What today's lab managers need to consider when making plans for the future
"Prediction is very difficult, especially if it's about the future." - Niels Bohr
Past predictions by notable authorities, such as Time magazine’s 1960’s assertion that computerized shopping will be a flop because “Women like to get out of the house, like to handle the merchandise, like to be able to change their minds,” or Wall Street’s 1990’s assessment that Apple was irrelevant and doomed to failure, are typical examples of how far we can go astray. But not all predictions are wrong. A 1942 prediction for push-button phones and a 1954 prediction of a television that can hang on the wall were right on. So, what’s the difference? Why are some predictions accurate while others appear comical? The answer is in the process used to make the predictions. The best predictor of the future is extrapolation of the emerging trends that we are seeing today to imagine how they might evolve to change our lives in the future. This is not foolproof but it at least provides some basis to add credibility for our vision. Using this approach, we’ll look first at current trends and then imagine what they might mean for the laboratory of the future.
These are current trends that are already established in our society that are affecting labs today and are likely to have a bigger impact in the future:
- For the past few decades, instruments have been getting “smarter” so that work that once required the knowledge and skills of a degreed scientist can now be done by a technician using the advanced features of modern instrumentation.
- Production plants are using on-line/at-line measurements to get more frequent data and to eliminate the logistics issues in getting timely samples to the lab.
- Instruments are being made smaller and more portable (such as the instruments used to investigate crime scenes on the television show CSI) so that they can be taken to the sample, rather than bringing the sample to the lab.
- Biotechnology R&D is finding ways to make everything from pharmaceuticals to fuels to chemicals to plastics out of biomass to replace the energy intensive industries of today.
- Big data (think Google and Facebook) and mathematical modeling are changing the way that we do science to establish relationships and make decisions.
- Outsourcing of laboratory functions, or even of the entire laboratory, has become a common business practice for pharma and even for traditional chemical companies.
- Laboratories are now included in the development of new business strategies where they once were somewhat isolated scientific entities whose only function was to provide test results.
- It is now possible to obtain scientific expertise or have lab work done remotely almost anywhere in the world with real-time collaboration.
- Social media has become pervasive in our society with billions of users, and has already affected the way that people interact with one another and the way that companies do business.
Not only is the way that labs operate possibly changing, but the way that we do science may also be changing. The scientific method traces its origins back to Aristotle and has served as the model for scientific inquiry throughout modern times, giving rise to the science and technology that we enjoy today. It is interesting that innovation in electronics, derived by applying the scientific method, has led to the unlimited data storage and accessibility of information that might now be leading to a new paradigm in the way that we do science. The success of Google and the gene sequencing project in gathering information and knowledge, simply through correlations using massive amounts of unfiltered data, might point to the way that science is done in the future. Chris Anderson, editor in chief of Wired magazine, went so far as to comment “…the data deluge makes the scientific method obsolete.” Just a few years ago, this notion would not have been taken seriously, but successes in this area have mainstream science taking a second look. It is a fundamental challenge to the ordered, structured way that we have approached data such as with our relational databases. The new way is unstructured, unfiltered data dumped onto a server with powerful mathematical models to extract useful correlations. Your next LIMS might not have a database but rather a large random storage area that has a gateway to the Internet for access to even more data— the powerful mathematical algorithms to extract useful information and translate it into knowledge may become the product differentiators.
The scientific instruments that are the heart of any lab are striking examples of the evolution of technology from room-size to tabletop, moving from degreed and technically trained operators to high school grads. We should expect instruments to continue to place more power in smaller packages with less need for human intervention. We see this trend in FT-IR spectroscopy, where the instruments from the 1960s that were large cabinets occupying most of a room and that required the expertise of a degreed chemist have evolved to the shoebox-sized desktop units of today that can be operated by a minimally trained technician. Going forward, instruments will be self-calibrating, able to detect and correct potential problems, and able to automatically configure themselves to provide the best quality of data and to minimize the possibility of human error. The analyst may be only minimally involved in obtaining a result, and the entire process may require minimal human intervention. Instruments of the future will surely incorporate more automation of sample preparation, with more interpretation of results and more operational diagnostic options than even the most advanced instruments of today.
The variety of proprietary software with different communication standards has been both a nuisance and productivity barrier for labs for decades. We might foresee that customer demand will drive the development of powerful generic software using industry standards that will be used on all instruments to simplify training needs for labs as well as to facilitate easy IT management. We are already seeing software evolve that offers more user help built into instruments, and this trend will almost certainly continue. As millennials populate the workforce, we anticipate software user interfaces that will transition from the PC style of the baby boomer generation to the Apple design of their successors.
Over the past decades, labs have changed from being numbers providers to problem solvers, from performing strictly technical functions to incorporating business functions, as they have become more integrated into the business. Scientists are now expected to solve problems for customers rather than just provide analytical results. Might this trend continue to a point where the scientists are no longer in the lab but actually reside in thecustomer department or business? With smart instruments handling more of the analytical chores, there might be less need for the scientist’s expertise in the lab, so the analyst’s job might become less skilled with fewer numbers. And, with the growth of big data, much of the lab’s analytical function might be replaced by a virtual lab where mathematical algorithms examine vast amounts of process data, combined with more data residing on the Internet, to propose solutions to a problem without actually analyzing samples. Perhaps posting a statement of the problem and symptoms to the Internet will produce the most likely solutions without having to send a sample to the lab.
New method development may be nearly completely automated in the future. The analyst may simply provide a smart system with the sample matrix and all of the analytes that they want to measure. The system then searches the entire Internet to find every piece of data related to those particular compounds and, using correlations, determines the optimum analytical technique and conditions. This information could be transmitted automatically to the correct instrument to set up the method, and the analyst need only present the sample to the instrument. The instrument then might not use traditional physical laws to generate the result but might use one of the correlation techniques, such as those already being used in near infrared spectroscopy.
So this brings us to the big question—what will the lab manager job look like in the future? With smaller labs, smart instruments, problem solving moved to customer organizations, remote technical expertise, more automation, machine knowledge, and other possible innovations, will there still be a need for a lab manager? Many of the current functions will likely need to be filled, but the manner in which they are addressed by the organization may be different. For example, the organization will likely need a technical voice, a safety leader, a regulatory compliance officer, a customer problem mediator, a lab staff director/developer, a strategic objective link, and other roles commonly filled by some lab managers today. The question is, “Where and by whom will these roles be performed in the future?” The management role in the future organization may not be part of the lab and may have only cursory ties to it but it will still be its voice and director. The role will likely be almost entirely a business function that supports integration of technology to generate profits and administer the ancillary functions necessary to accomplish this.
In conclusion, extrapolation of current trends was used to guess what the lab of the future might look like. However, the beginning of this article cautioned that some predictions that seemed perfectly reasonable at the time they were made turned out to be completely wrong. So how did they go wrong? The next big thing came along and changed everything! And that might happen in this case where something completely unexpected today, the proverbial “black swan,” comes along to shift our thinking to a new paradigm that leads to completely different outcomes than what has been conjectured in this article. In any event, the only thing that we can count on with absolute certainty is that there will be change and we will have to deal with it.
This article is based on a presentation delivered at the 35th Annual Conference of the Association of Laboratory Managers (ALMA).
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