What is DataOps, anyway?
3 July 2024 | 7 mins read | Data Culture
by Jessica, Tech writer
DataOps, short for data operations, is a transformative discipline that sits at the intersection of DevOps and data science, combining agile methodologies, automation, and cross-functional collaboration to streamline the entire data lifecycle.
By breaking down silos between data engineers, data scientists, IT teams, and business stakeholders, DataOps empowers companies to deliver high-quality, reliable data and insights faster, fueling innovation, enhancing decision-making, and supporting digital transformation initiatives.
Keep reading to learn more about the makeup and uses of DataOps in your organization.
What is DataOps?
DataOps, or data operations, is a modern practice in data management at the crossroads of DevOps and data science.
This practice, critical to digital transformation and the growth of data-driven companies, provides better data lifecycle management to optimize and improve data quality.
DataOps is designed to improve collaboration and communication between data engineers, data scientists, and other IT professionals who manage data analytics pipelines. Its goal is to enable organizations to deliver data and insights more quickly and reliably to their users while also reducing the complexity and cost of managing the underlying data infrastructure.
DataOps: The key to data-driven innovation
DataOps practitioners oversee the end-to-end data pipeline, from initial data collection and preparation to the development and deployment of data models, to the ongoing monitoring and optimization of the pipeline.
This often involves implementing automated processes and tools to streamline data management and fostering a culture of collaboration and continuous improvement within the organization.
DataOps is an agile approach to designing, implementing, and maintaining a data architecture. It encompasses many technological tools designed to make the most of big data. It involves the information systems development team and the operations team (DevOps). It can also involve teams specialized in data processing, including data scientists.
Combining these disciplines yields the tools, processes, and organizational structures necessary for data-centric growth.
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DataOps’ missions
DataOps improves data flow integration and automation while fostering collaboration between data scientists and the project teams that use that data.
These teams, in effect, analyze data with a practical value that can be measured by the knowledge it provides. DataOps project teams work in response to the changing needs of the customer. These groups organize themselves to achieve specific objectives based on scalability and stability, both for the team and process.
One of the primary responsibilities of DataOps teams is to enable project-scale orchestration of data, tools, environments, and code. Analytical pipelines function in the same way as lean production lines, with a strong emphasis on being able to reproduce results.
Who leads DataOps?
The key roles that lead DataOps include Data Engineers, Data Scientists, and IT professionals.
Data engineers
Responsible for designing and implementing the technical infrastructure for data pipelines, including the collection, storage, and preparation of data for analysis.
Data scientists are responsible for developing and deploying data models and algorithms, as well as analyzing and interpreting the data to generate insights and recommendations.
IT professionals, such as system administrators and DevOps engineers
Responsible for managing the underlying technology infrastructure and ensuring that the data pipeline is secure, scalable, and reliable.
The executive team, such as the CDO (Chief Data Officer) and the CTO (Chief Technology Officer)
Responsible for setting the overall strategy and direction for the organization’s data management practices. This often involves defining the goals and objectives of the data team and establishing the policies and processes to manage the data pipeline.
The executive team is also responsible for providing the necessary resources and support to enable the data team to succeed, such as funding for technology and personnel. Additionally, the executive team may make key decisions about data-related projects, such as which data sources to use and which data models and algorithms to deploy.
Together, these roles work closely to collaborate on the end-to-end data pipeline and to ensure that it is optimized to deliver data and insights to users in a timely and effective manner.
Why should my teams use DataOps?
While the primary goal of DevOps is to deliver functional software to the business quickly and continuously, DataOps involves delivering relevant, functional data to every stakeholder in business processes.
To put it another way, DataOps bridges the cognitive, temporal, and organizational gaps that exist between data scientists, business analysts, developers, and anyone who uses data within an organization.
There are several reasons why organizations should consider using DataOps:
Deliver data & insights
to their users more quickly and reliably by enabling faster and more efficient collaboration between different teams involved in the data pipeline.
This can help organizations to make better and more timely decisions and to stay competitive in an increasingly data-driven world.
Reduce the complexity & cost of managing data infrastructure
by implementing automated processes and tools to streamline data management.
This can help organizations reduce the time and effort required to maintain their data pipelines and free up IT resources for other tasks.
Foster a culture of collaboration & continuous improvement
by encouraging open communication and cooperation between different teams involved in the data pipeline.
This can help organizations better understand their users’ needs and develop more effective and innovative data-driven solutions.
The benefits of using DataOps in your business
DataOps streamlines the end-to-end data lifecycle by bringing agility, automation, and collaboration to data engineering and analytics processes. For data leaders, this means faster time-to-insight, improved data quality, and more reliable data pipelines that support critical business decisions.
By breaking down silos between data teams and encouraging continuous integration and delivery of data, DataOps enables organizations to respond more quickly to market changes and business needs.
It also reduces operational bottlenecks, enhances governance, and fosters a culture of innovation. This empowers data leaders to align data initiatives with business goals more effectively.
DataOps skills within your organization help you to:
Accelerate the process of creating data applications
Enhance & streamline collaboration
Build a collaborative data platform
Improve data transparency
Maximize data quality & reusability
In short, DataOps responds to new strategic trends in data management, such as the democratization of data use, the diversification of data processing technologies, and their commercial application.
The future of DataOps
It is difficult to predict the exact future of DataOps, as it will depend on various factors such as technological developments and changes in industry trends.
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Download the free guideHowever, using DataOps will likely continue to grow and evolve in the coming years. Some possible developments in the future include:
- The continued integration of DataOps with other IT practices, such as DevOps and Agile, to create a more comprehensive and streamlined approach to data management.
- The development of new and improved tools and technologies for data management, such as machine learning and artificial intelligence, to automate and optimize data pipelines.
- An increased focus on security and privacy as organizations seek to protect sensitive data and ensure compliance with privacy regulations.
- The growing importance of DataOps in enabling organizations to make better and more timely data-driven decisions in a world where data is increasingly seen as a strategic asset.
- The emergence of new roles and job titles related to data operations is due to the practice becoming more widely adopted and the growing demand for skilled practitioners.
FAQ
- How do I know if my data is “governed”?
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If your data assets are documented, owned, classified, and regularly validated — and if people across your org trust and use that data consistently — you’re well on your way.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/choosing-the-right-data-governance-tool/ - How do I implement data governance?
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To implement data governance, start by defining clear goals and scope. Assign roles like data owners and stewards, and create policies for access, privacy, and quality. Use tools like data catalogs and metadata platforms to automate enforcement, track lineage, and ensure visibility and control across your data assets.
- How do you improve data quality?
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Improving data quality starts with clear standards for accuracy, completeness, consistency, and timeliness. It involves profiling, fixing anomalies, and setting up controls to prevent future issues. Ongoing collaboration across teams ensures reliable data at scale.
- How do you build a data product?
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Building a successful data product begins with a clear business need, trusted data, and user-focused design. DataGalaxy simplifies this process by centralizing data knowledge, fostering collaboration, and ensuring data clarity at every step. To create scalable, value-driven data products with confidence, explore how DataGalaxy can help at www.datagalaxy.com.
- How does a data catalog help with AI risk management?
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A modern data catalog helps identify and track sensitive data, document lineage, and ensure data quality — all of which reduce AI-related risks. It also improves traceability across AI pipelines and enables proactive monitoring.
About the author
With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.
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