Table of Contents
Author
Nihar Rout
Managing Partner 4DAlert
Perfect data doesn’t exist — but with automated reconciliation, observability, and AI-powered checks from 4DAlert, your team can finally trust what they see.
“We’re not like your other organizations. Our data is terrible.” This is a familiar sentence that we hear from most people using or managing data in any organization. The good news for everyone is that no organization has perfect data.
Any organization claims that their data is perfect, most likely that claim is not true or they have not analyzed their data enough. The reality is that no organization could ever reach a perfect state of data. Perfect data is an imaginary target. However, the good news is that every organization could improve and make their data better.
In today’s data-driven world, organizations acquire and accumulate data from multiple sources on a continuous basis. Their data is constantly moving, drifting, and changing. On top of it, as an organization’s priorities shift, their requirement to generate insights from data changes, which in turn requires their data team to transform, process, and manage data differently.
Now the question is how should data teams prepare themselves to improve their data?
Well, getting to perfect data is a journey. Organizations need to take the journey and make it better by adopting the right strategy, tools, and techniques.
Let’s see the steps we need to take to make the journey to perfect data smoother and faster.
1. Develop a criteria for perfect data –
Every organization should develop its own Data Quality framework. Again, there is no single metric to quantify perfect data across the board. For example, the supply chain team might accept 98% accuracy in sales numbers for directional analysis, while finance or accounts receivable may need 100% match with invoices, and marketing may rely on 100% populated attributes for segmentation.
2. Have a dedicated team to maintain and store these rules –
Developing rules in silos is ineffective. Enterprise-wide governance is critical. Enterprise Data Quality Management platforms like 4DAlert provide a centralized, cloud Data Quality Platform to define and manage business rules. These rules should have clear ownership and traceability.
3. Benchmark the criteria –
Before starting the journey toward better data, it’s important to benchmark current data quality. Tools like 4DAlert offer Automated Data Quality checks and pre-built dashboards to categorize and visualize data issues. These serve as the starting point for improvement.
4. Measure continuously –
Once benchmarked, organizations need real-time data quality monitoring. This continuous evaluation helps data teams stay ahead of quality degradation and improves long-term trust in analytics.
5. Set up Data Quality alerts –
It’s okay to have small data quality issues, but knowing when they cross the threshold is crucial. For instance, 200 blank attributes out of 2 million records might be acceptable, but if that doubles, you want to know immediately. With AI and ML based Data Quality monitoring, 4DAlert provides intelligent Data Quality alerts when thresholds are breached.
6. Go beyond simple checks –
Traditional checks like non-blank, number ranges, or email validation are not enough. Advanced Data Observability tools like 4DAlert enable end-to-end Data Reconciliation, ensuring not just format but accuracy. This prevents silent failures that traditional tools often miss.
(Want a deeper dive into how observability and quality work together? Check out this blog for more insights.)
7. Automate with AI/ML –
Manual rule creation doesn’t scale. Organizations need automated, ML-based data quality. 4DAlert applies machine learning to assess freshness, row counts, and more. Over 70% of automated checks can run without manual effort. Manual rules can still be added for organization-specific needs.
In summary, no data is perfect, but data can always be better. Getting there requires a strong Data Quality framework, continuous monitoring, AI/ML-driven automation, and reliable Data Reconciliation tools like 4DAlert. With the right systems in place, organizations can trust their analytics and drive better business outcomes.
Author – Nihar Rout, Managing Partner 4DAlert, https://www.4DAlert.com
Contact-nihar.rout@performalytic.com