Implementing a new system can be a challenging task. Sometimes you have to change the way you work, adapt to new structures and templates, and change your habits. While this isn’t the case with Datarails, the implementation phase can be an enlightening experience. Our Customer Success Managers, who’ve worked with hundreds of customers, opened up and shared the top mistakes they found, and fixed, with their customers during the implementation process.
- A customer realized the accounting team was changing the data every month, almost all data from 2013 till today.
The client has an accounting system from which he extracts source data and uploads it to Datarails. After his financial reports were uploaded into the Datarails system, the team created a balance sheet for him based on past numbers. However, the numbers from Datarails did not match their past numbers. To investigate the discrepancies, the company looked towards their internal accounting team to figure out why the numbers were different. It turned out that the bookkeeper changed some numbers AFTER they had already filed their reports and an audit was conducted. They had been working manually on Excel without an organized system to handle their reporting. Once the numbers were inserted into the Datarails system, the long-existing mistakes were brought to light. Upon becoming aware of the issue, the company swiftly addressed it. By working with a system such as Datarails that doesn’t allow for miscalculations, the company diminished the chances of future errors.
2. A customer realized that the financials produced by Datarails were more accurate than hers, and she needed to reconcile and amend financials for 2020.
A customer used Datarails to create her 2020 financial statements. At the completion of 2020, once creating a new P&L report using Datarails’ formulas, she found many discrepancies between the new P&L and her existing P&L. She noticed that her GL would update at times after she completed her financials for any month. She wound up amending her entire 2020 financials, and she was able to do this using the Datarails formulas- she used Datarails’ drill down feature to see what made up each value in the financial statement in order to correct it.
3. A customer realized that he had filters in place for his sales report that were not necessary.
A customer wanted to use Datarails to create a weekly sales report. The Customer Success Manager was able to produce and automate the report using Datarails’ formulas, tables, and filters. Once the report was completed, after creating new weekly reports the customer realized that the numbers coming from Datarails were significantly different from the ones his original reporting system was creating. This led him to discover that he had filters in place that were not supposed to be in place, and he had to change his reporting system as well as his entire filtering process.
4. A customer realized that they were mapping some of their GL’s the wrong way all along.
A customer realized that she was associating a particular journal entry with the wrong rollup. As a part of the Datarails implementation, the customer had to go over her GL associations and their respective rollups. What she discovered along the way was that, for years, she was bulking up the wrong rollup, and inadvertently reducing another!
5. A customer realized that the allocation process was set up incorrectly. Using Datarails, they realized where the issue was and redefined the allocation method.
During the Datarails implementation process, a customer realized that the internal division of their P&L was done incorrectly- more specifically, an allocation of COGS to a specific client was not correct. The company had estimated in percentages which allocations were attributed to customer X and which for the rest. But when they checked the allocations with Datarails, they saw that the percentages they had attributed were not even close to the numbers obtained using the solution. Using Datarails, they identified all of that particular client’s transactions, and came to the conclusion that something on their end was off. They acknowledged that there was a mistake and trusted Datarails for the creation of their modern P&L.
6. A customer realized that manual correction entries weren’t booked in the system.
Our Customer Success Manager was putting together a high level P&L variance analysis report. While putting it together, he saw that there was a discrepancy between what was calculated and what the company reported in their previous report. When this discrepancy was brought up in a call with the CFO and Senior Financial Analyst, it became apparent why there was a discrepancy- the Senior Financial Analyst maintained an Excel where he recorded adjustments that needed to be made but weren’t actually booked in the system. Upon this discovery, it was agreed that all numbers be recorded in the system, no matter when amendments were made. Once this was done, the numbers from their local report tied out with the numbers that Datarails had produced.
7. A customer realized that their stock valuation was done incorrectly.
One of our Customer Success Managers shared that the customer they were implementing the system for had a weekly process which included counting their physical stock.When implementing the Datarails system, our Customer Success Manager created a management report for them that included P&L, cash flow, and production/operations details so they could keep tabs on their stock. However, they found a discrepancy between past and calculated figures. They turned internally to investigate, and it turned out that the logic for calculating price per kilogram of their product was off, resulting in an enduring miscalculation in their stock valuation. This mistake had existed for months! Of course, the mistake was amended in the new Datarails management report.
Minimizing Manual Work Will Minimize Errors
Our Customer Success team works with hundreds of customers and has come across a handful of miscalculations, missed datapoints, and inadvertent discrepancies. Once Datarails FP&A solution comes into play, it becomes apparent just how powerful working with a system, as opposed to working manually, can be in improving calculations and processes. Instead of subjecting your essential financial data to miscalculations and errors due to manual work, consider adopting a tool like Datarails. Working with a solution that draws directly from your organizational systems means that there’s no room for mistakes. Minimizing manual work and maximizing the use of your organizational systems will have you seeing more accurate numbers and reliable data in no time.
Interested in making sure your processes are error-free? We’d be happy to help! Reach out to us here.