A series on “the new world of investment work” was begun some time ago by way of postings about talent, how to find it, and how to judge it. This piece (and a follow-up to it) will provide additional blocks of the foundation for that overall topic by focusing on networks and organizational design.
Organic organizations
If you do a search on the words “organic organization,” you’ll come upon a distinction made by Tom Burns and G.M. Stalker in their 1961 book The Management of Innovation. The authors described the differences between a “mechanistic” organization and an organic one, which you can think of as the two ends of a spectrum.
Mechanistic organizations are centralized, hierarchical, and full of rules and standards. Interactions are often relatively formal in nature and a lot of communication is in written form or occurs in structured meetings. It is a network of defined positions, and status usually relates to the number and importance of those whom you manage.
An organic organization is the opposite of that: decentralized, free-flowing, and constantly adjusting, with responsibilities changing as needed. The paths and types of communication and the nature of relationships are hard to plot and hard to follow. The orientation away from rigid structures allows for more ready adaptation and also a bit of chaos.
You can think about where a given investment organization is on the continuum between those two endpoints. Certainly size plays a role in where one is located, although some larger organizations might be pretty mechanistic overall but allow for relative freedom in how individual investment teams or units function.
Using that simple framework, consider where you would map your organization (or your competitors or your agents) — and what implications might be drawn from that exercise.
The orgorg chart
About a decade ago, an Autodesk research team created something it called the organic organization (orgorg) chart. The video that was produced got a fair amount of attention at the time. It shows the evolution in the structure of Autodesk, day by day, for four years.
As it speeds by — you might want to slow it down to really see what’s happening — the degree of change is obvious. There are small movements as people are added or dropped or reassigned, as well as some major disruptions, which are probably because of reorganizations or acquisitions.
The main thing the video does is to get you thinking about changing structures. Even though many organizations are less hierarchical than they used to be, we still tend to think in terms of org charts. No matter where an organization is on the mechanistic-to-organic spectrum, an orgorg chart could greatly increase your understanding of it. It would illuminate the basic arithmetic of investment staffing (driven by asset levels and product additions and subtractions), the changes in structure due to modifications in approach, and how individuals are moving through the org chart. Looking at several static org charts across time can give you a sense of that, although some things won’t show up that way.
Network analytics
But org charts and orgorg charts only tell you what the formal structure looks like (or is supposed to look like). They often don’t tell you a lot about how an organization really works.
Buried in an org chart are people that are critical to the success of an organization, but you’d never know it by looking at a static diagram or even a dynamic one. (In fact, sometimes those in charge don’t really know much about those folks either.) There are undocumented, hidden networks at play — and people who are influential by virtue of their relationships with others or by their ability to broker ideas and/or connections.
The properties of organizations are increasingly being evaluated using network analytics that track the flows of information between people. If you add in psychometric and behavioral findings — and apply the tools of natural language processing — you can take the understanding of an organization to another level entirely. But, potential pitfalls come with that greater depth and breadth of knowledge.
A 2018 Harvard Business Review article, “Better People Analytics” examines some of the possibilities that come from the “digital exhaust” of emails, chats, file transfers, and the like, in a process the authors call “relational analytics” (and is elsewhere known as “organizational network analysis”). They examine six “structural signatures” that indicate important attributes:
Ideation ~ which employees will come up with good ideas
Influence ~ which employees will change others’ behavior
Efficiency ~ which teams will complete projects on time
Innovation ~ which teams will innovate effectively
Silo ~ whether an organization is siloed
Vulnerability ~ which employees an organization can’t afford to lose
The network characteristics that signify each are succinctly described and real-world examples are given. Some of them might be judged as very important for investment decision making, and others for investment operations. The broader point is that there is much to be learned from really understanding the patterns and variations of network interactions.
But you must wrestle with the subject of a sidebar within the article, “What About Employee Privacy?” It offers guidance as to different approaches — from using basic and fairly generic information gathering to applications that might be considered intrusive. No matter what, everything should start with complete transparency about what information is collected and how it is be used. That gives employees and prospective employees the opportunity to judge the trade-offs involved.
Given the ever-present need to generate performance, some organizations will be aggressive in applying networking analysis. Leaders need to think carefully about how to balance the power of the tools and the insight that they provide with the risks that they can spawn.
You can start with these questions: Does digital exhaust belong to the organization or the employee? Where should the lines be drawn regarding its use?
Diagnosis and design
What you would most like to know is how ideas flow through the organization — who originates the best ones, who propagates them, who uses them to great effect, and who kills them off. And you’d want to understand what functional needs are not being met and where there are skill shortfalls that should be addressed through hiring or training or the restructuring of roles. Insights like those are mostly the province of observation and intuition now; network analytics may provide more concrete evidence to support design decisions that will produce the investment performance of the future. Organizations should be examining the possibilities as part of their research and development efforts.
Published: December 4, 2022
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