Resilience Engineering, DevOps, and Psychological Safety – resources

With thanks to Liam Gulliver and the folks at DevOps Notts, I gave a talk recently on Resilience Engineering, DevOps, and Psychological Safety.

It’s pretty content-rich, and here are all the resources I referenced in the talk, along with the talk itself, and the slide deck. Please get in touch if you would like to discuss anything mentioned, or you have a meetup or conference that you’d like me to contribute to!

Here’s a psychological safety practice playbook for teams and people.

Open Practice Library

https://openpracticelibrary.com/

Resilience Engineering and DevOps slide deck  

https://docs.google.com/presentation/d/1VrGl8WkmLn_gZzHGKowQRonT_V2nqTsAZbVbBP_5bmU/edit?usp=sharing

Resilience engineering – Where do I start?

Resilience engineering: Where do I start?

Turn the ship around by David Marquet

Lorin Hochstein and Resilience Engineering fundamentals 

https://github.com/lorin/resilience-engineering/blob/master/intro.md

 

Scott Sagan, The Limits of Safety:
“The Limits of Safety: Organizations, Accidents, and Nuclear Weapons”, Scott D. Sagan, Princeton University Press, 1993.

 

Sidney Dekker: “The Field Guide To Understanding Human Error: Sidney Dekker, 2014

 

John Allspaw: “Resilience Engineering: The What and How”, DevOpsDays 2019.

https://devopsdays.org/events/2019-washington-dc/program/john-allspaw/

 

Erik Hollnagel: Resilience Engineering 

https://erikhollnagel.com/ideas/resilience-engineering.html

 

Cynefin

Home

 

Jabe Bloom, The Three Economies

The Three Economies an Introduction

 

Resilience vs Efficiency

Efficiency vs. Resiliency: Who Won The Bout?

 

Tarcisio Abreu Saurin – Resilience requires Slack

Slack: a key enabler of resilient performance

 

Resilience engineering and DevOps – a deeper dive

Resilience Engineering and DevOps – A Deeper Dive

 

Symposium with John Willis, Gene Kim, Dr Sidney Dekker, Dr Steven Pear, and Dr Richard Cook: Safety Culture, Lean, and DevOps

 

Approaches for resilience and antifragility in collaborative business ecosystems: Javaneh Ramezani Luis, M. Camarinha-Matos:

https://www.sciencedirect.com/science/article/pii/S0040162519304494

 

Learning organisations:
Garvin, D.A., Edmondson, A.C. and Gino, F., 2008. Is yours a learning organization?. Harvard business review, 86(3), p.109.
https://teamtopologies.com/book
https://www.psychsafety.co.uk/cognitive-load-and-psychological-safety/

 

Psychological safety: Edmondson, A., 1999. Psychological safety and learning behavior in work teams. Administrative science quarterly, 44(2), pp.350-383.

The four stages of psychological safety, Timothy R. Clarke (2020)

Measuring psychological safety:

 

And of course the youtube video of the talk:

Please get in touch if you’d like to find out more.

A Critique of SAFe – The Scaled Agile Framework

whats wrong with SAFe?

This is a critique of the Scaled Agile Framework (SAFe).

It’s a critique, so it’s pretty negative! There are some benefits of using SAFe in large organisations, some very good use cases for full or partial adoption as long as it’s considered part of a journey, as long as your eyes are open to the problems with SAFe, and your reasons for adopting it are sound.

However, here I’m describing ten key points emphasising why it’s not an appropriate approach for most organisations looking to scale software delivery.

I’m really interested in your opinion, so please do get in touch if you wish to make a comment or suggestion. Ultimately, we must remember to scale down the problem before scaling up the solution.

