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Databand.ai ensures the right users access data at the right time

Michael Novinson (MichaelNovinson) •
July 6, 2022

IBM bought a data observability startup to help organizations fix data errors, pipeline failures and poor quality before it affects their bottom line.

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The Armonk, New York-based tech giant says its acquisition of Tel Aviv, Israel-based Databand.ai will help companies ensure reliable data gets into the hands of the right users at the right time. . Databand.ai can alert data teams and engineers when the data they use to power an analytics system is incomplete or missing, according to IBM.

“Our customers are data-driven companies that rely on high-quality, trusted data to power their critical processes,” said Daniel Hernandez, general manager of IBM Data and AI. said in a statement. “When they don’t have access to the data they need at a given time, their business can come to a halt.”

Terms of the acquisition – which closed on June 27 and announced on Wednesday – were not disclosed, although Israeli publication Globes reported that IBM paid $150 million for Databand.ai. An IBM spokesperson declined to comment on the purchase price reported by Globes. IBM stock rose $0.46 – 0.33% – on Wednesday to $138.08 per share.

Former Sisense product manager Josh Benamram founded Databand.ai in 2018 and has led the company since its inception. Databand.ai currently employs 51 people and in December 2020 closed a $14.5 million Series A funding round led by venture capital firm Accel, according to LinkedIn and Crunchbase (see: IBM buys Red Hat for $34 billion).

“You can’t protect what you can’t see, and when the data platform is ineffective, everyone is impacted, including customers,” Benamram said in a statement. “Joining IBM will help us scale our software and dramatically accelerate our ability to meet the changing needs of enterprise customers.”

What does Databand.ai do for IBM?

Being part of IBM will allow Databand.ai to strengthen its observability potential for broader integrations through more open source and commercial offerings powering the modern data stack. Companies will have the option of running Databand.ai either as a service or as a self-hosted software subscription, according to IBM.

Databand.ai builds on IBM’s existing Instana platform to provide a more comprehensive and explanatory view of the entire application infrastructure and data platform system. This can help organizations avoid revenue and reputation loss, according to IBM.

“Data observability takes traditional data operations to the next level by using historical trends to calculate statistics about data workloads and data pipelines directly at the source, determining if they are working and identifying where problems may exist,” Mike Gilfix, IBM’s vice president of data management and AI products, written in a blog post.

According to Gilfix, Databand.ai collects data pipeline metadata about key modern data stack technologies and uses it to create historical benchmarks for data pipeline behavior. This allows Databand.ai to detect and generate alerts on anomalies while data pipelines are running as well as resolve anomalies in an automated manner without affecting delivery, according to Gilfix.

Databand.ai’s technology will improve mean time to discovery by detecting and executing data pipeline issues in real time instead of reacting afterwards, says Gilfix. Mean time to repair will also improve since the contextual metadata provided by Databand.ai helps data engineers focus on the source of the problem rather than debugging where the problem originated, according to Gilfix.

IBM will make Databand.ai’s data observability capability available on a stand-alone basis, but Gilfix recommends using it alongside multi-cloud data integration, data governance and privacy, and capabilities trusted enterprise AI tools to more effectively automate the data lifecycle. Databand.ai will integrate with these other use cases for best results where both are applied, according to Gilfix.

“Detecting data quality issues at the source helps provide more reliable data,” writes Gilfix in the blog post. “Monitoring static and moving pipelines while providing high-quality metadata enables faster time to value than would otherwise be possible.”

How can data observability benefit security?

Data observability platforms have historically been used to ensure an organization’s data is clean and useful to downstream teams by probing issues such as data lineage and algorithmic drift, said Jeff Pollard, vice president and principal analyst at Forrester, at ISMG. But security leaders are increasingly looking to gain visibility into their organization’s data to ensure it isn’t tampered with by adversaries, he says.

Security and data managers each mistakenly assumed the other team was responsible for ensuring data was not tampered with, but Pollard says it’s increasingly clear that CISOs have a role to play in data integrity. Pollard expects this to become a bigger area of ​​focus as companies work to ensure adversaries don’t tamper with or poison their data.

“The more you automate things, the more you rely on data and input from sensors and things like that,” Pollard says. “They have the motivation to make you make the wrong choice.”

Investment activity around data observability has accelerated over the past two to four years to ensure the integrity of data that is increasingly used in artificial intelligence and learning algorithms automatic, says Pollard. Data scientists generally understand what went into the algorithms, but Pollard says visibility and telemetry for those who aren’t practitioners is limited due to closed systems.

Security teams have traditionally focused on understanding and correlating log data, but observability platforms tend to be much closer to the applications and data actually in question, according to Pollard. It therefore encourages security leaders to create programs, processes, and practices around application and data observability pipelines to better align them with practices across the organization.

“Security leaders need to stay focused on observability concepts, as this will be an area where they begin to make significant investments in the future,” Pollard says. “And that’s definitely a change from what they’ve done in the past.”