Data Integration is key for optimal business performance. But, before integration, organizations need to consider the quality of their enterprise data. Ultimately, clean and integrated data works for your organization, while messy and siloed data does not.
In this blog, we will discuss why performing a robust data clean up before integration is crucial – plus, we spoke to our Geonexus Implementation Specialists to learn common data error patterns to look out for in the data clean up process.
Clean data allows for more productivity in the field and office, and provides reliable metrics for business leaders to utilize in decision making. While clean data is always vital to organizational and business success, it is especially important that organizations have clean, trustworthy data before integrating.
Integrations, whether they involve a middleware integration platform, like Geonexus’, or custom code, require sharing data between systems. Often, organizations choose one “system of truth” or “system of record” to share data to the other system. If the data in the “system of truth” is messy and full of errors, then that bad data will infiltrate the integrated systems as well.
Asset-intensive organizations have hundreds of thousands of data records – without robust reporting through an integration process, ensuring the accuracy of each record is extremely difficult. That’s why Geonexus Implementation Specialists recommend looking for larger data patterns, especially in the “system of truth,” to alleviate widespread discrepancies prior to integrating.
While manual data clean up is certainly an option, it is a time-consuming and labor-intensive activity. Geonexus offers an automated solution to speed up that process in the form of a Data Assessment. Before customers officially start synchronizing their data, Geonexus executes a “preview mode” synchronization. That preview mode sync point outs the large data patterns that need fixing prior to synchronization, and illustrates what data would be shared where so that clients can assess if the integration is configured to align with their goals.
Plus, once your data is cleaned up and ready for synchronization, Geonexus helps keep it that way. With Geonexus’ full compare approach to data integration, each time users run a synchronization in “commit mode” once the integration is up and running, they will receive reports pointing out discrepancies, errors, changes, and duplicates in the data.
Data Quality should be top of mind for asset-intensive organizations, especially leading up to a data integration project. To learn more about the Geonexus Integration Platform for data integration or data quality, visit www.geo-nexus.com/platform.