Data transformation and optimization, a task that many, if not most, large enterprises undertake, is not an easy task. However, thanks to the significant growth of AI and cloud technologies, the challenges appear to be increasing. A recent Gartner poll found that less than half (44%) of data and analytics leaders say their teams are effective at delivering value to their organization, but not for a lack of effort, but rather because of resources. , answered that this was due to a lack of funding and skilled staff.
Armon Petrossian and Satish Jayanthi encountered these inhibitors at their data automation company WhereScape. There, the two were responsible for solving data warehousing problems for WhereScape's clients. (Petrosyan was a national sales manager and Jayanthi was a senior solutions architect.) After spending about six years at WhereScape, Petrosyan and Jayanthi have found themselves at the top of their game when it comes to issues related to data transformation and data optimization. I started to believe I could do one (or two) well. —I was worried.
The result was Coalesce. Coalesce is a San Francisco-based company that builds a suite of data transformation services, apps, and tools. Coalesce announced Thursday that it has closed a $50 million Series B funding round co-led by Industry Ventures and Emergency Capital, bringing the startup's total funding to $81 million.
“The data transformation layer has long been the biggest bottleneck in analytics,” Coalesce CEO Petrosyan told TechCrunch. “Data science and engineering teams spend the majority of their time preparing data, including cleansing and transforming it, and building data pipelines to get data from its sources to dashboards or other business use. It involves manual coding and construction. These manual processes are time-consuming, labor-intensive, and most importantly, not scalable.”
The data supports Petrosyan's claims. His 2020 research for his provider of data science tools, Anaconda, found that data scientists spend nearly half (45%) of their time on data preparation tasks such as loading and cleaning data. got it.
Coalesce's response is a platform that standardizes data while automating more repetitive and routine data transformation processes. With Coalesce, data science teams can leverage metadata to understand how different pieces of data are linked and connected while managing transformations, Petrosian says.
“As a company's data grows, so does the complexity of the data pipelines and data models that must be built and maintained to make the data trustworthy and yield accurate insights and decisions,” he said. I am. “Therefore, scalability is critical for enterprises, and our product provides just that. By automating the data transformation process, we empower data engineers to build data pipelines faster and more efficiently. ultimately reducing costs and time to value for an organization's data.”
Coalesce is built to work exclusively with Snowflake's Data Cloud products. Not surprisingly, Snowflake Ventures, Snowflake's corporate VC arm, is an investor.
This type of vendor lock-in can hinder business expansion, especially given that Coalesce is not the only data conversion tool vendor in town. DBT and even traditional extract, transform, and load tools such as Informatica and Talend may be considered competitors. There are also startups like Prophecy, which secured a $35 million investment from venture capital firms Insight Partners and SignalFire last October.
Coalesce provides various settings and configurations to organize and normalize data within your Snowflake environment. Image credit: Coalesce
But Petrosyan says that's not the case.
“The Series B puts us in a position to become a profitable company if we want to,” he said. “Our company was born in the midst of a pandemic, which gave us the opportunity to focus on ‘stealth’ developing products that served enterprise Fortune 500 companies that were resilient to the possibility of an impending recession at the time. I got it. Its audiences are generally more resilient to economic changes, and our products and business are more resilient to market headwinds. ”
Petrosian notes that Coalesce has “multiple” Fortune 500 customers (her mother says exactly how many) and that recurring revenue will quadruple from a year earlier in the fiscal year ending January 2024. I have grown. As the company focuses on improving his Coalesce platform, Coalesce will increase its team size from 80 to around 100 by the end of the year by improving performance, introducing AI capabilities and reaching out to Snowflake's existing customers. We plan to expand to.
Petrosyan subtly hinted that generative AI and machine learning applications could double the power of Coalesce's business.
“Our customers tell us that executives must ask questions about AI and large-scale language models and base the conversation by first explaining why they need to ensure they have the right data foundation in place. ”, he specifically noted. The generative AI sector continues to experience tremendous growth. “This is where we come in. We are on a mission to fundamentally improve the analytics landscape by making enterprise-scale data transformation as efficient and flexible as possible. will enable you to quickly move to implementing and leveraging advanced use cases such as “AI, machine learning, and generative AI.” In short, we see the value of Coalesce's technology as a necessary catalyst to support the scalability and governance required for the future of cloud computing. ”
Across industries and startups, 11.2 Capital, DNX Ventures, GreatPoint Ventures, Hyperlink Ventures, Next Legacy Partners, Snowflake Ventures, and Telstra Ventures participated in Coalesce's Series B.