The vendor behind the open source Great Expectations data quality technology is looking to build a commercial cloud service to help data professionals accurately profile data.
Open source-based data quality startup Superconductive on Feb. 10 said it has raised $40 million in a series B round of funding.
Superconductive, based in Redwood City, Calif., was founded in 2017 and raised $21 million in a series A round in May 2021. The vendor is building out data quality technology -- based on the open source Great Expectations project -- that can be integrated with data pipelines that flow into operations, analytics, machine learning and business intelligence deployments.
To date, Superconductive has not yet offered an enterprise version of Great Expectations, and its plan with the new funding is to develop a commercially supported data quality service.
The need for data quality is growing and is a big theme for many organizations in 2022.
"This is the year of data quality," said Enterprise Strategy Group analyst Mike Leone. "In fact, our research shows that of all technology areas supporting data-driven initiatives, data quality will be receiving the most significant investment."
Expectations growing for data quality in 2022
Data quality is increasingly important for a number of reasons. Leone said that organizations are almost being forced to backtrack a bit in their adoption of emerging technology.
In his view, companies are realizing they may have moved too quickly and now must slow down as they work to address the early stages of the data lifecycle, such as ensuring data can be trusted and is of high quality before they analyze it or use it to train machine learning models.
The expansion of categories such as data observability is proving to be a landing zone for several emerging data quality vendors as they look to tie their messages to areas such as DataOps.
Superconductive's path forward to advancing data quality
At the most basic level, data quality for organizations means being able to effectively use data, said Abe Gong, CEO and co-founder of Superconductive.
"It's making sure that data is not missing and that you don't have big outliers, and the type of data that flows through systems is correct," he said.
A key part of good data quality in Gong's view is also an approach that he referred to as data profiling. A core concept within the open source Great Expectations technology is the data profiler, an automated way to establish the expectation or desired level of data quality for a given data set.
Data quality is being integrated into DataOps stacks
The open source community is seeing a lot of activity related to integrating Great Expectations with other data technologies.
This is the year of data quality. Our research shows that of all technology areas supporting data-driven initiatives, data quality will be receiving the most significant investment.
Mike LeoneAnalyst, Enterprise Strategy Group
Gong highlighted recent Great Expectations integrations, including data science notebook technology vendor Deepnote, the Apache Airflow workflow management project and the dbt Core data transformation effort.
Those integrations are all open source and will be able to flow through to Superconductive's commercial product when it's ready, Gong said.
"Down the road, we'll probably do channel partnerships and things like that for the sake of marketing and sales, but from a technical integration perspective, a lot of the integrations already exist in open source, and we intend for most of that value to live in open source," he said.
The big push for Superconductive in 2022 is for the vendor to build out a commercial platform for Great Expectations data quality technology. That platform will be a cloud software-as-a-service system offering and will add enterprise management, data collaboration and security capabilities.
"We just want to get to a place where in addition to being a solid open source project for data quality, we also have a solid commercial offering," Gong said.
Enterprise Strategy Group is a division of TechTarget.
