In this article, we will discuss what goes into making decisions from the data you gather and how you might want to approach your data-driven decision-making as a team.
As you may already be suspecting, gathering data and reviewing data isn’t the reason for transitioning your team to being more data-driven. That is only the beginning of the process. You need to have questions that you are trying to answer and multiple data points that you can bring together to determine the direction you will head.
It will be important to teach the skills internally among decision-makers on what methodologies to use to make decisions. In fact:
These artifacts from your business decisions will be important to further the learnings of the broader team and continue to make decision-making a focus and allow for improvement over time.
Here are some examples of some questions that you may be asking about your business and some of the data points you may bring together to determine the answer.
Question 1: Are we in an up trend or downtrend?
Most companies look at month-to-date, quarter-to-date, and budget vs. actuals. But if you want to understand trends and be more predictive you may want to consider looking at a four-month rolling average.
Question 2 - Is there an ideal situation we can compare our current situation (process, procedures, equipment)?
Let's say you are a manufacturer and you have one piece of equipment that is operating at an optimal rate. You can compare other machines and processes to this optimal rate to determine where you may be able to improve. This helps you to measure drift so that you can measure efficiency.
In a non-manufacturing environment, you can use this same methodology to measure a process that is working smoothly and is producing optimal results (i.e. sales process, return process, marketing process). Then measure other processes against this one and determine where there is drift, so you can bring process improvement.
Question 3 - Based on written feedback from my customer survey how can I determine the types of responses?
You can use clustering to code responses into positive, negative, and neutral feedback - so that you can determine the next step actions to take with particular customers. Then feedback can then be further coded into categorical areas so that they can be handed to specific teams to gain answers and predict changes that you can make to improve customer experiences.
I hope these tips are helpful to moving you towards a more data-driven decision-making model. If you have additional questions on these or more, please feel free to reach out to us to schedule a free consultation HERE.
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