How Can Enterprise System Integration Improve Data Quality?

Data quality drives the reliability of data-driven decisions and is therefore paramount to any organization, particularly asset-intensive organizations. Below we outline three ways that enterprise system integration can help improve data quality.

Evaluate Existing Data State

Enterprise system integration tools can be invaluable for evaluating the existing state of enterprise data. By comparing data in disparate systems, you can identify discrepancies between the systems.

The Geonexus Integration Platform helps with this through “Preview Mode” functionality and out-of-the-box reporting. Preview mode examines the records without committing changes to either system allowing operators to investigate potential errors caused by business logic validation. This information permits operators to address the issues before synchronizing data in commit mode. Preview mode will also generate a report that specifies information regarding specific record creates, updates, and various other asset life-cycle operations.

Solve Data Errors

Once application errors and discrepancies caused by bad data or logic have been identified, they must be resolved. Solving data errors often feels like a monumental task and can be if you are using manual processes to edit your data. Enterprise system integration can take the edge off this data clean-up process.

Enterprise system integration can resolve data quality issues through out-of-box methods. One of the ways the Geonexus Integration Platform can help resolve data issues is by configuring one-way synchronizations between the source (where data is available and accurate) and the target (where the data is missing) on a field-by-field basis. Configure a data owner within the Geonexus Integration Platform User Interface and synchronize records from the source of truth.

Additionally, the Geonexus Integration Platform provides dynamic translations and attributes as a method to solve application logic errors such as domain violations or data type discrepancies. All of which can be achieved through configuration in a user friendly, no code User Interface within the Geonexus Integration Platform.

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Ongoing Data Quality Management

After the data clean-up effort is complete, it is critical to have processes in place to maintain the quality of your data. This can also be accomplished through configuration within the Geonexus Integration Platform. Along with data quality reporting, which will continue to identify and report issues as they arise, there are also synchronization processes that can be put in place to promote data quality.

As mentioned above, dynamic translations are a powerful feature within the Geonexus Integration Platform. These data translations keep data aligned by converting data coming from a source system into a logical value in a target system. There may be a situation where you want to translate numeric values coming from one system to character descriptions stored in another system. For example, in GIS coded value domains are often used. Coded value domains store a numeric value in the database and display a character description to the user. An example of a coded value domain may be a Boolean type, which is storing the value “1” which equals “TRUE” and the value “0” which equals “FALSE” in the application context. When this data is synchronized with a system such as Maximo, the value stored in the database will be sent from GIS to Maximo. In the Geonexus Integration Platform, data translations are configured through a feature called “Value Lists.”

Watch this video to see a demo of the Geonexus solution and how it can help with data quality challenges:

As you can see, enterprise system integration can play a large role in improving data quality. First, as a tool to evaluate data quality, then supplying the flexibility to replace manual processes for resolving data errors, and finally delivering ongoing insight for data quality management.

At Geonexus, we believe data is the lifeblood of a business. Your business runs on data, and you need to be able to trust your data to make informed decisions. Therefore, data quality must be a priority. Our proven platform can help. Read more about our platform here or contact our team for more information today!