The Path to Better Data-Driven Insights
As data analysts, we are coached to add value for the teams or organizations we work with by ensuring that the output of our analysis includes actionable insights.
This means we need to go far beyond simply developing skills in creating technically correct charts or visually appealing graphs. We can also lean into being purposeful about crafting data-driven insights that inform and highlight the opportunities for improvement that will help achieve the business goals.
So, with the importance of creating good data-driven insights, you might wonder how you can gauge when your insights and recommendations are hitting the mark.
Here are a couple of best practices that you can look to incorporate into the workflow the next time you’re working with a team to drive improvements using data-driven insights.
1. Great data-driven insights go beyond simply reporting the data.
While it is essential that data-driven insights be derived from and defensible based on the analysis, great data-driven insights go beyond simply describing an issue by providing context into why something may have occurred.
Take, for example, a scenario where your analysis shows that there was a 20% decrease in a Key Performance Indicator over the last 30 days. Flagging this as a concern is a first good step, but a great data-driven insight digs deeper to ask why this may have happened…
- Was there a change in the process over the last 30 days?
- Was there an unexpected event that occurred that blocked performance?
- Did we start doing something new that may have interfered with performance?
Getting to the context of why something happened is often a team effort and requires you to be curious and ask questions.
There are some tried and tested root cause analysis tools like Five Whys or Fishbone analysis that can provide the framework to do a thorough evaluation. By leveraging these tools, you reduce the risk of stopping at symptom-level issues and getting down to the root of the problem.
2. Great data-driven insights are actionable.
Once you’ve adopted the idea of adding context to your data-driven insights, the next way to level up is to help the team focus on those areas that will bring the most improvement.
When it comes to potential root causes, there are two main types that teams will often run into: those resulting from special cause variation and those from common cause variation.
Special cause variation includes those potential root causes that occur from unplanned or unexpected glitches in a process. Examples include things like an unplanned power outage or unexpected damage that occurs to a critical piece of equipment. While a team may never be able to control for all the potential sources of special cause variation, the impact can be very high if or when they do occur, so it is often prudent for teams to take a proactive approach. This can include identifying what could potentially go wrong in a process and then where appropriate developing contingencies or back-ups.
Common cause variation are those potential root causes that occur as part of the regular fluctuation in the process. For example, the variability in the length of time it takes for a particular process to be completed. By addressing these sources of variation, teams can often achieve improvements in process predictability and overall capacity. These improvements can feel most approachable and rewarding to teams because they can often be implemented in a step-wise manner while still realizing incremental progress towards a larger business goal.
3. Great data-driven insights result in measurable change.
The ultimate validation of a great data-driven insight is measurable evidence of improvement after incorporating changes. This means committing energy to not only implementing the change, but also into what will be measured to validate when an improvement has occurred.
Using frameworks like a Plan-Do-Study-Act (PDSA) cycle can help with incorporating a pre-post data collection plan into the overarching change implementation process. This will support the team by having the data they need to demonstrate their improvement has moved the needle on the desired business goal.
Wrapping Up…
Developing better data-driven insights is a practice.
A practice that includes not only seeking to improve our abilities as analysts in analyzing data and crafting great data-driven insights but also leveraging our unique abilities to support teams as they test and implement changes.
Being able to do this as an analyst is a great way to demonstrate your abilities and add value to the team!
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