Organizations are increasingly relying on data to make informed business decisions. A result of this is them recognizing the need for more streamlined and efficient approaches to data management – a.k.a., for our purposes here, the adoption of DataOps.
Think about it. The benefits of DataOps (Data Operations) include faster time-to-market, improved data quality and accuracy, reduced risk and costs, increased productivity and collaboration, and better alignment between data and business objectives.
With organizations relying on data to drive business growth and innovation, DataOps is now an essential practice for staying competitive in today's digital age. In fact, a recent survey by Unravel Data shows that more than 44% of respondents reported they are employing DataOps methodologies, compared to just less than a quarter of respondents in 2022, representing a 110% increase from the year prior.
Further demonstrating the maturing DataOps practice, only 20% of respondents in this year’s survey said they were at the beginning stage compared to 41% last year.
Those stats make sense. By implementing DataOps, companies continuously seek to enhance their data management strategies, which can help them make data-driven decisions to achieve their goals.
One of the major benefits of DataOps is promoting teamwork across various departments, including data scientists, data engineers, data analysts, operations and product owners. These teams must collaborate seamlessly to optimize the use of data, and DataOps provides a framework to facilitate this synergy.
“In just the course of a year we’ve seen a significant shift in how these growing, cross-functional teams are prioritizing DataOps as an established discipline across their organizations in a similar way that DevOps became an entrenched practice among software teams a decade ago,” said Kunal Agarwal, co-founder and CEO of Unravel Data.
Unravel Data offers a platform that helps businesses optimize the performance and cost of their data applications and pipelines. It uses AI, machine learning, and advanced analytics to provide a unified view of the entire data stack, giving actionable recommendations to modern data teams to turn data into insights.
The Unravel Data survey is used to assess how data team stakeholders are meeting their big data analytics objectives and define the practices they are adopting to conquer the complexities of the modern data stack.
Edited by Alex Passett