The History of DevOps

devops loop

[Updated June 2023]

DevOps may be one of the most hyped concepts in the tech industry in recent times. Yet what it actually consists of is the subject of much debate: some describe DevOps as a culture of process improvement, whilst others describe it in purely technological terms of automation and cloud technologies.

The Origins of DevOps

What few disagree on though are its origins. In the tech industry, it has long been accepted that technologists are either “devs”: those who “create”, or “ops”: those who “build and maintain”. Developers write code while engineers build the system and keep it running. Conflict frequently emerges between these two camps and their seemingly incongruent goals –  whereas development teams are motivated and measured by their high change frequency and scale (deploying features, fixes, and improvements), operations teams are judged by reliability and consistency, qualities which are often seen as an outcome of low change frequency and scale (though we shall see later how this isn’t necessarily true). This often results in an antagonistic relationship between the two teams.

DevOps is or at least originated as, the effort to reconcile this fracture and improve business performance.

…all ideas are second-hand, consciously and unconsciously drawn from a million outside sources.” Mark Twain

At a high level, the practice of DevOps focuses on culture, process, velocity, feedback loops, repeatability via automation, responsiveness to change, and continuous improvement. (Also often condensed to CALMS – Culture, Automation, Lean, Measurement, Safety). These practices have accelerated the web-scale revolution behind high-performance tech giants such as Google, Netflix, Amazon, and Facebook.

However, these concepts are not new. They have been used by industrialists, researchers, and technologists to improve the quality and efficiency of production since the dawn of the industrial revolution.

Industry and Scientific Management

In 1620 Francis Bacon codified what was to become the fundamental basis for empirical knowledge: the origin of the scientific method. Bacon’s method described the conception of a theory based upon observation, and the use of experiments to test the theory. 400 years on, we still use Bacon’s approach to create and test theories, monitor systems and check technological functionality.

In the past, the man has been first; in the future, the system must be first.” Frederick Taylor

Frederick Taylor, in the 1880’s, applied the scientific method to management and workflows to improve labour productivity. He was one of the first people to deem work itself worthy of systematic study, using the principles that Bacon derived 200 years before. Whilst Taylor’s views on what makes a “good” worker were somewhat disturbing – he defined the “best” worker as “so stupid and so phlegmatic that he more nearly resembles in his mental make-up the ox than any other type.” – Taylorism had a huge impact on productivity across the industrialised world.

Taylor summed up his efficiency strategies in the 1911 book “The Principles of Scientific Management.” This was voted the most influential management book of the twentieth century by Fellows of the Academy of Management in 2011. Without Taylor, it’s unlikely that Apple or Google would even exist as they do now.

20th Century Production

At the beginning of the 20th Century, most manufacturing utilised inefficient techniques – cars for instance were built the way you or I would go about the task, by assembling the all the parts in one place: craft production. However when demand for cars increased, it became clear that a form of linear, or mass production was needed. One of the most well known examples of the production line is the one adopted by Henry Ford in 1913 for the Ford Model T, which was based on Taylor’s principles. Through the use of time and motion studies, Ford refined his production line until he had reduced the production time for a car from over twelve hours to just 93 minutes. He also introduced to mainstream manufacturing the concept of repeatability and standardisation. In contrast to Taylor, however, Ford always maintained his belief in the importance of the skill and craftsmanship of the worker.

Without data, you’re just another person with an opinion.” William Edwards Deming

In the 1950s, William Edwards Deming, a statistician, physicist, and management consultant, began to apply statistical analysis to manufacturing. Deming found that prioritising quality over throughput would actually decrease costs and improve productivity. Whilst Taylorism and scientific management had boosted productivity, quality had suffered. Defects were sent down the line and built into finished products because workers were incentivised to ignore flaws in order to meet quotas.

He defined what is now known as the Deming Cycle: Plan – Do – Check – Act. This is similar to the software development lifecycle most of the technology industry use today. Deming championed continual analysis and improvement of processes – one of the key tenets of DevOps.

He saw effective quality assurance as an essential function of high-performing organisations, the key message of the third of his “Fourteen Points”; key principles of management for transforming business effectiveness:

  1. Constancy of purpose, with the aim to become competitive and stay in business, and to provide jobs.
  2. Adopt the new philosophy. Embrace change.
  3. Cease dependence on inspection to achieve quality. Build quality checks and feedback loops into the process.
  4. End the practice of awarding business on the basis of lowest bid. Build long term relationships with suppliers, and value loyalty and trust.
  5. Continuously improve processes, aim to improve quality and productivity, which in turn leads to cost reductions through less wastage and higher efficiencies.
  6. Institute training on the job and integrate development into employees’ roles.
  7. Institute leadership. Leadership should help people and machines do a better job, remove barriers to working effectively, identify improvements, and develop teams.
  8. Drive out fear. Fear paralyses people and teams. Transparent communication, motivation, respect and care for each other and each other’s work will contribute to this aim.
  9. Break down barriers between departments. Cross-functional teams can solve problems more easily and effectively than single-function teams or siloes.
  10. Eliminate slogans and exhortations for the workforce asking for zero defects.
  11. Defects (and quality) are a result of the system, not the individual.
  12. Eliminate targets or quotas. Substitute quantity for quality, and quantity will follow.
  13. Permit pride of workmanship. Eliminate management by objective or by numbers. Employees feel more satisfaction when they get a chance to execute their work well and professionally, rather than trying to meet a quota.
    Institute training and self-improvement. Encourage employees to study for themselves and to see their studies and training as a self-evident part of their jobs.
  14. The transformation is everyone’s job. Transformation happens only when everyone in the organisation works to accomplish it.

