Launching Your Data Warehouse for Skip Tracing

The production data you receive can be simply classified into two categories. It is good data or it is bad data. We refer to the bad data as “garbage data” because it breaks your automated processes, will not load into your software, and in most cases requires some agonizing manual intervention. However, it is important to intelligently collect all skip tracing data and build automated processes for managing it. Technology solutions should be in place to manage your skip tracing data and to avoid your agency being buried in a landfill of skip tracing data.

Although, most collection software includes some sort of inherent module, feature, or perhaps even integrations for interfacing with skip trace service providers, a data warehouse should be implemented as a paired solution for housing and evaluating your skip tracing data. Your agency may already have a data warehouse that can be enhanced to include skip tracing data. Here are some considerations when launching or enhancing a data warehouse for your skip tracing data.

Basic Considerations

The basic, or not-that-exciting, considerations include:
1) Location – You need a server. If your security, compliance, and client requirements do not force you to host the data in-house, I recommend outsourcing to a hosting company. Most hosting companies can dedicate a physical server in a secure environment or implement a virtual environment with the same levels of safety and security.

2) System Sizing – Aside from the other data structures in your warehouse, how much disk space or how much processing power will you need to account for your skip tracing data? The disk space should be enough to house the production data from all systems feeding the data warehouse and from all the ancillary sources, such as your skip trace vendors. Regarding performance, there should be enough muscle to execute all jobs, rules, and interfaces in enough time to not interfere with any regu lar backups. If utilizing a hosting company, they can help you figure this out.

3) Software and Licensing – For most agencies, Microsoft Windows Server and Microsoft SQL Server will do the job. You may require .NET framework, Java, or special drivers based on your preferred technology for interfacing with the data warehouse. This is another area your hosting company can assist with.

Complex Considerations

The complex, and more stimulating, considerations include:
1) Data Structure – All skip tracing data should be captured and stored in your data warehouse even if it is not utilized today. After all, you are paying for it. It is safer to have the data and not need it than to need the data and not have it readily available. Try to construct an intuitive data structure. This will make future build out and scalability much easier.

2) Business Rules – There are many considerations on this topic but let’s focus on the skip tracing data your agency deems important. If you do not have documented business rules stating which pieces of returned data are imperative and how that data is used, you should. Let’s look at a simple business rule example. Most agencies run a skip for phone and address. Of course this information is imperative but how are you handling this returned data? Is it accepted as the best and always overwrites the primary phone and address in your system? Are you running a process to validate the returned phone and address prior to overwriting data in your system? Are you handling the returned data differently based on the source? These are some of the questions your documented business rules should answer. Finally, program these rules into your data warehouse so the process is fully automated.

3) Data Analytics – Many agencies overlook the importance of data analytics and thus lack the understanding of data trends. If you are going through the process of implementing or enhancing your data warehouse to include skip tracing data, you should absolutely run systemic analysis to understand your skip tracing data. This is more than identifying which skip tracing data points are important. Rather, it will show you what the data means. With proper build out, you will understand where you are getting the best skip trace results, what is not working within your business rules, and will likely identify cost saving opportunities.