Data has always been important to businesses. But in the post-pandemic era, it’s recognized as a critical differentiator. Yet unlocking data silos to generate business insight isn’t as simple as flicking a switch. The complexity of modern IT environments can make the process of building data supply chains time-consuming and resource intensive. New global research from StreamSets reveals it could cost organizations as much as $1.9m per year simply to fix broken data pipelines.
It’s time for a new approach – one in which technical data teams enable line of business teams with a single, automated platform designed to manage all pipelines at scale.
Data Integration Friction Increases Cost
Why is it so difficult to build resilient data supply chains? Part of the challenge lies in the current technology landscape. Many organizations have gradually bought a variety of technologies over the years, resulting in a patchwork of cloud and on-premises, bespoke, and siloed tooling. Data is trapped in different locations and inconsistent formats. Cloud migration can add extra complexity, especially if organizations choose a “lift-and-shift” approach, requiring extra work to orchestrate systems and connect them to pipelines.
Today’s chaotic environment causes data integration friction. This friction slows down the technical experts tasked with connecting, transforming, and processing data to build pipelines for their internal business customers. Two-thirds (65%) of global data leaders and practitioners responding to our survey say data complexity and friction can have a crippling impact on digital transformation.
Data teams are not just understaffed; they’re also struggling with existing toolsets. The result? Brittle solutions that break far too often. Pipeline breakage has a potentially serious impact on the organization and forces technical teams into fire-fighting mode. In fact, over a third (36%) of respondents say their pipelines break every week, and 14% claim they break at least once a day. Our study calculates total business spend on data experts at $6.13m annually. Repairing brittle pipelines alone could cost organizations nearly a third of this, totaling $1.9m.
This isn’t just about wasted spend. It’s also about lost opportunity. Every hour a valuable data engineer is forced to scramble in response to another outage, it’s an hour they’re not working on higher value tasks.
Organizations desperately need a way to introduce changes to their data environment without worrying about the stability of data pipelines. And they need to be able to ingest more data without being forced to build more infrastructure. Unfortunately, many are struggling. Nearly half (46%) of respondents say their ability to tackle broken data pipelines lags behind other areas of data engineering.
The first step is to reduce the load on high-value technical staff by handing off the “last mile” of data collection and analysis work to line of business users. Some 86% of those we spoke to say they’d like to see this happen. To make that a reality, business users need the right tools: highly automated, powerful, and simple-to-use platforms that build resilient, dynamic data pipelines across any environment and manage them from a single console. This would not only save organizations millions and free up technical teams to innovate for the company, but also put them on a fast-track to digital transformation and data-driven success.