Managing Data Clean Up: 3 Common Data Errors for Utilities to Look Out For

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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.

Why Clean Up Data Before Integrating It?

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.

Common Data Error Patterns to Look Out For

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.

• Null Values: Look out for null values throughout your data records. For example, if your organization’s “system of truth” is GIS, perform a quick analysis on the feature class in question to check for glaring patterns where the source attribute is null or incorrect. If glaring errors are apparent, then organizations may consider changing the “system of truth” for that specific attribute or performing a data load to populate that field before integrating. Ultimately, organizations want to avoid overwriting good data in either system during integration.
• Mismatched Values: Different systems use different methods to track data – some systems might use numeric values for a certain field, while others might use text or alpha-numeric values. Make sure your organization understands and is aligned (on both GIS and Asset Management teams) about how data is entered in the to-be integrated systems.
• Human Error: With manual data entry, mistakes are bound to happen. For example, if GIS editors manually type values for certain fields instead of using domains, there is room for human error. Plus, different GIS editors might enter the same values using different processes – also creating potential for error. While it would be impossible to find all of these errors manually, when looking through your data, you may find error patterns that can be fixed.

Data Clean Up with Geonexus

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.

Conclusion

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.