Data engineers are typically who we think of as the main data problem solvers in an organization. Indeed, they are on the front lines and must fix any data issues that arise, but increasingly, Chief Data Officers (CDOs) are also getting their hands dirty with any and all issues related to data. The approach and tool that all are using is data observability.
Data leaders and practitioners are increasingly interested in data observability—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.
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:
Theh Acceldata Data Observability 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 CDOs.
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.
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:
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.