Deming’s System of Profound Knowledge is the culmination of his work and ties together his seminal theories on quality, management and leadership into four interrelated areas:

  1. Appreciation for a system,
  2. Knowledge of variation,
  3. Theory of knowledge
  4. Psychology

Each area corresponds to one or more of his fourteen points, and we can reflect on how these four areas correspond to fundamental DevOps tenets too.

Appreciation for a system means that as a leader, engineer, developer or tester, you ought to understand the system that you are looking to work within – and that thoroughly understanding that system endows you with far greater capacity to improve it. This is systems thinking, a concept which will be revisited throughout this book.

Knowledge of variation refers to two types of “cause” determined by Deming: “Common” and “Special”. Common causes are those anticipated by, or inherent to, a system. An example of this would be scaling; for example, you might know that a particular system generates logs at a rate of 500GB per day, and as a result you build functions into your system to deal with this growing demand for storage. This growth (the “cause”) is understandable and predictable, and thus you are able to implement measures to manage the variation. Deming’s second cause is “Special”, and refers to those aspects that are unknown or unpredictable, such as a change made that had unintended consequences, or a datacentre outage, or action by a malicious third party. Deming estimated that over 94% of quality issues (in his case, in manufacturing, but the same principle applies to modern software delivery) are catalysed by “common” causes, but human nature looks for the “special” cause: the one-off event, the human error, or bad actor at play. If someone accidentally shuts down a production server, Deming’s solution is not to fire the human (thereby removing the unpredictable, unknown element), but to build improvements into the system to prevent a human making that mistake again, or preventing that mistake affecting the system.

Deming’s theory of knowledge concentrates on the importance of understanding our own knowledge. How do we discern what is true from what is false? How do we identify our own innate biases, and how can we make ourselves less susceptible to confirmation bias? Deming goes back to Bacon’s scientific method with the Plan-Do-Check-Act cycle, reflecting the concept of creating a hypothesis and then testing those assumptions. People appear to learn more effectively when they make predictions. Making a prediction forces us to think ahead about the potential outcomes and also causes us to examine more deeply the system that we’re working in or on.

At around the same time, after studying consumer behaviour in supermarkets, the Toyota Motor Corporation began using Kanban (which means “signboard” in Japanese) to control and record work. Kanban boards have vertical columns with work packages in the form of cards to represent stages in a process. Each process is a “customer” of the preceding process to the left – that is, the work is “pulled” from left to right, rather than “pushed”. This concept reduces inventory pile-up, enabling a delivery system called just in time and minimising waste. It also aids the identification of bottlenecks in the process by highlighting Work In Progress (WIP). Kanban makes “work” visible. And making work visible is crucial to further improvement, because “you can’t manage what you don’t measure”.

Any improvements made anywhere besides the bottleneck are an illusion.” Eliyahu M. Goldratt

The above constitutes Goldratt’s Theory of Constraints. In his 1984 management novel “The Goal”, Eli Goldratt built on Deming’s ideas and codified Lean Production, a precursor of DevOps methodology. He described a failing manufacturing plant where Alex, the main character, is brought in to turn things around within three months. Through a series of telephone calls and meetings with an acquaintance called Jonah (another physicist, like Deming), Alex solves the organisation’s problems by utilising pull rather than push processes, reducing WIP, and employing the Theory of Constraints. “The Goal” itself, Goldratt demonstrates, is simply to make money for the business. Anything else, if it cannot be demonstrated to help make money, is likely to be vanity.

the goal

People and Process

By the 1980s, the modern manufacturing revolution was in full swing, however, its often reductionist approach to workers wasn’t helpful, and staff turnover was high. Among those to recognise this was Burrhus Frederic Skinner, a psychologist, author, inventor and the Edgar Pierce Professor of Psychology at Harvard University. In describing the nature of quality work and happiness, he said:

It’s the difference between a craftsman who makes a complete chair and a person on an assembly line who makes only the legs. The craftsman’s work is constantly reinforced by the process of seeing the chair take form, and finally of producing the finished chair. But the assembly-line worker sees only chair leg after chair leg — never the completed product.

This is a near-definitive example of “systems thinking”- another key tenet of DevOps.

Being able to see the end result of the process is key to improving quality in the individual stages – how can someone build the perfect component if they don’t understand in the final product? Systems thinking is a cultural practice, rather than a process or tool, and relies on believing in the capability of team members to make small but important decisions regarding their part in the process, and thus being more invested in the outcome.

