What is DataOps?
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Published: 5 April, 2024
Contributors: Tim Mucci, Mark Scapicchio, Cole Stryker

What is DataOps?

DataOps is a set of collaborative data management practices intended to speed delivery, maintain quality, foster collaboration and provide maximum value from data. Modeled after DevOps practices, DataOps’ goal is to ensure that previously siloed development functions are automated and agile. While DevOps is concerned with streamlining software development tasks, DataOps focuses on automating the data management and data analytics process.

DataOps leverages automation technology to streamline several data management functions. These functions include automatically transferring data between different systems whenever it is needed and automating processes to identify and address inconsistencies and errors within data. DataOps prioritizes automating repetitive and manual tasks to free data teams for more strategic work.

Automating these processes protects data sets and makes them readily available and accessible for analysis purposes, while certifying that tasks are performed consistently and accurately to minimize human error. These streamlined workflows lead to quicker data delivery when needed because automated pipelines can handle larger volumes of data more effectively. In addition, DataOps encourages continuously testing and monitoring data pipelines to guarantee they are functioning and correctly governed.

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Why is DataOps important?

Manual data management tasks are time-consuming and business needs are always evolving. A streamlined approach to the entire data management process, from collection to delivery, ensures an organization is agile enough to handle challenging multi-step initiatives. It also allows data teams to manage explosive data growth while they develop data products.

A core purpose of DataOps is to break open silos between data producers (upstream users) and data consumers (downstream users) to secure access to reliable data sources. Data silos are effective at restricting access and analysis, so by unifying data across departments, DataOps fosters collaboration between teams who can access and analyze relevant data for their unique needs. Emphasizing communication and collaboration between data and business teams, DataOps drives increased velocity, reliability, quality assurance and governance. Plus, the cross-discipline collaboration that follows allows for a more holistic view of the data, which can lead to more insightful analysis.

Within a DataOps framework, data teams consisting of data scientists, engineers, analysts, IT operations, data management, software development teams and line of business stakeholders work together to define and meet business goals. So, DataOps helps avoid the common challenge of management and delivery becoming a bottleneck as data volume and types grow and new use cases emerge among business users and data scientists. DataOps involves implementing processes like data pipeline orchestration, data quality monitoring, governance, security and self-service data access platforms.  

Pipeline orchestration tools manage the flow of data and automate tasks like extraction schedules, data transformation and loading processes. They also automate complex workflows and ensure data pipelines run smoothly, saving data teams time and resources.

Data quality monitoring provides real-time proactive identification of data quality, ensuring that data used for analysis is reliable and trustworthy.

Governance processes make sure data is protected and aligns to various regulations and organizational policies. They also define who’s accountable for specific data assets, regulate who has permissions to access or modify data and track origins and transformations as data flows through pipelines for greater transparency.

Working in concert with governance, security processes protect data from unauthorized access, modification or loss. Security processes include data encryption, patching weaknesses in data storage or pipelines and recovering data from security breaches.

By adding self-service data access, DataOps processes allow downstream stakeholders like data analysts and business users to access and explore data more easily. Self-service access reduces reliance on IT for data retrieval and automating data quality checks leads to more accurate analysis and insights.

DataOps and agile methodology

DataOps uses the agile development philosophy to bring speed, flexibility and collaboration to data management. The defining principles of Agile are iterative development and continuous improvement based on feedback and adaptability, with the goal of delivering value to users early and often.

DataOps borrows these core principles from Agile methodology and applies them to data management. Iterative development is building something in small steps, getting feedback and making adjustments before moving to the next step. In DataOps, this translates to breaking data pipelines into smaller stages for faster development, testing and deployment. This allows for quicker delivery of data insights (customer behavior, process inefficiencies, product development) and gives data teams space to adapt to changing needs.

Continuous monitoring and feedback on data pipelines allow for ongoing improvements, ensuring data delivery remains efficient. The cycle of iteration makes it easier to address new data resources, changing user requirements or business needs, ensuring the data management process stays relevant. Changes in data are documented using a version control system, like Git, to track modifications of data models and enable simpler rollbacks.

