The development of data products is an intricate process, blending the complexities of data and code. Unlike traditional software development, the data dimension addiing additional unique challenges. Data must be available, understood, and accurate—key prerequisites that demand significant effort in acquisition and cleaning. The exploratory work, often led by data scientists and analysts, adds another layer of complexity. Furthermore, developing the infrastructure to release even small chunks of data products requires robust data pipeline environments. These complexities call for specialized methodologies called DataOps to overcome the hurdles of creating high-quality data products (Atwal, 2020, p.12; p.174).
What is DataOps?
DataOps, a fusion of "Data" and "Operations," addresses the challenges of developing data products by combining principles from Agile, DevOps, and Lean Manufacturing. It emphasizes collaboration, automation, and efficiency in handling data pipelines, enabling teams to deliver data products faster and with higher quality (Atwal, 2020, p.xxiii).
The Key Components of DataOps
DataOps incorporates methodologies from several established frameworks:
Agile: Focuses on creating the right product for the right people by adapting quickly to changing requirements. Agile enables data product teams to respond flexibly to unforeseen needs regarding functionality or content (Zimmer et al., 2015).
DevOps: Promotes a culture of collaboration and shared responsibility among teams. Automation, CI/CD pipelines, Infrastructure as Code, and automated testing ensure fast, reliable deployment with high quality (Macarthy & Bass, 2020b).
Lean Manufacturing: Aims to eliminate waste and focus on value-adding processes, resulting in higher efficiency, better resource utilization, improved quality, and reduced costs.
Why DataOps Matters
The complexity of building data products—from data ingestion to deployment—necessitates methodologies that streamline the process. DataOps supports:
Rapid Development of MVPs: DataOps enables teams to quickly create Minimum Viable Products (MVPs) to test ideas with customers and iteratively improve them. This approach reduces cycle times and accelerates delivery (Atwal, 2020, p.7).
Scalability and Robustness: By fostering automation and continuous improvement, DataOps enhances scalability and robustness, providing a competitive advantage to organizations (Atwal, 2020, p.136).
Customer-Centric Development: DataOps focuses on customer needs, breaking down technological and organizational silos to streamline activities toward delivering value (Atwal, 2020, p.81).
Definitions of DataOps
While there is no single definition of DataOps, it can be categorized into three perspectives (Mainali, 2020, p.17):
Goal-Oriented: DataOps goal is the “elimination [of] errors and inefficiency in data management, reducing the risk of data quality degradation and exposure of sensitive data using interconnected and secure data analytics models” (Mainali, 2020, p.17)
Activity-Oriented: DataOps describes the methodology as a key enabler for continuous flow of data through data pipelines “converting raw data into useful data products [which] can be treated as an end-to-end assembly line process that requires high level of collaboration, automation and continuous improvement” (Atwal, 2020, p.xxiv)
Team and Process-Oriented: The process or team-oriented definition of DataOps is focusing on the organizational framework underlining the relevance for cross functional teams, data governance management through the whole data lifecycle (Mainali, 2020, p.17)
Core Practices of DataOps
“DataOps is an integrated approach for delivering data analytic solutions that uses automation, testing, orchestration, collaborative development, containerization, and continuous monitoring to continuously accelerate output and improve quality” (Ereth, 2018, p.6). The focus is on “data products rather than data projects, and data flows rather than layers of technology or organizational functions” (Atwal, 2020, p.81). The core of the development of a data product are the customer needs. All activities are streamlined to achieve this goal by breaking of technological and organizational silos. The following practices are important for the success of DataOps methodology (Bergh et al., 2019, p.27):
Orchestration of Data Pipelines: Ensuring smooth, automated data flow from ingestion to delivery.
Automated Testing and Monitoring: Validating data quality and detecting issues in real-time.
Version Control: Managing changes to data and code efficiently.
Branch and Merge Strategies: Facilitating collaboration among multiple teams.
Multiple Environments: Supporting development, testing, and production workflows.
Reusability and Automation: Leveraging reusable components to reduce redundancy and improve efficiency.
The Future of Data Product Development
As organizations strive to harness the power of data, DataOps emerges as a vital methodology for delivering high-quality, scalable, and customer-focused data products. By combining automation, collaboration, and continuous improvement, DataOps bridges the gap between data and operations, enabling teams to meet the growing demands of data-driven innovation.
In summary, DataOps is not just a methodology; it is a mindset—a commitment to building better, faster, and more reliable data products by integrating principles from Agile, DevOps, and Lean Manufacturing. The journey toward effective DataOps may be complex, but its rewards are transformative for teams and organizations alike.
Sources
Atwal, P. (2020). DataOps: Delivering Data-Driven Value at Scale.
Bergh, J., et al. (2019). DataOps Cookbook.
Macarthy, R. W., & Bass, J. M. (2020a). An Empirical Taxonomy of DevOps in Practice. 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 221–228. https://doi.org/10.1109/SEAA51224.2020.00046
Mainali, S. (2020). "Exploring DataOps Definitions and Applications."
Zimmer, M., Kemper, H., & Baars, H. (2015). The impact of Agility Requirements on Business intelligence Architectures.
Ereth, J. (2018). DataOps – Towards a Definition.