In Summary: Problems with SAFe approaches:

  1. SAFe encourages normalisation of batch sizing across teams, incentivises increasing task sizes, and fundamentally misappropriates what story points are for.
  2. SAFe can cause increased localised technical debt.
  3. SAFe creates conflicts with support, operational and SRE functions.
  4. SAFe decreases inter-team (particularly value stream) collaboration.
  5. SAFe uses fallacies in estimation.
  6. SAFe decreases the agile focus on value in favour of “what management wants”.
  7. SAFe decreases the utility of, and the focus on, retrospectives.
  8. SAFe is not Agile – it encourages top-down, large-batch planning rather than small, iterative, feedback loops.
  9. SAFe is framed as a solution, rather than a stage of a journey.
  10. SAFe scales up the solution rather than scaling down the problem.

11 – (bonus point, thanks to Mathew Skelton) – SAFe encourages temporal coupling of teams.

In Detail: a critique of the Scaled Agile Framework:

1 – Nothing in agile suggests that we need to, or even *should* measure work units (i.e. story points) in uniform manners across teams. Story points exist to help the people *doing* the work break things down into optimum batch size, which makes deliverables achievable, less complex, and facilities flow. Indeed, SAFe actually encourages larger batch sizes through front-loaded planning, not smaller sizes planned through more iterative methods.

SAFe tries to normalise story points across teams for various reasons, but there is often a strong desire to measure and compare the delivery of teams and people. This is not what story points are for. Story points do not exist to measure how “productive” developers are.

2 – Technical debt tends to increase in SAFe organisations because the prioritisation of dealing with it is raised to a management level rather than team level. This is counter-productive for technical debt that originates at the team level (which most of it does). Management will tend to prioritise features and functions, delaying the pay-back of localised technical debt, and resulting in slower, higher risk, more brittle systems.

3 – If SAFe is applied to more operational functions, such as technology support, operations, or SRE, conflicts between delivery and support functions arise, because supporting teams typically need to work either responsively, dealing with issues as they arise, or on very short cycles – not the Programme Increment cycle time imposed by SAFe.

4 – Due to the focus on deliverables and accountability through project or product managers, teams may be discouraged from assisting each other, as they are measured by their own delivery and productivity: how much they assist other teams is rarely valued.

5 – The concept of “ideal dev days” is often used for estimating in SAFe. Everyone else knows that ideal dev days are a fallacy. Instead, look at past similar deliverables, and see how long they took. This is a much more predictive metric, and is less susceptible to optimism bias or wanting to please the boss.

6 – The concept of “value” often breaks down in SAFe, through a focus on volume of delivery and meeting the (often arbitrary) deadlines imposed by management in PI planning. As a result, what end-users actually want is often ignored in favour of what management wants.

7 – PI planning includes a small element of retrospective activity, but it’s too little, too late. The retrospective feedback loops need to be short and light, not tagged on to PI planning as an afterthought. Here’s a comprehensive guide to retrospectives that also covers some really useful suggestions for running them with remote and distributed teams.

8 – Agile was created as a response to frustrations felt across the industry from heavyweight, top-down project management methodology that was killing the sector. Trying to scale Agile up by applying heavyweight, top-down methodologies is antithetical. 

9 – Some SAFe practitioners describe it as a transition stage, a process through which organisations can achieve increased capability at scale. I would agree: if an organisation feels the need to adopt SAFe, it should be as training wheels, a structure through which great capabilities can be built, before throwing off the shackles of a rigid, top-down framework. If it was really true that SAFe is a transitionary framework, why does the SAFe model not include anything about the transition away from it?

10 – In reality, most organisations don’t need SAFe. They’re not so big that they need such a big solution. SAFe is a comfort blanket for organisations used to traditional, slow, heavyweight, command-control structures. Your projects and products actually aren’t that big – and if they are, then that’s the problem, not the management process.

Fundamentally, SAFE tends to ignore, or encourages management to ignore the possibility that those closest to the work might be the best equipped to make decisions about it. Scale the work down, not the process up. SAFe fits the delivery model to the organisational structure, rather than forcing the organisation to adopt new ways.

