Part of the idea behind the Mingle is the notion of breaking down silos.  Gillian Tett has written a book about it with an anthropologist lens threaded through it. (See second half of post on the anthropology insight, my previous post on Lewis Hyde’s Gift also examines anthropological insight for a work of art as a gift))

The stories are strong and while academically rigorous analytics are not applied the ideas about how we can improve through breaking silos and some practical case studies on how this has been done (often using data analytics as well as people insights) are thought provoking.

Here are some excerpts:

“Most of us have an uneasy sense that our world is marred by silos. We might not use that specific word to describe the problem. However, we encounter it all the time: in bureaucracies where one department does not talk to another; at companies where teams are fighting each other or hoarding information; in societies where rich and poor or different ethnic and political groups live in separate social and intellectual ghettos, side by side. Technology should help break these barriers down. In theory, the Internet could connect us all. However, social media will not do this automatically, or even easily. Silos exist in cyberspace too. We live in a world that is hyper-connected, yet often we barely know what is happening around us. That begs the question: what can we do? We cannot entirely abolish silos, any more than we could abolish electricity and maintain our modern lifestyles. We need to have specialists in the twenty-first-century world to create order in the face of extreme complexity and an ever-swelling deluge of data. Facebook could not operate as a company if everybody was trying to write the same piece of code all the time. Some autonomy and accountability is essential. Similarly, Cleveland Clinic would not be an effective hospital if everybody tried to treat the same patients. Central banks would not be able to conduct their monetary policy operations unless somebody inside the institution knew how economic models worked.

Silos, if you define this concept as narrow, specialist groups, are inevitable. But as this book shows, when our classification systems become excessively rigid, and silos dangerously entrenched, this can leave us blind to risks and exciting opportunities. The story of Sony, in Chapter Two, shows those perils. So does the tale of UBS, or the story of the economics profession before 2007. These stories are not necessarily the worst examples out there: silos have caused problems at numerous other institutions, such as Microsoft, General Motors, the White House, Britain’s National Health Service, the BBC, BP. To name but a few. So is there anything we can do to mitigate this problem? I believe there is.

... 

One lesson is that it pays to keep the boundaries of teams in big organizations flexible and fluid, as Facebook has done. Rotating staff between different departments, as in the Hackamonth program, makes sense. Creating places and programs where people from different teams can collide and bond is also a good idea, be that through hackathons, off-sites, or other types of social collisions. It can also be beneficial to design physical spaces that funnel people into the same area, forcing constant, unplanned interactions. The corridors at Cleveland Clinic do this well. So do the squares at Facebook. Either way, people need to be mixed together to stop them becoming inward-looking and defensive.

A second lesson is that organizations need to think about pay and incentives. When employees are rewarded purely on the basis of how their group performs, and when groups are competing with each other internally, they are unlikely to collaborate—no matter how many expensive off-sites an institution holds, or open plan offices it creates. A key reason why UBS was so fragmented, as I described in Chapter Three, was that it had an “eat what you kill” incentive structure. The same problem besets most large financial groups. It also affects medicine, where the “eat what you treat” approach has raised health care costs in America. Collaborative pay systems, of the sort seen at Cleveland Clinic or Blue-Mountain Capital, are needed—at least in part—if people are going to think as a group.

A third lesson is that information flows matter too. The stories of UBS or Sony show that when departments hug information to themselves, huge risks can build up. One solution to this is for everybody to share more data, and modern computing technology now makes that much easier. However, it should be stressed that you cannot combat silos simply by opening the data spigot and letting information spill out. What is equally important is to create a culture that enables everyone to interpret information—and let different interpretations be heard. This is not easy to do when there are teams of experts who use complex technical language that only they understand, or when they refuse to listen to alternative ideas. Or as Paul Tucker (formerly of the Bank of England) points out, what big institutions really need are “cultural translators,” people who are able to move between specialist silos and explain to those sitting inside one department what is happening elsewhere. “You don’t need everyone to be a cultural translator—perhaps just 10 percent of the staff, or so. Most people can be specialists, and you need different types of specialists,” argues Tucker. “But any large organization needs to have somebody, or some people, who can play that translation role because they are literate in a number of specialisms.” Mutual respect for different “languages”—be that economics jargon, trader-speak, or anything else—is important too. “It is about epistemology, about what counts as knowledge. If someone is saying something in a different language from the one you use, that does not mean you should just ignore it.”

