Match, Merge, Purge

We will work with you to document the range of duplicate types in your data, and agree on processing rules which will remove the significant issues, without losing your valuable data. Identified duplicates in your data will be removed or flagged to be processed by your own systems. This process can be a one-off project or completed on a regular basis to keep your data clean continuously.
 
The three key stages of de-duplicating data are:

• Identify duplicate records, based on data matching algorithms which best suit your particular data.
• Merge duplicate records into one super-set which becomes your primary record.
• Purge the waste data, leaving behind a clean, efficient marketing data list.

We will help you decide on the key issues such as:

• Automated matching algorithms: the business rules that determine how aggressive or relaxed the data matching approach is; this determines the balance between losing valuable data and keeping the largest possible data universe.
• Methodology and approach : with large data sets, perfection costs a lot of money. The approach affects the cost, and in particular the higher the level of manual labor involved, the greater the cost.

You want a sensible, cost-effective approach to data deduplication that strikes the right balance between cost and effectiveness. We will give you a cost-benefit analysis that allows you to make a simple choice to get the right approach.

Over 12 years we have fine-tuned our tools to achieve a higher match rate than our competitors, but with the flexibility to strike the right balance between keeping valuable data for segmentation and targeting purposes, and eliminating duplicate data for efficiency and anti-spamming purposes.

The majority of matching tools and services are rigidly automated and therefore can only work through standard queries. Often these standard approaches miss duplicates that would be obvious to the human eye, or lose valuable data in the merging process. Our manual browse and review process augments our automated rules to ensure that as many records are matched as possible, and the right data is merged into the final record.

As with most of our services the key to successfully merging data is to document all field mapping and priority rules before starting the project. Once these have been agreed, we can view the data sources and provide you with a field mapping document (if merging more than one file) as well as recommendations for the priority of each field group.