Sergey Nivens - Fotolia

News Stay informed about the latest enterprise technology news and product updates.

Collibra acquires predictive data quality vendor OwlDQ

With OwlDQ, Collibra is bringing data quality capabilities to its data intelligence platform to help users better understand and evaluate the impact of different sources of data.

Data intelligence vendor Collibra on Wednesday said it has acquired privately held predictive data quality firm OwlDQ.

Financial terms of the deal were not disclosed.

OwlDQ was founded in 2017 as Owl Analytics and had raised $2.5 million in funding. The vendor, based in Glenelg, Md., is in the business of data quality, ensuring that data is useful for business intelligence and data analytics.

The OwlDQ platform provides data profiling capabilities that can identify what's in a given data set and can determine patterns to measure data quality.

For its part, Collibra was busy in 2020 building out its Data Intelligence Cloud platform, which provides data governance and data catalog capabilities. In a December 2020 interview, Collibra co-founder and CEO Felix Van de Maele said his plans for the vendor were to continue working on data quality.

Data quality metrics are part of the intelligence about data that Collibra collects and manages, noted IDC analyst Stewart Bond. He also noted that Collibra has had to rely on third-party data quality software to provide data quality metrics and relied on these same third parties to be an active part of data cleansing, until now.

"The acquisition of OwlDQ adds data quality reporting and cleansing capabilities to the Collibra software portfolio, providing the opportunity to introduce its own data quality technology into existing, and new accounts," Bond said.

In Bond's view, OwlDQ takes a continuous predictive approach to data quality management, which is different than the declarative approach historically used in data quality software.

Screenshot of OwlDQ platform
The OwlDQ data quality platform automatically profiles datasets without the need for manual user intervention.

With a declarative approach, data quality metrics are often driven by static policies that define how to measure data. Bond noted that the dynamic nature of data can potentially bypass an existing declarative rule or a data quality problem could slip through because a rule doesn't exist.

"The continuous and predictive approach is becoming an attractive alternative in the modern enterprise, where the diversity and distribution of data can result in an exponential number of data quality rules," Bond said.

Why Collibra chose OwlDQ for data quality

For Jim Cushman, chief product officer at Collibra, it is the predictive data quality capabilities that OwlDQ provides that set it apart and made it an attractive acquisition.

"What really makes OwlDQ different and stand head above everyone else is that they focus on automation; they focus on using predictive measures using machine learning to generate rules," Cushman said.

The acquisition of OwlDQ adds data quality reporting and cleansing capabilities to the Collibra software portfolio, providing the opportunity to introduce its own data quality technology into existing, and new accounts.
Stewart BondAnalyst, IDC

According to Cushman, OwlDQ is able to automatically generate data quality metrics for approximately 70% of all the data columns it accesses, a large productivity gain over a manual approach.

While Collibra had been enabling data quality capabilities on its platform through third parties before, Cushman emphasized that with OwlDQ users will get native integration in a complete offering.

What's next for OwlDQ data quality at Collibra

Cushman said the plan is to integrate the OwlDQ capabilities directly into Collibra Data Intelligence. He expects that the OwlDQ brand name will disappear over time as the technology moves into Collibra.

As part of Collibra Data Intelligence, Cushman said the data quality tools will inform users about the impact of using a given piece of data.

"Since we track a data lineage, we understand what other systems might actually take information from that system that has poor quality, so we can then understand the overall impact that it might have to a business," Cushman said.

Next Steps

Bigeye raises $17M Series A funding to boost data quality

Superconductive raises $21M for open source data quality

Dig Deeper on Data quality management and governance

SearchBusinessAnalytics
SearchAWS
SearchContentManagement
SearchOracle
SearchSAP
SearchSQLServer
Close