When it comes to innovation, corporate dataism has perpetuated an evidence-based culture of creativity. But imagination always proceeds evidence. So how do we operationalize innovation at scale in a way that creates space for generative imagination and discovery? How do we discover new opportunity in environments that incentivize the protection of the status quo? The answer is generative research. In this article I outline what learning through research looks like, and how it contrasts the normative approach to determining where to create new value.
Learning & Creating New Value
The activities of today’s organizations are many — from the most mundane of processes to solving the most complex challenges — knowledge work is infinitely diverse. Though what it means to be a knowledge worker is radically evolving as our relationship to information is radically evolving. Our relationship to knowledge is no longer characterized by retention, rather our relationship to knowledge is primarily characterized by recall — the ability to access the right information at the right time. We typically do this through search heuristics, (i.e. “hey Google…”) which requires that we ask a question. This means that while the era of retention concerned itself with knowing the right answers in any given situation, the era of recall is concerned with knowing the right questions. Intelligence in the era of retention was measured by the quantity of existing information one held in their mind. Intelligence in the era of recall is measured in how quickly one can access, synthesize, and apply new information in any given moment. Said simply, what you know isn’t as valuable as your ability to know. Some have called this ‘information asymmetry,’ I call it an ‘epistemology of inquiry.’ Those who excel at asking new questions and integrating new knowledge (otherwise known as learning) day-in and day-out will now outperform those who do not.
The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn. – Alvin Toffler
I bring up search heuristics and the epistemology of inquiry because when we consider the activities of our everyday work and the creation of new value, it’s important to understand that generative learning is a core element of these activities. Gone are the days of rote labor and processing in the enterprise where one would merely apply the same knowledge to the same task repeatedly. Even the most minuscule of tasks today require learning and iteration, as the problems we solve today are rarely the problems we solved yesterday. This truth is commensurate with the increasing complexity of the world. With greater complexity comes greater interdependency and system dynamics — the challenges we face are not static.
Much of task-based enterprise learning takes the form of optimization and efficiency methodologies. Think lean, six sigma, etc. These are good and necessary forms of learning inasmuch as they rely on feedback loops, though these are methodologies born from Tayloristic notions of value creation, competitive advantage, and market resilience. While optimization and efficiency are foundational and necessary, they are no longer a sufficient means to the sustained competitive advantage and market relevance.
All this is especially true for the grander challenges that today’s companies are facing. And with the scale of the challenge comes a greater scale of learning. But how do we operationalize learning at scale? What does an enterprise epistemology of inquiry look like? How should organizations create new value for new customers? This is the realm of research, and it is the imperative of every organization of every size.
Quantitative & Qualitative Research
Enterprise research can be categorized into two primary categories — quantitative and qualitative. Quantitative research is concerned with empirical data that can be represented in numbers. This kind of information is critical to effectively growing a business. From simple metrics to web traffic and downloads (primary data) to more complex global economic and minute-to-minute market data (secondary data), the ability to mine this information is critical. But this information is becoming evermore accessible, and often a simple subscription to a SaaS platform can provide a two-person startup with the same secondary data and analytic power that international conglomerates are accessing.
Qualitative research can be both empirical and subjective, sometimes simultaneously. (This is where our post-enlightenment epistemology trips us up as our common theory of knowledge has fallen behind our modern psychology, and especially quantum physics… but I digress). While quantitative research is most often concerned with the ‘what’ of a phenomenon such as the number of products sold, qualitative research is concerned with the ‘why’ of a phenomenon such as why consumers are buying a particular product over another. The domain of qualitative inquiry is grounded in human experience and the meaning that individuals and groups ascribe to particular experiences — with products, places, services, interfaces, etc. The theory and practice of semiotics, teleology, hermeneutics, epistemology, and phenomenology are the tectonic foundation for the qualitative research of sociologists, anthropologists, historians, and other social scientists inasmuch as they are seeking to understand how humans interpret their subjective human experience.
