Data profiling is a foundational step in pharmaceutical data management that helps identify anomalies, inconsistencies, and quality issues before they affect analytics or regulatory reporting. This guide explains how the Datagaps DataOps Suite automates profiling of pharma datasets by analyzing key patterns, detecting outliers, monitoring data distributions, and tracking list-of-values (LOV) changes. These capabilities help organizations maintain data integrity, improve governance, and ensure reliable clinical, patient claims, and drug sales data for informed decision-making.
Key Takeaways
- Data profiling improves pharmaceutical data quality by identifying missing values, anomalies, pattern changes, and inconsistencies before they impact downstream analytics or reporting.
- Monitoring primary key patterns helps detect unexpected format changes, such as shifts from numeric to alphanumeric identifiers, preventing data integration and governance issues.
- Outlier detection and distribution analysis enable teams to identify unusual trends in patient claims, drug pricing, and sales data that may indicate ETL errors or business anomalies.
- Automated profiling with DataOps Suite provides statistics, distribution analysis, and list-of-values (LOV) tracking to continuously validate pharma datasets and improve data trust.
Pattern Recognition and Tracking of Keys and Strings
In the pharmaceutical industry, it is common for different vendors to provide datasets that contain information on the same subjects or entities. For example, a vendor may provide a dataset containing information on clinical trial participants, while another vendor may provide a dataset containing information on patient outcomes.
In order to accurately merge or join these datasets, it is important that the primary keys used to identify the subjects or entities are consistent. For example, if one dataset uses a 9-digit numerical key to identify participants, it is important that any other datasets that contain information on the same participants also use a 9-digit numerical key.
If the pattern of the primary keys is not consistent, it can make it difficult or impossible to accurately link records from different datasets. This can lead to errors or incorrect analyses and can compromise the overall integrity of the data.
As seen in the example below, originally the only pattern seen in the datasets was a 9-digit key. However, in the latest run post, an update from the client we see a new alphanumeric pattern is also seen in the system. This might indicate a data-type change and a definite notification in data governance.
Outliers in Patient Claims and Drug Sales Datasets
Outliers can have a significant impact on the results of any analyses or modeling efforts, as they can distort the overall patterns or trends in the data. For example, if a dataset contains an outlier value that is significantly higher or lower than the majority of the other values, it could skew the average or standard deviation, leading to incorrect or misleading results.
A few examples of how variations in min-max values and standard deviations can help identify anomalies in patient claims and drug sales datasets:
- If the minimum value for a dataset decreases significantly over time, it could indicate an anomaly or error in the data. For instance, if the minimum value for a column containing drug prices decreases significantly from one month to the next, it could indicate that the price was entered incorrectly or that the drug is being sold at a significantly discounted rate.
- If the maximum value for a dataset increases significantly over time, it could also indicate an anomaly or error in the data. Such as, if the maximum value for a column containing drug prices increases significantly from one month to the next, it could indicate that the price was entered incorrectly or that the drug is being sold at a significantly inflated rate.
- If the standard deviation for a dataset increases significantly over time, it could also indicate an anomaly or error in the data. For example, if the standard deviation for a column containing drug prices increases significantly from one month to the next, it could indicate that the prices are becoming more variable than expected, which could be a sign of an anomaly or error.
Also Read: Data Drift Using DataOps Data Profiling
Distributions and List of Values Deltas
In addition to monitoring the distribution of values, it can also be useful to monitor a list of values (LOV) deltas. LOV deltas refer to the difference between the list of values used in one dataset and the list of values used in another dataset. For example, if you are comparing a dataset of patient claims from one year to a dataset of patient claims from the previous year, you might be interested in the LOV deltas between the two datasets.
The DataOps Suite’s profile node can provide statistics on LOV deltas, which can help you identify any changes in the list of values used in your datasets. This can be useful for ensuring the quality and consistency of your data, and for identifying any potential issues or discrepancies.
As seen below 2 examples:
Example A deals with showcasing a change in the number of distinct values seen in a geography key of a patient claims dataset.
Example B showcases how the distribution of sales among different “Lines of Therapy” has been drastically changed indicating either an issue in the calculation of LOT, a change in behavior of the LOT in the drug in question, or worse a bug in the ETL.
Conclusion
In conclusion, data profiling is an important step in the data preparation process, and it is especially important in the pharmaceutical industry where data quality and integrity are critical. The DataOps Suite’s profile node is a powerful tool that can help you perform data profiling on your pharma datasets, and it can provide valuable insights and help you identify any potential issues or inconsistencies.
Some of the key features of the profile node include overview statistics, column statistics, and column distribution plots, which can all be useful in understanding the contents, structure, and quality of your data. In addition, the profile node can help you identify anomalies and outliers in your data, and it can provide statistics on LOV deltas, which can be useful for ensuring the consistency of your data.
Overall, the DataOps Suite’s profile node is a valuable tool that can help you ensure the quality and integrity of your pharma datasets and support more accurate and reliable analyses and modeling efforts.
Frequently Asked Questions
1) What is data profiling in the pharmaceutical industry?
Data profiling is the process of analyzing pharmaceutical datasets to understand their structure, quality, patterns, and distributions. It helps identify anomalies, missing values, inconsistencies, and data quality issues before the data is used for reporting, analytics, or regulatory compliance.
2) Why is data profiling important for pharma datasets?
Pharma organizations rely on accurate clinical, patient, and drug data to support research, compliance, and business decisions. Data profiling helps detect inconsistencies, outliers, and unexpected data changes early, reducing the risk of inaccurate analyses and reporting.
3) What types of data quality issues can data profiling detect?
Data profiling can detect changes in primary key patterns, null values, duplicate records, outliers, unexpected value distributions, and list-of-values (LOV) changes. These insights help identify ETL issues, data integration problems, and governance risks before they impact downstream systems.
4) How does the DataOps Suite support automated data profiling?
The Datagaps DataOps Suite automates data profiling by generating column statistics, identifying outliers, analyzing value distributions, tracking key patterns, and monitoring list-of-values changes across datasets. This enables continuous monitoring of data quality and faster detection of anomalies in pharmaceutical data pipelines.