Here’s a bonus point 11, thanks to Matt Skelton of Team Topologies: SAFe, via the enforced Program Increment approach, encourages (or very possibly forces) at least a temporal coupling of teams that isn’t warranted. In fact, any sort of forced coupling is an antipattern for a fast flow of change, and via Conway’s Law, probably introduces architectural coupling too (which is bad). Given that SAFe adopts the PI as the core foundation of the approach, it’s unlikely that any SAFe practitioner would suggest dropping PI when the teams are mature enough to do so… or would they?

2023 update: Here’s a fantastic Creative-Commons document that outlines similar criticisms of SAFe along with case studies and expert commentary from practitioners and researchers alike.

 

Resilience Engineering and DevOps – A Deeper Dive

robustness vs resilience

[This is a work in progress. If you spot an error, or would like to contribute, please get in touch]

The term “Resilience Engineering” is appearing more frequently in the DevOps domain, field of physical safety, and other industries, but there exists some argument about what it really means. That disagreement doesn’t seem to occur in those domains where Resilience Engineering has been more prevalent and applied for some time now, such as healthcare and aviation. Resilience Engineering is an academic field of study and practice in its own right. There is even a Resilience Engineering Association.

Resilience Engineering is a multidisciplinary field associated with safety science, complexity, human factors and associated domains that focuses on understanding how complex adaptive systems cope with, and learn from, surprise.

It addresses human factors, ergonomics, complexity, non-linearity, inter-dependencies, emergence, formal and informal social structures, threats and opportunities. A common refrain in the field of resilience engineering is “there is no root cause“, and blaming incidents on “human error” is also known to be counterproductive, as Sidney Dekker explains so eloquently in “The Field Guide To Understanding Human Error”.

Resilience engineering is “The intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions.Prof Erik Hollnagel

It is the “sustained adaptive capacity” of a system, organisation, or community.

Resilience engineering has the word “engineering” in, which makes us typically think of machines, structures, or code, and this is maybe a little misleading. Instead, maybe try to think about engineering being the process of response, creation and change.

Systems

Resilience Engineering also refers to “systems”, which might also lead you down a certain mental path of mechanical or digital systems. Widen your concept of systems from software and machines, to organisations, societies, ecosystems, even solar systems. They’re all systems in the broader sense.

Resilience engineering refers in particular to complex systems, and typically, complex systems involve people. Human beings like you and I (I don’t wish to be presumptive but I’m assuming that you’re a human reading this).

Consider Dave Snowden’s Cynefin framework:

cynefin

Systems in an Obvious state are fairly easy to deal with. There are no unknowns – they’re fixed and repeatable in nature, and the same process achieves the same result each time, so that we humans can use things like Standard Operating Procedures to work with them.

Systems in a Complicated state are large, usually too large for us humans to hold in our heads in their entirety, but are finite and have fixed rules. They possess known unknowns – by which we mean that you can find the answer if you know where to look. A modern motorcar, or a game of chess, are complicated – but possess fixed rules that do not change. With expertise and good practice, such as employed by surgeons or engineers or chess players, we can work with systems in complicated states.

Systems in a Complex state possess unknown-unknowns, and include realms such as battlefields, ecosystems, organisations and teams, or humans themselves. The practice in complex systems is probe, sense, and respond. Complexity resists reductionist attempts at determining cause and effect because the rules are not fixed, therefore the effects of changes can themselves change over time, and even the attempt of measuring or sensing in a complex system can affect the system. When working with complex states, feedback loops that facilitate continuous learning about the changing system are crucial.

Systems in a Chaotic state are impossible to predict. Examples include emergency departments or crisis situations. There are no real rules to speak of, even ones that change. In these cases, acting first is necessary. Communication is rapid, and top-down or broadcast, because there is no time, or indeed any use, for debate.

