Synthetic Management

Key takeaways:

  1. The synthetic nature of programming has driven the adoption of experiential and empirical development practices, like TDD and continuous integration
  2. Synthetic management approaches like Agile have emerged to support the practices used to build software systems
  3. Synthetic management contrasts with the traditional “analytic” approaches used to run a business.
  4. The tension between the analytic and synthetic sides of the business creates new constraints to the flow of value.
  5. The pendulum is swinging away from managing software like the business, and towards managing the business like software.

Software needs a management model that supports the synthetic nature of programming. To this end, a variety of management practices have emerged to couple with the practices used to build software systems. I like to call this Synthetic Management: capitalizing on the experiences used to produce repeatable value from creative work.

We know this dragon well. It has come in many forms over the years. Big Ball of MudThe Mythical Man MonthWorse is Better, the Agile Manifesto; each of these was a step forward in explaining the fundamental truth of our work – that synthetic work needs synthetic management.

Unfortunately, it goes against the patterns that gave us digital technology:

  • The earliest days of programming, forced programmers to use analytic patterns to manage how they solved problems. The first computers were so expensive and inaccessible that programmers had to work through their algorithms with pencil and paper before trying anything out (think, Knuth’s Art of Computer Programming). The practice of running experiments to see what works was simply not an option.
  • In those days, programmers had to do the analysis needed to get as close to a solution as possible before committing it to code. And managers had to do the analysis needed to ensure that their incredibly constrained resources were used most efficiently. The pattern of working that developed involved a lot of planning and up-front design. That pattern persists to this day, despite being irrational in the world of cheap computing power.

Synthetic thinking asks us to set aside those traditional roots, leave behind the historical memory of how to solve problems. Instead of formal proofs, we prove things by seeing them work – inside the context of the systems they are intended to work inside.

Management practices like Agile and DevOps de-prioritize the classic analytic approach (think, Project Management). We understand that analysis can only take us so far before we reach the edge of understanding, absent of emergent knowledge, collective learning and systemic properties.

Agile practices are predictably useful because in software development there is no substitute for experience. We use tests and demos, pair programming and fast feedback, customer interactions and user research. These are all designed to get our work, as quickly as possible, into the “experiencing” part of the process.

Our systems, environments, organizations, and markets are constantly changing, and our teams need to be equipped with the same responsive and adaptive capacities that we expect our systems to have.

  • To manage software systems we adopt flexible approaches that allow us to experience and learn. The inspecting and adapting of Scrum; the sensing and responding of Cynefin; the observing and orienting of OODA – these practices all embrace the non-linear nature of our systems, and the synthetic approach to building understanding within them.
  • We have expanded out definition of what a software system is to include the team that builds and runs it. This brings together the technical, organizational and cultural elements into a single end-to-end value delivery process. We now recognize that a software system is a symmathesy in which learning is done across through emergent networks of people, processes and code. We recognize that synthetic work necessitates a diverse, collectively learning system to get fullness from experiential knowledge and subjective meaning.

To this end, I see our industry struggling to define and refine what we should correctly call Synthetic Management approaches.

This approaches allow our teams to close the gap between the act of creation and the moment of experience, connecting people to their work and to the contexts they inhabit. The next challenge, then, is bringing our work into the orbit of a powerful force that we often call, simply, “the business”.

New Kinds of Constraints

Our fundamental truth is contradicted by the need for business to be stable and analytic, calculating and certain. The business cares about money and metrics, contracts and deadlines. To deliver value to customers, we need to continuously interface with this contrary, yet complementary, set of intentions.

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The tension between the synthetic nature of software and the analytic needs of the business creates constraints that put pressure on our work.

These constraints can be either valuable or harmful, depending on how we design our organizational system. They demand trade-offs, sacrifices and debts, which can be managed deliberately, or left ad-hoc and made cumbersome. Note that it’s not the tension itself, but rather the mismanagement of it, that creates problems. The differences are huge:

  • When organizational systems are not optimized to transfer knowledge between these two contexts, it becomes a painful exercise of extraction. We have to spend time in work that provides the analytic side with confidence, but the synthetic side with nothing. We force our work inside opaque reporting abstractions that do not map to our work structures. We use patterns that inadvertently put developers under constant pressure to compromise on their synthetic values.
  • On the other hand, successful organizations actively facilitate the flow between the analytic and synthetic contexts. Interfaces are created for the business to gather information; social learning is used to route around organizational hierarchies; abstractions like OKRs provide direction without disrupting execution. We optimize the organizational system to create feedback loops, allowing us to share our learning and diffuse knowledge easily and appropriately.

The ways that we manage constraints across this Janus-face of business are critical to how we build dynamic learning organizations, which depend on a balanced flow of information and knowledge. But with an understanding of the analytic-synthetic dichotomy in hand, we can think more deeply about how to be effective.

While there is more investigation to be done around how to manage these constraints to flow across the intellectual boundaries of the business, new ways of working have already emerged to help us.

New Ways of Working

In many ways, computers have changed the world’s understanding of what we consider to be “verifiable information”. Code provides us with new ways to discover truths about the world by opening up a synthetic approach to solving problems that were previously only the domain of analysis.

The synthetic management practices born in software have opened up the frontier of ways to manage a business. We now see greater openness to different ideas, and the digital world demands it: we have at the center of work a complex system that demands looking at how we can push new ways of working right across the business.

The pendulum is swinging away from managing software like we manage the business, and towards managing the business like we manage software.

Significant momentum is growing to soften the analytic sides of the business, align it in a way that interfaces more easily with synthetic thinking. 

  • Beyond Budgeting moves us past the constraints of quarterly budgets, Design Thinking popularizes empirical techniques for discovery and experiential learning, Business Agility seeks to align business operations with the nature of complex adaptive systems, and the Teal movement is gaining traction as an alternative way to manage a business as a self-organizing, collective-learning system.

If we create organizations in which we can harness the power of emergent knowledge – if we are successful in designing a synthetic management system for our business – we are rewarded. But, if we spend too much lost in the paralysis of analysis, we fail.

As we break the management moulds to deliver software products, we get the opportunity to break them somewhere else. With software, we have demonstrated success with practices that do not fit the analytic style. Will they work elsewhere? There’s only one way to find out: go and see.


Portions of this article were  originally published on InfoQ on Jan 6th, 2020

Published by jrause

Eng Mgr @ Cisco

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