Organizing for AI&ML Success – from Conway’s Law to the CDAO

Here’s a topic that I have given a great deal of thought to after observing lots of examples of how companies organize to identify, sense, collect, and use their business data. In a nutshell, HOW a company chooses to organize their data strategy and teams determines how successful they will be in delivering business value through data. Why is this? Conway’s Law gives us the reasons…

Conway’s Law

In short, in 1967, Melvin Conway, a computer programmer proposed that organizations design systems that mirror their own communication structure. This sounds very simple, but I’ll give some examples of why this provides really great insight into the power of architecting organizations around desired business outcomes.

First, why does this make sense?

Conway suggested that the architecture of products by organizations who are broken into functional competencies will tend to reflect those functions. For instance, an application developed by a firm with four functions: mechanical engineering, electrical engineering, software engineering, and signal processing will develop applications with distinct modular capabilities that reflect those functions. A module that manages thermal loads, center of gravity, control systems, structural sensing, and power will emerge and be developed by the mechanical engineering group. This module will interface to another module that contains embedded processing and memory through interfaces that carry power and sensors that provide data. This second module, of course, will be developed by the electrical engineering team. The software engineering team will develop a module that will be loaded into the electrical engineering’s processing system through some programming interface and will receive signals from sensors as well as elements within the mechanical engineering modules and will use logic to make decisions. The signal processing team will also develop a module that will be triggered by signals from the software engineering module and will provide outputs that interface with control modules in the mechanical engineering module. Phew! See below for a very high level visualization of how this might occur. Note how each department “owns” their own content and then someone (hopefully a systems architect or systems engineer) manages the interfaces.

Very high-level block diagram demonstrating Conway’s Law – Tod Newman, 2022

Conway’s Law and Data Science / AI&ML

I have seen Conway’s Law borne out over and over with regards to Data Strategy in an organization. Organization one (lets say Mechanical Engineering) understands their business function well and is intent to optimize for this function. They develop a strategy around data collection, storage, and analysis that helps them achieve their goals. Organization two (Finance, we’ll say) does the same thing. Then Organization three follows suit, and so on. Eventually what we have is 10-15 different data silos, each of which works relatively well for the owner (but each of which requires attention and sustainment — something that’s not always present). However, in traditional organizations (companies not named Uber or Google or SpaceX or similar) there is rarely a central figure like the systems architect who designed the complete business data system and who manages the interfaces. Therefore, Conway’s law results in the isolation of multiple, locally-valuable data sources. Frequently because these organizations design their data strategy to their own unique needs, there’s not even a clear way to connect these data stores!

Are there Solutions?

There are lots of examples of companies who have avoided the bulk of this negative effect by designing a centralized data strategy up front. As I alluded earlier, these companies are often data firms that offer a service like Google or Uber. They were born as data companies and developed from the ground up. If you’re not lucky enough to be a company that was born a data firm, however, there may be some possibilities, but I think they might be difficult and involve culture change management.

  1. Centralize the Data Strategy and Empower an Owner: This role has traditionally been called the Chief Data Officer and these days I’m noticing a positive trend towards redefining this role as the Chief Data and Analytics (or AI) Officer. Here’s a good explanation of the difference. This will have the effect of making the statement to the organization that data is now seen as a central business asset vs. simply a local asset. As the Harvard Business Review states, the trend towards naming CDO’s or CDAO’s “reflects a recognition that data is an important business asset that is worthy of management by a senior executive” and that it is “also an acknowledgement that data and technology are not the same and need different management approaches.” Note that redefining and centralizing the organization can leverage the positive aspects of Conway’s Law towards the goal of integrated, aligned data sources.
  2. Identify “low-hanging fruit” in your existing data silos for integration. You may be lucky and have a common key (employee number, part number, etc.) between two data silos that enables the data to be joined. This assumes that you can get permission to see the data by the silo owner, however, which might be a large assumption. Regardless, a demonstration of the power of integrated data could make the case for the difficult decisions and culture shifts (from local to collective ownership of data).
  3. Make a mandate. Jeff Bezos (legendarily) made his API Mandate at Amazon which required all data and functionality to be exposed publicly across Amazon through a defined interface called an Application Programming Interface (API). This interface managed both access to the data as well as insight into the structure of the data. It is said that this mandate changed the company and enabled their future high-value Amazon Web Services business.


If you’ve made it this far, then you probably have the gist of my argument. If you’ve skipped to the conclusion, here’s what I’d want you to know:

  1. Organizations the build a Data Strategy from scratch will fall into the Conway’s Law trap and are unlikely to have the ability to understand data interfaces.
  2. Conversely, a carefully-architected Data Strategy (everything from design of information to be sensed, sensing approach, collection, and application of Data Science, etc.) can be a surprisingly powerful lever for gaining business value. Some of the largest return on internal investments in process improvement I’m aware of inside large firms involve joining previously-unconnected data sources and gaining a new valuable insight for decisions, risk management, or even better understanding of the flow of business value from suppliers to the hands of the customer.
  3. It is hard to apply a new Data Strategy to an existing business culture. Unless you are leading an amazing business culture, it will require change management techniques (like John Kotter’s 8 steps) to succeed.
  4. An empowered role like the CDO, or better, the CDAO, may help this culture change and can make the kinds of “Bezos API mandates” that might be needed can aid success. It can also help with the next challenge, Sustaining the Data Business.

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