This article is based on a piece originally published on devops.com.
The DataOps Gap in Most Organizations
Despite the growing importance of data-driven software, dedicated data science teams remain rare outside a small number of large enterprises. The practical consequence is straightforward: in most organizations, responsibility for data operations falls on whoever is already managing the technology stack — and that increasingly means DevOps engineers.
If a DevOps team doesn’t take ownership of data operations, the applications that depend on healthy data flows are put at risk. Recommendations engines, predictions features, monitoring pipelines, and security tooling all rely on data moving reliably through the system. Ignoring DataOps is not a neutral choice — it is an active risk.
What Is DataOps and Why Does It Belong in the Delivery Pipeline?
DataOps refers to the collection of processes, workflows, and tooling required to collect, store, manage, and analyze data reliably and at scale. The term draws a deliberate parallel with DevOps: just as DevOps applies agile and automation principles to software delivery, DataOps applies similar thinking to data management.
Historically, DataOps was siloed from software delivery. Application developers, operators, and data specialists worked in separate teams with separate toolchains. That separation was manageable when data volumes were small and data’s role in applications was peripheral.
That is no longer the case. Modern applications are often data-first by design:
- Feature functionality — recommendation engines, personalization, and predictive features all require continuous, high-quality data input.
- Observability — application health monitoring depends on metrics, logs, and traces flowing correctly through data pipelines.
- Security operations — threat detection and incident response rely on fast, accurate analysis of event data.
A broken data pipeline can be just as damaging to a production application as a broken build pipeline. For this reason, DataOps can no longer be treated as a separate concern — it must be integrated into the overall software delivery lifecycle.
Practical Ways to Integrate DataOps into a DevOps Workflow
DevOps engineers don’t need to become data scientists to handle DataOps effectively. The following principles provide a practical starting point.
1. Automate Data Workflows Like You Automate Code Delivery
The automation mindset that underpins effective DevOps applies equally to data operations. Manual data collection, transformation, and validation steps introduce the same risks as manual deployments: inconsistency, human error, and poor scalability.
Tools that automate ingestion, schema validation, data quality checks, and pipeline orchestration reduce the operational burden on generalist engineers. They also make DataOps failures visible earlier — much like CI/CD surfaces broken builds before they reach production.
2. Use AI-Assisted Data Management Where Available
A growing category of data management tooling uses machine learning to reduce manual decision-making in DataOps workflows — for example, automatically detecting anomalies in data pipelines, suggesting data quality rules, or classifying data assets. For teams without deep data expertise, these capabilities lower the barrier to maintaining data health without requiring specialist knowledge for every decision.
Evaluate whether the data tools you already use expose AI-assisted features, and assess purpose-built options against your team’s maturity level and budget constraints.
3. Prioritize Data That Directly Affects Application Behavior
Not all data carries equal weight. Within a software delivery context, it helps to categorize data by its operational impact:
- High impact — data that directly drives application features or user-facing behavior (e.g., training data for a model, session data for personalization).
- Medium impact — operational data used for monitoring, alerting, and post-incident analysis.
- Low impact — data collected speculatively or for future use cases that have not yet materialized.
Applying your team’s limited DataOps capacity to high-impact data first ensures that the most critical pipelines receive the most attention. Not every data set you collect needs to be stored, transformed, and analyzed with equal rigor.
4. Choose Infrastructure-Agnostic DataOps Tooling
Cloud providers and platform vendors often bundle basic data management utilities with their infrastructure. These tools are convenient to start with, but relying on them exclusively can create deep vendor lock-in for your data layer. If you migrate to a different cloud, change your data warehouse, or adopt a new observability platform, proprietary DataOps tooling may need to be replaced entirely — along with all the workflows built on top of it.
Wherever feasible, prefer tools that support open standards and work across multiple infrastructure environments. This keeps your DataOps portable and reduces migration costs over time.
5. Build Baseline DataOps Literacy Across the Team
DevOps engineers don’t need to master statistics or data science, but they do benefit from understanding a core set of data management concepts:
- Data quality dimensions — completeness, accuracy, consistency, timeliness, and validity.
- Pipeline observability — how to identify where data is delayed, dropped, or corrupted within a pipeline.
- Schema management — understanding how schema changes in upstream systems can break downstream consumers.
- Data lineage basics — being able to trace where a given data point originates and how it has been transformed.
Targeted internal training sessions, documentation of your organization’s data systems, and pairing with any available data specialists are all effective ways to build this literacy without formal data science education.
Conclusion: DataOps Is Now Part of the DevOps Remit
Modern software delivery pipelines depend as much on the health of data flows as on the health of code deployments. With dedicated data teams absent from the majority of organizations, DevOps engineers are the practical owners of DataOps by default — whether or not that responsibility is formally recognized.
The good news is that the core DevOps disciplines — automation, observability, iterative improvement, and cross-functional collaboration — translate directly into effective DataOps practice. The gap is not one of capability; it is one of awareness and deliberate skill-building. Closing that gap starts with treating data pipelines with the same operational rigor you apply to your software delivery pipeline.