Written by Troy Robinson
I am an Integration Engineer. My job is to connect your different HR systems allowing them to function as if they were one big harmonious system. It is critical to ensure that employee data from multiple systems your organization may use (such as your recruiting system, talent management system, onboarding, people management/core HR, timekeeping, payroll, benefits, and IT data warehouse), can be shared information – meaning that these various systems “talk to each other.” Otherwise, your administrators perform dual data entry in multiple systems, which leads to errors and data inconsistency, ultimately resulting in risk to your organization over time as bad data compounds.
As more and more organizations move to cloud-based solutions, organizations realize with an ever-increasing frequency just how out of sync the data in their various HR systems have become. This is particularly problematic when an organization is in the process of implementing a new HCM system, as the new vendor will request files and data feeds from your different existing systems. In addition, for organizations with disparate systems, providing that data in a coherent manner can be a struggle.
Now is the time to work through data cleanup and harmonization. First, identify your SPOT (single point of truth) for your different data types and make the necessary data structure changes that will benefit your organization in the long run. Do not rely on manually keyed entries. These typically result in many headaches if you are trying to provide accurate data reporting and aggregation. Instead, create additional maintenance screens to set up picklists for user entry and add user input validation automation to enforce data rules and consistency.
Once you have your SPOT, connect the interdependent systems via available APIs or other necessary means. This will allow your administrative team a single point of data entry, which ultimately reduces the number of data entry errors and provides consistent, accurate data throughout all systems.
Many issues that arise during the user acceptance testing (UAT) phases of a new software implementation occur due to poor data. The data provided to integrate the new system may not be what was initially planned or agreed upon. This is often a result of “sample” data delivered early in a project that was more than likely manually put together in the format required by the new system to meet specific deadlines. “Mock” data can cause more harm than good. It is essential to have TRUE source data created by the automated source system early in the project. Differences between “mock” data, such as date formatting, spelling errors, or code variants, can cause copious amounts of re-work for your team and delays in your project. Here are some examples.
Let’s say with your “mock” data, the format for the date field is MM/DD/YYYY. This is how you would likely see dates formatted if you are in the U.S. and use Excel. However, when the source system finally provides the data, the dates may arrive in YYYY-MM-DD format.
Let’s look at another scenario. If you are looking at your workforce management/payroll systems, perhaps your “mock” data says that the premium code for shift 2 employees is “2-Premium”. However, when the source system sends the data, the premium now appears as “Shift2Prem”. Suppose the developer writes custom logic to perform specific calculations looking for “2-Premium”. In that case, you can see that this would not match, and you would find that out when you’re checking calculations during user acceptance testing.
Once your system is in production after a thorough user acceptance testing, most support tickets arise due to source data errors or effective dating issues with data. “Effective dating” means that the system data can have different values during different periods of time. If your systems implement effective dating, confirm the data is accurate for the period your issue is occurring. Countless support tickets for data support issues arise where the effective dated record is not in the period in question or the dates in the records have been entered incorrectly. New job, location, or other data codes are added to systems and not communicated to all downstream teams dependent upon the source data.
Good communication, planning, and end-user training can help eliminate many data entry errors and source data issues impacting downstream systems. In addition, data migration and integration tools can help provide the necessary data validation needed to enforce good data in all connected systems.
Let HRchitect help you and your organization evaluate your HR systems, identify your SPOT and help connect/integrate your HR systems to provide a seamless, secure flow of HR data to all who require it. Check out our various levels of support and reach out to us today to request a consultation.
Our expert consulting teams have decades of experience with HCM systems, and we work alongside you every step of the way to ensure your project is a success. Over the past two decades, HRchitect has helped thousands of organizations worldwide align their HCM technology initiatives with business objectives to achieve extraordinary results. HRchitect is a name you can trust and the one-stop-shop for all your HCM technology needs, including strategy, evaluation and selection, implementation, change management, ongoing support, and everything in between.
Troy Robinson is an HCM Integration Engineer with more than 20 years of experience as a software consultant. In his role at HRchitect, he leverages his unique experience as both a consultant and end-user of HCM systems to lead clients through successful workforce management systems implementations.
About Troy Robinson