Acceldata raises $50M to boost data observability platform

Data observability vendor Acceldata on Wednesday raised $50 million in venture capital funding, bringing the startup’s total funding to more than $95 million.

Founded in 2018 and based in Campbell, Calif., Acceldata offers a data observability platform designed to enable customers to monitor their data and gain insight into the overall health of their data and analytics infrastructure.

The funding was the vendor’s series C round, following a series B round of $35 million in September 2021. March Capital led Acceldata’s series C round, with participation from Industry Ventures, Sanabil Investments and Insight Partners.

Once a relatively simple process when organizations had fewer data sources, kept most of their data in a single on-premises database and used fewer tools to build their analytics stack, data observability is becoming more complex. Organizations now ingest data from an exponentially growing number of sources, store it in myriad databases — both in the cloud and on premises — and use a wide array of tools to integrate, prepare and analyze data. And without quality data, an organization’s entire data operation doesn’t work effectively.

As a result, vendors such as Acceldata, IBM Databand and Monte Carlo have emerged to address what is both a growing and essential need, according to Kevin Petrie, an analyst at Eckerson Group.

Data observability helps improve both the performance of data pipelines and the quality of the data itself. Data teams need these tools to ensure they deliver timely, accurate data to the users and applications that need them for analytics. “Data observability helps improve both the performance of data pipelines and the quality of the data itself,” Petrie said. “Data teams need these tools to ensure they deliver timely, accurate data to the users and applications that need them for analytics.” He added that tools to observe data quality are not new.

What is new, however, is the use of augmented intelligence and machine learning (ML) to foresee trouble within a data pipeline and enable organizations to take proactive measures. Also new are the scale and complexity of the data pipelines that the observability tools must now monitor. “Data quality tools have existed for some time, [but] modern data observability tools such as Acceldata apply new AI/ML techniques and new levels of automation to detect and correct data quality issues,” Petrie said. “They also help predict, measure and optimize the performance of data pipelines, all in the context of hybrid or multi-cloud environments.” Beyond the need for better data observability due to the exponential increases in data volume and complexity, a shortage of data engineers is increasing the need for data observability tools, noted Rohit Choudhary, founder and CEO of Acceldata.

Data engineers prepare data for analysis, which includes monitoring data throughout its lifecycle to ensure its quality. But since 2016, demand for data engineers has outpaced supply, according to QuantHub. Acceldata and its peers automate data observability, enabling organizations to not only overcome the shortage of data engineers — at least when it comes to data observability — but also gain greater vision into their data, given that machines can oversee vast amounts of data far more quickly and thoroughly than even a team of humans.

“Talent continues to be flat,” Choudhary said. “There are not enough high-quality data engineers.