Best Practices for Data Aggregation in the Age of “Big Data”

Lab Informatics

As your lab transitions into the age of “big data,” following certain best practices for data aggregation can improve the efficiency of your research processes. Image Credit: Flickr user luckey_sun

As your lab transitions into the age of “big data,” following certain best practices for data aggregation can improve the efficiency of your research processes. Image Credit: Flickr user luckey_sun

“Big data” has recently become a major buzzword in both the materials science and life science fields. As a researcher today, you have the ability to generate enormous datasets in a matter of hours, minutes or even seconds. This has opened up unprecedented opportunities for innovation, but it also makes it a lot harder to keep track of data. If the researchers in your lab are used to running small experiments and recording your results in a single lab notebook, the transition to the age of “big data” can be a significant challenge.

As your lab takes advantage of the benefits of “big data,” there are certain best practices for data aggregation that researchers in your lab can follow in order to maximize research efficiency. Today’s lab informatics innovations can support these practices.

Dispensing with Paper-Based Data Aggregation Methods

In the past, the paper lab notebook was the mainstay of data aggregation, but keeping track of all your data in a notebook just isn’t feasible anymore. Obviously, it is impossible for you to copy down thousands of data points every time you run an experiment — but copying down data file names and locations in a lab notebook can end up being just as problematic. Keeping track of “big data” in this way can just end up sending you (or another researcher in the future) into a labyrinth of information, wasting time searching for lost files on different lab computer or in different accounts. With today’s electronic lab notebooks and cloud-based data aggregation options, it’s a lot easier to keep track of “big data” without the time efficiencies associated with trying to document datasets in lab notebooks.

Documenting and Automating Data Generation Processes

Even though it is now possible to generate larger datasets than ever before, the fundamental guidelines of good science still apply. In both the materials science and life science fields, all experiments need to be well-documented and repeatable. This can be a challenge, given the fact that generating a dataset that qualifies as “big data” involves conducting a series of hundreds or thousands of experiments. Using lab informatics software to standardize processes can ensure that all experiments and tests are properly documented. That way, when you look back on your aggregated data, you know exactly what every data point means, regardless of the size of the dataset.

Process automation is also a best practice that can support data aggregation in materials science and life science labs. By automating processes, you can limit variability and ensure that all of the experiments you have conducted when generating a massive dataset are repeatable. Automation can also reduce the risk that manual error will lead to accidental data loss when results are being aggregated into a single, massive dataset.

Standardizing Data Analysis Processes

One of the things that distinguishes “big data” from large datasets generated in the past is the challenge that analyzing “big data” presents. Traditional data processing methods simply won’t cut it, so researchers are challenged to find creative ways to parse massive datasets. When your lab finds a method that works, lab informatics software makes it easier to standardize the method so that it can be repeated and shared between researchers.

With today’s informatics solutions, standardized methods can also be adjusted to deal with unique datasets. Instead of having to start from scratch, a researcher can simply tweak a previously validated method to meet the processing requirements for different datasets. This can significantly cut down on time spent with manual programming and other time-consuming tasks related to processing method development..

Opening the Door to Data Sharing

Another one of the hallmarks of the age of “big data” is the fundamental need for data sharing. In many cases, it is simply no longer feasible for a single scientist to carry a project from conception to execution to analysis and conclusion. Therefore, when you are aggregating data, you need to make sure that it is easily accessible to other researchers and any relevant external collaborators. By embracing informatics software that supports data sharing and collaboration,  the research group can avoid any bottlenecks in end-to-end research processes that might arise as a result of data accessibility problems.

Storing Aggregated Data Securely

Storing “big data” securely is just as important as being able to share it with collaborators. The very size of the datasets that are being generated today make them uniquely challenging to store, just as growing data security threats raise further concerns for scientists in all fields. The latest lab informatics solutions resolve this problem by implementing strong security measures without interfering with data accessibility for authorized parties.

BIOVIA provides a wide range of innovative lab informatics solutions that can support efficient data aggregation in the age of “big data.” Whether you are working in the materials science or life science field, our offerings can help you take advantage of the ongoing revolution in data generation and processing. Contact us today to learn more!