This post is the third in a seven-part series that uses a regulatory lens to explore how different dimensions of data quality can impact organizations—and how GX can help mitigate them. Read part 1 (schema) and part 2 (missingness).
Data volume isn’t just a statistic about the size of your datasets—it’s a critical dimension of data quality that can make or break your business’ operations.
Managed effectively, data volume can be a goldmine of insights and opportunities. It can help you make better decisions, optimize processes, and stay ahead of the competition.
On the flip side, when your data volume spirals out of control it can lead to performance issues, inaccuracies, and missed chances to innovate.
In this post, we'll explore the world of data volume management and how it can transform your organization's operational excellence.
We'll share real-world examples and strategies for tackling data volume challenges head-on. And we’ll show how the Great Expectations platform can help you monitor and validate your data volume seamlessly, ensuring that your data’s quality remains top-notch.
A double-edged sword
Data volume is a pervasive challenge facing organizations across industries. While the explosive growth in available data presents immense opportunities, it also poses significant challenges:
System strain: Unexpected spikes in data volume can overwhelm processing systems, leading to performance issues and even system failures.
Inaccurate insights: Anomalies in data volume, if undetected, can skew analyses and lead to flawed decision-making.
Compliance risks: Inconsistencies in data volume across different stages of a pipeline can raise red flags during audits and compliance checks.
Operational inefficiencies: Managing fluctuations in data volume often requires manual intervention, diverting resources from more strategic initiatives.
To show how volume-based data quality checks can improve your data pipelines, we’ll return to the example of FinTrust Bank: a fictional financial institution that’s serving as our lens for this series.
Leveraging GX for effective volume management
Continuing our journey with FinTrust Bank, let's see how Samantha—the data engineer leading the quality improvement effort—and her team tackle data volume challenges.
Having chosen GX as their data quality solution, Samantha already has what she needs to start tracking and checking data volumes. This proactive approach can help Samantha and her team prevent problems down the line.
Using GX Cloud, Samantha's team collaboratively developed and refined their volume Expectations.
GX Cloud allows team members to work on developing their data tests (called Expectations) simultaneously, share insights, and track changes in real time. Its support for collaboration significantly accelerated the team’s data quality work.
For detailed information on volume Expectations and their implementation, see our technical guide on volume management.
Key Expectations for data volume
Samantha and her team implemented key Expectations for data volume.
She used "Expect table row count to be between" to ensure daily transaction volumes remained within expected bounds, setting thresholds based on historical patterns.
For reconciliation tasks, she employed "Expect table row count to equal" to verify that specific batch operations processed the exact expected number of records.
As these Expectations ran, Samantha's team gained valuable insights into their data volume. They identified unusual spikes in transaction volume during peak periods, inconsistencies between data pipeline stages, and discrepancies in batch processing records.
Strategic approaches to manage data volume
Armed with insights from GX, Samantha's team developed a multifaceted strategy to manage data volume effectively:
Implemented scalable data processing architecture to handle volume spikes gracefully.
Automated data volume checks using GX Cloud, enabling proactive issue detection.
Established data volume SLAs across departments, ensuring consistent expectations.
Integrated volume checks into their CI/CD pipeline, catching issues early in development.
They also set up automated email alerts using GX Cloud, enabling rapid responses to critical volume deviations.
Outcomes and impacts
FinTrust Bank's system performance improved significantly as volume spikes were proactively managed, leading to a more stable and reliable data infrastructure.
In particular, downtime was reduced for critical business processes. The accuracy of financial forecasting models also improved, thanks to more consistent and reliable data inputs.
With more reliable output from existing models, FinTrust Bank's data scientists could now build on those to develop more precise models, enabling better decision-making across the organization.
Automated volume checks made reconciliation efforts more efficient, allowing time and resources previously spent on manual data reconciliation to be redirected to higher-value tasks.
When FinTrust Bank ran a promotional campaign, its systems seamlessly accommodated a surge in transactions. The marketing department made real-time optimizations based on reliable data, resulting in a more successful campaign outcome.
FinTrust Bank's robust data volume validation processes were even recognized by auditors as industry-leading practices, boosting the bank's reputation and demonstrating their commitment to maintaining the highest standards of data quality.
While FinTrust Bank may be a fictional entity, the benefits it reaped from effective data volume management can be realized by organizations in the real world.
Prioritizing data quality and implementing smart volume management strategies means that you’re taking steps to improve your operational excellence and successful outcomes for your data-driven initiatives.
Mastering data volume: A catalyst for operational excellence
Managing data volume is not just about keeping systems running smoothly—it's a strategic lever for driving operational excellence. FinTrust Bank's journey showcases the transformative power of effective volume management:
Improved system performance and reliability
More accurate and timely insights for decision-making
Increased operational efficiency and agility
Enhanced regulatory compliance and audit readiness
By getting a handle on your data volumes (leveraging techniques detailed in our technical guide), you're not just optimizing operations—you're fueling your organization's growth and innovation.
Join the conversation
How has tackling volume challenges transformed your data operations? Join the discussion in our community forum to exchange ideas and best practices with other data practitioners. Together we can continue to build a new vision of what it means to excel in data quality.
Stay tuned for the next installment of our series, where we’ll explore how monitoring your data distribution can elevate your data quality capabilities and your data outcomes.
