Data engineers might be the first group that comes to mind when discussing the topic of data observability. No doubt, data observability technology has become mission-critical for many data engineering teams seeking visibility into their data, processing, and pipelines.
But, data engineers aren’t the only stakeholders with a seat at the table.
Data leaders are increasingly interested in data observability, too—especially as data environment complexity and costs continue to increase. During a recent Data Engineering Podcast, we explained how CDOs can benefit from data observability. This blog article builds on a few of these key points.
Shared Challenges that Speak to the Need for Data Observability
It’s tempting to think that the work life of a CDO has very little in common with the work life of a data engineer. After all, CDOs spend most of their time advocating for data, defining data strategy and policy, making technology decisions, negotiating with vendors, and collaborating with other C-level executives. Data engineers, on the other hand, are usually heads-down on the latest data integration project, troubleshooting data pipeline issues, working with stakeholders on specific use cases, and managing data projects.
Despite the many differences in their day-to-day responsibilities, data leaders and engineers should be aligned around at least one important objective: leveraging data to advance the organization’s goals. Unfortunately, that’s easier said than done when companies are dealing with:
- Increasingly complex data environments: Modernizing an organization’s data stack can require years of careful planning and effort. Even after the modernization efforts are “done,” some use cases may still be best suited for on-prem technology—leading to a hybrid environment.
- Rising cloud costs: Cloud technologies make it possible to bypass many of the upfront data infrastructure costs that were previously necessary. But, cloud services can add up quickly, especially as demand for data and insights increases throughout the organization. Failing to implement proper control mechanisms can lead to costs that exceed budgetary expectations.
- Recruiting challenges: Modern organizations are struggling to find and retain top technical talent. Data teams have to make difficult decisions about which projects to prioritize and how to allocate scarce resources. And, for some teams, this could mean focusing on new data products without fully understanding the related costs and technical impacts on other workloads.
How Enterprise Data Observability Can Help Data Engineers & CDOs
Acceldata’s platform provides enterprise visibility into the modern data stack. Our technology helps data-driven organizations reduce complexity, scale innovation with data, and optimize costs.
Here’s what that means for data engineers and data leaders.
Gaining end-to-end visibility into the organization’s data repositories and pipelines helps data engineers ensure optimal performance across cloud, on-prem, and hybrid data environments. Being able to quickly drill down into root causes puts data engineers in a better position to fix issues in less time and keep data flowing. Automated data quality monitoring supports data reliability with less engineering effort. Spend intelligence helps data engineers ensure that the organization’s data-related costs are staying within budget, thereby reducing the chances for an unexpected cloud bill at the end of the month.
Better yet, Acceldata’s platform supports all of this without placing too much extra work on time-strapped data engineers. The goal is to have reliable data, but it shouldn't require a major change to workflows.
Chief Data Officers
Acceldata’s platform can be incredibly useful to CDOs, too. For starters, CDOs can use our technology to easily visualize how data and technologies are being used across the organization. This map can be very helpful for every sort of manager, flowing up to the data executive.
CDOs can use this “map” and other features in Acceldata’s platform to gain a high-level view of organizational data use and answer complex questions, such as:
- What is the relationship between our data systems?
- Who is using what technology?
- Is our data high quality?
- Where do data issues originate?
- How much are we spending on our data?
- What did we spend relative to data growth?
- How does our data map into revenue?
Data leaders can also find peace of mind knowing that their data teams—particularly their data engineers—are getting the necessary visibility to ensure data quality, hit SLAs, and, ultimately, move faster on value-added projects.
Increase Visibility for Your Data Leaders & Data Engineers
Interested in learning more about enterprise data observability? Request a free trial of Acceldata’s data observability cloud.
Photo by MARIOLA GROBELSKA on Unsplash