Originally sourced from eWeek (December 2017). This article preserves and expands on the original industry perspectives for archival and reference purposes.
DevOps at the End of 2017: A Foundation Being Tested
By the close of 2017, DevOps had spent roughly half a decade moving from buzzword to mainstream methodology. The core premise — that software development and IT operations teams should collaborate closely, share responsibility, and automate wherever possible — had proven itself in measurable ways. High-performing DevOps organizations were deploying software significantly more frequently, recovering from failures faster, and reducing the number of change-related incidents compared to organizations still operating in siloed models.
But as adoption grew, so did the complexity of the problems DevOps teams faced. Security, database management, AI integration, and organizational culture were all emerging as friction points. Going into 2018, a number of industry practitioners and executives shared their predictions on where the discipline was headed.
DevSecOps: Security Moves Left — and Into Everything
One of the clearest themes across 2018 predictions was the convergence of development, operations, and security into a unified practice increasingly called DevSecOps.
“Breaches and attacks are only going to increase and viruses are getting more sophisticated. Application development is going to need to natively include specific safeguards and protections. Security tools that are layered into the infrastructure are still needed — they just aren’t going to be enough as more and more apps adopt the cloud model.”
Manoj Nair, Product Director, HyperGrid
The argument here is structural: bolting security on at the end of the delivery pipeline — or relying solely on perimeter-level controls — was no longer viable as cloud-native architectures, microservices, and container-based workloads became the norm. Security needed to be embedded at every stage of the CI/CD pipeline, from code commit through deployment.
“We will continue to see end-users make a tighter connection between a company’s brand and the quality of its code, based on their experiences across a company’s applications. As a result, more organizations will look to integrate security across all phases of development and intensify their automated continuous testing efforts as they work to release higher quality code, faster.”
Aruna Ravichandran, VP of DevOps Product and Solutions Marketing, CA Technologies
This prediction aligned with what was already being observed in the market: security scanning tools being integrated directly into CI pipelines, infrastructure-as-code being linted for misconfigurations before deployment, and compliance checks being automated rather than performed manually at release gates.
Cloud Security and the Shared Responsibility Model
A specific prediction tied to cloud adoption was the growing acceptance — and operationalization — of AWS’s shared responsibility model. The model divides security obligations between the cloud provider (responsible for the security of the cloud) and the customer (responsible for security in the cloud). In practice, many organizations had struggled to act on their side of that boundary.
The 2018 prediction was that DevOps practices — particularly the ability to script, version, and automate infrastructure configuration — would become the primary mechanism through which organizations fulfilled their cloud security responsibilities. Automated policy enforcement, configuration drift detection, and secrets management were all expected to mature as core DevOps capabilities rather than afterthoughts.
The Database: DevOps’ Forgotten Component
Several contributors independently identified the same blind spot: while DevOps had made significant progress automating application builds, tests, and deployments, the database remained largely manual — and was becoming a serious bottleneck.
“The database is the hardest part in the application stack to manage, so it just doesn’t make sense that it’s always the forgotten piece of the puzzle. IT teams have been so focused on time-to-market and getting development to push out applications at the speed of light, but still manually manage the change process of databases that contain massive amounts of information.”
Robert Reeves, Co-founder and CTO, Datical
Reeves went further, framing DevOps not as a toolchain but as a continuous process of identifying and eliminating friction — and that the database was an obvious source of that friction that the industry had been reluctant to address:
“It’s time to stop having DBAs perform manual SQL script review prior to a release and start automating the review so that they can continue to innovate and bring strategic value to their organization.”
Robert Reeves, Datical
Patrick O’Keeffe of Quest Software reinforced this, predicting that 2018 would see a shift in DevOps focus from upstream concerns (source control, CI) to downstream delivery — and that DBAs would need to become active participants in that pipeline rather than gatekeepers outside it.
“Due to their complex nature and common data movement challenges, databases tend to pose major bottlenecks to DevOps teams and processes. The DBA will play an integral role in alleviating these challenges and play a critical role in how businesses move to enabling DevOps digital transformation.”
