Intelligent Data Centres Issue 39 | Page 25

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New research finds data automation adoption to climb from 3.5 % to 88.5 % over the next 12 months

scend . io has announced

A the 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 US – based data scientists , data engineers , data analysts , enterprise architects and 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 .

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 .
Data initiatives are ballooning beyond team capacity
Nearly all data teams ( 93 %) anticipate the number of data pipelines in their organisation 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 organisation , nearly three in four respondents ( 72 %) indicated that the need for data products is growing faster 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 life cycle
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 organisation ’ 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 resourcedemanding compared to their peers .
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 . �
DATA AUTOMATION IS EMERGING AS THE MOST PROMISING PATH TO INCREASE DATA TEAM CAPACITY AND PRODUCTIVITY .
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