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IBM expanded its data fabric platform with the acquisition of data observability startup Databand on Wednesday.
Financial terms of the deal were not disclosed.
Databand, founded in 2018 and based in Tel Aviv, Israel, had raised $14.5 million in funding to build out its data observability technology, which provides organizations with visibility and monitoring for data pipelines, which can be used for machine learning training, data analytics and business intelligence.
Databand -- formerly known as Databand.ai -- is the second observability vendor IBM has acquired in as many years, after picking up application observability vendor Instana in November 2020.
There is a growing need for data observability, according to Paige Bartley, an analyst at S&P Global Market Intelligence's 451 Research. Data is increasingly being used by less technical workers in enterprise settings, so enterprises need more data observability tools to maintain access to quality data.
"While periodic, cyclical clean-up efforts for individual data sets will still remain necessary in certain cases, data observability efforts offer a more preventative and real-time approach to data pipeline maintenance, helping ensure a steady flow of high-integrity data through the organization," Bartley said.
Data observability technology today is most often closely associated with data reliability and data quality assurance applications. In Bartley's view, data observability technology has room to evolve to be more widely used for other key business objectives, such as optimizing the cost of data systems and the cost and allocation of cloud resources.
Paige Bartley Analyst, S&P Global Market Intelligence's 451 Research
Why IBM needs data observability to enable its data fabric
Databand will fit into IBM's data fabric platform, which enables organization to govern and use data for analytics, BI and machine learning.
The data fabric helps organizations connect the consumers of data to wherever the data resides, whether it's on premises or in the cloud, said Michael Gilfix, vice president of product management for data at IBM.
An example of a typical application that Databand will now enable for IBM users is making sure a BI dashboard is accurate and up to date.
A BI dashboard is typically powered by a data pipeline that takes data from different sources. The Databand technology can detect if the data is inaccurate or if something went wrong in the pipeline that affects the data. Databand alerts the users of the error and pinpoint its source so it can be remediated.
The intersection of data observability and data quality
The IBM data fabric already includes the IBM Watson Knowledge Catalog, which provides data governance and data catalog capabilities to enable users to identify and use data for machine learning training or data analytics.
The Watson Knowledge Catalog enables organizations to create rules for how data should be used and also provides capabilities to enforce those rules. The combination of the data catalog and Databand's technology in the data fabric will provide better data quality, according to Gilfix.
The Databand technology provides visibility into the generation of data through the pipeline. That visibility into how data is created will help improve the quality of data that organizations catalog and use, Gilfix said.
"Data observability is going to help people trust that the data that comes from different parts of the organization is reliable," he said.