Collaboration and communication are central to Agile and DataOps reflects this. Engineers, analysts and business teams work together to define goals and ensure pipelines provide business value in the form of trustworthy, usable data. Stakeholders, IT and data scientists have an opportunity to add value to the process in a continuous feedback loop to help solve problems, build better products and provide trustworthy data insights. 

For example, if the goal is to update a product to please and delight users, the DataOps team can examine organizational data to gain insights about what customers are looking for and use that information to enhance the product offering.

Benefits of DataOps

DataOps promotes agility within an organization by fostering communication, automating processes and reusing data rather than creating anything from scratch. Applying DataOps principles across pipelines improves data quality while freeing data team members from time-consuming tasks.

Automation can quickly handle testing and provide end-to-end observability across every layer of the data stack, so if anything goes wrong, the data team will be alerted immediately. This combination of automation and observability allows data teams to proactively address downtime incidents, often before these incidents can affect downstream users or activities.

As a result, business teams have better-quality data, experience fewer issues and can build trust in data-driven decision-making across the organization. This leads to shortened development cycles for data products and an organizational approach that embraces the democratization of data access.

With increased data use come regulatory challenges in how that data is used. Government regulations such as general data protection regulations (GDPR) and the California consumer privacy act (CCPA) have complicated how companies can handle data and what data types they can collect and use. The process transparency that comes with DataOps addresses governance and security concerns by providing direct access to pipelines so data teams can observe who is using the data, where the data is going and who has permissions up or downstream.

Best practices and implementation of DataOps

When it comes to implementation, DataOps starts with cleaning raw data and developing a technology infrastructure that makes it available.

Once an organization has its DataOps processes running, collaboration is key. DataOps emphasizes collaboration across business and data teams, fostering open communication and breaking down silos. Like in Agile software development, data processes are broken down into smaller, adaptable chunks for faster iteration. Automation is used to streamline data pipelines and minimize human error.

Building a data-driven culture is a crucial step as well. Investing in data literacy empowers users to leverage data effectively, creating a continuous feedback loop that gathers insights to improve data quality and prioritize data infrastructure upgrades.

DataOps treats the data itself as a product, so it’s crucial for stakeholders to be involved in aligning KPIs and developing service level agreements (SLAs) for critical data early on. Finding a consensus about what qualifies as good data within the organization helps keep teams focused on what matters.

Automation and self-service tools empower users and improve decision-making speed. Rather than operations teams fulfilling stopgap requests from business teams, which slows down decision-making, business stakeholders always have the access to the data they need. By prioritizing high data quality, enterprises ensure reliable insights for all levels of the organization.

Here are a few best practices associated with implementation:

  • Define data standards early: Set clear semantic rules for data and metadata at the outset.
  • Assemble a diverse DataOps team: Build a team with various technical skills and backgrounds.
  • Automate for efficiency: Leverage data science and business intelligence (BI) tools to automate data processing.
  • Break silos: Establish clear communication channels, encourage diverse teams to share data and expertise, employ data integration and automation tools to eliminate silos and bottlenecks.
  • Design for scalability: Build a data pipeline that can grow and adapt to increasing data volumes.
  • Build in validation: Integrate feedback loops to continuously validate data quality.
  • Experiment safely: Utilize disposable environments to mimic production for safe experimentation.
  • Continuous improvement: Embrace a "lean" approach, focusing on ongoing efficiency enhancements.
  • Measure progress continuously: Establish benchmarks and track performance throughout the data lifecycle.
The DataOps lifecycle

This lifecyle is designed to improve data quality, speed analytics and foster collaboration across the organization.


This stage involves collaboration between business, product and engineering to define data quality and availability metrics.


Here, data engineers and scientists build data products and machine learning models that will go on to power applications.


This stage focuses on connecting the code and data products with an organization's existing technology stack. Like integrating a data model with a workflow automation tool for automatic execution.


Rigorous testing ensures data accuracy aligns with business needs. Tests could involve checking for data integrity and completeness and that data adheres to business rules.

Release and deploy

Data is first moved to a testing environment for validation. Once validated, the data can be deployed to the production environment to be used for applications and analysts.