A fourth lesson is that it pays if people can periodically try to reimagine the taxonomies they use to reorganize the world, or even experiment with alternatives. Most of the time, most of us simply accept the classification systems we have inherited. But these are almost never ideal: they can become outdated, or end up serving just narrow interest groups. At Sony, the engineers did not question their silos, and ended up missing huge opportunities to innovate as a result. The economics profession before 2008 suffered a similar flaw, and as a result economists failed to see the scale of leverage that was developing in the system. But at Cleveland Clinic, doctors have tried to flip their mental maps of how medicine should be organized upside down, to visualize the world around how the patient experiences health, rather than how a doctor is trained. The same principle could be applied to numerous other businesses. Media groups, for example, are often arranged into departments defined according to how journalists have traditionally organized themselves (as, say, “political reporters” or “banking reporters” or “sub-editors” or “writers”) rather than how consumers experience the news. Banks tend to offer their financial products in departments defined by bankers, not investors or savers. Industrial companies often organize themselves according to how products were made fifty or a hundred years ago, or the different skills that engineers have, rather than around the problems that their modern customers want to solve. If those patterns are rigid, they risk becoming outmoded or clumsy and cause people to do foolish things. Changing them can spark innovation or, at the very least, a broader perspective.

And a fifth lesson is that it can also pay to use technology to challenge our silos. Computers do not automatically remove silos from our lives. Far from it. The sheer volume of digital data that now exists in our system forces us to constantly keep creating new systems to organize data, which inevitably forces us—or, more accurately, prompts computers—to put information into specific buckets. But the beauty of computers is that they are not born with indelible mental biases. They can be programmed to rearrange information in different ways and test out different ways of organizing data. Indeed, it is usually dramatically faster and easier to rearrange computer bytes than people, particularly given the power of data processing in modern computing systems (and the fact that data, unlike real-life people, cannot rebel against an order or foot-drag). The story of the New York skunkworks shows how effective this process of data reorganization can sometimes be in driving subtle, but potentially important, policy shifts. So does the tale of Brett Goldstein’s battle to cut the murder rate in Chicago. But these stories also reveal an important caveat. Data does not reorganize itself, or break down silos by itself; somebody needs to program the computers. What is needed above all is a big dose of human imagination.

.......

One potential tool is to borrow some of the principles of anthropology. This does not mean studying far-flung exotic cultures, lurid rituals, or dusty bones. ...

Instead anthropology is best viewed as a mind-set, or a way of looking on the world. It has several defining traits.

First, anthropologists tend to take a bottom-up view of life. They usually get out of their offices and experience life on the ground, trying to understand micro-level patterns to make sense of the macro picture.

Second, they listen and look with an open mind and try to see how all the different pieces of a social group or system interconnect. They tend to be flies on the wall.

Third, because anthropologists try to look at the totality of what they see, they end up examining the parts of life that people do not want to talk about, because they are considered taboo, dull, or boring. They are fascinated by social silences.

Fourth, they listen carefully to what people say about their life, and then compare it to what people actually do. Anthropologists are obsessed with the gap between rhetoric and reality.

Fifth, anthropologists often compare different societies and cultures and systems. A key reason they do this is because comparison can help illuminate the underlying patterns of different social groups. That is useful when looking at another culture. It is also invaluable if we want to understand our own society. When we immerse ourselves in another world, we not only learn about the “other” but can look back on our own lives with fresh eyes and a clearer perspective. We become insider-outsiders.

The sixth and most important point about anthropology, though, is that the discipline celebrates the idea that there is more than one valid way for humans to live. That sounds obvious. But humans in any society tend to assume that their own culture is natural. Our own social rules and classification systems feel so normal, if not inevitable, that we rarely devote much effort to thinking about them at all. But anthropologists know that the classification systems we use to organize our worlds and minds are not inevitable; they are usually a function of nurture not nature. We can change our cultural patterns if we really want to do that. We can also change the formal and informal rules that we use to organize the world. Or we can if we stop and think.

These six principles from anthropology can offer a good perspective for thinking about silos. ...

You can buy the whole book on this Amazon link. 

The current Arts blog, cross-over, the current Investing blog.  Cross fertilise, some thoughts on autism.  Discover what the last arts/business mingle was all about (sign up for invites to the next event in the list below).

My Op-Ed in the Financial Times  (My Financial Times opinion article) about asking long-term questions surrounding sustainability and ESG.

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A long read on Will Hutton looking at Brexit causes and solutions.

Some writing tips and thoughts from Zadie Smith

How to live a life, well lived. Thoughts from a dying man. On play and playing games.

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