Data Harmonisation – Contact Centre


Our client provides contact centre solutions to a wide range of clients. They utilise a central database, that they also market for sale to other organisations. Due to a number of inconsistencies and errors (and the ongoing requirement to update and add to their database), they wanted to check its integrity.

DDC AS cleansed and de-duplicated their vast database, in addition to automating and future-proofing further updates.


Our client operates a contact centre with a central database that was used internally, and it was also sold as a product. Much of this data was acquired at a considerable cost over time, either through purchasing data or through manual research and data capture. Maintaining and cleaning their database used resources and time.

However, they saw the positives in this, suggesting to DDC AS that their database was 99.5% clean with virtually no duplicates anywhere. As this database was a key asset to their company, they wanted to check its integrity to be able to provide it to third parties with confidence.

The Challenge

Whilst on the surface, matching structured and semi-structured data can appear to be simple, it is an extremely challenging and complicated task that can be dismantled by human error. Not only is the process incredibly time-consuming, it also can be marred by mistakes.

However, the most frequent time-consuming and business-critical activity is the comparison of names and addresses.

The reason for this is because name and address data are not structured. People will input these details in a variety of formats, and it can be challenging to digest. To further this issue, people may change their details and will mix old details with new details. Other issues following in this line of complication are:

  • Operational systems using different data formats and rules
  • An abundance of spelling mistakes and abbreviations
  • Data could be entered by subject (or via a contact centre)
  • There are multiple legitimate versions of name and addresses
  • Potential fraud whereby individuals (and companies) may deliberately use different forms of name and address to obfuscate identities.

Our client needed a checking procedure to see how clean their database was.

They had also questioned human efficiency related to matching datasets and data input. The harsh reality facing a larger number of firms today is that databases can include an incredible amount of variance through inputs.

The Solution

DDC AS deployed our tried and tested data solution that can match simultaneously on a variety of fronts.

In the case of our client, during the initial consultation they had suggested that their database was “99.5%” clean. However, our solution identified, for example, one case where there were 27 master file records for the same company at the same location but with enough variations in names and addresses for the normal database and search process not to detect them. Luckily, our client had deployed a tireless data solution that had detected these duplicates and reported back instantly.



DDC AS provided a full data solution that:

  • Matched data sources
  • Cleansed their database
  • Eliminated duplicates
  • Provided ongoing error/potential error notifications
  • Provided a report for our client to consider actions
  • Enabled the client to put a framework and repeatable process in place to future-proof additional updates/changes


After review, and agreeing thresholds, the client received a cleansed database, with duplicates removed and remaining data content cleansed and standardised in line with agreed business rules. Not only did DDC AS locate and notify our client of false matches, but the relationship has continued with the client, and the database was cleansed to a standard whereby all discrepancies had been removed.

Our client received a detailed report of the numbers of strong duplicates, likely duplicates and possible duplicates through our data software technology.

The chances are that without DDC AS’ data solutions, a costly and time-consuming process would have returned fewer assured results in a longer time period, at a higher price.

Goals achieved

  • Improved customer satisfaction
  • Accurate overview of customer information
  • Improved decision making
  • Develop more effective strategies
  • Save money
  • Reduce waste
  • More marketable product for the client

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