Resilience

As Erik Hollnagel has said repeatedly since Resilience Engineering began (Hollnagel & Woods, 2006), resilience is about what a system can do — including its capacity: 

  • to anticipate — seeing developing signs of trouble ahead to begin to adapt early and reduce the risk of decompensation 
  • to synchronize —  adjusting how different roles at different levels coordinate their activities to keep pace with tempo of events and reduce the risk of working at cross purposes 
  • to be ready to respond — developing deployable and mobilizable response capabilities in advance of surprises and reduce the risk of brittleness 
  • for proactive learning — learning about brittleness and sources of resilient performance before major collapses or accidents occur by studying how surprises are caught and resolved 

(From Resilience is a Verb by David D. Woods)

 

Capacity Description
Anticipation Create foresight about future operating conditions, revise models of risk
Readiness to respond Maintain deployable reserve resources available to keep pace with demand
Synchronization Coordinate information flows and actions across the networked system
Proactive learning Search for brittleness, gaps in understanding, trade-offs, re-prioritisations

Provan et al (2020) build upon Hollnagel’s four aspects of resilience to show that resilient people and organisations must possess a “Readiness to respond”, and states “This requires employees to have the psychological safety to apply their judgement without fear of repercussion.”

Resilience is therefore something that a system “does”, not “has”.

Systems comprise of structures, technology, rules, inputs and outputs, and most importantly, people.

Resilience is about the creation and sustaining of various conditions that enable systems to adapt to unforeseen events. *People* are the adaptable element of those systems” – John Allspaw (@allspaw) of Adaptive Capacity Labs.

Resilience therefore is about “systems” adapting to unforeseen events, and the adaptability of people is fundamental to resilience engineering.

And if resilience is the potential to anticipate, respond, learn, and change, and people are part of the systems we’re talking about:

We need to talk about people: What makes people resilient?

Psychological safety

Psychological safety is the key fundamental aspect of groups of people (whether that group is a team, organisation, community, or nation) that facilitates performance. It is the belief, within a group, “that one will not be punished or humiliated for speaking up with ideas, questions, concerns, or mistakes.” – Edmondson, 1999.

Amy Edmondson also talks about the concept of a “Learning organisation” – essentially a complex system operating in a vastly more complex, even chaotic wider environment. In a learning organisation, employees continually create, acquire, and transfer knowledge—helping their company adapt to the un-predictable faster than rivals can. (Garvin et al, 2008)

“A resilient organisation adapts effectively to surprise.” (Lorin Hochstein, Netflix)

In this sense, we can see that a “learning organisation” and a “resilient organisation” are fundamentally the same.

Learning, resilient organisations must possess psychological safety in order to respond to changes and threats. They must also have clear goals, vision, and processes and structures. According to Conways Law:

“Any organisation that designs a system (defined broadly) will produce a design whose structure is a copy of the organisation’s communication structure.”

In order for both the organisation to respond quickly to change, and for the systems that organisation has built to respond to change, the organisation must be structured in such a way that response to change is as rapid as possible. In context, this will depend significantly on the organisation itself, but fundamentally, smaller, less-tightly coupled, autonomous and expert teams will be able to respond to change faster than large, tightly-bound teams with low autonomy will. Pais and Skelton’s Team Topologies explores this in much more depth.

Engineer the conditions for resilience engineering

“Before you can engineer resilience, you must engineer the conditions in which it is possible to engineer resilience.” – Rein Henrichs (@reinH)

As we’ve seen, an essential component of learning organisations is psychological safety. Psychological safety is a necessary condition (though not sufficient) for the  conditions of resilience to be created and sustained. 

Therefore we must create psychological safety in our teams, our organisations, our human “systems”. Without this, we cannot engineer resilience. 

We create, build, and maintain psychological safety via three core behaviours:

  1. Framing work as a learning problem, not an execution problem. The primary outcome should be knowing how to do it even better next time.
  2. Acknowledging your own fallibility. You might be an expert, but you don’t know everything, and you get things wrong – if you admit it when you do, you allow others to do the same.
  3. Model curiosity – ask a lot of questions. This creates a need for voice. By you asking questions, people HAVE to speak up. 

Resilience engineering and psychological safety

Psychological safety enables these fundamental aspects of resilience – the sustained adaptive capacity of a team or organisation.:

  • Taking risks and making changes that you don’t, or can’t, fully understand the outcomes of. 
  • Admitting when you made a mistake. 
  • Asking for help
  • Contributing new ideas
  • Detailed systemic cause* analysis (The ability to get detailed information about the “messy details” of work)

(*There is never a single root cause)

Let’s go back to that phrase at the start:

Sustained adaptive capacity.

