I’ve just bought Amy Herman’s Visual Intelligence book. She teaches visual intelligence to doctors, intelligence analysts and the NYPD and uses art to teach observational skills. While waiting for the book, I’ve been reading her website and liked these two phrases in her post about the importance of objectivity in observation:
“We all make assumptions more often than we think, and like a snowball, even the smallest ones get bigger as they go downhill.”
“The earlier the assumption is made, the more dangerous it is because it skews subsequent observations.”
In this brief video she explains the steps to follow – the Four A’s – to get better at observation:
- Assess – what’s in front of me? You can also ask someone else ‘this is what I am seeing? Is there anything I’m missing?’ Amy believes that eveeryone sees things slightly differently
- Analyse – break what you are looking at down into components. What’s most important in the scene? What information do I need, what might I need, and what don’t I need? She advises getting rid of things that are not needed
- Articulate – How you explain what you observe. This is, she says, the most important step. Every word counts, and your choice of words are important. (this is where I think ethnographic training really excels)
- Adapt (or Act) – You make a decision based on what you have observed and act on that decision
It’s a good framework to explain what a lot of ethnographic researchers do naturally, and may not always be able to articulate as it often becomes an unconscious skill after several year studying the craft of observational research.
Have you noticed that the benefits of enhancing Big Data are espoused as providing insight? Qualitative research also exposes the same thing. Is every methodology within a client’s grasp “insightful”???
An article from anthropologists at Arizona State University on the types of methodological challenges of studying online environments and behaviour. The article is an interview with the research team who were investigating online channels for social support for weight loss. The interview covers a range of research challenges they came across, from collecting data to ethics and privacy, and is an interesting read and argument for the evolution of ethnography to incorporate more of online behaviour as it has become so integrated into daily life for many societies.
From one of the researchers, Professor Amber Wutich:
“Ethnographic research has always been adaptable as humans themselves are in the myriad environments we inhabit. Understanding the new, social world humans experience online helps us more scientifically and definitively answer questions like, how do people construct meaning there? What sorts of cultural norms govern online behavior? How do social worlds created online differ from the spaces people physically inhabit, and how does that affect life in the “real world”? ”
Let me introduce Martha Bird to you via this article in Computerworld. She has a PhD in anthropology and has worked in a range of industries including a non-profit, telco and in e-commerce. She helps match user needs across multiple markets to products and services for global brands. Right now she is a business anthropologist working on how chatbots need to function for customers of an HR product and service provider.
She says that in her role she “is always about thinking about the intersections of technologies and people or, put another way, about the human-machine relationships in cultural context“. As an anthropologist, she is building on work done to date around UX and customer journeys in the company and explains: “the user’s journey must also account for the cultural landscapes – organizational, culture, national culture, geography, tech infrastructure, gender – on which these journeys are mapped“. This is something that an anthropologist can offer.
One of the interesting things involved in her work is identifying “cultural precisions” where cultural differences need to be built into how chatbots function in order to meet user needs globally.
Full article here
Triangulation in social research is a technique to increase credibility of research outcomes. Borrowed from navigational and land surveying techniques, it aims to overcome biases in single method, researchers, data sets and/or theories.
There are 4 types of triangulation:
- Methodological – two or more methods are used to collect data
- Data – two or more different data sets
- Researcher – two or more researchers investigate
- Theoretical – two or more theories approach the often described as a way of ensuring some form of ‘truth’ in research results. This is one reason to use triangulation, but another is to intentionally draw out some of the differences
As an anthropologist I think we constantly aim to reduce the influence of bias in research we do. A competent, self-aware anthropologist is very good at observing phenomena from multiple viewpoints. Of course we can’t always overcome our own biases, so designing your research (and synthesis) with triangulation in mind can contribute to overall outcomes.
If customer-centricity is the new metric for organisational success, a new study has revealed what a company’s insights team needs to look like in order to make this happen. The Insights 2020 study by global consultancy Kantar Vermeer highlights the evolution of internal insights teams from being part of marketing to becoming an ‘insight engine’ within an organisation. An insight engine has particular ‘alchemy’ of capabilities that were found to be the key differentiator for customer-centric growth across 10,000 companies. Organisations that possess these capabilities (and that ahve obviously proved their worth) have the ear of the CEO twice as often as other functions. Key to these teams success was having independence as a function with direct reporting into executive level. Another KEY capability of these front and centre, empowered and valued insights functions in customer-centric organisations is data. Not just lots of it, but organised, clean, useable, searchable – and sharable. This combination seems to me to lead to the customer having a voice at the table and not just locked away in market research insights reports kept in particular parts of an organisation. Love it!
Harvard Business Review picked up on the Kantar Vermeer study and uses Unilever as a case study for how its working and to do it. Have a read 🙂
A few key points about “successful insights engines” for those short on time. HBR identifies 10 characteristics of these ‘superior’, strategic insight teams which can be grouped into Operational and People characteristics:
- Data – making sense of it and extracting value
- Independence – sit outside marketing (its traditional home) and report in to C-suite
- Integrated planning – being involved in where to play and how to win strategic conversations within the business
- Collaboration – with customers and other areas with the business (evolving from being an effective service provider to shared goals and partnerships)
- Experimentation – embrace a culture of experimentation such as hackathons, mentoring programs with startups and collaboration platforms and Shark Tank like pitching of ideas to execs
- Forward-looking orientation – less focus on history to predict future performance and more focus on real-time
- Affinity for action – focus as much on strategy as on data and who they recruit
- Whole-brain mindset – recruiting and consciously supporting holistic, creative, right-brain skills as well the more traditional, (and organisationally familiar), left-brain thinking to move away from default thinking and gather multiple perspectives (and strengths)
- Business focus – programs to build business acumen, linking staff bonuses to business performance
- Storytelling – constructing a message through engaging, even provocative, narratives
The article concludes:
Much of what insights engines at any firm do is gather and analyze data. But today that is the minimum needed for success. Being able to translate this capability into customer-centric growth is what distinguishes winners from losers.
I’d add that your insights engine doesn’t need to just analyse data and translate into growth, but also internal insight teams need to know what and how to research otherwise the data just won’t be there to achieve those important strategic insights.
I am always interested to hear the ways fellow researchers, designers or consultants reach their insights. I found this article today that talks about distracting your brain or creating ‘incubation periods’ to allow the creative process to come forth and almost magically give you the clarity and insight you need.
I’ve written on this before about what constitutes productive time as a researcher. This new article has a bunch of links to other research on why disconnecting and doing something different can often create the spark needed for the major a-ha! moment. The article concludes that “what all of this research suggests is that peak creativity happens when we’re pleasantly absent-minded”.
Pleasantly absent-minded – sounds blissful!
One of the suggestions the article puts forward is to plan time for disengagement and distraction. I imagine this is what we intuitively know (but can sometimes forget) when we go for a walk outside to get out of the office, or go eat something or do a load of laundry when working from home. These meditative, repetitive or seemingly mindless tasks carry great power and may in fact lead you to your next insight!