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Garbage In, Garbage Out: The Need for Crush Errors for Reconciliation


Image by Mohamed Hassan


There’s a fundamental data problem that’s existed forever, and everyone accepts its veracity: Garbage in, garbage out.


You can have the greatest, most expensive software on the most powerful computers, but if you start with garbage, you’ll end up with garbage.


More than ever, when users silo versions of official data in spreadsheets, the reality of garbage in, garbage out is ubiquitous across every organization, and everyone knows it.


There are numerous tools to clean data. However, they only handle the basics, like address formatting, parsing first, middle and last names, and making phone numbers follow a designated format.


These cleaning tools are weak in two areas: matching and math.


Let’s say you have two customers that are identical to your human eyes, like ‘Dan’s Bar and Grill’ and ‘Dans Bar & Grill’. We can catch it automatically.

However, not many software products will see the difference. (Sometimes people miss it too.) It’s certainly not easy for people to see these errors if there are thousands of vendors.



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And why does something like this happen?

The focus now is ‘garbage in, garbage out’. So, when is this important?


And why does something like this happen?


It could be human error.

But maybe it’s not human error. Maybe it’s a problem that is intrinsic to your organization. Maybe it’s an inevitable error because your organization has an accounting department and a sales department.


Each one tracks what’s important to them separately. Some of the data overlaps, but they don’t have a single, centralized source of truth. This happens all the time.

Here's another situation. Let’s say you have the same situation and there are many variations of Dan’s Bar & Grill, like “Dan’s Bar” or “Dan’s Grill” or “Dan’s Bar/Grill”. What if there are 3 variations in your accounting department and 4 variations in your sales department?

It would be easy to say that they are all the same and then just pick a proper name.


But what if the records in accounting show a balance for the 3 variations of $280 while the ones in sales show $290?



Looking at the above, customers A and B total customer D and E. So those 4 variations have identical totals. We can assume they are all one entity.


Customer C has the same balance as customer G, so we can assume they are the same. Or maybe there are two separate customers with similar names. That’s not so uncommon.


That leaves customer F as a red flag. Where does that $10 balance belong?

This could take some human intervention to resolve, but if there are thousands of rows and the software lets you focus on less than 5%, that’s a huge savings.

We don’t think this is an extreme example. We’ve seen cases with reconciling transfer agent data. The variations of ‘TTEE’ and ‘in trust for’ are voluminous.


Many times, our clients live with this defective data and assume it’s materially correct. That is often true.


However, there are times when such an issue needs urgent resolution. Like when an important customer receives the wrong invoice or is annoyed to be confused with another customer.


The focus now is ‘garbage in, garbage out’. So, when is this important?


Importance is elevated when you’re migrating from one accounting system to another or you’re trying to absorb an acquisition into your accounting framework. Whether it’s QuickBooks, NetSuite, or even Adaptive Planning or SAP, this same issue surfaces.


NextGen, using patented proprietary software, can clean data in ways other products cannot.

How do we work with all these different programs? We rely on the most common data file format: The simple CSV. All software products export and import data using CSVs, including Excel.


Our proprietary software will import data from SAP and Excel and reconcile the differences in ways that are impossible with other products.


It does not independently resolve the issues. It flags them. Then you can focus on the few discrepancies without having to spend an inordinate amount of time trying to resolve data that already reconciles.


Want a demo? Contact us today! You can send us some sample data and we’ll show you how much simpler your work can become.


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