Against the global adoption of enterprise-level cloud data warehouses (CDW), the challenges of cost-efficient CDW use loom upon the data engineers. A sound data management strategy is just the right thing to implement. It should plan for preventive data quality monitoring and acting upon its findings to optimize data-driven workflows.
As a part of data quality management, data observability has become one of the elements for building a lean, high-performing, and supervised CDW-centered data ecosystem. Now that we’ve briefly defined “what is data observability,” let’s see what practical aspects of organizational data operations it covers.
6 Application Areas of Data Observability
Data observability system implementation targets 5 key aspects related to the quality of data sets and the sustainability of enterprise data operations.
- Verification of Data Completeness.Missing data points can undermine co-dependent calculations and skew their results. Timely detection of incomplete data can prompt data engineers to work around such an error. For instance, if the data is linear, they can programmatically set a replacement with the mean/median value.
- Integrity of Data Quality. Observability fulfills data governance standards of data quality throughout its entire lifecycle. In other words, the data quality team ensures that data consumed by business users is reliable and meets the established standards: refreshed/deduplicated within strict periods, kept relevant, etc.
- Understanding Data Lineage.Observability platforms foster backtracking through the data evolution cycle. They fast-track debugging processes and help data engineers eliminate erroneous and anomalous data. Thus, they can prevent data issues from snowballing and affecting downstream business operations.
- Data Schema Analysis. Shema is a model of how data inputs form integral ledgers and how users request data from CDW to work with it in external apps. Observability tools reveal excessive data use and redundant assets that lead to overspending on CDW.
- Optimizing Data Flows.Maintaining a data pipeline performance smooth and fast-flowing becomes troublesome if ETLs/reverse ETLs remain unsupervised. With observability systems, data engineers can identify redundant extract and rewrite operations induced by unwanted user access or data architecture gaps. Hence, they can restrict excessive requests or even shut down ETLs that do not contribute positively to the business.
How Data Observability Tools Work?
Observability products are layered on the existing CDW-centered data stack and typically demand no coding. However, not every product is low-code software, and you should primarily look for a user-friendly observability platform with auto-deployed monitors and detection thresholds.
The best-of-breed observability solutions leverage machine learning and predictive analysis to comprehend data use patterns. Thus, their prompts on data quality issues and anomalies are accurate and actionable. Human-readable prompts are also crucial in nurturing an enterprise-wide data culture: they help data stewards report on data health problems in easy-to-digest form and share essential data health insights across all stakeholders.
Taking CDW Management Beyond Data Observability
Even though data observability has made a big splash in the last 2-3 years, business adopters don’t fully understand how to determine data observability efficiency. Presumably, you get the integrity of data quality, but how does it contribute to workflow stability and productivity? Do you know how costly manual debugging is and what data issues cause excessive CDW resource use?
The stakeholders want to implement data observability tech to gain peace of mind about data that underlies business operations, feeds business analytics, and backs up the sustained performance of internal applications. Data teams are all for zero-touch solutions that will guide them on what, where, and when to troubleshoot without diving headfirst into weeks or even months of deployment and setting things up.
From those circumstances, you can clearly see what shortcomings of the generic data observability offering to be aware of:
1. A Manual-First Approach
It leads to long deployment cycles. Observability systems are meant to be time-savers, so it is ridiculous that some take weeks and months to deploy.
Secondly, there has to be a human-supervised monitoring setting. The enterprise will need to assign people to monitor what, how, and when. So, it’s no good when the observability offering doesn’t resolve what to check and define performance metrics and thresholds.
2. Insufficient Automation
Let’s be honest—data teams should dedicate their time to mining new data and devising more agile data schemas that would drive business growth and scalability. The last thing they should be wasting their time on is managing data observability systems and programming data health monitors.
The top-pick observability solution must offer sufficient automation for data quality management. It will likely remain shelfware if it doesn’t eliminate a specialist’s manual intervention.
3. Fixation on Data Quality for Its Own Sake
Fair enough, data quality is paramount to driving key business decisions and strengthening trust in data insights. However, dwelling on it can’t help enterprises understand whether they’re investing rationally in the data stack, and it will not pay off with higher data adoption and ROI.
You need to ensure you’re getting quality data at the best cost. So, if you can say that your observability product cuts CDW costs and ensures lean resource consumption, then your investment is worth it.