Sampler postings republish pieces previously available only to paid subscribers.
This one is comprised of two postings published in December 2022.
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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 the follow-up to it that appears below) 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.
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The above posting explored some structural attributes of organizations and how network analytics might be used to assess the relationships and flow of ideas within them. This one considers the networks that go beyond the boundaries of the organization.
Our networks
We are all parts of multiple networks, from those at the places that we work to our webs of family relationships, social connections, and affiliations spawned by hobbies, cultural activities, political persuasions, faith communities, etc.
In our investment-related duties, we have external networks as well as internal ones. While a few organizations could be considered isolationist in their approach, in general the range and quality of those external networks are critical factors in determining what ideas are considered. They also are a main driver of the social pressure that motivates many decisions.
Even in today’s wired world, cultural and geographic differences linger, although they are more muted than they were in decades past. For example, how people thought about and approached global investing used to vary quite a bit depending on whether they were in the United States or Europe. And there was a noticeable clustering of investment styles and strategies by city, often because they were the specialty of the dominant firm or firms in the region. The propagation of ideas was natural — people went to the same meetings (most of which were oriented to the established interests of the biggest players), migrated from firm to firm within the area, and socialized together.
Analysts and salespeople from investment banks and research firms, while in many ways less influential than they were in the past, are conduits for a lot of ideas. Their conferences have served for years as gathering places where networks are strengthened and extended (we’ll see whether they retain their significance post-pandemic), and the corporate access that they provide has become more important than ever (despite Reg D).
In an industry of specialization, it isn’t surprising that most networks are based upon commonalities in investment strategy, organizational type, or functional role. They provide opportunities for learning and establishing new connections, surely, but they also create reinforcement loops of belief that can inhibit independent observation and analysis. It’s a social system, and security analysts, asset owners, investment advisors, and all of the other kinds of investment professionals are susceptible to the pressures of the crowds around them.
One other type of network arises from the technology platforms that we use. The platforms that include a communication element within them (think Bloomberg) can shape who you interact with — and all of them influence your world view, primarily by virtue of how investments are categorized and analyzed. Standardized tools yield standardized views.
Analyzing the networks
The previous posting held out hope that some network analytics might provide data that is useful for considering how an organization works. External networks present different challenges.
Information that flows into an organization (electronically) from the outside can be tracked, just as it can be internally, allowing a better understanding of where ideas come from and how they develop. But the privacy issues highlighted in the last piece get even more complicated. While an employee who understands how their digital exhaust will be used can decide whether the promised benefits are worth it, what about someone sending a business email from the outside? Does an organization have a right to track and analyze the contents of it, given that the sender has no understanding of those practices? (And, since personal and business matters often get mixed together in messages, where should the lines be drawn as to what is analyzed?)
However you address those kinds of questions, you have less ability to evaluate external networks in a systematic way than internal ones, given that the electronic evidence is limited to points of exchange. But that doesn’t mean you shouldn’t try to understand the quality of the networks that lie outside; they are (usually) where the seeds of your actions originate.
With that in mind, how would you judge the quality of your personal external network (when it comes to investment pursuits)? What about the whole range of those networks that your organization relies upon?
One place to start is by pondering how uniform the networks are. Innovations in methods and ideas usually come from recombining concepts and applications in new ways; that’s much more likely when there is diversity in the sources of information and opinion. There are benefits to imitation, but alpha comes from differentiation, and too many investment networks are tribal echo chambers.
This comes from Social Chemistry: Decoding the Patterns of Human Connections, by Marissa King:
When Yo-Yo Ma, an iconic classical musician, looked around, he noticed that “the most interesting things happen at the edge. The intersections there can reveal unexpected connections.” Within ecology, this is known as the edge effect. Where the edges of two ecosystems meet, there is the greatest biodiversity.
Ma’s interest in fostering new musical amalgams might seem far afield from the investment world, but the same principles apply. Combinations occur at the edges of asset classes and strategies, creating new categories. Insights from other disciplines offer the potential to improve investment theories, tactics, and organizations, but are often untapped, since (despite its dynamism in other respects) the investment industry is quite insular and slow to change.
Searching for quality
One aspect of the autonomy provided in most investment organizations is that you can build your outside networks on your own without much interference. That hands-off approach ignores the fact that many of us aren’t very good at building diverse networks of quality. That creates weak spots for an organization; helping people analyze and improve their network of sources should be an important concern, but it rarely is. We fend for ourselves — and usually develop narrow and conventional networks that deliver narrow and conventional ideas.
The standard inference is that if there is good performance then there is a good process behind it — and a good network which feeds it. But conditions change and a network that is optimized for one environment can be totally out of sync with the next, missing the transition from one to another.
While a diversity of inputs is essential, assessments need to be made about the quality of work done by those in the network. That requires looking beyond the surface level of the current idea flow (and performance); a good network is built on an understanding of how your contacts do their work and what beliefs and incentives motivate them.
That depth of knowledge takes time and comes not from osmosis during the normal investment discourse, but from purposeful inquiry of a different sort. That is a rare and valuable practice.
These postings are part of “The New World of Investment Work” series.
Published: August 17, 2023
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