4 Tips to Manage a Clean Data Exchange

We’ve Been Approaching Data Accuracy 90’s Style. The Rules of Thumb are Changing – Are We?

Chris ReedMay 25, 2017

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The Approach to 3rd Party Data Accuracy is Flawed

The traditional approach is to figuratively deploy a traffic cop to check data accuracy between a 3rd party data source and the destination in the enterprise. Good plan, but is that sufficient? What often occurs is that downstream systems that consume the data are vulnerable to consuming dirty data. The fact is, data accuracy doesn’t just happen by coincidence and the traditional check between source and destination provides an incomplete picture.

Why this doesn’t work effectively is that data spans not one or two systems, but a wide array of systems, some internal and some from third-party sources. What is required is to change the “traditional” approach to a “business process” approach. By following an end-to-end business process you turn three risks into benefits:

  1. Ensure data accuracy from beginning to end of business process
  2. Standardize control points and business rules that provide continuous metrics
  3. Drill down visibility from business process view to individual control points

Add this to the explosion of data and regulatory compliance and it becomes necessary to change approaches to perform data accuracy checks in order to gain control over data management, workflows, and business processes.

In a business process, data errors can result in multiple billing issues including overbilled, under-billed or double-billed causing unhappy customers and possible customer loss. The key is to not just deploy one-off traffic cops, but rather a synchronized and automated traffic system that validates data accuracy across a business process to stop data errors where they occur. Sounds easy, right?! We recognize it’s not that simple.

While your “in-house” data is consistent and reliable, your vendors might not have the right processes, data controls or data formats in place to achieve clean and accurate data, let alone a seamless data transfer of information. If data is not checked for quality and confirmed for integrity, it introduces data errors into your environment. How can you ensure this does not happen?

4 Problems to Watch Out For

When you receive data from multiple vendors, there are problems you need to keep an eye out for to ensure data errors don’t proliferate downstream. When these problems are ignored it greatly affects your bottom line. These problems include:

  1. Data Format. It would great if all vendors followed a common data format, but the reality is that will never happen. When file types aren’t consistent from vendor to vendor you must find a way to read the files in their native format and translate the information into one single format. 
  1. Timeliness. Different vendors send data at various times. One vendor might submit their files once a week, and another vendor might submit it once a day. To complicate things even more, vendors can often be late when sending data over which impacts other processes dependent on receiving that data in a timely manner. 
  1. Duplicate files. Duplicate files are a common occurrence when files are being received daily or weekly from another application. Sometimes, the previous day’s file has been sent in error. When, not if, this goes undetected, it’s a nuisance to back out all of the collateral damage caused by processing a duplicate file. 
  1. Data Integrity. Some vendors might consistently send inaccurate data or continuously send data late, which can have a major effect on the accuracy of your data as a whole. To avoid underperforming vendors you will need a way to figure out which vendors are doing just that by measuring their data accuracy to ensure they are meeting their agreed upon SLA’s. And if they are not, provide evidence to back up SLA contract violations.

Now to accomplish accuracy in financial transactions and reporting you have two options. You can manually double and triple check the numbers hoping nothing is missing, but this sounds like a painful endeavor if you have more than one system to review. The other option is to institute deliberate and repeatable processes to automate and validate 100 percent of your data for accuracy automatically.

Getting Ahead: Data Integrity

By implementing a data integrity solution, you’re able to automatically validate 100 percent of your data for accuracy. The solution should be able to extract data from a source system with no transformation – read in native file format – as input into a destination system. It should have the ability to handle extremely high volumes of data across multiple systems. Finally, by continually monitoring your data to ensure the bad data is immediately flagged and stopped before it impacts business operations, you should achieve data quality.

Additionally, your data integrity solution should reconcile transactions through automation and prioritized alerts to operators about issues that require manual intervention. The solution should also verify that a transaction passes from one process point to another processing point within a specific time frame, and have the capability to ensure the correct day’s file will be processed, or in the case of an incorrect file being received, an exception report will be immediately flagged and the process will prevent the wrong file from processing.

An automated system can also help you set-up and implement a 3rd party vendor scorecard,  to regularly  track which vendors are consistently sending inaccurate data, error-prone formats, and late or duplicate files. A scorecard not only provides you the information on how your vendors are performing, but the easy-to-read dashboard also helps you hold your vendors accountable.

To learn more about ensuring data quality during data exchange, download the eBook below.

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