Operate and monitor

This is an ongoing stage. Data pipelines run continuously, so data quality is monitored using techniques like statistical process controls (SPC) to identify and address anomalies promptly.

DataOps tools and technology

The proper application of tools and technology supports the automation necessary to succeed with DataOps. Automation employed in five critical areas helps establish a solid DataOps practice within an organization. Additionally, because DataOps is a holistic framework for managing data throughout an organization, the best tools will leverage automation and other self-service features that allow more freedom and insight for DataOps teams.

Implementations of tools is a way to show progress in adopting DataOps, but successfully implementing the process requires a holistic organizational vision. An enterprise that focuses on a single element to the detriment of others is unlikely to see any benefit from implementing DataOps processes. Tooling does not replace ongoing planning, people and processes; it exists to support and sustain an already strong data-first culture.

Here are areas that benefit most from automation:

Data curation services

DataOps relies on the organization's data architecture first and foremost. Is the data trusted? Available? Can errors be detected quickly? Can changes be made without breaking the data pipeline?

Automating data curation tasks like data cleansing, transformation and standardization ensures high-quality data throughout the analytics pipeline, eliminating manual errors quickly to free up data engineers for more strategic work.

Metadata management

Automating metadata capture and lineage tracking creates a clear understanding of where data comes from, how it's transformed and how it's used. This transparency is crucial for data governance and helps users understand the trustworthiness of data insights. DataOps processes increasingly use active metadata as an approach to managing information about data. Unlike traditional metadata, which is often static and siloed, active metadata is dynamic and integrated across the data stack to provide a richer and more contextual view of data assets.

Data governance

When it comes to data governance, automation enforces data quality rules and access controls within pipelines. This reduces the risk of errors or unauthorized access, improving data security and compliance.

Master data management

Automating tasks like data deduplication and synchronization across various systems ensures a single source of truth for core business entities like customers or products, which is the key to effective data management. This eliminates inconsistencies and improves data reliability for analytics and reporting.

Self-service interaction

Automation also empowers business users with self-service tools for data access and exploration. By applying automation to self-service interactions, users can find and prepare the data they need without relying on IT, accelerating data-driven decision-making across the organization.

Functions of a DataOps platform

With a strong DataOps platform, organizations can solve inefficient data-generation and processing problems and improve poor data quality caused by errors and inconsistencies. Here are the core functions that such platforms provide:

Data ingestion: Generally, the first step in the lifecycle of data starts by ingesting it into a data lake or data warehouse to transform it into usable insights through the pipeline. Organizations need a competent tool that can handle ingestion at scale. As an organization grows, an efficient solution for data ingestion is required.

Data orchestration: Data volume and type within organizations will continue to grow and it's important to manage that growth before it gets out of hand. Infinite resources are an impossibility, so data orchestration focuses on organizing multiple pipeline tasks into a single end-to-end process that enables data to move predictably through a platform when and where it's needed without requiring an engineer to code manually.

Data transformationData transformation is where raw data is cleaned, manipulated and prepared for analysis. Organizations should invest in tools that make creating complex models faster and manage them reliably as teams expand and the data volume grows.

Data catalogA data catalog is like a library for all data assets within an organization. It organizes, describes and makes data easy to find and understand. In DataOps, a data catalog can help build a solid foundation for smooth data operations. Data catalogs serve as a single point of reference for all data needs.

Data observabilityWithout data observability, an organization is not implementing a proper DataOps practice. Observability protects the reliability and accuracy of data products being produced and makes reliable data available for upstream and downstream users.

The five pillars of data observability

DataOps relies on five pillars of data observability to monitor quality and prevent downtime, By monitoring the five pillars, DataOps teams get an overview of their data health and can proactively address issues affecting its quality and reliability. The best observability tools should include automated lineage so engineers can understand the health of an organization's data at any point in the lifecycle.


When was the data last updated? Is the data being ingested promptly?


Are the data values within acceptable boundaries? Is the data formatted correctly? Is the data consistent?


Is any data missing? Has all data been ingested successfully?


What is the current structure of the data? Has there been any changes to the structure? Are the changes intentional?


What's the upstream source of the data? How has the data been transformed? Who are the downstream consumers?

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