Adoption Rising as the Need for Data Products Grows Faster Than Team Size and a Staggering 95% of Data Teams Are Still at or Over Capacity

Ascend.io, the Data Automation Cloud, today announced results from its third annual research study, The DataAware Pulse Survey, about the work capacity and priorities of data teams. Findings from more than 500 U.S.-based data scientists, data engineers, data analysts, enterprise architects, and—new this year—chief data officers (CDOs) reveal that despite 81% of respondents indicating that their team’s overall productivity has improved in the last 12 months, 95% of teams are still at or over capacity—just a 1% decrease from the 2021 study. The study also found that data automation is emerging as the most promising path to increase data team capacity and productivity, with a majority (85%) planning to implement automation technologies in the next year even though only 3.5% of the same respondents reported currently have automation technologies in place.

Marketing Technology News: Launch of MicroGrid Intelligent Connect (MIC- Let’s Talk) Conversational AI Platform

Ascend.io, the Data Automation Cloud, announced results from its third annual research study, The DataAware Pulse Survey, about the work capacity and priorities of data teams.

Data Initiatives Are Ballooning Beyond Team Capacity

Nearly all data teams (93%) anticipate the number of data pipelines in their organization to increase between now and the end of the year—with 57% projecting an increase of 50% or greater. Amid the rising number of data pipelines across their organization, nearly three in four respondents (72%) indicated that the need for data products is growing faster than their team size. This was especially true among data engineer respondents, of which 82% stated that the need for data products was increasing at a faster rate than their team size.

“Data team productivity remains the single biggest threat to the success of data projects and workloads,” said Sean Knapp, CEO and founder of Ascend.io. “In fact, data team capacity has only marginally improved year over year, yet the demands on these teams continue to grow exponentially—far beyond what teams can feasibly keep up with.”

Team Backlogs Have Emerged Across the Data Lifecycle

One major roadblock to data team productivity remains fast access to data. When asked how much time they spend trying to gain access to the data they need to do their job, respondents said they spend an astounding 18.91 hours on average per week. Data scientists spend the most time trying to gain access to data each week at 24.6 hours, followed by data engineers at 19.1 hours.

However, data access is not the only roadblock. When it comes to the other top bottlenecks for team productivity, 66% cited team size or hiring constraints as their biggest productivity roadblock, followed by technology limitations (42%).

When asked which activities or tasks in their organization’s data ecosystem are the most backlogged, respondents were split. However, data scientists, data engineers, data analysts, and enterprise architects all agree to disagree—they each were all more likely to identify their own function as the most backlogged or resource-demanding compared to their peers.

• Data scientists are 3.3 times more likely to say data science is the most bottlenecked
• Data engineers are 2 times more likely to indicate data engineering
• Data analysts are 1.9 times more likely to say data analysis
• Enterprise architects are 1.5 times more likely to indicate data architecture

Data Teams Look to Automation, Flex Code, and Data Mesh to Increase Productivity

As data teams look for ways to overcome bandwidth limitations, many data professionals are turning to automation to improve data workload efficiency and productivity. In fact, while only 3.5% currently use them, 85% of respondents indicated that their team will likely implement data automation technologies in the next 12 months.

Marketing Technology News: MarTech Interview with Justin McDonald, Senior VP and GM of Conversational Marketing at Terminus