Embarking on a Data Quality journey is a crucial endeavor for data-driven organizations. The initial fundamental steps often include confirming data frequency and volume through metadata analysis. This preliminary phase lays the groundwork for what lies ahead.
So, what happens next?
How can you ensure that the data being processed is not only the right volume and frequency, but also reliable, accurate, and trustworthy?
This is where having the right features and capabilities in your Data Quality Monitoring platform becomes paramount.
To eliminate the guesswork, we’ve compiled a list of 10 essential features that’ll elevate your Data Quality analysis — moving beyond the surface-level metadata checks. From ensuring data freshness and detecting hidden trends and anomalies in your data, to incorporating seasonality and harnessing the power of AI-based monitoring, these combined features will enable your organization to have a comprehensive, robust, and scalable Data Quality framework.
Here are the 10 must-have features that’ll empower you to dig deeper into the intricacies of Data Quality analysis.
1. Data Freshness
Incorporate a data arrival schedule into your freshness checks. The schedule for data arrival may vary by day or season.
Avoid extra alerts or missed alerts by taking that schedule into account.
2. Data Volume
Detect arrival of “old” data. If the total volume of data arriving is “normal,” but the timestamps are “old,” the data volume observed is likely inaccurate, which won’t be detected by metadata checks.
To really understand the data volume of time series data, query the data rather than just looking at data generated by metadata warehouse reports.
3. Root Cause Analysis
Support root cause detection based on business dimensions.
For example, it’s not enough to know that sales are low. Your team most likely needs to know what market or store is reporting low sales.
To understand the root cause, you’ll need to investigate which records failed. With failed record analysis, data engineers can see examples of the violation and all the failure records when incidents are detected.
4. Low-code Metrics
Implement metrics to quantify the health of your business.
Data freshness and volume provide insights into data pipelines, but they don’t provide the deep insights needed to build confidence in business processes.
Data teams often progress from automated metrics (freshness and volume), to low-code metrics (basic aggregations or percentage checks), to custom SQL metrics that represent their business-specific use cases.
5. Seasonality
If your business is seasonal, incorporate seasonality into your monitoring methodology.
Most businesses have a seasonal component. This seasonality can be hourly, daily, weekly, or based on holiday schedules.
If you don’t incorporate seasonality into your monitoring methodology, your Data Quality framework will either be overly noisy — alerting at unexpected times — or incorrect, missing alerts when it should be generating alerts.
6. AI-based Monitoring
If your business metrics are trending in some direction, make sure your monitors can handle those trends.
For example, if sales are trending up, a manual threshold will soon become outdated. AI-based anomaly monitoring with trend is necessary to accurately alert when data is trending.
7. Triggered Metrics
Trigger metrics from your pipeline to ensure that metrics are collected only after the data is available.
If you have a schedule for collecting metrics, but your pipeline is unreliable, you’ll collect unreliable data. Triggering collection from the pipeline itself will ensure that the data collected by your Data Quality platform has actually been written to the warehouse.
8. Scheduled Metrics
If you’re not triggering Data Quality Checks from your pipeline, create a schedule for Data Quality Checks that ensures they only happen after your data has arrived.
Make sure that you aren’t doing Data Quality analysis on data that is not yet “complete” or stable. Mistakenly checking your data before it has arrived will likely cause false positives, reducing the trust in your system.
9. Dimension Table Checks
Run Data Quality Checks on dimension tables.
Why? Though most new data arrives via time series data in fact tables, dimension tables are also updated periodically and hold data that is referenced by your fact tables.
Data Quality Checks on dimension tables will typically be full table scans that occur at repeating intervals.
10. Dashboards
Create simple, table-focused dashboards that enable your data quality engineers to quickly understand coverage and health, so they can take action as soon as problems are detected.
Equally important, surface this information at the datasource- and project-level, so all stakeholders and leadership teams can observe Data Quality trends in the organization.
Advancing Your Data Quality Journey
When it comes to Data Quality analysis, laying the foundation with initial metadata checks is indispensable. But to truly understand the health of your data, you’ll need to dig deeper by using advanced features. Leveraging these 10 features allows your organization to perform deep Data Quality analysis for insights beyond shallow metadata checks.
By incorporating arrival schedules for data freshness checks, performing root-cause analysis, and using low-code metrics and AI-based monitoring, organizations can fortify their data pipelines against Data Quality blindspots, mitigating the risks of missing silent data outages and other incidents that impact downstream business operations.
Moreover, triggered metrics, scheduled checks, and dimension table checks help ensure that Data Quality analysis is not just a routine, but rather a meticulous process aligned with the nuanced dynamics of your business. As a final touch, simple Data Quality dashboards give teams a bird’s-eye view of the coverage and health of their tables across the organization.
While the journey to achieve high Data Quality is an ongoing process, by integrating these critical features, organizations can navigate the complexities of data with confidence. The result? A modern Data Quality framework that not only identifies issues, but also empowers teams to proactively monitor and self-check their Data Quality, building a stronger foundation of data trust for data-driven organizations.
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