How Data Engineering Can Empower Your Research

Academia and industry are two distinct fields that have completely different requirements. If we were to make a comparison, independent research labs are closer to small businesses or startups, where resources are tight and funding runways are short, often leading to decisions that focus on short-term gain and survival. Often, that’s why labs in academia have poor data infrastructure. It’s hard to justify prioritizing something when it is considered non-essential for survival, when instead that time can be spent on ad hoc data analyses to write a research grant.

However, I think this is a mistake. Science is not only about discovery but also about reproducibility. Well-designed code, data management, and automated data pipelines will ensure that the data is consistently prepared the same way, leaving less room for error. Furthermore, engineering away this time consuming task quickly pays itself off, and frees up time for more thinking, analyses, and writing. Below, I will talk about a few of the benefits of investing some time into data engineering for your research needs.

Reliability

One of the primary benefits of robust data engineering is reliability. In my experience, I’ve seen fragile data pipelines written, and then the code is inherited by the next graduate student or research assistant, with little understanding of how it works. This person then launches a version of R, Python or MATLAB that was completely different from what the pipeline was written in years ago, and they run into an unknown error. When people run data pipelines manually, it introduces the potential for systematic errors or small changes in configurations that people may not be aware of. These inconsistencies can lead to skewed results and unreliable findings.

Automated data pipelines, on the other hand, ensure that data is processed consistently every time. By encouraging production-level code, we can version the data pipelines and traceback how any data discrepancies may have happened. This traceability is crucial for identifying and correcting errors, ensuring that your research data remains accurate and reliable.

Furthermore, standardized data pipelines contribute significantly to the reproducibility of research findings. In scientific research, reproducibility is essential for validating results. By minimizing human error and ensuring consistent data processing, robust data engineering practices help maintain the integrity of your research.

Efficiency and Time Management

Another significant advantage of investing in data engineering is the efficiency it brings to your research workflow. Automation of routine data processing tasks can free up considerable time for researchers, allowing them to focus on more complex and creative aspects of their work. This is especially true for researchers that have data pipelines that take hours to finish. Rather than setting a reminder to run some pre-processing script for the day, a pipeline can instead be triggered to scan for changes in a given directory and then run a batch process. This shift can lead to more innovative thinking and potentially groundbreaking discoveries, or simply more time to make very pretty figures for their papers.

Automated systems can also handle larger datasets more efficiently than manual processes. As data volumes grow in research, having scalable and efficient data processing systems becomes increasingly important. A well designed system on a high performance computing cluster should be able to utilize distributed computing to scale from processing 1GB of data to more than 1TB of data. This would ensure that your research can keep pace with the ever-increasing amounts of data being generated, without sacrificing quality or accuracy.

Data Quality and Integrity

Data quality and integrity are critical for any research project. Usually these can be considered band-aid fixes to some data collection issue, where code may be commented and uncommented to process a specific subject’s data. However, properly cataloging and continuously adding these rules to a data pipeline ensures that the rule can be tracked and reused in the future. Robust data engineering involves effective error handling and data validation steps to prevent the use of corrupted or flawed data in analyses. By implementing these practices, researchers can trust that their data is of the highest quality, leading to more reliable and valid research findings.

Conclusion

Investing in data engineering can significantly enhance the quality, efficiency, and reliability of your research. While it may seem like a non-essential task in the short term, the long-term benefits are substantial. By ensuring consistent data processing, freeing up time for more complex tasks, maintaining data quality, and reducing technical debt, robust data engineering practices can empower your research and contribute to scientific progress. With a well-designed data management system, future research projects can be started and managed more efficiently, as the foundational infrastructure is already in place.

I encourage all researchers and lab managers to consider the importance of good data infrastructure. Start small, implement basic automation, and gradually build a comprehensive data management system. The initial effort will pay off in the long run, leading to more reliable and impactful research outcomes. Interested? Then we encourage you to reach out to our team for a professional assessment of your current data infrastructure needs.