The 5 Most Common Pitfalls of Informed Decision Making

Dec 2, 2014
Written by
Ton Baas
Ton Baas

The 5 Most Common Pitfalls of Informed Decision Making

We have all experienced moments where we have had to make a big or important decision, without any form of guidance, help, or support. It’s a well-known fact that managers make many decisions based on gut feeling. This may be fine for smaller decisions, but what about big(ger) strategic decisions? Informed decision making is becoming a trend, with many organizations dispelling gut-based decisions and incorporating tools and analytics to help reach the best possible decision.

With our expertise in change management and decision making, we help you to make the right decisions. However we realize that there are many pitfalls regarding informed decision making. We can warn you about the following five most common pitfalls:

5. Treating all decisions the same way
Decision LevelsQuite an obvious pitfall, yet one that comes back more often than you would think. Roy Schulte and Rita L. Sallam from Gartner have written an article in which they discuss the difference between operational, tactical and strategic decisions. Operational decisions tend to be structured – easier to automate and they can be made relatively quickly. However there is a catch regarding the automation – it does not work for every decision. Schulte and Sallam point out that it is best to have different decision-making processes and analytical techniques for the three different levels of decisions.

4. Not refining your decision models
Another point that often comes up regarding informed decision making is the models and tools used. In today’s world it is nearly impossible not to think of automation or influences from IT. This also goes for decision making, in which ‘big data’ has become the basis for a lot of analytical views on decision making. However one decision does not necessarily use the same data or even model as another decision – in this case you have to ensure that your model is aligned with the decision ahead and incorporates the right data.

3. Not combining computers with human judgment
Always keep in mind that models or tools are not a replacement or perfect solution for decisions. Schulte and Sallam point out that “where human judgment is required, decision makers should run operational intelligence systems in a management-by-exception mode”. As we get used to the fact that systems take over our work, we tend to become unaware and lose control over any possible mistakes made. It’s good to keep a collaboration between sharp minds (examples are data scientists, statisticians or operations researchers) and computers just to make sure that there is a person who can make the call when something unusual pops up.

2. Know your audience
When we think of informed decision making, the link to business rule representation is quite strong. A business rule is a way of structuring decisions, so it’s important to represent it well. Nicholas Gall has written a paper on business rule representation and points out that currently most business rules (or decision rules for that matter) are embedded in software code, making it difficult to view, understand or modify anything. Decisions lead to changes which you need to sell to your audience. With business rule representation ranging from easy to understand (with less depth) to extremely complex (with more depth), you need to understand the people you make your decisions with, and also the people you are selling the decision to.

As an example: we helped a government institution to remove hard-coded business rules from systems and visualize them alongside the actual decisions made. This created a context and gave a clear image of the connections, improving the ability to analyze and evaluate. This in turn made it easy to track down inconsistencies and improve the decision making process. By knowing the audience and changing the representation of the business rules you can improve the usefulness significantly.

1. Detaching consequences
One of the biggest pitfalls, is detaching decision making from what it actually entails for the organization and its future. Every decision has a consequence which can quickly be lost in the process of automation or the complexity that comes with the process of making a decision. Making sure that the model takes into account any consequence stemming from the decision is essential in order to establish solid decision-making.

All in all, we can see that automation has become an integral part of decision making. It makes it easier to do day-to-day jobs and save time (and consequently money) with every single decision made. However it is essential to differentiate between types of decisions, and to not let models and tools take over everything. It is important to realize that there are a lot of things that require human input. As long as you take into account what model is right for your decision, who your audience is and what shortcomings there are regarding models, your decisions will be made with a near-perfect basis – improving your businesses operations.