Building robust models starts with data quality and integrity

By Carla Paquet based on an interview with Chandramouli Ramnarayanan

Imagine investing months into a predictive model – only to realize too late that data inconsistencies have rendered it useless. Flawed data leads to flawed decisions. And in industries where precision matters, unreliable predictions can be costly.

Poor data integrity doesn’t just affect model performance; it can introduce bias, compromise regulatory compliance, and lead to incorrect conclusions.

Across industries, companies struggle with data fragmentation, legacy systems, and inconsistent validation processes.

If data is the foundation of predictive modeling, what happens when that foundation is unstable?

⚠️The cost of ignoring data quality

When data has inconsistencies, organizations face:

• Unreliable predictions that lead to incorrect insights and flawed business strategies.
• Increased costs that are the result of repeated experiments, wasted resources, and misinformed decisions.
• Regulatory risks, especially in regulated industries, where inaccurate data can lead to compliance issues and delays.
• Inefficiencies and delays mean that teams waste time troubleshooting errors instead of focusing on innovation.

Chandramouli Ramnarayanan, Global Technical Enablement Engineer at JMP, highlights how these costs accumulate over time: “Errors in key data sets lead to operational inefficiencies, regulatory risks, and costly delays. The inability to trust data can slow down decision making and create long-term business challenges.”

So how can companies ensure their data remains accurate and reliable?

📌Three ways to ensure data consistency and reliability in predictive models

1. Validate your models before trusting the results A model is only as good as the data it’s trained on. Set aside a portion of your data to test performance in real-world scenarios to prevent overfitting and to ensure that predictions hold up under different conditions.
2. Use statistical monitoring to detect anomalies Chris Wells, Study Statistician at Roche Pharmaceuticals, advocates for statistical monitoring as a key tool for ensuring data integrity. By proactively identifying irregularities and inconsistencies, you prevent errors from propagating into decision making.
3. Use key metrics to detect inconsistencies early

• Central tendency metrics: Mean, median, mode to identify unexpected shifts in data.
• Dispersion indicators: Variance, standard deviation, and interquartile range to understand variability.
• Process capability indices (Cp, Cpk): To assess how well processes meet specifications.
• Error metrics (MAE, RMSE, R²): To monitor predictive performance.
• Control charts and trend analysis: To detect gradual drifts over time.

Emphasizing the power of real-time monitoring, Ramnarayanan says, “Dashboards displaying these indicators allow immediate action, preventing anomalies from cascading into flawed decisions.”

⚙️ Use DOE (design of experiments) for robust models

As Christian Bille, Statistician Scientist at Bavarian Nordic, demonstrates in JMP’s DOE for Robustness and Optimization, a well-designed DOE strategy ensures models remain stable under variable conditions.

Ramnarayanan explains the impact of DOE on data integrity, “Randomization mitigates biases, replication ensures consistency, and factorial design reveals interactions between variables.”

🧠 How to improve data without collecting new samples

When collecting new data isn’t an option, you can still enhance the quality and reliability of your data sets. Ramnarayanan highlights three techniques:

• Bootstrap sampling: Resample existing data to test model stability.
• Monte Carlo simulation: Generate synthetic data to assess uncertainty impacts.
• Imputation methods: Replace missing values with plausible estimates.

"Automated data-cleaning pipelines, combined with statistical monitoring, significantly reduce human error and improve efficiency in large-scale data environments," says Ramnarayanan.

With software like JMP, these techniques become faster and easier to implement, leading to higher data accuracy and reliability.

✅The business case for high-quality data

Companies that prioritize data quality and integrity see tangible benefits:

• Faster decision making: Greater confidence in predictive models.
• Lower costs: Fewer errors and unnecessary rework.
• Regulatory readiness: Compliance without last-minute corrections.

 The bottom line? Investing in data quality isn’t just about accuracy – it’s a strategic move that spurs innovation, efficiency, and growth.

 📈See data integrity in action

Chandramouli suggests using an interactive dashboard combining:

-> Control charts: Tracking data quality over time (error rates, variance, drift over time).

-> Scatter plots: Showing model predictions vs. actual results.

“As data integrity improves, control limits tighten, and predictions cluster more closely around actual values—demonstrating better model accuracy,” concludes Ramnarayanan.

📢 Read the full interview with Chandramouli Ramnarayanan

These insights are just the beginning. In the full interview, Ramnarayanan takes a deeper dive into:

• The biggest data integrity pitfalls across industries.
• How to quantify the cost of unreliable data.
• The best strategies for anomaly detection and statistical monitoring.

Read the complete interview on the JMP Community.

❓What’s your biggest data challenge?

Have you ever struggled with messy, incomplete, or unreliable data? How did you handle it?

👇 Drop a comment below 👇. Your experience could help others!

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