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.
