Bad data in = bad data out
Bad data in = bad data out
“Without a systematic want to start and keep data clean, bad data will happen.” – Donato Diorio, the founder and CEO of DataZ.
When I first read that quote, it really hit me. This is so true, and while artificial intelligence (AI) is decreasing the amount of work we have to do to process the data, we still need to oversee the data to ensure that there is no bad data going in.
Bad data can easily cripple any organization. This means that we need to know our craft – even more so than before – to be able to see, at a glance, what isn’t right. Enter Xennet Autoreview.
Autoreview is a newer tool on the market that will quickly and easily see what isn’t right – with the help of AI – so that you can dig in to get it fixed in record time. The more you use it, the more time you will save, and the more accurate your data is.
If we don’t ensure our data is accurate, we can suffer from:
- Offering wrong business strategies to our clients.
- Damaged reputations.
- Increase in financial costs and time fixing the errors.
- Missed opportunities, as you are chasing your tail to find and fix all of these errors and don’t have the time to look at new clients.
Since using Autoreview, I have found that it’s very easy to find bad data, and equally as easy to find the source of the bad data for a quick and easy correction. In addition, I have been able to refine the data collection process on more complicated files to find items that would be more complex to notice without digging deep.
There are 4 R’s to using Autoreview:
- Rule: Set up your rules based on supplier, amount, ledger, transaction type, memo, description, and currency – just to name a few.
- Resolve: Any abnormality or rule breach.
- Review: Any items flagged for deeper investigation.
- Report: Provide accurate reports to help your clients with effective decision-making.
A rule breach or abnormality could be any of the following:
- Poor AI: Your receipt capture app may have used wrong numbers or wrong data.
- People: You or your team member may have inadvertently entered incorrect information, or coded an expense or sale wrong.
Either way, you can now fix this so that the abnormality or rule breach does not happen again.
- Poor AI: You can fix the vendor/supplier rules to match up so that the error does not happen, or even take that vendor/supplier off auto publish, if you have that turned on.
- People: It may be necessary to have more training, or set up procedures to ensure the error does not happen again.
At the day, I find that the ease of Autoreview gives me peace of mind that we are providing the correct data to my clients.