Have you ever sat down with your business where they claim that a significant percentage of your report's information seems incorrect? Data problems can be hard to spot, let alone repair. By reading this blog you will be able to challenge even the most difficult data issues with several hints and tricks. The main goal is to find and fix the root cause of the problem. (And you know what? Often the root cause can be very easily explained)
The root cause? let's do an analysis!
Root cause analysis is the process of identifying the underlying causes of problems or issues. In the context of this blog, the root cause analysis would involve to identify the reasons why the data in the report is inaccurate.
There are many potential causes of incorrect data in reports, including errors in data entry, calculation, or interpretation; incorrect assumptions; faulty data sources; and so on.
Root cause analysis can help to identify the specific cause or causes of incorrect data in a report, so that steps can be taken to correct the errors and prevent them from happening again in future reports.
What types of discrepancy exist?
There are a few things that could be causing the discrepancy between the data in your report and the actual data.
Granularity
The first thing to check is the granularity of the data. If you're looking at data that's been aggregated to the highest level, it's likely that some of the details are being lost in translation. To get an accurate picture, you'll need to get down to the lowest grain possible. This means drilling down into the data to see what's happening at a more granular level.
Collection
Another possibility is that there's something wrong with the way the data is being collected. If you're relying on manual data entry, for example, there's a greater chance for error. There might also be issues with the way the data is being coded or processed. If you suspect there are problems with the data collection or processing, you'll need to work with your team to figure out what's going on and how to fix it.
Definition
Finally, it's possible that the discrepancy is simply due to different definitions or interpretations of what constitutes "correct" data. If you're working with team members who have different ideas about what the data should look like, it can be difficult to achieve agreement. In cases like this, it's important to take a step back and agree on a common definition of what constitutes correct data. Once you've done that, you can move forward with confidence that everyone is working from the same baseline.
Minimize data impact (use filters)
It is common for reports to contain incorrect data. The data in your report may be incorrect for a number of reasons, including: -The source data is incorrect -The report is based on outdated data -There are errors in the report itself.
Fortunately, there are ways to minimize the impact of incorrect data in your reports. One way is to use filters. Filters allow you to focus on specific data points and exclude others. This can help you identify where the problem lies and find the problem.
If you find that the data in your report is incorrect, don't panic! There are ways to minimize the impact of the incorrect data. By using filters and multiple sources, you can identify errors and correct them. Explain this to your business on why it happened and how it can be solved. It is still a human process to make mistakes, so own them.
Does the bug happen all the time
If you are finding that the data in your reports is not correct, it is important to first determine if the issue is happening all the time, or if it is intermittent. If the bug only occurs occasionally, try to identify any patterns that may be associated with it (e.g. does it happen after a certain type of action is taken?) This can be helpful in narrowing down the potential causes of the problem. However, if the bug is happening all the time, it is likely that there is an issue with the data sources themselves, and you will need to investigate this further.
Have something ready to compare
If you're not sure whether the data in your report is accurate, it can be helpful to have something to compare it to. This could be another data set from a different source, or previous data, or even a data dump from a database.
Having something to compare the data to will help you spot any errors or discrepancies. Take in mind that your business almost always have something to compare your report with, so be vigilent in the fact that their source can be wrong as well. Ask yourself questions like, how old is the source? Are there some additional calculations?
Conclusion
When troubleshooting data issues, it is important to look for the root cause of the problem. To do this, you need to get down to the lowest grain of data possible. This means looking at the smallest pieces of data that make up your report. Once you have isolated the smallest grain of data that is causing the issue, you can then start to look at ways to minimize the impact of the data on your report. Getting to the root cause of your data problem often involves using filters to exclude certain data points from your report. It is also important to determine if the data issue is happening all the time or only sometimes. This will help you to understand whether there is a bigger problem that needs to be addressed. Finally, it is helpful to have something ready to compare your data against. This could be a previous version of your report or another similar report. Having a reference point will help you to identify whether the data issue is a one-time occurrence or part of a larger trend.