The recent trend toward internet research has invoked a predictable glut of web-based research tools. The consolidation of research technology is inevitable, especially in the chaotic world of web survey software. Hundreds, if not thousands, of web survey tools currently crowd the market, many of them initially built for a specific purpose and then turned into commercial products. I have met countless software developers who delved into research solutions on the basis of a single contract, with the belief that applications like opinion polling could be lucrative and would be easy enough to implement. Many of these firms have survived the initial shock of complexity—realizing that it’s not easy to satisfy a user base as analytical and pragmatic as research professionals. But does the world really need hundreds of web survey tools, most of which offer little or no substantial differentiation from their competition? This parallel-invention phenomenon, so typical in the early stage of any burgeoning market, is ripe for consolidation. Though there will always be room for “boutique” players, I suspect that in a few years we will see only a handful of industry-leading web survey tools.

Lest we get too comfortable with current survey technology, I reiterate that converging modes of interpersonal communication will prompt innovation that is difficult to comprehend today. The internet, despite its widespread use, is still in its relative infancy; and the mobile phone is already having an impact on research methodology as a growing number of people abandon their landlines in favor of mobile devices. I would not suggest that current survey techniques will be abandoned overnight, but I do believe that by following a logical trajectory for each mode of communication it is possible to foresee where convergence may occur, and to embrace it in an evolutionary way rather than waiting for the next “Internet Revolution” to take place. I assert that such innovations will only appear revolutionary to people who do not recognize the incremental steps that comprise them

How do we make sound business and methodological decisions about technology innovation in the midst of a shifting landscape? The crux of the solution lies in the fact that people—not technology—are the regulators of change. While people collectively drive the demand for innovation, people are also its greatest bottleneck. The classic adoption curve states that for every early adopter of technology, there is a laggard who will resist change as long as possible—and in between lies the majority of the population. So while early adopters generate a perceived need for revolution, it is usually ideal to take part in the second wave of adoption—after the pioneers have broken ground but before the masses have trampled it.

So how can we ride the leading edge without being on the bleeding edge? By constantly investigating and testing new solutions while continuing to employ tried-and-true methods, then absorbing successful new methods into our existing toolkits. This is a difficult but necessary practice for anyone aspiring to be an effective innovator. One problem is that as demand for our time increases, too many people are choosing one of the two obvious paths that appear before them: wholesale change or the status quo. Both of these options are risky, and the most successful innovators among us will often explore less obvious paths without fear of “wasting”—that is, investing—some time and money.

In practice, any new technology should be viewed with a healthy blend of excitement and skepticism. Once you’ve assessed your needs and done some high-level due diligence, determine a shortlist of solutions for further evaluation. Then find a way to test your top choice in a setting that is relevant to your needs before committing to large-scale implementation. Testing may involve a “live” field project, preferably in parallel to an existing method; an extensive in-house mock-up of one or more projects; or a customized demo provided by the technology vendor using parameters that you provide. Naturally, the more realistic your test scenario is, the more reliable your test results will be.

If you are trying to improve on an existing method, for example moving a group of interviewers from paper to a computerized interviewing system, it is ideal to test a new solution in parallel to an existing one. If you can’t do that, at least ensure that a contingency is in place until you establish confidence in the new solution. During my introduction to socioeconomic research at Parks Canada in the early 1990s, the wisdom of both testing and redundancy was instilled in me by a manager who had built a reputation for successful innovation. Whenever we implemented a new research technology—such as handheld data collection, Optical Mark Recognition (OMR) scanning, or barcode scanning—we always allowed time for a proper pretest period, and we provided traditional paper surveys to back up the technology solution. We rarely had to revert to manual data entry, but on the rare occasions when a battery died or a scannable form wouldn’t scan, our research projects were able to continue while we resolved the problem. Over a span of five years that I spent at Parks Canada, I cannot recall a single example when technology failure jeopardized the integrity of a research project.

Experience has also taught me that it is beneficial to micromanage any project that incorporates new technology, constantly monitoring every element of the project until you are confident that it is working smoothly. All too often, innovation goes awry because project managers simply expect technology to work, and do not invest any energy into managing or monitoring the changes they are testing. In my current role as a software provider to commercial market research firms, I have seen many instances when a lack of pretesting or an inattentive project manager has caused avoidable problems to go undetected, in turn bringing survey results into question.

For many people, the most difficult question about innovation is not if or how to change, but when? Given the turbulent picture I’ve painted about the present and future state of research technology, how can one innovate without fear of betting on the wrong horse? My belief is that you can’t worry too much about that. Change is a constant force, and those who embrace it as an evolutionary process will learn the art of innovation—which is a knack for knowing when to embrace new technology and when to discard old techniques, combined with an acceptance that false starts are a necessary part of process improvement.

Increasingly, research is becoming more of a “dashboard” than a “snapshot.” Technology is enabling us to collect, process, and report survey data faster than ever, and our thirst for knowledge is driving a trend away from static reports to more dynamic and timely methods for measuring public opinion. Technology is the vehicle that carries our precious intellectual cargo; as such it requires constant maintenance and the occasional replacement. In response to the growing demands being placed upon us, innovation—like research itself—must be viewed as a process, not an event.

Mark Cameron is president and CEO of Techneos Systems Inc.