Provider of reference data distribution products PolarLake has launched a standalone reference data reconciliation solution aimed at fixing the problem of poor reference data quality in downstream systems. The vendor has created the solution as part of plans to develop modular solutions for the market and extend its client reach.
According to PolarLake, the new solution allows organisations to reconcile centralised golden copy with the distributed copies of reference data in downstream systems.
“The key driver for this solution is really the question of data quality once you start distributing reference data. A lot of the
focus up until quite recently has been on creating golden copy and building data warehouses, but I think that as the industry has matured, people as realising that is only a part of the problem,” explains John Randles, CEO of PolarLake.
Once that reference data is integrated into downstream applications – fund accounting systems, order management systems and portfolio management systems – the golden copy or reference data can be destroyed in the process, he says. There is also a huge risk around intervention from other users and other processes that mean data you thought was clean ends up as dirty data.
“Our reconciliation solution is really targeting the control process around that data. Whether they use PolarLake for distribution or they use any other mechanism, our solution gives the business management the confidence that what they are distributing still retains its data integrity. This is critical to ensuring the success of enterprise data management,” claims Randles.
PolarLake has been working for over a year on this project and has evolved this offering from the RDD solution into a
standalone solution. One of the key reasons that the vendor chose to produce a standalone solution was to meet the needs of firms that have distribution problems but because they have invested a large sum in their data infrastructure, they are looking for specific control around data distribution rather than replacing the whole system, explains Randles.
“The key challenge around producing a solution like this is the ability to deal with multiple formats and multiple ways of describing what may be the same piece of data and putting them all into a common format where you can reconcile them. Reconciliation is a well known process and the industry has been doing it for years but the challenge around reference data is dealing with these irregular data structures and formats,” he says.
PolarLake has two customers using the system and, according to Randles, both are getting visibility reports on data quality and data effectiveness and how data becomes corrupted in downstream systems much more readily than they were previously.
He believes that the standalone nature of the solution addresses the ability for customers to take components of PolarLake’s data offering without having to replace their entire system. It is compatible with any other data distribution mechanism, so it can be used with home grown solutions on an existing middleware platform, he adds.