Photo by <a href="">Lenny Kuhne</a> on <a href="">Unsplash</a>
Photo by Lenny Kuhne on Unsplash

Further developments in understanding of how to develop an aspirational working culture came once again from Toyota when in 2001 they defined their philosophy, values and manufacturing ideals in four key headlines, “The Toyota Way”. These were:

  1. Long-Term Philosophy – Base your management decisions on a long-term philosophy, even at the expense of short-term goals.
  2. The Right Process Will Produce the Right Results – Focus on pull processes, managing WIP, and making work visible.
  3. Add Value to the Organization by Developing Your People – Provide effective training, highlight team success over individual success, and challenge your partners and suppliers.
  4. Continuously Solving Root Problems Drives Organizational Learning – Continuously improve (in Japanese, kaizen), use the “5 whys” to get to the root cause of problems, standardise, decide slowly and act quickly, and encourage a knowledge sharing culture.

Everything in The Toyota Way and Lean Production aligns with, and indeed comprises part of the DevOps principles.

The Agile Manifesto

Also that year, at Snowbird resort in Utah, seventeen developers, frustrated with traditional heavyweight project management methodologies, came up with the Agile Manifesto. At the time, industry experts estimated that the time between a validated business need and an actual application in production was around three years. There was a real desire to find more lightweight ways to deliver value from technology, faster. The Agile manifesto is as follows:

Individuals and interactions over processes and tools

Working software over comprehensive documentation

Customer collaboration over contract negotiation

Responding to change over following a plan

Image result for agile manifestoThe Agile Manifesto gives a clear guide to what to prioritise. For example, whilst documentation is valuable, it is more important to the business that the software works. The most well-known element of Agile is possibly the fourth line: Responding to change over following a plan. Given how quickly customer requirements, finances, and technology can change, it is often unrealistic to believe that specifications created at the start of a project will remain 100% accurate and true throughout the lifetime of the project. Thus, responding to change is one of the ways that software teams can provide a competitive edge over teams that do not.

Whilst Agile methodology is not fundamentally part of DevOps, the two usually go hand-in-hand. In technology teams, one is certainly easier to achieve in the presence of the other.

The First DevOps “Role”

Shortly after the Agile manifesto was written, Google was undergoing rapid expansion. As one of the few web-scale tech businesses at the time, they experienced the unprecedented challenge of trying to rapidly introduce new features whilst maintaining a highly complex, always-on, massive scale platform. The Site Reliability Engineering (SRE) team, led by Ben Traynor, was their solution.

A Site Reliability Engineer (SRE) would typically spend up to half their time performing operations-related work such as troubleshooting system issues and performing maintenance. They would spend the other half of their time on development tasks such as new features, scaling challenges, or automation. An SRE is an example of one of the first true DevOps roles in technology.

DevOps Detractors

…the opportunities for gaining IT-based advantages are already dwindling… And as for IT-spurred industry transformations, most of the ones that are going to happen have likely already happened or are in the process of happening.” Nicholas Carr

It’s worth noting that not all in business recognised the potential of DevOps. In May 2003, Nicholas Carr published an article in the Harvard Business Review, titled “IT doesn’t matter.” In this now infamous piece, Carr defines IT as a commodity, in the same category as electricity or water. He suggests that being the first to utilise a particular technology provides only a small competitive advantage, since your competitor can purchase the same system or replicate the same technology, but you incur the lion’s share of the cost by doing it first. He stated:

The key to success, for the vast majority of companies, is no longer to seek advantage aggressively but to manage costs and risks meticulously. If, like many executives, you’ve begun to take a more defensive posture toward IT in the last two years, spending more frugally and thinking more pragmatically, you’re already on the right course. The challenge will be to maintain that discipline when the business cycle strengthens and the chorus of hype about IT’s strategic value rises anew.

Carr’s piece was taken very seriously at the time, and still is by many business leaders. Perhaps it is fortunate for organisations such as Salesforce and Google that they pursued technology as a competitive advantage, and disregarded Carr’s advice.

Improving IT

It is not unsurprising however that technology had such a poor reputation at the time, since research suggests that at least 80% of outages were (and potentially still are) self-inflicted. A book by Kevin Behr, Gene Kim and George Spafford, The Visible Ops Handbook (2004), described a methodology to improve operational IT. This methodology of “Visible Ops” is described in four stages:

  1. Stabilize Patient, Modify First Response – This first step controls risky changes and reduces MTTR (Mean Time To Resolution).
  2. Catch and Release, Find Fragile Artifacts – Here assets, configurations and services are inventoried in order to identify those with the lowest change success rates, highest MTTR and highest downtime costs.
  3. Establish Repeatable Build Library – This creates repeatable builds for critical services, to make it “cheaper to rebuild than to repair.
  4. Enable Continuous Improvement – This implements metrics to enable continuous improvement of processes.

