Providing Insight at the Speed of Light: The Challenge and Necessity of Embracing Innovation

By Mark Cameron

 

We live in a world where growth is demanded at every turn. In business, shareholders demand growth of revenues, profit, and share value. In government, taxpayers demand growth of industry, tourism, and gross domestic product. Even in our family lives, we try to grow our nest eggs while our children—who provide a sobering reference for the speed at which this world turns—cannot wait to grow up.

This “demand to expand” fuels an ever-increasing “need for speed.” More than ever, we need to know what people are thinking, and we need to know now. The requirement to deliver more information in less time asserts tremendous pressure on managers, media, and government, which in turn pushes researchers to find innovative solutions to both old problems and emerging challenges. Ironically, the technology that is supposed to help us manage this pressure all too often fuels even higher expectations, thus creating a vicious, accelerating cycle of demand.

For the past few decades, technology has been a frequent catalyst for disruptive innovation in the field of survey research. Starting with fundamental changes to the way paper surveys were produced and processed, through the introduction of Computer-Assisted Interviewing (CAI) tools, to the adoption of the internet as a primary mode of communication, we have seen many generations of innovative technology come and go within a relatively short timeframe. Within each generation have been numerous sub-innovations, compounded by differences in technology adoption throughout the world.

This complex web of technology represents a global game of “innovation leapfrog,” whereby new methods that are suitable for one region or market are bypassed elsewhere for logistical or cultural reasons. For example, with the advent of the personal computer, survey researchers in North America wholeheartedly embraced Computer-Assisted Telephone Interviewing (CATI) thanks to the inexpensive, pervasive landline infrastructure that was available from Anchorage to Miami. By contrast, the more costly and complex telephone infrastructures in Europe, combined with much greater population density, cultivated advancement of Computer-Assisted Personal Interviewing (CAPI). Now we find both of these continents embracing the Internet with similar fervor, but from very different perspectives. As a result, few standards exist for creating and processing survey instruments, and thousands of technology solutions have been invented in parallel around the world. Factoring in the rapid emergence of other global research markets, the inevitable but unpredictable impact that wireless technology will have on how people communicate, and the increasing homogenization of global culture, it is difficult to paint a cohesive picture of research technology.

 

Popular culture has created a romantic view of revolution. Whether viewing the history of the world or developing business plans for global expansion, we liberally use the word “revolution” to describe a positive, energetic activity. I admit that I have used the “R” word on many occasions, but I cannot help seeing our revolutionary attitude as a key contributor to the pressure we face to deliver on the promises of technology.

I believe that most effective “revolutions” are actually a series of “evolutions.” Though many success stories stem from revolutionary ideas, execution is at least as important as invention in the innovation process. One need only look as far as the world’s most successful software company, Microsoft, to see the benefit of execution over invention. Microsoft did not invent the operating system, the word processor or the spreadsheet, yet it has become the predominant player in all three of these areas.

There have been overnight successes in technology, such as the Napster file-sharing phenomenon of the late 1990s, but many of these have fallen under the weight of their own expectations. In a field that is near and dear to me—mobile survey computing—innovative companies like Wireless Opinion, CodeSurveys, and ZingData all fell short of expectations, despite strong backing and good people. From about 2000 to 2003, these companies developed wireless polling technology to conduct surveys using mobile phones. It quickly became apparent that mobile phone research was a good idea that was ahead of its time, at least in the North American and European markets that were being targeted (I am not aware of whether there have been success stories for related technology in Asia). A number of similar products are now emerging to tackle this same problem, and I believe we will see a wider adoption of mobile polling in 2007 and beyond. It remains to be seen who will be the leading players in this space, and whether text-messaging will be widely adopted as a polling tool or surpassed by newer, more robust mobile technologies.

By contrast, the now ubiquitous data analysis software, SPSS, began as an academic development effort at Stanford University in 1968 and was initially intended only for local consumption. As if to challenge my assertion, a visit to the SPSS website defines this invention as a “revolutionary statistical software system”; but I would argue that while the research behind SPSS software may have been revolutionary in nature, its adoption within the research community has been a long, evolutionary process.

It is human nature to favor revolution over evolution, to seek a more direct path to the imaginary finish line that we are constantly chasing. But it is important to pursue innovation with a pragmatic, iterative approach. The rate of technological change is sure to continue accelerating as historically separate means of communication—telephony, face-to-face, and electronic modes such as e-mail, web browsing, and text messaging—converge upon a new world of interpersonal connectivity. I expect the most successful survey organizations will be those that are grounded but innovative in their view toward technology. Those who implement change for the sake of change will break ground for the rest of us—at their own expense—while those who seek new opportunities based on proven technology will position themselves for success.

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 socio-economic 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.