The indicator for success
Spoiler alert: it doesn’t exist
You may probably expect to see some sort of formula for a parameter that would indicate the net value of success that your company or project might (will!) have.
And since you just read that you probably may realise that talking about something so radical like a formula for success in this life made out of uncertainty, is possible. Absolutely ridiculous. Remember the COVID-19 pandemic, you weren’t expecting it, didn’t you?
Well, you are right. and wrong at the same time. Here’s why:
The problem is to assign the entity of object to something like information that indicates something. This is what happens when, with all good intentions, companies look for ways to express clearly and briefly something that they learned about and is relevant to them. Also, the road to hell is paved with good intentions. Even the best ones.
Because, for the sake of objectivity, we tend to objectualize (fethishise) without realising it. We focus more on the thing than in how we relate to that, and without that understanding it is impossible to really find value in what that thing indicates. Although this may sound pretty obvious, how many times did find yourself or your company seeking desperately for results to meet a deadline? The road to hell is right there.
As business strategists, it is key to understand that information may come in such diversity and be embedded in a complex set of conditions or contexts, meaning that the power of an indicator relies on how it works in a specific context. Therefore, is all about the vision: what is the best strategy to meet this business need in this particular situation.
Understanding business needs takes sensitivity. Which is not like staring at numbers and rates in a slideshow, no matter how delightful data visualisation might be.
“I like sentimental people, who do not separate reason from the heart. Who feels and thinks at the same time. Without divorcing the head from the body, nor emotion from reason” — Eduardo Galeano
How can we design an indicator or KPI from sensitivity, then?
Thinking about how we relate to the information is a good start. Are we thinking of data as immutable absolutes or are we considering data as an expression of human activity in a certain set of circumstances?
Are we mere information consumers who treat data as objects or are we thinking about what that information is telling us and how it impacts our goals for the people’s sake? Are we being aware on the calling for responsibility that such information inspires or are we just eager to make a passive conscious-less decision just because a percentage says so.
Where’s the people experience standing in how we’re interpreting that information and connecting the dots?
When we are resorting to data is because we want to make a sense of it. Do something else. And yes, data is not only quantitative but also qualitative, both are equally valid to tell something. We just need to be aware on what part of the story is that data telling us and with what focus. In terms of Design Research this is sorted out by planning the study considering the right weight of the amplitude — depth relation. Whereas qual data looks for significant depth in findings, quant data will cover for the scale of what’s being studied.
How we build relationships between qual and quant data is the process where we can apply sensitivity whilst we try to make sense out of it (so we can start talking about actual information) and ultimately, the knowledge we are producing. Here’s where how we think of ourselves (in anthropology we call this reflexivity) impacts on how we come across in data interpretation. There’s no such thing as objectivity since we are people working with and through or subjectivity in inter-subjectivity with others.
Producing knowledge is a social endeavour and takes critical thinking and commitment that’s in line with social sensitivity.
So, a while back I wrote about how targeted sensitivity can help drive innovation through ethnographic methodology. Actually, ethnography is the key to drive real, impactful innovation. Simply because innovation is just not possible outside the boundaries of sociocultural understanding. “The only important thing about design is how it relates to people” said Victor Papanek.
So, how do we work with sensitivity then? Here’s a bulleted list for the reader’s sake:
- Learning to really listen (no that “active listening” — that is actually paying attention). Focus in what’s important for the others, listen to understand, not to reply. Take time to think and feel, there’s no rush.
- Gaining real awareness of oneself prejudices, biases, and projections.
- Taking accountability of oneself limitations and ask for help (collaboration didn’t kill anybody throughout human history, quite the opposite).
- Humility can only lead to success. Not only because of what’s listed above, but also because there are better chances to be heard in this position rather than coming across like some Mr. Knowitall.
- Forgetting about “empathy”. Choosing concepts already known by their meaning and are crystal clear. Thinking in terms of respect and consideration (the less the confusion, the better we can act on it).
- Encouraging a transparent, straightforward, and kind communication style. When shared, the sense of humor can help a lot! (Beware on how to come across: being straightforward is not a green light to hurt anyone’s feelings).
- Fostering a culture of diversity and inclusion in the workplace. (Diversity also helped a lot in human history).
The above wrongly named “soft skills” — what’s harder than working with the complexity of social relationships and the human condition? — are the tools we can resort to, to develop the most effective strategy to design a particular measurement. Understanding the complexity and being able to read and interpret the implications of a measurable indicator enables us to create the appropriate conditions to translate something we observed into something that can be also estimate or confirm quantitatively.
How can we create such conditions?
- Making sure we understand the context in which what we want to measure is behaving.
- Taking into consideration the different variables (both quantifiable and non-quantifiable) that are shaping that object studied.
- Remembering that the chaos theory doesn’t quite apply in this case: different conditions and different contexts result in different outcomes.
- Understanding uncertainty is not an enemy to tackle, but a wave to surf.
Last but not least:
- Understanding that a quantifiable stand-alone indicator doesn’t mean anything. It’s meaning comes from the aforementioned context and specific conditions of existence.
- Following-up KPI’s and requesting indicators doesn’t mean you are conducting research of any kind. Be careful: You might be collecting meaningless “data”.