To some degree, these four stages are evolutions of elements of The Toyota Way. They formed an embryonic codification of what was to become the principles of DevOps.

Over the next few years, the technology industry underwent a paradigm shift, where methods of working were analysed, and technology became far more fundamental to the success of organisations (possibly to the chagrin of Nicholas Carr).


In 2008, the term DevOps was used in the industry for the first time. There’s some confusion and misinformation regarding how this came about, but I spoke to Andrew Clay Shafer and Patrick Debois, both widely credited with creating the term “DevOps”, to get the full story…

Andrew clay Shafer DevOps Ghent 2009

In August 2008 at the Agile Conference in Toronto, software developer Andrew Clay Shafer posted notice of a discussion group session entitled “Agile Infrastructure.” Just one person, system administrator Patrick Debois attended. Debois had become frustrated by the now ubiquitous conflicts between developers and operations while working on a data centre migration for the Belgium government and was looking for solutions. Shafer actually skipped his own session because he didn’t think anyone was interested, but Debois later tracked him down for a chat in the hallway. Inspired by that hallway discussion, they formed an “Agile Systems Administration” Google Group”

Patrick Debois


In November the following year, 2009, Patrick organised the first DevOpsDays conference in Belgium, though it was Shafer who (it’s believed) coined the term DevOps by tweeting using the #DevOps hashtag at the Velocity conference in June 2009 whilst watching the now famous “10 deploys a day” talk by John Allspaw and Paul Hammond of Flickr.

The Role of Cloud Technology

Image result for amazon cloud

It wasn’t long after the #DevOps hashtag was first used that adoption of cloud technology accelerated rapidly. The AWS EC2 service (virtual servers on-demand) only went out of beta in late 2008. It was (and still is) a fast evolving technology. Cloud technology tends to align well with DevOps practices, because its features lend themselves to elasticity and scaling, automation, measurement and repeatability, key fundamentals of DevOps.

The tide had turned. Increasingly organisations began looking at ways of improving software deployments, moving away from large, disruptive (and frankly, stressful) deployments, towards a model of more frequent, smaller, low-risk deployments.

Jez Humble and Dave Farley wrote what is still one of the definitive texts on this approach: “Continuous Delivery” in 2010. It describes in detail how to automate your build, deployment, and testing pipeline so that you can release changes in hours or even minutes. That might not seem that impressive today, but at the time, a release cycle of months or years was very common.

Continuous delivery, according to Farley and Humble, requires:

  • Comprehensive configuration management
  • Continuous integration and short lived branches (in reference to Trunk-Based Development)
  • Continuous testing

The automation of the build, deployment, and testing process, coupled with better collaboration between development, test, and ops teams, means that changes can be released rapidly. These smaller, low risk changes are more easily rolled back should something go wrong. “Continuous Delivery” showed how to increase velocity of change, whilst reducing risk and improving quality.

With cloud technology becoming mainstream and a desire to release software more rapidly, automation technology and tools took off. Software firms such as Puppet and Chef grew fast as developers and engineers strove to streamline their build processes and manage ever-increasing scales of infrastructure in the cloud. These tools also provided a new ability to fire up duplicate environments, such as staging, QA, test and validation, within minutes rather than weeks or months. Organisations exploiting these automation tools and using native cloud technologies felt that they were gaining significant competitive advantage by doing so, and what evidence there was, was in their favour. Even Gartner, in a 2011 report, stated that:

By 2015, DevOps will evolve from a niche strategy employed by large cloud providers into mainstream strategy employed by 20% of Global 2000 organisations.” Gartner, March 18, 2011.

In the same report, Gartner recognised that ITIL and other “top-down” best practice frameworks had not delivered on their goals, and IT organisations were looking for something new. They understood that because DevOps was primarily a cultural shift, driven from the ground up, it could prove far easier for technology departments to adopt than ITIL or similar frameworks

The Codification of DevOps

Two years later, Gene Kim, Kevin Behr and George Spafford wrote The Phoenix Project, a novel about a failing organisation struggling to meet the demands of modern technological complexity and competition. This novel inspired technology leaders and engineers alike, because it described with eerie familiarity what it was like to work in a technology organisation with poor change control, problematic “Ops vs Devs” cultures and inadequate visibility and monitoring of work or performance.

The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win: Gene Kim; Kevin Behr;...The Phoenix Project was inspired by The Goal by Eli Goldratt. It demonstrated a number of actionable ways to improve the performance of your IT organisation, such as effective (but lean) change control, effective (and again, lean, testing), reducing WIP and unplanned work, and avoiding letting anyone become the bottleneck for processes. The “bottleneck person” in the book is Brent, a character who knows everything but hasn’t documented anything. A key message of the book? Don’t be Brent.

