Strategies are catching up with a multicloud reality
DevOps and Cloud are highly synergistic. The reproducability and availability of cloud resources removes barriers to DevOps practices. The automation focus of DevOps multiplies the benefits of cloud computing.
Multicloud is the practice of using more than one cloud service. IBM found that 85% percent of enterprises already operate in multicloud environments and that by 2021 this number will have risen to 98%. Why is this a trend to watch?
The adhoc way that multicloud has appeared at companies (as a cost saving method, for disaster recovery, or as new services are added on) means that in 2020 strategy is the priority.
Formal procedures and strategies for using multiple cloud services are needed to get the most financial benefit in a multicloud world. This also prevents the creep of shadow ops, when a company is not fully aware of what cloud services they have in use.
DevOps will continue to shape the 5G revolution
The onset of 5G has made DevOps adoption a pressing reality for communication service providers (CSPs). The investments demanded of them are significant, which is why accessing B2B customers such as IoT services who will be making use of 5G’s real-time network performance is vital.
Over half of CSPs admit their current IT systems does not seamlessly link up with partners that would allow them to meet the demands of 5G. Automation and a pipeline which can be used by many vendors are some of the areas where DevOps has a lot to offer here.
DataOps and actionable insights
Measuring is a foundational pillar of DevOps. It’s what the M stands for in CALMS. For companies, data is only as valuable as the actions they lead to. We like to call this actionable insights.
DataOps is the adoption of DevOps principles by the data industry. DataOps was picked up as a term by the Gartner hype cycle for data management in 2018. The data industry is huge, and growing, reaching values of $189.1 Billion in 2019 with double-digit annual growth through 2022. DataKitchen argues that there are more people working in data science than there are in software development.
Gone are the days when a single data scientist hero could do it all. Gene Kim’s new book, the Unicorn Project covers the trials of data analytics teams. Currently, the data science arena has a high error rate (87% of data projects never make it to production), very little when it comes to automated testing, and a deploy to production rate of months.
The DevOps principles of version control, measurement and monitoring of results, and collaboration across varied teams, technologies and environments has a lot to offer here.
As said by Christopher Bergh of DataKitchen: “The ideas in Lean and Agile and DevOps that apply to manufacturing, software development, need to be applied to the world of data science and engineering. It’s a world that’s broken, it’s a world that needs your help, and you have a unique perspective and ideas”.
DevOps culture: psychological safety leads to increased productivity
Culture is important to DevOps. Without cultural change, you may be running the same broken workflow even if you’ve made investments in cloud services and other technological advances.
Psychological safety is a new theme when it comes to the cultural element of DevOps. It featured in the Accelerate State of DevOps report (our consultant and trainer Johan Abildskov wrote a blog about the key takeaways from the report). In brief, it is being able to work and be oneself without fear of negative consequences.
The data in the Accelerate State of DevOps report showed that a culture of psychological safety contributed to organizational performance and productivity.
Psychological safety fits in neatly with DevOps culture, where experimentation and moderate risk taking is a must. It also improves job satisfaction overall, which is important to software-driven companies in a tight job market.
Investments in information systems obviously affect the psychological safety of those who work with them. Reducing technical debt, tools as a managed service, and other investments make work more productive and error-free for dev and ops alike. Other DevOps best practices like a clear change process also contribute to psychological safety.
A note on scaling DevOps
I’d like to end with a note on scaling DevOps. At Gartner’s IT Infrastructure, Operations & Cloud Strategies Conference in November 2019, they highlighted the following two elements for scaling DevOps across an enterprise:
- platform teams that operate self-service platforms
- communities of practice, groups of people learning and sharing insights around a shared interest which can withstand change.
We’ve been encouraging our clients to adopt communities of practice, and we adopted communities of practices within Eficode earlier this year. We’ve had a DevOps Platform team for quite some time now as well, where we build security into the pipeline, a key element of DevSecOps (security took center stage in Puppet's State of DevOps report in 2019). Both of these projects are done for the benefit of our clients and their DevOps journeys.
2020 will be yet another big year for DevOps.