Visual Knowledge Cycles

Visualisation is a key tool for the management of knowledge, especially knowledge from data. We’ll explore different states of knowledge, and how we can use visualisation to drive knowledge from one state to another, as individual creators of visualisation and agents within an organisation or society.

Visualisation Cycle
Visualisation Cycle

(There’s some justifiable cynicism about quadrant diagrams with superimposed crap circles. But, give me a chance…)

Awareness and Certainty about Knowledge

We’re used to thinking about knowledge in terms of a single dimension: we know something more or less well. However, we’ll consider two dimensions of knowledge. The first is certainty – how confident are you that what you know is right? (Or wrong?) The second is awareness – are you even conscious of what you know? (Or don’t know?)

These two dimensions define four states of knowledge – a framework you might recognise – from “unknown unknowns” to “known knowns”. Let’s explore how we use visualisation to drive knowledge from one state to another.

Knowledge states
Knowledge states

(Knowledge is often conceived along other dimensions, such as tacit and explicit, due to Nonaka and Takeuchi. I’d like to include a more detailed discussion of this model in future, but for now will note that visualisation is an “internalisation” technique in this model, or an aid to “socialisation”.)

Narrative Visualisation

I think this is the easiest place to start, because narrative visualisation helps us with knowledge we are aware of. Narrative visualisation means using visuals to tell a story with data.

Narrative Visualisation
Narrative Visualisation

We can use narrative visualisation to drive from low certainty to high certainty. We can take a “known unknown”, or a question, and transform it to a “known known”, or an answer.

“Where is time spent in this process?” we might ask. A pie chart provides a simple answer. However, it doesn’t tell much of a story. If we want to engage people in process of gaining certainty, if we want to make the story broader and deeper, we need to visually exploit a narrative thread. Find a story that will appeal to your audience and demonstrate why they should care about this knowledge, then use the narrative to drive the visual display of data. Maybe we emphasise the timeliness by displaying the pie chart on a stopwatch, or maybe we illustrate what is done at each stage to provide clues for improvement. (NB. Always exercise taste and discretion in creating narrative visualisations, or they may be counter-productive.)

Here is a brilliant and often cited narrative visualisation telling a powerful story about drone strikes in Pakistan.

Drones Visualisation
Screen shot of Pitch Interactive Drones Visualisation

The story also provides a sanity check for your analysis – is the story coherent, is it plausible? This helps us to avoid assigning meaning to spurious correlation (eg, ski accidents & bed-sheet strangulation), but do keep an open mind all the same.

Discovery Visualisation

But where do the questions to be answered come from? This is the process of discovery, and we can use visualisation to drive discovery.

Discovery Visualisation
Discovery Visualisation

Discovery can drive from low awareness, low certainty to high awareness, low certainty – from raw data to coherent questions. Discovery is where to start when you have “unknown unknowns”.

But how do you know you have “unknown unknowns”? Well, the short answer is: you do have them – that’s the thing about awareness. However, we’ll explore a longer answer too.

If someone drops a stack of new data in your lap (and I’m not suggesting that is best practice!), it’s pretty clear you need to spend some time discovering it, mapping out the landscape. However, when it’s data in a familiar context, the need for discovery may be less clear – don’t you already know the questions to be answered? We’ll come back to that question later.

A classic example of this kind of discovery can be found at Facebook Engineering, along with a great description of the process.

Facebook friends
Facebook friends visualisation

In discovery visualisation, we let the data lead, we slice and dice many different ways, we play with the presentation, we use data in as raw form as possible. We don’t presuppose any story. On our voyage of discovery, we need to hack through undergrowth to make headway and scale peaks for new vistas, and in that way allow the data to reveal its own story.

Inductive Drift

What if you’ve done your discovery and done your narration? You’re at “known knowns”, what more need you do?

If the world was linear, the answer would be “nothing”. We’d be done (ignoring the question of broader scope). The world is not linear, though. Natural systems have complex interactions and feedback cycles. Human systems, which we typically study, comprise agents with free will, imagination, and influence. What happens is that the real world changes, and we don’t notice.

We don’t notice because our thinking process is inductive. What that means is that our view of the world is based on an extrapolation of a very few observations, often made some time in the past. We also suffer from confirmation bias, which means we tend to downplay or ignore evidence which contradicts our view of the world. This combination makes it very hard to shift our superstitions beliefs. (The western belief that men had one less rib than women persisted until the 16th century CE due to the biblical story of Adam and Eve.)

So where does this leave us? It leaves us with knowledge of which we are certain, but unaware. These are the slippery “unknown knowns”, though I think a better term is biases.

Unlearning Visualisation

Unlearning visualisation is how we dispose of biases and embrace uncertainty once more. This is how we get to a state of “unknown unknowns”.

Unlearning Visualisation
Unlearning Visualisation

However, as above, unlearning is difficult, and may require overwhelming contradictory evidence to cross an “evidentiary threshold”. We must establish a “new normal” with visuals. This should be the primary concern of unlearning visualisation – to make “unknown unknowns” look like an attractive state.

Big data is particularly suited to unlearning, because we can – if we construct our visualisation right – present viewers with an overwhelming number of sample points.

Unlearning requires both data-following and story-telling approaches. If we take away one factually-based story viewers tell themselves about the world, we need to replace it with another.

Recap

Visualisation Cycle
Visualisation Cycle

Your approach to visualisation should be guided by your current state of knowledge:

  • If you don’t know what questions to ask, discovery visualisation will help you find key questions. In this case, you are moving from low awareness to high awareness of questions, from “unknown unknowns” to “known unknowns”.
  • If you are looking to answer questions and communicate effectively, narrative visualisation helps tell a story with data. In this case, you are moving from low certainty to high certainty, from “known unknowns” to “known knowns”.
  • If you have thought for some time that you know what you know and know it well, you may be suffering from inductive drift. In this case, use unlearning visualisation to establish a new phase of inquiry. In this case, you are moving from high certainty and awareness low certainty and awareness, returning to “unknown unknowns”.

Of course, it may be difficult to assess your current state of knowledge! You may have multiple states superimposed. You may only be able to establish where you were in hindsight, which isn’t very useful in the present. However, this framework can help to cut through some of the fog of analysis, providing a common language for productive conversations, and providing motivation to keep driving your visual knowledge cycles.

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