Gene also introduces in the Phoenix Project one of the first efforts to codify DevOps, using “The Three Ways”:

  • Flow (or Systems Thinking)
  • Feedback Loops
  • Continuous Improvement

These “Three Ways” are concepts that echo the Toyota Way, Deming’s “Plan-Do-Check-Act” cycle, and other best practices, made specific to the DevOps context.  Gene’s subsequent book, written with Jez Humble (of “Continuous Delivery”), Patrick Debois and John Willis in 2016, “The DevOps Handbook”, goes deeper into the technical application of The Three Ways. It explores how to measure what matters to the business, and how to implement technical processes such as Continuous Integration and Continuous Delivery.

devops handbook

it didn’t take long to realise that there was another functional silo with a somewhat different set of interests than Dev or Ops: Security. Security, the IT profession realised, should be built into code as it is developed rather than added later on by a different team. Predictably, the idea became known as DevSecOps.

Gene also introduces the concept of “DevSecOps” – the integration of DevOps practices into the application of information security. If The Phoenix Project was the “why” to do DevOps, The DevOps Handbook provides the “how”.

Measuring DevOps

Given that DevOps is at least partly about effective measurement and continuous improvement, it’s self-evident that we, as an industry, should measure the success of DevOps itself. In 2012, Puppet began surveying people working in technology to understand the adoption and development of DevOps practices. They published “State of DevOps” reports which focussed on twenty key capabilities.These fall along familiar categories:

  • Technical (version control, test automation, deployment automation, trunk-based development)
  • Process (WIP limits, visual management, visualisation of the value stream)
  • Cultural (team culture, learning cultures, and job satisfaction).

Now taken over by DORA (DevOps Research and Assessment), an organisation created by Nicole Forsgren, Gene Kim, Jez Humble and Soo Choi, the State of Devops Report is being improved every year. According to Alanna Brown at Puppet, they “have built the deepest and most widely referenced body of DevOps research available, drawing on the experience of more than 30,000 technical professionals around the world.” The data from these reports demonstrates that Carr’s view of IT as a cost centre was misguided. It is clear that IT is a powerful driver of value to an organisation where velocity, security and stability are essential for success.

“…software delivery is an exercise in continuous improvement, and our research shows that year over year the best keep getting better, and those who fail to improve fall further and further behind.” Nicole Forsgren

On the back of the last four years of State of DevOps reports, Nicole Forsgren wrote the illuminating book “Accelerate”. It explains which metrics correlate to organisational performance, and what you should measure in order to find out where and how to improve.

Accelerate Nicole Forsgren

Forsgen states that the key metrics separating high from low performers in tech organisations are:

  • Deployment frequency (and pain!)
  • Lead time for change (from code commit to code deploy)
  • Mean Time To Restore (MTTR)
  • Change failure rate

Interestingly, the first two of these metrics are throughput (traditionally development-oriented) measures; the last two are stability (traditionally operations-oriented) measures.

The State of DevOps in 2019

As of the 2018 State of Devops report, the findings consistently show that:

  • Software delivery and availability unlock competitive advantages.
  • How you implement cloud infrastructure matters.
  • Use of open source software improves performance.
  • Outsourcing by function is rarely adopted by elite performers and hurts performance.
  • Key technical practices drive high performance. (i.e monitoring, automated testing, security integration)
  • Industry doesn’t matter when it comes to achieving high performance for software delivery.

The statistics show that the high performers exhibit 46 times more frequent code deployments than low performers. They have a 7 times lower change failure rate, over 2,500 times faster lead time from code commit to deployment, and are over 2,600 times faster to recover from incidents.

When an organisation can deploy quickly, recover rapidly, and suffer few outages, it has the ability to reach the market before competitors and respond to customer demand quickly. It will also provide more stable and secure service. This results, ultimately, in Goldratt’s “Goal”, making more money for the business.

Such a state is not reached by simply automating, using cloud technology, or recruiting a “DevOps Engineer” – it is the culmination of great team culture, continuous improvement, feedback loops, systems thinking, and a rigorous approach to using the right technology. DevOps is not a framework (like ITIL), an industry standard, a suite of tools, or a job title.

DevOps encompasses the culture, technologies, tools, skills and processes that enable organisations to go from idea to production as rapidly as possible, incurring low risk and cost, and providing high security and reliability at scale.

The definition of DevOps itself is continually evolving and improving, and while I may offer a definition as above, it will be out of date within days of writing, because, like the technology and services we build, it is continuously in flux, and being improved by the same people practising it.

Where does DevOps go next? I believe that the scope of DevOps needs to widen. As mentioned above, a large reason why DevOps is so successful is that it’s a ground-up movement, created and progressed by the actual people doing it (unlike ITIL, for example). However, this has meant that DevOps, naturally, focusses tightly on the technological functions of an organisation.

The next phase of DevOps includes practices and approaches such as “Platform as a Product” and also broadens the scope of DevOps to the wider organisation, evolving into “digital transformation” using Andrew Clay Shafer’s 5 Elements, Jabe Bloom’s Three Economies, and the broader, cross-sectoral concepts of resilience engineering in sociotechnical systems.