What we’re trying to create is an organisation, a complex system, and sub systems (maybe including all that software we’re building) that possesses a capacity for sustained adaptation.

With DevOps we build systems that respond to demand, scale up and down, we implement redundancy, low-dependancy to allow for graceful failure, and identify and react to security threats.

Pretty much all of these only contribute to robustness.

robustness vs resilience

(David Woods, Professor, Integrated Systems Engineering Faculty, Ohio State University)

You may want to think back to the cynefin model, and think of robustness as being able to deal well with known unknowns (complicated systems), and resilience as being able to deal well with unknown unknowns (complex, even chaotic systems). Technological or DevOps practices that primarily focus on systems, such as microservices, containerisation, autoscaling, or distribution of components, build robustness, not resilience.

However, if we are to build resilience, the sustained adaptive capacity for change, we can utilise DevOps practices for our benefit. None of them, like psychological safety, are sufficient on their own, but they are necessary. Using automation to reduce the cognitive load of people is important: by reducing the extraneous cognitive load, we maximise the germane, problem solving capability of people. The provision of other tools, internal platforms, automated testing pipelines, and increasing the observability of systems increases the ability of people and teams to respond to change, and increases their sustained adaptive capacity.

If brittleness is the opposite of resilience, what does “good” resilience look like? The word “anti-fragility” appears to crop up fairly often, due to the book “Antifragile: Things that Gain from Disorder” by Nassim Taleb. What Taleb describes as antifragile, ultimately, is resilience itself.

I have my own views on this, but fundamentally I think this is the danger of academia (as in the field of resilience engineering) restricting access to knowledge. A lot of resilience engineering literature is held behind academic paywalls and journals, which most practitioners do not have access to.  It should be of no huge surprise that people may reject a body of knowledge if they have no access to it.

Observability

It is absolutely crucial to be able to observe what is happening inside the systems. This refers to anything from analysing system logs to identify errors or future problems, to managing Work In Progress (WIP) to highlight bottlenecks in a process.

Too often, engineering and technology organisations look only inward, whilst many of the threats to systems are external to the system and the organisation. Observability must also concern external metrics and qualitative data: what is happening in the marketspace, the economy, and what are our competitors doing?

Resilience Engineering and DevOps

What must we do?

Create psychological safety – this means that people can ask for help, raise issues, highlight potential risks and “apply their judgement without fear of repercussion.” There’s a great piece here on psychological safety and resilience engineering.

Manage cognitive load – so people can focus on the real problems of value – such as responding to unanticipated events.

Apply DevOps practices to technology – use automation, internal platforms and observability, amongst other DevOps practices. 

Increase observability and monitoring – this applies to systems (internal) and the world (external). People and systems cannot respond to a threat if they don’t see it coming.

Embed practices and expertise in component causal analysis – whether you call it a post-mortem, retrospective or debrief, build the habits and expertise to routinely examine the systemic component causes of failure. Try using Rothmans Causal Pies in your next incident review.

Run “fire drills” and disaster exercises. Make it easier for humans to deal with emergencies and unexpected events by making it habit. Increase the cognitive load available for problem solving in emergencies.

Structure the organisation in a way that facilitates adaptation and change. Consider appropriate team topologies to facilitate adaptability.

In summary

Through facilitating learning, responding, monitoring, and anticipating threats, we can create resilient organisations. DevOps and psychological safety are two important components of resilience engineering, and resilience engineering (in my opinion) is soon going to be seen as a core aspect of organisational (and digital) transformation.

 

References:

Conway, M. E. (1968) How Do Committees Invent? Datamation magazine. F. D. Thompson Publications, Inc. Available at: https://www.melconway.com/Home/Committees_Paper.html

Dekker, S. 2006. The Field Guide to Understanding Human Error. Ashgate Publishing Company, USA.

