Quality In, Quality Out ... Decisions Made the Right Way

Posted by John H. Heinrichs

Jan 7

Quality In, Quality Out….Decisions Made the Right Way

Data Decorated_Garbage_Can_-_2drives financial decisions and is the cornerstone of building a quality financial model.  Data is either used to make a decision or justify a decision.  Bad data can drive bad decisions while good data can drive good decisions.  Without a doubt, it can work in the opposite fashion, but most would agree with the previous statement.  Without data, decision makers are left to their instinct.  Basing decisions on pure gut instinct can yield positive outcomes, but the opportunity to open the risk window is staggering. 

(Photo by Dr. Avishai Teicher [CC-BY-2.5 (http://creativecommons.org/licenses/by/2.5)], via Wikimedia Commons.)

Having spent countless hours collecting data with clients from various industries, managing this process poorly can have terrible consequences.  Data from multiple business units and service lines is regularly required to build a multi-layered business case.  It is not uncommon to have an initial model with thousands of data points. 

Decision makers can be so set on achieving an end result; they often forget that data quality is always step number one.  Forget a future savings and forget the payback, focus on the data or else nothing may be achieved!  Project timelines and the incontestable thirst to meet those deadlines all too often keep the data collection process short-lived.  In the end, those analyzing the data are left with garbage.  We all know the saying that follows after “garbage in.” Yes, it is “garbage out.”


Data is always dirty, and that’s the dirty truth!  Very rarely, data comes in having been cleansed properly and in no need of normalization.  Data in its purest form, before any manipulation, will always need to be cleansed.  However, there are steps that can be taken to minimize these efforts. This ensures the cleansing process does not turn into a elongated mess:

  1. The DATA must be CURRENT.  One of the worst ways to botch a model is to use old data.  The common goal of most models is to present a current financial picture.  When old data is used, assumptions are needed.  Assumptions can usually uplift or down lift the data enough to put it in the ballpark, but this adds too much risk.  Make the time to pull current numbers.
  2. The INDIVIDUAL making the decision must OWN the data.  Trust is unique in the business world.  It is often hard to come by, but not when it comes to data collection.  Key decision makers often put too much trust in the source of the data (be it human and software!).  Those tasked with management responsibilities have a RESPONSIBILITY to check the data for completeness and accuracy.  Build time for multiple review sessions into the project plan.  If management doesn’t sign off on the source for their decisions, potential problems are inevitable.
  3. TIE the data back to numbers people KNOW.  Data is often passed from resource to resource.  Things can get bumpy along the way.  The end result is what I like to call “mystery manipulation.”  It can happen at any time and no one knows why.  Data used for decision making purposes must always tie back to the original source.  If there are slight modifications along the way, make sure they are documented.  If it doesn’t tie, you must seek out the delta.
  4. PLAN appropriate TIMING to collect the data.  Data collection isn’t something that can be done overnight.  If the data set is small, then perhaps that is an exception to the rule.  The data collection process must never be hurried along just because an arbitrary date was put on paper during the initial planning stages.  It is helpful to associate percentages of completion around the collection process (i.e. 45% of Accounts Payable business unit data collected).  This allows decision makers to associate the potential for results based on the amount of data initially collected.

Data collection always seems to be a daunting task when discussed during the planning phase of engagements or projects.  The level of stress associated with this task can be minimized by utilizing the steps I outlined above.  While the steps won’t put out every fire that arises, it will definitely assist in minimizing their potential to start.

In the coming months, I will be publishing a whitepaper with a fellow colleague on a similar topic.  The whitepaper discusses issues that can arise when a business case is immediately shelved after a contract with a service provider(s) is finalized.  The assumptions used to build the business case (often times because of a lack of source data) are never revisited or updated to ensure the financial savings were actually correct.  This issue happens very frequently in the world of outsourcing and shared services and tends to derail savings in the long run.

If you have had similar experiences with data collection for a client or employer, please reach out to me and share your story.   I welcome any insight to how you managed the process to a successful outcome.

(All accolades and credit for this tremendous blog post are for Paul Buckles who is the author and creator of this blog post.)

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