DataOps has been part of our vernacular only since 2015, but its value is so well-known that it has made huge inroads into the enterprise. In fact, 72% of respondents to a recent study by 451 Research said they are actively pursuing initiatives to deliver more agile and automated data management: the very definition of DataOps.
In addition, 91% said they already have defined, or are in the process of defining, a formal DataOps strategy, while 86% plan to increase spending, investment or development related to DataOps over the next year.
It’s clear that organizations are evaluating DataOps strategies and processes, but figuring out where to start can be challenging. What aspects of your data infrastructure and processes need to change, and where, specifically, will IT need to put its investments?
The 451 Research study confirmed that processes and technology will evolve across the board, but there are some buckets that will see more investment – at least at the beginning of the journey. According to respondents, the top three buckets for investment include: analytics and self-service data access (40%), data virtualization (37%) and data preparation (32%).
It’s not surprising that analytics and self-service data access were the most commonly cited investment targets, since end users, looking to get more out of data, have clamored for such functionality for years. Until recently, analytics fell squarely on the shoulders of the IT department. But now, by investing in technology, those same users can create reports, access dashboards and data models, and provision their own data warehouses.
“This technology enables your application developers [and] your business people to get access to data for themselves and not have to wait to request it and have those requests fulfilled,” says Hitachi Vantara’s Director of Product Marketing, Madhup Mishra. “The return on investment (ROI) is very clear. Once data users are empowered, they have access to data much faster: It’s not taking days or even weeks the way it used to.”
Data virtualization and data preparation also have strong ROIs and direct correlations to the success of DataOps. The two, when combined, “have the potential to improve the efficiency of self-service analytics,” according to the report.
From an operational standpoint, data virtualization gives users access to multiple sources of previously siloed data. And data preparation – one of the practices that few people think about – impacts every person in an organization, says Mishra.
“We see that engineers can spend more than 50% of their time simply preparing data. It’s not clean. It’s got gaps. It needs to be tagged. You have to prepare data before you can start to use it successfully,” Mishra explains. “Today, that’s a very manual process and one that’s extremely time consuming. Virtualization automation and data preparation automation are game changers, which is why we see companies looking to invest in both to make it happen.”
While there is no single road map for any organization looking to embrace DataOps, these three technologies deserve careful consideration for investment.
Looking to learn more about how DataOps helps you get the most out of your data? Want to turn your business into a data-driven engine? Download 451 Research’s latest report, DataOps Unlocks the Value of Data to read more about the importance of DataOps and what you need to know to move forward.