As enterprise data becomes more expansive, it also becomes more convoluted. Poor management of high-volume data can quickly create chaos for an organization’s system of records, thus compromising data integrity.

But data mapping tools can help organizations avoid this nightmare scenario by maintaining clean records and ensuring that duplicate data is identified and eliminated. When choosing a decision management system for your data, pick a solution that offers all of the mapping tools you need to ensure integrity at any scale.

Here are some key data mapping tools you’ll want to consider.

Address Validation

Addresses in customer and contact lists are a common source of duplicate data in a master system of record. Thanks to small inconsistencies in how addresses are filled out in forms, a single address can produce multiple data entries across your records.

A decision management system should offer address validation as a simple tool to standardize address entry in forms to eliminate the possibility of duplicate entries. The best way to do this is through a third-party solution like SmartyStreets, which references the U.S. Postal Service’s address system to validate addresses in any list. This system ensures that all addresses entered are matched to an official USPS-recognized address, thus validating those entries before they’re submitted into the system.

Interceptor Cleaning Rules

Although source systems may not be accessible to your business when looking to change or correct “dirty” data, an interceptor can implement a system of rules to identify dirty data and convert it when it’s entered into reporting system databases.

An automated interceptor helps standardize your master records data instantly, thus preserving the integrity of your master data in spite of flaws discovered in source data.

Form Validation

Just as an interceptor cleaning corrects data at the point of entry into a data system, form validation can employ a number of tools to standardize data and ensure that data entering a management system is clean from the start.

Drop-down menus, standardized data lists, data ranges for numerical fields, character restrictions, and data calls to search for duplicates are all simple ways a form validation tool can improve the integrity of your incoming data.

Master Data Storage

A decision management system should be able to function as a master system of record, identifying any existing records located within other software applications or data sources. As a master system of record, such a platform can also ensure that a system of record is created using clean data—even when bringing together multiple distinct records.

Automated rules can help sort and manage this data, and workflows can be deployed to assign tasks to employees when the system of rules isn’t able to resolve a data discrepancy. This allows you to investigate and validate data that goes into the master system of record.

Account/Product Code Reconciliation

When codes are used to categorize products, sales, customers, or other business data, reconciling this data is sometimes a challenge. This data may be moved, consolidated, transferred, or otherwise manipulated in ways that create data validity issues.

Business acquisitions, mergers, relationships with affiliates, and/or poor existing data controls can all create situations in which code discrepancies develop. With a tool capable of account/product code reconciliation, you can institute better controls over that data, thus enabling better management oversight and more accurate master records.

System Integration

Disparate systems—and/or poor integration of those systems and their data management practices—can create blind spots and blockages that prevent the flow of data from one system to another.

Over time, the integrity of this data is placed at risk. Organizations need access to tools that facilitate seamless system integration, resulting in better data mapping that supports your rules engine and overall data integrity.

The more comprehensive this integration, the better the movement of data between systems—and the easier it will be to ensure consistency and standardization of this information.

ESB & Message Bus Integration

Within a service-oriented architecture, ESB and Message Bus can bring separate systems and solutions closer together—especially where data is concerned. This integration enables greater consistency of data as it moves from one application to another—even when those applications are set up to run independently from each other.

An Important Choice

Your choice of a decision management system affects not only how data is managed, but also how reliable it is as a resource for your business. With the right data mapping tools in place, you can reliably manage this information at any scale, building a master system of record that delivers valuable insights and enables greater efficiency within your organization.