Given the increased use of data products across industries in this pandemic era, professionals are struggling to keep pace.
Increased data pipelines are driving most of the data-driven initiatives across the enterprises. However, with innovations at the infrastructure layer enable more significant volumes and velocities of data processing, organizations face new challenges.
It is primarily around scaling, including how to allow technology professionals to achieve more, faster. A recent study from Ascend.io shows team sizes are not scaling at a fast enough rates to keep up with the evolving business needs.
Almost all data professionals at present are already at their capacity or beyond, which leaves little room for strategic business models and innovation. Exhibiting current pressures, nearly 96% of the data teams are at or over capacity, which is only 1% less YOY.
The 2021 DataAware Pulse Survey observes the workability and priorities of teams which includes data scientists, data engineers, data analysts, and enterprise architects. It was observed that requirements for data projects had accelerated faster than team capacity.
Basically, lack of data engineering resources leading to downstream delays. Every team identified their own function as the most backlogged of the overall business – compared to their peers. Indeed, this unprecedented time sees a rising backlog of data engineering tasks.
Accordingly to the statistics, the most common bottlenecks spanning different teams include the support of existing and legacy data systems (39%), data system settings and prototyping (30%), as well as the needing to ask for access to systems (26%).
The vast majority of the professionals believe the number of data pipelines in their companies to increase by the end of 2021 – with 56% anticipating the number of data pipelines to surge by more than 50%.
This increased rate appears to be impacting some roles more – data scientists 2.4x more likely than other teams to expect significant growth in their pipeline demands. In fact, many professionals reported that their infrastructure could scale to meet the rising data volume processing needs.
Amid the significant growth in data pipelines across organizations, almost 74% of data professionals noted that their need for data products is growing quicker than the team size. And nearly 81% cited that the requirement for data products is increasing faster than the size of their team.
Certainly, data pipelines are fuelling almost every data-driven advantage across businesses. Recent years have marked a sudden increase in low- and no-code technologies available in the market. Despite the benefits of such tools, businesses are fast discovering some serious limitations.
In this context, Sean Knapp, CEO and founder of Ascend.io, explains, “However, as innovations at the infrastructure layer continue to enable processing of greater volumes and velocities of data, businesses face a new scaling challenge: how to enable their teams to achieve more, and faster.”
Certain low- and no-code solutions do not allow professionals to customize code when needed complex business logic. This often ends with the scenario like making about 95% of the job more accessible, and the remaining 5% becomes impossible. As a result, most data teams would prefer to use a low- or no-code tool with the preferred programming language.