eprentise Data Quality software standardizes data (abbreviations, punctuation, etc.) within a single database or across disparate databases. The standardization function of eprentise Data Quality is like a massive search and replace across all tables and works on configuration data and master data, permeating all changes in every transaction across the system. It is particularly useful for changing area codes for all types of phone numbers or changing units of measure across instances. Once data is standardized, candidate duplicates can be identified and resolved. eprentise Data Quality merges all duplicate data within a single database or across database instances into a single record and then changes all the referenced data everywhere in E-Business Suite.

Organizations need high quality data to inform sound decision-making but have typically focused only on improving data at the Master Data level. Addressing issues at all other levels was seen as too difficult, too costly, and likely to consume countless IT hours. Now, with eprentise Data Quality software, organizations can take a holistic approach to improving data quality by addressing discrepancies that may occur at any data level, not just in Master Data.

Beginning with database objects, eprentise Metadata Analysis Software identifies problems within an Oracle E-Business Suite (EBS) instance or differences across instances, such as broken constraints1 or inconsistent data types, and then eprentise Data Quality standardizes configuration or setup data. After the database objects and configuration issues are resolved, the user identifies duplicate master data, and finally eprentise Data Quality resolves and promulgates the standards and data definitions to every single transaction, everywhere in the EBS instance.

eprentise Data Quality software ensures that all the data in Oracle E-Business Suite is complete, consistent, and correct. From an operational perspective, this means:

• Complete – All business data, including all history, is in one place – the database of record. There are no sunset instances. There are no spreadsheets for reconciliation or corrections. There are no interfaces or data flows between EBS instances.
• Consistent – Since the data is in one place, if it is used multiple times in the instance, it means the same thing everywhere. There is no “translation” to go from one instance to another or from one organization to another. If you are creating a report, consolidating financials, or developing a data warehouse query, you are comparing apples to apples. Standards are in place for abbreviations, naming conventions, and business processes such as payment terms, credit limits, and account numbers.
• Correct – Users rely on the accuracy of the information. There are clear data governance structures in place, as well as standards and processes to create, retrieve, update, and delete data. It is possible to audit the data and prove it is correct according to all the business rules.

After the database objects are standardized and the invalid data is identified and corrected, the software analyzes all the configuration or setup parameters. Comparing data across operating units or even across instances, data is standardized. For example, multiple values or units of measure for the same item are eliminated. Standard lists of abbreviations can be imported and automatically applied throughout the system.

Once the configuration data is consistent, the software automatically identifies and resolves duplicates in the master data. When a candidate duplicate set is identified, the software identifies the duplicate items everywhere in the database, allowing the business user to decide on the attributes of the survivor record. An unlimited number of duplicate criteria can be used anywhere in the database. Once the duplicate is identified and resolved, that change is percolated throughout the database—even to the transaction data. For example, if a unit of measure is changed, it is changed on every invoice, order, statement, shipment, and for all history.