Data mesh startup Nextdata raises $12M in funding
Nextdata, a startup making it easier for enterprises to implement the data mesh architecture, today announced that it has raised $12 million in seed funding.
Greycroft and Acrew Capital led the investment. A number of angel investors participated as well. Nextdata said that it will use the capital to accelerate product development initiatives and grow its workforce, which currently comprises 10 people.
Nextdata was founded in 2022 by Chief Executive Officer Zhamak Dehghani (pictured). In 2018, while working as the director of emerging technologies at Thoughtworks Inc.’s North America division, Dehghani invented the concept of a data mesh. The concept encapsulates a new approach to managing business data that promises to help companies streamline their analytics projects and reduce costs.
Today, enterprises often take a centralized approach to data analytics. The records produced by a company’s various business units are pooled in a single environment for processing. In many cases, that environment is managed by a dedicated engineering team.
The centralized approach to data analytics presents a number of challenges. Pooling information from a large number of business units in a single environment is difficult and time-consuming, while accessing the information can be challenging as well. The data mesh architecture promises to address those challenges.
At a company that implements the data mesh architecture, the different business units don’t move all their information into a centralized environment. Rather, each unit is responsible for managing the information it produces. When data scientists require a certain set of records for an analytics project, they can simply request the records from the business unit that produced them instead of navigating a complex, centralized analytics environment.
This arrangement reduces the amount of effort involved in retrieving information for business projects. It also offers other benefits, including cost reductions.
Nextdata has built a software platform, Nextdata OS, that makes it easier for companies to build an internal data mesh. The platform allows a company’s business units to package the information they produce into so-called data product containers. Those containers, Nextdata says, make it simple to share a business unit’s information with the employees who require it for their work.
“When you think about data as a decentralized problem, the cost of integrating these technologies in a cohesive developer experience is what’s missing,” Dehghani explained in an interview on SiliconANGLE Media’s theCUBE studio earlier this year. “And we want to focus on that cohesive end-to-end developer experience to share data responsibly in these autonomous units.”
Each data product container combines the business records it holds with relevant metadata. That metadata includes lineage information, which describes where the files being shared originated. There are also data quality metrics that can be used to validate the records’ accuracy.
According to Nextdata, its platform pairs the business information and metadata in a data product container with governance policies “embedded as code.” Those policies regulate how the information being shared may be used by workers. In Nextdata OS, developers can embed usage restrictions into a data product container when it’s created and enforce them whenever it’s used.
The platform makes the information that a business unit shares with other teams available via application programming interfaces. According to the company, those APIs make it easier for users to find the specific records they require for their work. It says the productivity improvements provided by its platform can reduce the duration of some analytics projects from weeks to hours.
Nextdata’s platform is drawing significant interest from enterprises. On occasion of its funding announcement today, it disclosed that more than 1,000 organizations have signed up for early access. Multiple Fortune 100 companies are currently piloting the platform.
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