Objective & Non-Objective Research
At this point it’s important to note the difference between objective and non-objective research. We are most familiar with objective research, commonly known as the scientific method. Using deductive reasoning, one would start with a hypothesis and conduct a series of experiments to prove or disprove it. In other words, objective research begins with an answer. But remember your ability to know has superseded the value of what you know — we’ve learned through technology to engage the world around us and to drive learning through asking questions. In this regard objective research is no longer sufficient for sustained learning and relevance. Even so, objective research analyzes and validates – it is the method of choice for quantitative analysis and validation oriented experiments like prototyping — it’s a great approach to optimizing and designing things right, but not helpful in determining whether or not you're designing the right thing.
Non objective research, on the other hand, is widely understood and practiced among those in the design research and innovation community, though its theoretical roots (primarily grounded theory) are in the social sciences. Non objective research is the best way to determine what should be designed because it doesn’t begin with a hypothetical answer to be validated, rather it begins with a broad question or what I call a ‘domain of inquiry.’ Using inductive reasoning, this research embraces an unknown outcome and allows the hypothetical framework to emerge as the researcher(s) learn more about their particular domain of inquiry. Instead of beginning research with an answer, the answer emerges through the course of research by way of patterns, themes, and concepts determined through rigorous synthesis, coding, and sensemaking. This process ensures that the landscape of the possible and plausible opportunities have been mapped before deciding on the most probable opportunity. In qualitative non-objective [design] research, preferable opportunities are most often those that are customer desirable, technologically feasible, and business viable.
Non objective research is extremely valuable as a methodology for enterprise learning in a hyper-competitive environment where most markets have reached peak saturation. When exponential value is found in the creation of all-together new markets, services, and product spaces, organizations need methods for connecting imagination to insight — non objective qualitative research facilitates this beautifully.
“The path to success is through not trying to succeed. To achieve our highest goals, we must be willing to abandon them.” — Associate Professor Kenneth Stanley, University of Central Florida, in a presentation on non-objective research.
As learning becomes evermore critical for organizational relevance and value creation, and operationalizing this learning through non objective qualitative research becomes the primary way of probing the future, we’ll hopefully see more companies move away from market-reactive growth strategies towards market-proactive growth strategies — defining a future through imagination and insight that is good for humans, our society, and our environment, creating products and services that advance that vision.
Armed with the knowledge that perpetual learning is a core activity of value creation, let’s now explore how the theoretical frameworks above translate to innovation in the enterprise.
Microsoft & The Future Workforce
Recently Microsoft’s Real Estate and Facilities (RE&F) team wanted to study how GenerationZ is and will continue to drive evolutions in work. The research set out to understand (1) how individual workers and the workforce at large is evolving, (2) the differences in this evolution across cultures and regions, and (3) design and planning implications for real estate, workplace, and the organization at large. The research was non-objective in nature and sought to discover and illuminate unconsidered areas of opportunity for Microsoft RE&F to explore.
True to the tradition of non-objective research, the research team (comprised of our team at Gensler, International Connector, and Microsoft RE&F) began with a domain of inquiry — the Future Workforce — but did not approach the study with a predetermined hypothesis. As stated above, this ‘grounded theory’ approach allowed for generative exploration. And generative it was. Through a mixed methods approach utilizing interviews, focus groups, secondary data analysis, and surveying, we discovered that there are two unprecedented and transformative shifts happening in the world of work. Furthermore, we identified eight new opportunity areas along with 87 provocative questions that Microsoft RE&F had yet to consider — essentially a list of the critical challenges they will need to face to design exceptional working experiences for future talent. In a matter of months we were able to explore a broad swath of what could be, and were able to arrive at a focused imperative for what should be.
This effort was, as illustrated in the diagram below, phase one. Through rapid learning we were able to discover and define new opportunities that were previously unconsidered.
Conclusion
We’re now kicking off phase two with Microsoft RE&F— developing and delivering workplace solutions to the challenges we identified in our research. This investment in mapping the landscape of the future workforce has equipped Microsoft RE&F with insight that nearly no other real estate organization has, and thus a significant upper hand in designing exceptional working experiences for future talent. This dedication to rapid learning through research has also exponentially accelerated their innovation cycles. Finally, by inviting over forty key stakeholders from across the company into the process, the research team has effectively translated learnings to actionable insights in real time, adding a layer of operational value that is rarely seen. This is operationalized learning through research and innovation in action.