Edmondson, A., 1999. Psychological safety and learning behavior in work teams. Administrative science quarterly, 44(2), pp.350-383.

Garvin, David & Edmondson, Amy & Gino, Francesca. (2008). Is Yours a Learning Organization?. Harvard business review. 86. 109-16, 134.

Hochstein, L. (2019)  Resilience engineering: Where do I start? Available at: https://github.com/lorin/resilience-engineering/blob/master/intro.md (Accessed: 17 November 2020).

Hollnagel, E., Woods, D. D. & Leveson, N. C. (2006). Resilience engineering: Concepts and precepts. Aldershot, UK: Ashgate.

Hollnagel, E. Resilience Engineering (2020). Available at: https://erikhollnagel.com/ideas/resilience-engineering.html (Accessed: 17 November 2020).

Provan, D.J., Woods, D.D., Dekker, S.W. and Rae, A.J., 2020. Safety II professionals: how resilience engineering can transform safety practice. Reliability Engineering & System Safety, 195, p.106740. Available at https://www.sciencedirect.com/science/article/pii/S0951832018309864

Woods, D. D. (2018). Resilience is a verb. In Trump, B. D., Florin, M.-V., & Linkov, I.
(Eds.). IRGC resource guide on resilience (vol. 2): Domains of resilience for complex interconnected systems. Lausanne, CH: EPFL International Risk Governance Center. Available on irgc.epfl.ch and irgc.org.

John Allspaw has collated an excellent book list for essential reading on resilience engineering here.

The State of DevOps Report 2020 – A Summary

Every year, for the past decade, Puppet have carried out their “State of DevOps” report, apart from 2019, when it was carried out and released by DORA through Google.

This year, Puppet took the reins again and despite 2020 being the year from Hell, they managed to survey 2,400 technology professionals and released their report on 12th November.

The State of DevOps report attempts to gather, aggregate and analyse progress across the technology industry, backed by data and statistical analysis.

Here are the key takeaways from the 2020 State of DevOps Report:state of DevOps report

DevOps continues to evolve.

One of the things I like about the Puppet approach is that they see DevOps as a continual evolution towards improved delivery, quality and security, and steer away from a more traditional “maturity model” that implies a possibly fictional end state where DevOps is “done”.

From personal experience and what we’ve seen over the past ten years of data, we need to recognise that technical practices are important, but practices that are isolated to a few teams simply aren’t enough to help organisations achieve widespread DevOps success. DevOps is not a CI/CD pipeline, it’s not technology, public cloud, or automation. DevOps is people, culture, mindset, technology, constraints, experience and expertise.*

As the 2019 report by DORA showed, a culture of psychological safety is crucial to both team & organisational performance, and productivity.

psychological safety and devops

Internal platform teams

One major evident transition is the shift to internal platform teams. Unlike product teams, which are responsible for the end-to-end delivery of a product, internal platform teams are responsible for providing a platform that provides the infrastructure, environments, deployment pipelines and other internal services that enable internal customers (such as those product teams)  to build, deploy and run their applications.

The platform model can make product teams far more efficient by allowing them to focus on their primary goals and their core competencies: building and delivering products. A platform team can improve governance, compliance and cost efficiency through providing a standardised toolset that can be easily understood and consumed by value stream-oriented teams.

The 2020 State of DevOps report shows that high performing organisations are six times more likely to report the use of internal platforms as compared to low performing organisations.

devops and shared platforms

Shared internal platforms provide a balance between standardisation and team autonomy. Finding where to place this balance and draw the line can be challenging, but the important thing is to start.

A really useful resource is Manuel Pais and Matthew Skelton’s book “Team Topologies”, which will help you understand what team structures will contribute to building high performing products and services, and how internal platform teams could work in your organisation.

Product over project

More organisations are transitioning away from a traditional project mindset, towards value-stream-aligned, product approaches. Organisations that still possess a traditional “project mindset” may suffer from the proliferation of temporary teams that form and disperse as projects begin and end, impacting team cohesion and performance.

