Data teams are under more pressure than ever to move fast without breaking things, and most are still figuring out how to do both at once.
Gartner estimates that 85% of big data projects fail, often because businesses treat data delivery as a one-off engineering task rather than an ongoing discipline. DataOps blends DevOps, Agile development, and statistical process control into a single approach designed to fix exactly that.
In this article, we'll be covering the full DataOps Manifesto, organized into three practical groupings: the collaboration principles that bring teams together, the quality and reliability principles that make outputs trustworthy, and the technical and automation principles that make the other two achievable at scale.
What is DataOps? The Essential IntroductionThe DataOps Manifesto lays out 18 principles for applying Agile development, DevOps, and statistical process control to data analytics, not just a single collaboration framework.
The principles group naturally into three practical categories: collaboration between teams, quality and reliability, and technical automation.
Embracing change, treating DataOps as a team sport, and favoring simplicity remain foundational collaboration principles, but they work best alongside the quality and technical principles covered in this guide.
Treating analytics as code, version-controlling everything from data to environments, and using disposable environments are technical principles that make the collaboration principles achievable at scale.
Reducing heroism, the reliance on one person's manual effort to keep a pipeline running, is one of the more distinctive and often overlooked principles in the Manifesto.
Not every organization needs to prioritize all 18 principles equally; identifying which grouping represents your team's biggest current gap is more useful than treating the list as a checklist.
The DataOps manifesto consists of 18 principles spanning technical and operational practices for applying Agile and DevOps thinking to data analytics. As the Manifesto states, the beginning-to-end orchestration of data, tools, code, environments, and the analytic team's work is a key driver of analytic success.
Rather than listing all 18 with equal weight, which tends to overwhelm more than it clarifies, we’ve organized them into three common groupings: collaboration, quality and reliability, and technical and automation.
Most teams will have a gap in one of these three areas, and knowing where that gap sits matters more than being able to recite all 18 from memory.
The Manifesto has also evolved since it was first published. As the data landscape has changed, the principles have been refined and new ones have been added. What hasn't changed is the underlying premise that team communication matters more than any single tool, and that experimentation and iterative feedback beat trying to design a perfect pipeline upfront.
Collaboration is central to the DataOps Manifesto, and three principles capture most of what it takes to bring teams together.
Data, by its very nature, is constantly changing. New sources appear, schemas shift, and business requirements evolve mid-project. The Manifesto treats this as a given to design around, not a disruption to resist. Teams that build rigid, one-time pipelines find themselves rebuilding constantly, while teams that expect change from the outset build in the flexibility to absorb it.
In practice, this often means favoring configuration over hard-coded logic, wherever a source or rule is likely to change, and treating a request for a schema change as a normal Tuesday rather than an emergency that derails the sprint.
DataOps only works when data owners, technical teams, and business users are talking to each other regularly, not passing requirements over a wall and waiting for the finished product. By emphasizing communication, collaboration, and cooperation, DataOps can unlock the true value of your organization's data, and that value depends on people across different roles staying in sync, not on any single team working in isolation.
This is also where a lot of DataOps initiatives quietly stall. It's easy to buy a tool and call it DataOps. It's much harder to get a data engineering team and a business analytics team into the same regular conversation, especially if they've historically operated with almost no overlap.
Complexity creeps into data pipelines quickly, and once it does, every change becomes slower and riskier. To avoid these issues, the DataOps manifesto recommends simplification and standardization. You can achieve this by using templates, playbooks, or simply recycling processes and code that you've previously built. This helps to create a common language that boosts collaboration and decision-making.
Simplicity isn't the same as avoiding necessary complexity, where a problem truly requires it, it's about not adding complexity that exists only because nobody paused to ask whether there was a simpler way to solve the same problem.
This second group of principles focuses on making data outputs trustworthy and consistent, not just fast.
The Manifesto treats data quality checks as something built into every stage of a pipeline, not a final inspection before release. This lines up directly with the case for data validation in data ingestion, catching problems at the point they occur rather than downstream, where they're more expensive and more visible to fix.
Treating quality this way also changes who's responsible for it. Rather than one quality assurance step at the end that everyone else can safely ignore, every stage of the pipeline shares some responsibility for the data leaving it in good shape.
A pipeline that produces slightly different results each time it runs, for reasons nobody can quite explain, isn't a reliable pipeline, no matter how fast it runs. Reproducibility means the same inputs and the same process should reliably produce the same outputs, which sounds obvious until you've debugged a system where it isn't true.
Non-reproducible pipelines are also expensive in a way that's easy to underestimate: every unexplained discrepancy costs someone hours tracing back through logs and configuration to figure out what happened, time that a reproducible system would have made unnecessary.