2023 Update: Safety Cultures and Platform Engineering

Over the past two to three years, DevOps has seen further maturity and adaptation to new norms, driven by unprecedented global circumstances and evolving technological trends. A particularly noteworthy shift has been the focus on building robust ‘Safety Cultures’. This approach emphasizes the creation of an environment where experimentation is encouraged, failures are seen as opportunities for learning, and psychological safety is paramount. Teams are given the latitude to innovate while knowing that missteps are not only tolerated but expected as part of the process of continuous improvement. This aspect has greatly enhanced the resilience of DevOps, fostering a more transparent, responsive, and adaptive culture.

Platform Engineering has also been a rising trend, presenting a shift in how organizations perceive their development infrastructure. Rather than treating platforms as a collection of tools and services, they are viewed as integrated products that evolve with the needs of the end-users, who are the developers. This perspective empowers developers, reduces overhead, and ultimately accelerates the delivery of value to the business.

The COVID-19 pandemic brought its own set of challenges and lessons. The necessity of remote working underlined the importance of strong communication channels, reliable cloud-based tooling, and the autonomy of distributed teams. It revealed the strength of DevOps practices in enabling organizations to maintain their pace of innovation even in the face of major disruptions. Companies that had already embraced DevOps were better positioned to navigate the transition to a remote work environment, demonstrating the value of adaptability inherent in the DevOps philosophy.

As we look to the future, the trajectory of DevOps and related methodologies appears more integrated and comprehensive. The focus will likely continue to expand beyond the technological realm, permeating deeper into business strategies and driving broader digital transformation initiatives. The trends of Safety Cultures and Platform Engineering are expected to solidify, with even greater emphasis on psychological safety, learning from failures, and treating internal platforms as products.

Furthermore, the remote working lessons from the pandemic will likely catalyze a shift towards more distributed, asynchronous ways of working. We may see a rise in ‘RemoteOps’, an evolution of DevOps practices adapted for a world where remote and flexible work arrangements become the norm. In this era, principles of effective remote communication, time-zone friendly practices, and trust-based management will become critical. In essence, the future of DevOps is about expanding its boundaries, integrating more closely with business goals, and continually evolving to meet the demands of our ever-changing world.

GDPR, and how I spent a month chasing my data.

In May 2018, I received a letter from a local firm of solicitors, Roythornes, advertising a property investment event. I hadn’t heard of them and I was damn sure I hadn’t given them my permission write to me at home. They were wide of the mark to say the least- I’m an unlikely potential property tycoon, unless we’re playing Monopoly. Even then, I’m a long shot.

It was a quiet week at work so given the recent implementation of GDPR and the fact that I really don’t like junk mail, I thought I’d give the new Data Subject Access Request (DSAR) process a whirl.

I couldn’t find contact at Roythornes to send a DSAR to but, helpfully, GDPR places no restriction on the medium someone can use to make a request, so at around 9am that day I filled in their online contact form, despite my concerns that it would get picked up by a clueless admin assistant. I requested a copy of the data they hold on me, the source of that data and the evidence of my opt-in for direct mail. I also asked that they delete the data they hold on me and send no further marketing material.

At 1:42pm, I had a from “Norma” of Roythornes (not joking, sorry Norma), asking for a copy of the letter and stating that she couldn’t find me in their database. So far, so good…

At 1:55pm, I received an automated recall email from Norma.

A few hours later, another email arrived, this time from the firm’s “compliance partner”, stating that they had acquired my personal data in a mailing list they purchased from Lloyd James Media, on the 1st of May 2018, and that my letter was sent out on the 21st May 2018. She stressed that the purchase of the list, and the sending of the letter itself was prior to the GDPR implementation date of 25th May 2018, and therefore legal.

Solicitors abiding by the letter of the law, not the spirit of the law? Imagine that.

Dancing around technicalities notwithstanding, Roythornes did confirm that my data had been deleted and I wouldn’t be hearing from them again. Phase 1 complete, but who exactly were Lloyd James Media, and how did my data fall into their hands?

For Phase 2 of my quest, a quick google told me that Lloyd James Media “is a multi-channel data agency focusing on the intelligent use of data to optimise customer acquisition and retention.” I can only assume this translates as “we make and sell mailing lists”. So, off went my DSAR email to their sales email address, because yet again, there was no contact information available for non-sales enquiries, let alone DSARs.

Andrew of the compliance team at Lloyd James Media only took 24 hours to get back to me. He confirmed that they sold my personal data to Roythornes for a postal campaign. They had acquired my data from another firm, “My Offers”, and the consent for postal marketing was obtained by them, apparently. Helpfully,  Andrew suggested I get in touch with the “compliance team” at My Offers. Evidently, this is a team of one, someone called Saydan, whose email address Andrew provided. I reminded Andrew to remove my data from their system and headed off to continue the hunt for the true source of my data, feeling like a geek version of Bear Grylls tracking an elusive squirrel. Phase 3 had begun.

