect from trusted advisors.  If you have someone with more answers than questions claiming to be your trusted advisor, you may want to ask some questions of your own!

Sometimes after going through this process, the perfect answer may not already exist.  That doesn’t mean one should always resort to custom-building.  Sometimes tradeoffs are necessary, but now the trade-offs can be made deliberately.  When starting with an arbitrary selection of predetermined answers, the best solutions may not even be considered!

The best way to counteract the blind certainty surrounding us is to take a step back in our own process:  ask questions, collect data, make connections, and iterate.  This kind of technique is effective in breaking down the most sophisticated business and technology problems, and yet it is still useful in everyday life.

In fact, I’m absolutely sure it is the right solution for you!

Anthony J. Algmin is a Manager in the Business Intelligence Practice at West Monroe Partners.

Phone: 312-602-4000
222 W. Adams
Chicago, IL 60606
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