This is one of the Manifesto's more distinctive and often overlooked principles. A process that only works because one specific person remembers the undocumented workaround, or manually intervenes every time something goes wrong, isn't a sustainable process, it's a single point of failure wearing a badge of dedication. Reducing heroism means building processes robust enough that they don't depend on any one person's memory or manual effort to keep running.
The person doing the heroics is often skilled and well-intentioned, and the fix isn't to blame them, it's to document and automate what they're doing so the whole team, and the business, isn't exposed the day they're unavailable.
A third group of principles is what makes the collaboration and quality principles achievable at scale, rather than aspirational.
Treating a data pipeline with the same rigor as a piece of software, code review, testing, structured change management, catches problems before they reach production instead of after. Pipelines built without this discipline tend to accumulate the kind of undocumented, fragile logic that makes every future change riskier than the last.
This shift can feel unfamiliar to teams that came up building analytics in spreadsheets or ad hoc scripts, but the payoff is direct. A change that would have been a nervous, manual edit becomes a reviewed, tested, reversible one.
Data, code, configurations, and environments should all be version-controlled, not just the code. When something breaks, being able to see exactly what changed, and roll back to a known-good state, turns a multi-hour investigation into a five-minute fix.
Teams that only version their code, and not their configuration or data schemas, often find that's precisely where the untraceable changes creep in, a config value someone tweaked manually and never documented, quietly breaking something three deployments later.
Testing and development environments that can be spun up and torn down on demand let teams experiment freely without risking production systems. If setting up a test environment is a multi-day undertaking, teams will avoid testing changes properly, not because they don't care, but because the friction makes shortcuts feel reasonable.
The sequencing and dependencies between pipeline steps need to be automated and managed centrally, not held together by manual triggers and tribal knowledge about what needs to run before what. This is exactly the kind of end-to-end orchestration that turns a collection of scripts into a reliable pipeline.
Good orchestration also makes the other technical principles visible rather than theoretical: a well-orchestrated pipeline shows you exactly which step failed, why, and what depended on it, instead of leaving someone to reconstruct that picture from scattered log files after the fact.
DataOps and MLOps are complementary disciplines, not competing ones.
MLOps depends on DataOps to deliver the clean, versioned, well-governed training and inference data that models require. Poor data quality costs the average enterprise approximately $12.9 million annually, and a growing share of that risk now sits specifically with AI initiatives that depend on data being fresh, accurate, and well-governed.
The technical principles covered above matter even more once AI and ML models depend on them. Versioning means you can trace exactly which data trained which model version. Reproducibility means a model's behavior can be debugged rather than treated as an unexplainable black box.
Disposable environments mean a data scientist can experiment with a new feature set without risking anything running in production. None of these principles were written with AI specifically in mind, but they turn out to be exactly what AI-driven workflows need most.
This is also where the collaboration principles matter more than they might seem to on the surface. An AI initiative that lives entirely inside a data science team, disconnected from the engineers who own the underlying pipelines and the business users who'll act on the model's output, tends to produce technically impressive models that never quite fit how the business operates day to day. The same cross-functional communication the Manifesto calls for is exactly what closes that gap.
CloverDX is built around exactly the technical principles covered above: visual, versionable pipelines where every change is tracked, and environments that can be spun up, tested, and torn down without touching production.
The quality principles are supported directly too: validation and profiling run at every stage of a pipeline rather than as an afterthought, and full audit trails mean reproducibility isn't just a goal, it's something you can verify after the fact.
For the collaboration principles, shared, visualized workflows mean technical and business teams are looking at the same picture of a pipeline, rather than translating between a developer's mental model and a business user's spreadsheet. Giving business users safe, self-service access to validated data directly supports the "team sport" principle in practice, not just in theory.
None of this replaces the cultural work of getting teams talking to each other regularly, no platform can do that on its own, but it removes a common excuse. A shared, visual pipeline means there's no longer a real barrier to a business stakeholder and a data engineer looking at the exact same thing and having a productive conversation about it.
Why CloverDX should be part of your DataOps toolkitThe DataOps Manifesto isn't a checklist to complete, it's a set of tensions to manage well. It provides you with speed against reliability, flexibility against consistency, and individual expertise against sustainable processes. The three groupings, collaboration, quality, and technical automation, aren't ranked in importance, they're interdependent.
Weak collaboration undermines even the best technical setup, and weak technical foundations make good collaboration unsustainable once a team scales past a handful of people.
Let's talk about how CloverDX can help your organization with its DataOps approach, and increase collaboration between your teams.