My Offers are “Europe’s leading online direct marketing company with a database of 22.2million registered users.” I fired my third DSAR email off to Saydan later that day. One week later, I’d heard nothing. According to GDPR, there is no need for an immediate response, as long as the DSAR is executed within a month, but the silence was unnerving. Was Saydan trying to ghost me? I found their Facebook page and sent a message to whichever poor soul supports their social channels. For good measure, I also dredged LinkedIn for their employees, emailed Ivan, one of their directors, and in true annoying millennial style, tweeted at them. The only response was from their Facebook team, who reiterated that I should email the enigmatic Saydan, then also went quiet on me.

Over the next few weeks, because nothing seemed to be happening, I pinged their facebook team a courtesy message once each week with a gentle reminder of the impending deadline for a response. Part of me was relishing the prospect of not getting a reply, and I began googling, “What to do if someone doesn’t respond to a DSAR.” I was way too invested in this.

Then, exactly one month to the day since the original request, an email from Ivan, the My Offers director arrived in my inbox. Ivan’s email was straight to the point and only had a few spelling mistakes. Attached was a password protected CSV file containing all the information I’d requested. The password was sent in a separate email. So far, so good (though, yet again, I had to remind him to remove my data from their systems).

The CSV file was interesting. And by interesting, I mean in the way that hearing the creak of a stair when you’re in bed, and there’s nobody else in the house is interesting. The data contained my full name, birth date, gender, home address, email address, phone number, data subject acquisition date and source (Facebook), as well as a list of businesses that my data had been shared with in the past year. The list totalled around 60, including Lloyd James Media, various PPI and no-win no-fee firms, and more. That explains all the marketing calls over the past year then.

This CSV file was the smoking gun. However, the trigger was evidently pulled by my own fair hand. At some point, possibly whilst drunk, bored at work, or both, I’d clicked on a campaign offering me a beard trimmer. I still don’t have a beard trimmer (I do have a beard), so I presumably I didn’t pursue this purchase but in getting only that far, I inadvertently provided My Offers with access to my personal data, and consent for direct marketing. Sounding eerily familiar, I wondered if my voting choices in the last election were my own making.

So, just over a month after I sent my first DSAR to a local firm, what have I learned from this?

Firstly, GDPR actually works. Not only was the DSAR process easy to do, it was free (for me), and two out of three firms responded within 24 hours. Presumably GDPR is also helping to reduce unwanted junk mail; after all, Roythornes as good as admitted that they wouldn’t have posted the initial letter to me after the GDPR implementation date.

Secondly, once your data is out there, it gets around. It only takes one “online direct marketing company” to get hold of it, and your personal information will spread faster than nationalism in Europe.

Finally, don’t be dumb on facebook (like me). We know about Cambridge Analytica of course, but they’re not the only guys trying to harvest information and profit from it. Resist the lure of data-harvesting surveys and competitions, even when drunk.

Streaming music services and the future of consuming music

I’m listening to Spotify while I write this. I’ve been a premium subscriber since early 2010, which means I’ve so far paid spotify £390 of which around 70% has gone to the artists. It took me a while to get used to the idea that i didn’t “own” the music I was listing to, but the benefits of being able to listen to anything I wanted to, whenever i wanted, and the chance to discover new music made up for it and I now believe that as long as streaming services exist, I’ll never buy a CD again. I won’t bang on about how great it is, because you’re generally either into streaming or not, and that usually depends on how you listen to your music.

There’s a lot of bad press about streaming services and the supposed bad deal that the content creators (artists) get paid from it.  Atoms for Peace pulled their albums from Spotify and other streaming services, with band members Thom Yorke and Nigel Godrich criticising these companies for business models that they claimed were weighted against emerging artists. I disagree. Anyone that thinks they can create some music and make a living from it using streaming services is living in a dream world. The music business has changed, and for the better in my opinion. Gone are the days when a band could release a CD, sell hundred of thousands or millions of copies and rake in the big bucks (but don’t forget the record labels and other third parties taking their lion’s share). Some people compare streaming to that old business model, and that’s where it looks like the artists are getting a worse deal, but it’s not a fair comparison.

Musician Zoë Keating earned $808 from 201,412 Spotify streams of tracks from two of her older releases in the first half of 2013, according to figures published by the cellist as a Google Doc. Spotify apparently pays 0.4 cents (around 0.3p) per stream to the artist. When artists sell music (such as a CD), they get a one-off cut of the selling price. When that music is being streamed, they get a (much smaller) payment for every play. Musician Sam Duckworth recently explained how 4,685 Spotify plays of his last solo album earned him £19.22, but the question is just as much about how much streams of the album might earn him over the next 10, 20, 30 years.

If you created an album yourself, and you had a choice between two customers – one who would by the CD, giving you a £0.40 cut, and one who would stream it, providing you with £0.004 per stream, which customer would you choose? Part of this actually might depend on how good you think your music is, and how enduring its appeal will be. If it’s good enough, and al the songs on that album are good (all killer, no filler!), then it’s going to get played a lot, making streaming more lucrative over time, but if it’s poor, with only a couple of decent tracks, and maybe not as enduring as it could be (think Beatles vs One Direction), then a CD is going to be more lucrative, because after a year or so that CD is going to be collecting dust at the bottom of the shelf never to be played again.