Patrick O’Keeffe, Executive Director of Software Engineering, Quest Software
The Emergence of DataOps
Alongside the database conversation, O’Keeffe introduced a term that was just beginning to gain traction in late 2017: DataOps.
“Where DevOps aligns developers and IT teams to accelerate software delivery and infrastructure changes, DataOps is all about streamlining the preparation of data so developers can leverage it during the application building process.”
Patrick O’Keeffe, Quest Software
DataOps applies DevOps-style automation, version control, and collaboration principles to the data pipelines that feed analytics and machine learning workloads. In 2017, this was a nascent concept; by the early 2020s, it had developed into a recognized discipline with dedicated tooling and teams at larger data-driven organizations.
AI, Open Data, and What It Meant for DevOps Teams
Dr. John Bates, CEO of Testplant, offered a broader view that extended beyond DevOps mechanics into the emerging role of AI and data sharing in software engineering:
“AI and deep-learning will now drive the creation of ‘open data’ communities where people share data repositories to help train algorithms — much as open-source communities have shared code for decades.”
Dr. John Bates, CEO, Testplant
This prediction foreshadowed the growth of model hubs, shared datasets, and collaborative ML infrastructure that would accelerate in subsequent years. For DevOps practitioners, the practical implication was that managing data pipelines and model artifacts would become as important as managing application artifacts — a further argument for the DataOps discipline.
Bates also predicted that monitoring and testing would converge in DevOps pipelines — a trend consistent with what later became observable reliability engineering (SRE) practices, where production telemetry feeds back into test design and release decisions.
Product Owners Pulled Into the Pipeline
One organizational prediction from Bates deserves particular attention for how it reflected evolving thinking about continuous delivery:
“DevOps has been about ‘Dev’ and ‘Ops’, but in 2018 the business will be pulled directly into DevOps, especially product owners. This will enable the true continuous delivery envisaged.”
Dr. John Bates, Testplant
The argument is that genuine continuous delivery requires fast feedback not just from automated tests, but from business stakeholders who can evaluate whether features are achieving intended outcomes. Without that loop closed, teams can deliver code continuously while still building the wrong things — quickly.
The CIO Reclaims the Information Mandate
Bates made one further prediction that speaks to a governance challenge that grew more acute over subsequent years:
“As companies realize that their data is their most valuable asset, but that it’s now fragmented across functions who have all deployed independent SaaS solutions, they will look to the CIO to bring their data together. So the CIO will once again be the center of information.”
Dr. John Bates, Testplant
This framing — data fragmentation as an organizational risk, not just a technical one — anticipated the data governance conversations that would dominate enterprise IT strategy through the early 2020s, particularly around data mesh, data catalogs, and the difficulty of deriving consistent analytics from dozens of independent SaaS tools.
Key Themes in Retrospect
Looking at these 2018 predictions as a set, several themes stand out:
- Security shifts left: DevSecOps was no longer optional; embedding security into the pipeline was treated as a prerequisite for sustainable delivery velocity.
- The database bottleneck: DevOps automation had largely bypassed database change management, and this was increasingly recognized as a critical gap.
- DataOps as a discipline: Applying DevOps principles to data pipelines was an emerging need, particularly as AI and analytics workloads grew.
- Broader stakeholder inclusion: True continuous delivery required pulling product owners — and eventually business stakeholders — into the feedback loop.
- AI and open data: Machine learning was beginning to create new collaboration and tooling requirements that paralleled the open-source software model.
Many of these predictions proved accurate in direction if not always in timing. DevSecOps became a standard term and practice. Database DevOps tooling matured. DataOps became a recognized field. The CIO data governance question remains unresolved for many enterprises to this day.
This article is based on industry commentary originally published by eWeek in December 2017, covering predictions from practitioners at HyperGrid, Quest Software, Testplant, CA Technologies, and Datical.