With the advancement when it comes to automation and new drug modalities, modern labs happen to generate more data than ever; however, turning data into intelligence is indeed another story. Lab leaders stress on making the data actionable or achieving data intelligence from their data pools. They know that their laboratory data can go on to help their businesses better; all they need is to harness it.
AI doesn’t give you the right intelligence automatically
Laboratory data happens to be fertile ground when it comes to AI. Data-driven quality control can go on to alert labs to instrument trends as well as deviations. Analyzing data can enhance resource allocation as well as budgeting and also go on to identify emerging patterns that surround degrading data or process integrity. Data can be actionable; it can go on to generate powerful insights, shape decisions, and at the same time improve business outcomes, and with AI, one can go on to extract greater insights from data than ever before. For instance, AI pattern recognition can help in process monitoring as well as optimization. But there also happens to be a risk. If AI models get trained on data that is biased or incorrectly contextualized, they can go ahead and generate biased results.
So as to reach digital maturity, labs require the right intelligence in so as to get the right intelligence out. The phrase garbage in, garbage out is apt here in this context when we say- bad data will lead to bad outcomes. Intelligence in, apparently, is all about making use of high-quality data, and at the same time, it is also about harnessing human intellect. In order to succeed, AI and ML need the right data as well as the right people who ask the right questions.
Begin with the right data
Garbage in goes on to include data having transcription errors or stripped of context; for instance, just inputting the method and results of an experiment sans the context of what the experiment is. But when we talk of AI or ML, garbage in can also go on to mean insufficient data. Typically, if a lab goes on to run an experiment and does not get the desired outcomes, that data happens to be archived but rarely retrieved for review analytically. But in ML models, data coming from failed experiments can go on to yield information that’s useful about how parameters interact. Models get more precise due to lots of data on what does and does not go on to achieve desired outcomes. Hence, intelligence in should go on to include data from successful experiment runs as well as assays and failures.
Apart from being accurate, high-quality data has to be absolute, comprehensive, present, and also unique. Complete data happens to have no missing entries, and metadata or associated data are also included. Next, the data has to be comprehensive for the questions that the lab looks to ask. For instance, attempting to come up with golden batches but only offering a dataset with laboratory information management system- LIMS data may go on to generate an inaccurate as well as biased response. A LIMS may only go on to have partial data, which would require pulling data from other sources within the lab for a more absolute picture. Data has to be present. Training an algorithm with out-of-date data can go on to produce an out-of-date answer. And finally, the data should be unique. If values happen to be accidentally duplicated, it may further bias the data.
Getting the right data to the right people
Next, for good data to be useful, it must be made available and should be intelligible to both humans as well as machines. Often, data gets stored in varied silos and formats; even high-quality data can be difficult to retrieve.
Many companies have started to funnel data across all systems into a single data lake. This collection of structured as well as unstructured data can go on to offer a single source for data-consuming algorithms. But this approach is resource-intensive and is no longer necessary. Newer tools are designed so as to provide access to data in spite of the data location, necessarily de-siloing the system architecture with no involvement of IT.
It is well to be noted that wherever data is stored, a well-architected data backbone goes on to add layers on top of the data so as to maintain integrity and also provide context for data from numerous sources. These architectures are often built surrounding the FAIR data principles: making sure that the data is findable, interoperable, accessible, and, at the same time, reusable. In the past, it often took a trained IT professional operating side-by-side with the subject matter expert in order to construct the complex queries required to gain the desired solution sets. New tools happen to be reaching the point where anyone can go on to learn how to construct meaningful queries sans knowing how to program. Putting low- and no-code tools in the hands of lab workers can speed process development as well as experimentation.
In the current scenario, AI and ML have become integral so as to enhance low- and no-code platforms, thereby making it easier for non-technical users to go ahead and also perform sophisticated data analysis. The synergy between AI and ML and low-code as well as no-code tools makes sure that high-quality data happens to be accessible and actionable, helping users with unique levels of expertise to go ahead and contribute to data-driven decisions.
Intelligence in and intelligence out go on to mean that outcomes get affected by the people looking for answers as much as by way of quality of the data analyzed. This is indeed true once a data backbone gets established and optimized, but it is also true going up to that point. When designing a data backbone, human intelligence happens to hold the key to ensuring that data gets optimally captured, contextualized, stored, as well as accessed.
Get the right people ask the right questions
Apparently, having the right people in the room when it comes to a big data project often goes on to mean having all the roles getting represented. Diverse perspectives aid in ensuring that the right questions are going to be asked internally. Which is the data that matters? Given those objectives, how should the data be organized? These are all the questions that may differ from lab to lab.
Bench scientists as well as technicians should get involved from day one of a new data strategy; very often they are best placed to gauge the challenging space and to qualify that the apt questions are being asked in the first place.
Business leaders and data experts are also crucial to ensuring that the architecture captures data in ways that can be queried to answer business questions and achieve the desired business outcomes.
It is well to be noted that the most successful labs often go ahead and partner with industry experts who comprehend scientific and process development business requirements and have data science skills as well as expertise and capabilities. These external partners, often, can also go on to serve as helpful training resources.
While the industry goes on to develop in digital maturity by way of wet experiments to in silico techniques, knowledge gaps as well as communication can be barriers. For all team members, a shared digital literacy foundation around how AI as well as ML models work is necessary; that foundation should go on to include a shared commitment to the significance when it comes to stewarding high-quality data. A shared vocabulary can go ahead and help stakeholders communicate well with one another and with technical partners pertaining to data architecture and feasibility.
While AI tools happen to be indeed democratizing access when it comes to insight, true data intelligence requires an intelligent approach right from the very beginning to the end, with high-quality, well-organized data that is supported by knowledgeable and thoughtful humans across every phase of the business.