I can’t easily find a way to show the number of plays per track in my spotify library, apart from my scrobble stats, which won’t be entirely accurate as they only record what I listen to in online mode, but I’ve pasted the top plays per artist below:

The Gaslight Anthem (621 plays)

Chuck Ragan (520 plays)

Frank Turner (516 plays)

Silversun Pickups (425 plays)

Biffy Clyro (305 plays)

Ben Howard (302 plays)

Sucioperro (241 plays)

Eddie Vedder (225 plays)

Blind Melon (173 plays)

Foo Fighters (166 plays)

Iron & Wine (141 plays)

Saosin (121 plays)

Benjamin Francis Leftwich (119 plays)

Cory Branan (116 plays)

Twin Atlantic (112 plays)

Kassidy (101 plays)

Funeral for a Friend (94 plays)

Molly Durnin (89 plays)

Crucially, of the 18 artists above, at least 4 or 5 are artists that I discovered on spotify. The radio and “discover” tools on it are actually really good (90% of the time), and of those 4-5 discovered artists, I’ve seen two of them live in the past year or so. If we stop trying to think in pure instant revenue terms, streaming services provide a great part of a business model that includes long term small payments to artists and allows consumers to discover new music more easily.

Artists need to build themselves a business that incorporates records, songs, merchandise and/or tickets, and look for simple ways to maximise all those revenues.

Crucially, they also need to start developing premium products and services for core fanbase – fans who have always been willing to buy more than a gig ticket every year and a record every other, but who were often left under-supplied by the old music business. Which is why, for artists, the real revolution caused by the web isn’t the emerging streaming market, but the boom in direct to fan and pre-order sites.

Frank Turner believes we may eventually move towards a model where all music is free, but artists are fairly compensated. Talking about piracy and torrenting, he says:  “I can kind of accept that people download music without paying for it, but when the same people complain about, say, merch prices or ticket prices, I get a little frustrated.” “I make the vast majority of my living from live, and also from merch. Record sales tick over.”

If you look at Frank Turner’s gig archive, you’ll see he’s performed at almost 1500 live shows from 2004 to 2013. Most of the musicians I know do what they do because they love playing music, and particularly so in front of an audience. I personally believe that live music should be the core of any musician’s revenue stream, with physical music sales, streaming, merchandise, advertising, sponsorship, and other sales providing longer term revenue. Frank seems pretty hot on spotify, and has released a live EP exclusive to the service.

I also believe the format of live shows will change too. I love small gigs in dark little venues such as the Rescue Rooms in Nottingham, but as artists become more popular and play larger venues, there is naturally some loss of fan interaction. With the use of mobile technology, social networks, and heavy duty wifi (802.11ac for example), large venues can begin to allow the artists to interact with fans and provide a more immersive experience. Prior to or while the artist is on stage, content can be pushed to the mobile devices of those in the audience, telling them what track is being played for example, with links to download or stream it later, provision of exclusive content such as video and photo, merchandise, future gig listings, and event the ability to interact with other fans in the venue or otherwise.

The future is a healthier relationship between services like Spotify and musicians, where both can find more ways to make money by pointing fans towards tickets, merchandise, box-sets, memberships, crowdfunding campaigns such as songkick’s detour, and turning simple concerts into fuller experiences for fans.

Find out which security group members are in one or more Exchange 2010 databases

First, run this command on a domain controller to extract the members of a security group:

net group "Security group name" >c:\groupmembership.csv

Then run this in an Exchange 2010 shell to extract the mailbox names from the database:

Get-Recipient -PropertySet ConsoleLargeSet  -ResultSize '9000' -SortBy DisplayName -RecipientType 'UserMailbox' -Filter '((Database -eq ''CN=DATABASENAME,CN=Databases,CN=Exchange Administrative Group (YOURADMINGROUP),CN=Administrative Groups,))' | Export-csv C:\userslist.csv

Then paste your names lists into excel in two columns, one named group membership, and one database users. Use the below formula in the third column to find the names that occur in both columns of data.

=VLOOKUP([first column entry,[Range E.G. A:A],1,FALSE)



Orange vs. Vodafone in Nottingham

The data coverage and bandwidth in Nottingham on Orange has been dismal for quite some time. I actually spoke to them last week about this, and they said that they’re aware of issues in Nottingham, as there are too few 3G masts, and one of them is faulty. They were rather hoping that things would improve once they merged the T-Mobile network into theirs (apparently around the 6th October), though as far as I can tell, it hasn’t.

The photo below shows two speed tests, run on iphones, with the same app, at the same time. The one on the left is on Vodafone, and the one on the right is on orange. You can see the Vodafone download speed is around 3Mbits/sec, which is comparable to ADSL. The Orange speed, on the other hand, is 0.24Mbits/sec; this is around just 6 times faster than a standard dial-up connection.