During CloudSuite implementations in the past, several clients have asked us to bring all of their historical data over to the new system. Obviously this is possible, but it's definitely not our recommended best practice. You can bring as much data as you want, whether the cost/benefit makes sense. If you bring over every piece of data from your old system just in case of a potential reference someday, then you’re just muddying up the reporting waters. We always have this conversation as part of a pre-planning engagement because it’s vital to the project to decide what data you absolutely must have to have correct accurate reports.
If you only bring your year-to-date historical data, you can expect a relatively short timeline. You will still have archive data to access the information that you need, which is another thing to keep in mind when having that discussion about what to bring over and the resources that will be required. As you go through a scoping exercise, your implementation partner will address this before you can even get a proposal. There’s a lot of cooks in that kitchen when it comes to deciding what should be brought over, so make sure the right voices in the room when working out those details.
Let’s talk about the different methods of bringing data from the legacy system into GHR payroll. One of them is the data migration factory that Infor provide with their own proprietary set of tools. That’s really good for companies who aren’t making any changes to the design and data structures. While the provided spreadsheet designer and add-in where you can query and upload data is useful, this options has limitations because it's more of a line-by-line upload. It takes a good amount of time to load something like 30,000 records, but our proprietary conversion tool set is evolving to speed this up as we speak.
Every client that we bring on board has needed enhancements. We've added a lot of new creations to the tool set, with the newest being data validation. This is very time-consuming and involves going through multiple processes, so directly from a client request, RPI’s tech team created a data validation tool. It’s working very well and everyone is really excited about it, especially the fact that it is an evolving tool set that makes the implementations go a lot smoother.