Data Ladder

DataMatch Enterprise includes data profiling, data matching and deduplication, data cleansing of various types (including database cleansing, list cleansing and so forth) data enrichment, and address data standardisation. Address Verification is available as a plug-in extra.

Of course, data quality is a mature market and most of Data Ladder’s capabilities are more or less table stakes. Where it aims to differentiate itself is in its price/performance, especially within its match engine, which uses proprietary (fuzzy) algorithms running against in-memory data. A notable capability is cross-jurisdictional matching, which is important for PII purposes.

Fig 2 - Relationships uncovered using ProductMatch

While DataMatch Enterprise is the company’s main product it is arguable that ProductMatch is also a differentiator as many data quality vendors are not very good at matching the sort of textual data you get in product descriptions, for example. The product has been specifically designed to uncover hidden relationships – thanks to its semantic matching capabilities – and create hierarchical representations of these, as illustrated in Figure 2.

A notable fact is that ProductMatch makes use of machine learning to improve its matching processes but that this is not the case with DataMatch Enterprise. We understand that the company has experimented with this but found that there was still too much manual intervention required. Data Ladder might be better advised to use AI to optimise its matching rules rather than the matching process per se.

Another difference between ProductMatch and DataMatch Enterprise is that the former includes some elements of data governance, though this more oriented towards compliance – identifying UNSPCC numbers for example – than supporting corporate policies.

Using data quality tools is not a one-off process but one that is ongoing. However, not many products in this market have the sort of data quality firewall that Data Ladder’s API extension offers. While you can’t prevent typos and other errors of that sort, being able to ensure the uniqueness of new records – preventing duplication – is a significant boon.

We think that the decision to provide a separate product targeted at product data is also a sensible one. Few, if any, other vendors offer this separation: they either ignore unstructured data completely or try to use the same facilities as for structured data, despite the fact that they are very different environments.

Next, there is the issue of price/performance. Data Ladder’s website promotes the company’s superiority in this area based on independent research, which is fair enough. However, this research is out of date and far from comprehensive. Moreover, performance is a tricky beast: it depends how many resources you throw at it. Nevertheless, based on our own research, we are happy to accept that Data Ladder does indeed offer better price/performance than many of its competitors.

And finally, we should comment on the product’s ease of use. This has been attested to by many users.

The Bottom Line

DataMatch Enterprise seems worthy, and complete, but does not have outstanding features that cannot be found elsewhere, though the company would claim that its matching accuracy and performance sets it apart. On the other hand, its pricing is attractive and that alone should make it worth consideration. That said, we especially like both ProductMatch and the API extension to DataMatch Enterprise. Both of these strike us as significant differentiators for appropriate use cases.