A project mindset encourages teams to focus on the next shiny thing, and throw things over the wall for ops to support, rather than own a product or service longer term and ensure that it’s not only fit for purpose, but constantly improving.

Adopting a product-oriented approach and tying work to value streams improves the delivery of features, reduces defects, increases security, and lowers technical debt. Mik Kersten’s Project To Product is an excellent book to learn more about how to adopt a product approach.

The 2020 State of DevOps report shows that a product mindset is a key enabler of performance in the technology space, and accelerates DevOps adoption and evolution.

product oriented approach and devops

Change management

Ever since Gene Kim wrote The Phoenix Project, we’ve known that fast and lean change management is a precursor for technology performance. Nicole Forsgren describes in her book Accelerate how lead time for changes is an essential trailing metric for high performing teams.

The 2020 State of DevOps report revealed four different approaches to change management based on approval processes (orthodox “gatekeeping” approaches versus adaptive and collaborative), automated testing and deployment, and advanced risk mitigation techniques.

The four approaches described by Puppet are:

  • Operationally mature: High levels of both process and automation.
  • Engineering driven: High emphasis on automation.
  • Governance focused: High emphasis on manual approvals and low emphasis on automation.
  • Ad hoc: Low emphasis on both process and automation.

Puppet also showed that organisations that trust in their change management processes are more likely to adopt automation, which further improves performance.  Additionally, organisations that encourage high engagement with employees in the change management process are five times more likely to have effective change management processes.

devops and change management

It is interesting to note that ITIL, originally intended to improve the quality and performance of technology, has been adopted by many organisations (90% of Fortune 500 firms have adopted ITIL) and has resulted in cumbersome bureaucratic processes that actually resulted in slower change and higher risk. Fortunately, the latest version of ITIL, v4, departs from this heavyweight approach and instead encourages change enablement and collaboration.

To put it simply:

  • Orthodox approvals damage performance
  • Automation gives teams confidence in change management
  • Giving people agency over the process results in higher performance

Challenges to improving change management practices include incomplete test coverage, organisational mindsets of fear and compliance instead of trust and value, and tightly coupled and or monolithic architectures.

As with any DevOps transformation, improve change management processes but primarily focus on people and culture. Break down silos and build empathy across people and teams: enable and encourage engineers to understand and empathise with the concerns of compliance and risk teams, whilst working with governance to create a culture of shifting security and compliance left.

TL;DR:

  1. The industry still has a long way to go and there remain significant areas for improvement across all sectors.
  2. Internal platforms and platform teams are a key enabler of performance, and more organisations are adopting this approach.
  3. Adopting a product approach over project-oriented improves performance and facilitates improved adoption of DevOps cultures and practices.
  4. Lean, automated, and people-oriented change management processes improve velocity and performance.

 

Thanks to the team at Puppet and DORA for carrying out the State Of DevOps reports every year, including the team for this years report, Alanna Brown (@alannapb) , Michael Stahnke (@stahnma), and Nigel Kersten (@nigelkersten).

 

If you’re here looking for a summary of the 2021 State of DevOps Report by Puppet, it’s located here.

*Thanks to Tom Hoyland for the articulate description of DevOps.

Remote Working – What Have We Learned From 2020?

Remote working improves productivity.

Even way back in 2014, evidence showed that remote working enables employees to be more productive and take fewer sick days, and saves money for the organisation.  The rabbit is out of the hat: remote working works, and it has obvious benefits.

Source: Forbes Global Workplace Analytics 2020

More and more organisations are adopting remote-first or fully remote practices, such as Zapier:

“It’s a better way to work. It allows us to hire smart people no matter where in the world, and it gives those people hours back in their day to spend with friends and family. We save money on office space and all the hassles that comes with that. A lot of people are more productive in remote setting, though it does require some more discipline too.”

We know, through empirical studies and longitudinal evidence such as Google’s Project Aristotle that colocation of teams is not a factor in driving performance. Remote teams perform as well as, if not better than colocated teams, if provided with appropriate tools and leadership.

Teams that are already used to more flexible, lightweight or agile approaches adapt adapt to a high performing and fully remote model even more easily than traditional teams.

