How Unravel Data optimizes data apps for enterprises
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Even as data-driven apps, such as artificial intelligence (AI), machine learning (ML) or advanced analytics, continue to see a surge in demand, companies are facing challenges in making the most of them. In a recent survey conducted by Alteryx and IDC, 73% of 1500 business leaders said they see spending on analytics growing. However, at the same time, many of the respondents also claimed that only about half of their business decisions are currently based on analytics.
While there can be multiple reasons for this gap, reliability challenges often come out as a major roadblock. Essentially, most data apps, even though effective, fail to deliver the performance that decision-makers expect consistently. They run on a complex data stack, which makes it difficult for data teams to track performance or understand the interactions and dependencies critical to optimizing it. In fact, modern data teams spend more than half of their day just on debugging and troubleshooting issues on the thousands of data pipelines in their environment and still end up struggling with performance issues.
This also results in higher costs and impacts the productivity of teams.
Unravel Data to the rescue
To address the challenges, Palo Alto, California-headquartered Unravel Data offers a full-stack data operations (dataops) platform that uses AI and ML to not only monitor the entire data stack for issues leading to application performance gaps, but also provide insights and automation capabilities to address those issues. The company today announced it has raised $50 million in a series D round of funding, led by Third Point Ventures.
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Founded by Sun Microsystems veteran Kunal Agarwal and Shivnath Babu, Unravel works in three stages to manage complex data pipelines.
The platform gathers metadata from across your entire stack to paint a picture of performance across data apps and their platforms, regardless of whether they are on-premise or on the cloud. Then, using this data, it builds and analyzes a fully correlated model of the entire environment. Finally, this model, combined with the platform’s knowledge base and intelligence engines, results in specific answers and insights about the data stack and what might be affecting app performance.
But, that’s not the end of the story.
Beyond insights, the solution also provides AI-powered recommendations to help teams take quick action on the issues detected, as well as the ability to automate actions for automatically improving performance over time.
“Just as the devops market united the practice of software development and operations a decade ago to transform the application lifecycle — data teams require the same kind of full-stack visibility, automation, and actionable intelligence that meet their needs around data pipeline performance, cost, and quality,” Agarwal said.
The company claims that its offering can ensure 100% reliability, making data apps available all the time, while reducing costs by up to 60% and time spent on troubleshooting by 98%. It has already seen adoption from leading giants, including Adobe, 84.51 (a Kroger company) and Deutsche Bank.
“Unravel enables us to see across our entire data environment, enabling our team to quickly understand which workloads are running well and which ones are having issues, and make recommendations on how we can fix the problems,” Jeff Lambert, VP of data solutions at 84.51, said.
With this round of funding, which takes Unravel’s total capital to $107.5 million, the company will focus on extending its platform to help connect the dots from every system in the modern data stack, including within and across the most popular data ecosystems such as Databricks, Snowflake, Amazon EMR, BigQuery, and Dataproc.
“The dataops observability market is poised to explode as enterprises invest in building data products that increase customers, revenue, and efficiencies,” Curtis McKee, partner at Third Point Ventures, said. “We’re excited to partner with Unravel Data, as the company has paved the way and established a proven track record of success helping some of the world’s most recognized brands simplify their data operations so they can bring new data-driven innovations to market.”
Other players in the dataops observability space are Dynatrace, New Relic, AppDynamics and Datadog.
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