Accessing Historical Life Science Data with ETL: A Path to Insights

In the rapidly evolving life sciences industry, access to historical data is crucial for operations from research to drug development, manufacturing, and clinical trials. However, the necessary data often resides in disparate systems, formats, and locations, making it challenging (and expensive) to harness its full potential. This is where ETL (Extract, Transform, Load) processes come into play, acting as a vital tool for data integration and analysis.

What is ETL?

ETL is a data integration framework that enables organizations to extract data from various sources (e.g., LIMS), transform it into a usable format, and load it into a centralized system for analysis (e.g., data lake or platform). In the life sciences domain, ETL processes can streamline the handling of vast quantities of historical data from clinical trials, research studies, and laboratory results, thus facilitating better decision-making through visibility. This is applicable in a merger or acquisition when various units of a new or divested business would have their own data sources.

Extract: Gathering Data from Multiple Sources

The first step in the ETL process is extraction, where data is collected from various sources, including:

• Clinical Databases: These databases often contain patient records, trial results, and treatment outcomes.
• Laboratory Information Management Systems (LIMS): The LIMS holds detailed (typically structured) lab results, sample information, and operational data.
• Public Repositories: Numerous public databases offer valuable historical data (e.g., GenBank, RefSeq, Behavioral Risk Factor Surveillance System, etc.).

By systematically extracting data from such diverse sources, scientists can begin to build a comprehensive historical dataset.

Transform: Cleaning and Standardizing Data

Once the data is extracted, it must be transformed to ensure consistency and usability (FAIR; Findable, Accessible, Interoperable, and Reusable). This transformation step includes:

• Data Cleaning: Removing duplicates, correcting errors, and filling in missing values to enhance data quality.
• Data Standardization: Converting data into a consistent format, such as standardizing date formats or unit measures, to facilitate accurate comparisons and analysis. This is where decisions are made surrounding how the data will be used (e.g., European or United States markets and regulations).
• Data Enrichment: Integrating additional data sources or deriving new variables that add context or enhance analytical capability.

These transformation activities are crucial in ensuring that the data is not only accurate but also relevant to any future questions that might be asked of it.

Load: Centralizing Data for Analysis

The final step in the ETL process is loading the transformed, disparate data into a target system, typically a data warehouse, data lake, or platform (e.g., ZONTAL, Sapio Science). This centralized repository enables researchers to perform complex queries and analytics, facilitating the discovery of insights that can drive innovation and optimization. As an example, linking supply chain data to LIMS forecasting of sample workloads can help prevent unnecessary stoppages in QC testing due to running out of a particular stock reagent.

With historical life sciences data now accessible in a unified FAIR data format, data scientists/engineers can use advanced analytics, machine learning, and artificial intelligence tools to derive meaningful conclusions. For instance, analyzing historical scientific instrument trends can reveal predictive models to save downtime and costly non-conformance issues in finished product testing.

Putting It All Together: ETL to Optimize Processes

Once all your data is centralized and adhering to the same ontology, what do you do with it? In other words, why is ETL useful?

At this point, you might find yourself asking, “Why would I do all this to my data? It sounds expensive, and isn’t data management the whole reason for having a LIMS?” Well, yes… and no. LIMS is only part of an ETL design structure. LIMS certainly helps introduce and maintain FAIR data principles, but it’s only (usually) concerned with lab results. What about… 

• supply chain data? 
• Quality documents? 
• Instrument preventive maintenance schedules? 
• Lab staffing? 
• Market forecasting?

ETL helps optimize stored data relationships and uses them to operate more efficiently. For example, with the LIMS data, quality control management can see what product goes out of specification. That’s very useful for their purposes. Next, you must consider why it went out of specification. It may be important to get to the root cause of this event. Often, this task is assigned to a compliance investigator who will look for the root cause or causes in other upstream systems (e.g., manufacturing, documentation, supply chain). Maybe a trend could be identified if we could combine these disparate systems. That’s where ETL comes in! By extracting the data from disparate systems, transforming them to the same (or compatible) format, and then loading them into the same accessible location, data scientists/engineers can mine for root causes quicker and develop more effective corrective and preventive actions (CAPA).

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How could you use ETL in your informatics environment? Let us know in the comments.