Data Pipeline Automation Platform Improves Engineering Efficiency up to 700% and Eliminates At Least $156K in Annual Tooling Costs, According to ESG Economic Impact Report

Enterprise Strategy Group’s economic analysis found Ascend’s Data Pipeline Automation Platform delivers faster time to insight, consolidation of tool spend and cost containment, improved team productivity and more

Ascend.io, the leader in data pipeline automation, released an economic analysis report conducted by Enterprise Strategy Group (ESG) of its Data Pipeline Automation Platform. The analysis found that the platform delivers multiple economic benefits, including major improvements to the productivity of data analytics teams, reduced overall cloud infrastructure costs, lower data platform tooling costs, and greater pipeline reliability.

As enterprise usage of data analytics grows, the field has become a significant area of IT expenditure. The collection and preparation of data used for analytics are achieved by building data pipelines that ingest raw data and transform it into useful formats leveraging cloud data platforms like Snowflake, Databricks, and Google BigQuery. These data pipelines span multiple stages of data preparation, often involve blending data from several different cloud services, and are interdependent on each other. Changes in one pipeline often cascade down to different teams and projects. Manually building and maintaining those pipelines is a complex process that is costly and time-consuming for engineering and analytics professionals. Ascend’s platform addresses this pain by automating up to 80% of repetitive data engineering tasks as reported by Ascend customers interviewed by ESG for this report.

To understand the economic impact of Ascend’s platform, ESG completed its industry-renowned economic analysis focusing on both the quantitative and qualitative benefits organizations can expect from Ascend.

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Significant savings and benefits were revealed in the following categories:

• Greater analytics productivity: Organizations using Ascend are able to make decisions faster with more complete data. Many users declared 80% reductions in the time required to create new data pipelines, while others said processes that previously took hours are accomplished in mere minutes.
• Consolidation of tool spend and cost containment:  The end-to-end nature of the Ascend platform enables teams to reduce up to four-point solution components of the modern data stack with data pipeline automation. This lowers the hard costs associated with data software and lost productivity from having to manually integrate various parts of the stack in-house. Multiple examples that were studied found that the reduced time it takes to build or modify intelligent pipelines directly improved analytics output. In the ESG’s financial model, the sample company was able to eliminate $156K in annual costs for tools.
• Improved data team productivity: Reducing the labor required to achieve the same outcome is super-charging data teams’ throughput thanks to automation. Interviews found that engineering teams using Ascend data pipeline automation spent only 25% of their time on building and maintaining manual data stack integrations; which led to a 500-700% boost in engineer efficiency when compared with a traditional modern data stack approach.

“The data world is filled with useful but disjointed products, which is why when we set out to build automation we found the need for a common data pipeline platform. It is awesome to have a third-party group like the ESG validate that through the voice of our own customers,” said Tom Weeks, Chief Operations Officer at Ascend, “Our platform is designed to help data teams achieve leverage by automating the mundane tasks required to create cutting-edge data products. It’s so rewarding to see that translate into less money spent on data processing and more team capacity for advanced projects like machine learning and predictive analytics. We can’t wait to see what the next wave of automation will unleash in this part of the stack.”

Ascend has announced a number of major innovations on its platform this year, including Ascend for Data Mesh that allows businesses to share and link data across multiple data clouds from a single console. The company has also expanded its ecosystem presence, having deepened its partnerships with both Snowflake  and Databricks to offer free ingest on both platforms, and announced new Databricks integrations to further enable transparency and team collaboration on the lakehouse.

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