Four Types of Futures Intelligence and How to Obtain Them
Together, the four different types of Futures Intelligence help us get a comprehensive overview of the probable, plausible and possible futures.
FUTURE PROOF – BLOG BY FUTURES PLATFORM
Futures Intelligence is a versatile set of futures-related knowledge that provides insights on future changes and guides decision-making. From data modelling to science-fiction prototyping, it utilises a wide range of methodologies, each fit for different purposes. In our webinar, Futures Platform’s Content Director Dr Tuomo Kuosa discusses the four different types of Futures Intelligence, their use cases and the methods to obtain them.
Watch the webinar recording for practical insights on Futures Intelligence and learn how you can strategically apply foresight into all decision-making.
JOHARI WINDOW AND FUTURES INTELLIGENCE
Dr Tuomo Kuosa begins his presentation with the Johari Window, which is one of the key methodological principles behind Futures Intelligence. Composed of four quadrants, Johari Window is a tool that is used to map out different domains of knowledge. Together, these four quadrants can provide a comprehensive 360-degree overview of a research topic, industry or operating environment.
Figure 1: Johari Window, Rumsfeld modification
The four types of Futures Intelligence correspond to these four quadrants of the Johari Window. We take a look at each category below.
KNOWN KNOWNS: UNDERSTANDING THE BIGGER PICTURE
Known knowns are things that we know we know, and as such, they refer to objective facts and widely available knowledge. Within the context of futures studies, known knowns include megatrends, trends and change drivers, which are usually quantifiable.
Kuosa says that the ‘known knowns’ domain of knowledge is most commonly utilised in investment proofing, regulation and policymaking processes, and they can also reveal changes in consumer mindsets.
Methods to analyse known knowns include trend extrapolation, trend impact analysis, and megatrends & drivers analysis. The first two methods are quantitative and involve a data modelling process, whereas megatrends & drivers analysis is a qualitative process focused on prioritising the key pushing cornerstones and interconnections of trends.
Kuosa adds that this type of knowledge is easy and fast to obtain with foresight tools. As there is already a plethora of analysis and insights on such well-known trends, one doesn’t need to start from scratch and invest resources into researching them.
KNOWN UNKNOWNS: LOOKING FOR ALTERNATIVES
The second category, the known unknowns, refer to the uncertainties that we are well aware of. For instance, we know that our flight may get cancelled, or we know that megatrends such as artificial intelligence or climate change will significantly reshape our lives, but we don’t precisely know how or when.
Such uncertainties are most commonly explored through scenario-building methods. For example, scenarios can be built quickly with the Axes of Uncertainty method, or more detailed narratives of alternative futures can be constructed via methods such as the Futures Table.
Figure 2: Scenarios via Axes of Uncertainty
Dr Kuosa lists the main use cases for this type of knowledge as investment proofing, regulation and policymaking, and validating existing strategies and risk assessments.
UNKNOWN KNOWNS: HORIZON SCANNING AND VIGILANCE
Unknown knowns are things that are too close or familiar for us to see directly. Within the foresight context, unknown knowns usually refer to all the changes happening around us in the present time. We can observe and identify these changes, but we don’t yet know whether they will disrupt the status quo or fade away without any significant impact. This domain of knowledge plays a key role in early warning & risk identification, market intelligence and competitor analysis, adds Kuosa.
It is possible to get a deeper understanding of unknown knowns through various methodologies, such as emerging issues analysis, weak signals analysis, and signs of discontinuities analysis.
Signs of discontinuities analysis focuses on identifying changes in the development of known trends. It must be conducted on a continuous, ideally monthly basis for a robust early warning system. Weak signals analysis has a broader outlook and scans across industries to spot very early signs of potentially disruptive future changes. In contrast, emerging issues analysis scans a particular environment to analyse what may expand.
UNKNOWN UNKNOWNS: FREE IMAGINATION AND VISIONING
Unknown unknowns are the toughest to spot, as they are real blind spots and often outside the scope of our imagination – things that we don’t even know we should be aware of or worried about.
Unknown-unknowns can be best obtained through visionary methods such as science fiction prototyping and wild card analysis, Kuosa explains. These methodologies help us expand our thinking beyond the probable futures and explore future technological, societal, environmental, political or scientific changes, as well as their implications.
The creativity-based foresight methods used in exploring unknowns can be entirely subjective and imaginative, or they can also be based on present issues that are boldly projected into the future.
This domain of knowledge is most commonly used in product, service and concept development as well as innovation management processes. For example, GPS, robot friends, flying cars and teleportation all have their roots in science fiction.
BRING YOUR FORESIGHT WORK TO THE NEXT LEVEL
The methods outlined above and how to utilise them in organisational settings are explained in-depth in our webinar Futures Intelligence: How to Bring Foresight into Action. Watch the full webinar for practical tips on foresight tools and workflows that will help you bring your foresight work to the next level.
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