Data automation has ability to boost team productivity
- 85% of companies plan to implement data automation technologies next year
- Data professionals are turning to data automation, flex code, and data mesh
- 81% of respondents say they would be more inclined to use a no-code or low-code tool if it offered the ability to use their preferred programming languages
In today’s data-driven world, it’s more important than ever for businesses to access the correct data at the right time. However, with so much data generated every day, this is becoming increasingly difficult.
Data professionals are struggling to keep up with the explosive growth of data. Despite 81% of respondents indicating that their team’s overall productivity has improved in the last 12 months, 95% are at or over capacity, according to Ascend.io’s 2022 DataAware Pulse Survey.
Data Automation to boost team productivity
Though data automation is not a new concept, its potential for boosting data team productivity is only now being fully realized. This can be seen as companies (85%) plan to implement automation technologies next year, even though only 3.5% of the same respondents currently have automation technologies in place.
Data pros are turning to data automation, flex code, and data mesh
Automation can take many forms, but often involves scripts or software to streamline or replace manual steps in the data processing process. It can also include using artificial intelligence (AI) to help identify patterns and insights in data that would otherwise be difficult or impossible for humans to find.
The use of automation can save time and improve accuracy, making it an essential tool for data professionals seeking to make the most of their limited time. As data teams assess new solutions, many consider low-code tools and data mesh frameworks to unlock greater team efficiency and business value.
Technologies like Puppet and Chef help automate the installation, configuration, and management of servers and data pipelines. At the same time, tools like Hadoop and Spark make it possible to analyze massive datasets in parallel.
In addition, many teams are beginning to adopt agile development practices, which allow them to rapidly develop and deploy new features in response to customer feedback.
Respondents indicated a strong interest in low-code tools that provided greater flexibility (i.e., flex code), with 81% saying they would be more inclined to use a no-code or low-code tool if it offered the ability to use their preferred programming languages, up from 73% in 2021.
Team Backlogs emerging across the data lifecycle
For data teams to be productive, they need fast access to the data to answer business questions.
Often this data is spread out across the organization in different data stores and formats. Extracting, cleaning, and preparing this data for analysis can take up a lot of time, reducing the team’s ability to respond to new business opportunities or solve pressing problems quickly.
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. Other roadblocks to team productivity include size or hiring constraints (65%), followed by technology limitations (42%).
11 December 2023
11 December 2023