It's time for go-live! As cut-over approaches, the "this is real" feeling sinks in. The project plan, quality of data, migration programs, and solution readiness are all examined under a microscope. With the weight of go-live, the team knows that despite months of hard work, the project success is ultimately determined by a few short days of carefully-coordinated activities. Nobody wants any surprises to pop up; everyone wants the new system to work as expected. Anxiety levels ramp up, and suddenly everyone second-guesses themselves.
Will my primary vendor convert? Will sales orders get shipped twice? How many open purchase orders are left in the system? Are we missing any salesperson configurations? Will general ledgers get hit twice when we age open invoices?
No matter how many test cycles have been completed or how well-prepared the team is, these questions will be asked. However, there is a tool that preemptively answers these questions, calms nerves, and creates go-live confidence, the pre-validation report. Pre-validation reports identify data quality issues, show what the data will look like after conversion, and provide high-level metrics regarding go-live readiness.
Getting Ahead of Data Issues
One major benefit of pre-validation reporting is the identification of data issues and load errors before upload into the target application. By identifying data issues early, the team is afforded time to address and reconcile any records that might fall out during a load. Additionally, since pre-validation reports can be run at any stage of the project, consistent generation prevents surprises, ensuring that the project’s data quality strategy is properly executed, and the data is go-live ready.
What’s Reported
The reported data issues on pre-validation reports typically fall into three main categories: critical error, warning, and informational.
Critical errors are typically the highest priority issues that need to be resolved and indicate one of two severe consequences. The first is that the data will outright fail to load, either for a specific record or the entire data set. Alternately, the data will load, but introduce poor data quality situations that are technically allowed but decrease the efficiency of the new solution or prevent the data from being used at all. Examples of this include missing configurations in the target application, suppliers with invalid payment terms, salespersons missing in the target, taxpayer ID duplicated across multiple vendors, and non-purchasable items on a purchase order.
Warning messages flag situations that will not cause a data conversion failure, but should still be corrected. Warnings are typically data quality issues that are allowed into the new solution, but should be reviewed and addressed to increase the effectiveness of the solution and improve overall data quality. Examples of warnings include gaps in cross references where a default value needs to be used, when non-critical data needs to be dropped such as duplicate phone numbers or email addresses, and when address standardization cannot be utilized.
Informational messages indicate both situations that will occur during the conversion, and metrics about the data that will be converted. These messages provide a solid sanity check about the data, and even provide an opportunity to validate the data before it’s too late—after the data has been loaded. Examples of informational messages include record creation or consolidation to help with record count auditing.
Know What the Data Will Look Like Post-Conversion
Go-live often entails a list of requests regarding how the data will look in the target application. Pre-validation reports are a helpful tool for showing the business previews of converted data, such as customers, sales orders, or items. These previews are even more critical in cloud applications, like in Oracle’s, SAP’s, or Microsoft’s SAAS Cloud solutions, where it is impossible to back data out and often difficult to correct incorrect datasets.
There are typically two types of preview reports: summary metrics and detailed data views. Summary metrics contain statistics about important fields and overall record auditing. They allow the business to see how many records exist in legacy, how many are excluded (and for what reason), how many are created to enrich the data, and the final expected load count. Examples of important data sets include distinct sales order types, on-hand quantities, open accounts receivable invoices (in quantity and amount), and general ledger balance totals.
Detailed data views allow the business to sort and find information on any record or field in question. Large datasets can be segmented or filtered to increase usability, and the views will also show the legacy data matched to its target. This type of information is key for complicated and business-critical “dollar amount” conversions such as general ledger balances and asset depreciation reconciliations.
Seeing the data at both summary and detail levels alleviates anxiety and provides crucial insight. The team can trace key data like on-hand, open orders, and customers through the data transformation and prove to the business that nothing will be lost.
Boost Project Leadership Confidence & Prevent Project Timeline Slippage
It is standard for the PMO to receive consistent feedback on the project status so that they can gauge go-live readiness for an implementation. The readily-available metrics within the pre-validation reports provide visibility to the data migration track, and allow the PMO to increase magnification on the data and make predictions on the success of the data conversions. Pre-validation reports provide management with the tools to see the summary statistics and conversion previews; anxiety can be instantly alleviated with positive reports, or if there are issues, resources can be assigned to focus on resolution before deadlines are missed and timelines slip.
In addition to the team’s boost in morale in the data quality and conversions processes, a comprehensive pre-validation reporting process can help dramatically reduce project implementation and conversion cycle load times. Time (and budget!) is saved whenever issues can be preemptively identified. When Premier joins projects that are struggling with data, one the most critical components we implement is a pre-validation reporting process. Once this process is in place, not only do projects stabilize, but we have seen cutover cycles drop by up to 40% and increase the amount of data/business units that can simultaneously go-live. (Read Case Study)
If you have any questions regarding data quality, data conversions, and ways to reduce risk for large-scale business system implementations, email me at steve_novak@premierintl.com or call me at 773.549.6945. I have spent the past 20 years helping some of the world’s most recognized brands focus on what makes them great by removing the riskiest obstacle of their transformation and would like to do the same for you.
