“Garbage in, garbage out”*, or as it’s abbreviated in the computer science field, “GIGO”, means the quality of information output from the system is dependent on the quality of information input into the system. If the initial data imported in the system is incorrect, then the data in your reports will also be incorrect, which ultimately defeats the purpose of an ERP implementation. Having correct data is imperative to a successful project, which is why data reconciliation is so critical.
Data reconciliation is the process of matching the data imported into the new system to the source in which the data originated from (e.g. previous ERP system, Excel, payroll system, banking system).
Here are some strategies on the data reconciliation process:
- Make time for it! This activity is largely completed by the client and not the implementer because the client knows their data and has to sign-off on it being correct. Data validation is going to require dedicated time, so make sure you’re aware of the project plan ahead of time and schedule time accordingly.
- Since the data being reconciled typically spans multiple years and business areas, it is important to appoint an owner of data reconciliation to track the progress of the effort. It is important to ensure the owner has the proper knowledge, training, and resources necessary to complete the task.
- Validate the data in newly built reports. As part of an ERP implementation, the implementers will likely build new financial statements as well as other reports. Be sure to validate the data in those newly built reports—this accomplishes two things:
- It ties out the data
- It ensures the reports are built to the business’s needs.
It’s incredibly important to make sure the data is correct before relying on a new ERP system for reports, so make sure to dedicate an adequate amount of attention to reconciling the data.
“Garbage in, garbage out” was coined by George Fuechsel, an IBM programmer and instructor—you can’t make this stuff up!
To Wrap It Up
Implementations are a marathon that involve managing a lot of different variables (e.g. timelines, people, functionality, testing, data, etc). While every implementation is unique, these are the common pitfalls we’ve come across. By knowing these pitfalls ahead of time, having strategies to prevent them, and understanding how to deal with them if they occur will give the implementation the best chance at being successful.