The opportunity to work remotely, more flexibly, and save on time spent commuting helps to improve the lives of people with caring, parenting or other commitments too. Whilst some parents are undoubtedly keen to get into the office and away from the distractions of home schooling, the ability to choose remote and more flexible work patterns is a game changer for some, and many are actually considering refusing to go back to the old ways.

What works for some, doesn’t work for others, and it will change for all of us over time, as our circumstances change. But having that choice is critical.

However, remote working is still (even now in 2020 with the effects of Covid and lockdowns) something that is “allowed” by an organisation and provided to the people that work there as a benefit.

Remote working is now an expectation.

What we are seeing now is that, for employees at least, particularly in technology, design, and other knowledge-economy roles, remote working is no longer a treat, or benefit – just like holiday pay and lunch breaks,  it’s an expectation.

Organisations that adopt and encourage remote working are able to recruit across a wider catchment area, unimpeded by geography, though still somewhat limited by timezones – because we also know that synchronous communication is important.

Remote work is also good for the economy, and for equality across geographies. Remote work is closing the wage gap between areas of the US and will likely have the same effect on the North-South divide in the UK. This means London firms can recruit top talent outside the South-East, and people in typically less affluent areas can find well paying work without moving away.

But that view isn’t shared by many organisations.

However, whilst employees are increasingly seeing remote working as an expectation rather than a benefit, many organisations, via pressure from command-control managers, difficulties in onboarding, process-oriented HR teams, or simply the most dangerous phrase in the English language: because “we’ve always done it this way“, possess a desire to bring employees back into the office, where they can see them.

Indeed, often by the managers of that organisation, remote working may be seen as an exclusive benefit and an opportunity to slack off. The Taylorist approach to management is still going strong, it appears.

People are adopting remote faster than organisations.

In 1962, Everett Rogers came up with the principle he called “Diffusion of innovation“.

It describes the adoption of new ideas and products over time as a bell curve, and categorises groups of people along its length as innovators, early adopters, early majority, late majority, and laggards. Spawned in the days of rapidly advancing agricultural technology, it was easy (and interesting) to study the adoption of new technologies such as hybrid seeds, equipment and methods.

 

Some organisations are even suggesting that remote workers could be paid less, since they no longer pay for their commute (in terms of costs and in time), but I believe the converse may become true – that firms who request regular attendance at the office will need to pay more to make up for it. As an employee, how much do you value your free time?

It seems that many people are further along Rogers’ adoption curve than the organisations they work for.

There are benefits of being in the office.

Of course, it’s important to recognise that there are benefits of being colocated in an office environment. Some types of work simply don’t suit it. Some people don’t have a suitable home environment to work from. Sometimes people need to work on a physical product or collaborate and use tools and equipment in person. Much of the time, people just want to be in the same room as their colleagues – what Tom Cheesewright calls “The unbeatable bandwidth of being there.”

But is that benefit worth the cost? An average commute is 59 minutes, which totals nearly 40 hours per month, per employee. For a team of twenty people, is 800 hours per month worth the benefit of being colocated? What would you pay to obtain an extra 800 hours of time for your team in a single month?

The question is one of motivation: are we empowering our team members to choose where they want to work and how they best provide value, or are we to revert to the Taylorist principles where “the manager knows best”? In Taylors words: “All we want of them is to obey the orders we give them, do what we say, and do it quick.

We must use this as a learning opportunity.

Whilst 2020 has been a massive challenge for all of us, it’s also taught us a great deal, about change, about people and about the future of work. The worst thing that companies can do is ignore what they have learned about their workforce and how they like to operate. We must not mindlessly drift back to the old ways.

We know that remote working is more productive, but there are many shades of remoteness, and it takes strong leadership, management effort, good tools, and effective, high-cadence communication to really do it well.

There is no need for a binary choice: there is no one-size-fits-all for office-based or remote work. There are infinite operating models available to us, and the best we can do to prepare for the future of work is simply to be endlessly adaptable.