Streamlining the Data Product Development with DataOps
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The development of data products is an intricate process, blending the complexities of data and code. Unlike traditional software development, the data dimension adding additional unique challenges. Data must be available, understood, and accurate. 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. There are three main challenges in the development of Data Products:
Waste during the whole lifecycle
Misalignment with business goals
And challenges in scaling data science/ analytics outputs (Productionizing)
Are you interested to learn more about the challenges of developing Data Products? Please have a look here: LINK
These complexities and challenges call for specialized methodologies called DataOps to overcome the hurdles of creating and maintaining 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: 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).
Customer-Centric Development: DataOps focuses on customer needs, breaking down technological and organizational silos to streamline activities toward delivering value (Atwal, 2020, p.81).
Scalability and Robustness: By fostering automation and continuous improvement, DataOps enhances scalability and robustness, providing a competitive advantage to organizations (Atwal, 2020, p.136).
Data Governance: DataOps integrates automated governance and compliance checks into the pipeline, ensuring data integrity and regulatory compliance. This helps mitigate risks without slowing development.
Continuous Improvement: DataOps fosters a culture of continuous improvement by automating feedback loops, allowing teams to refine data pipelines and products in real-time. This ensures ongoing optimization and relevance of data products. New ideas can be tested without breaking the production model.
Innovation and Competitive Advantage:
DataOps accelerates innovation by enabling faster iteration and adaptation to market changes. It provides a competitive edge by ensuring data products remain relevant and responsive to customer needs.Cross-Functional Collaboration:
Collaboration between data engineers, analysts, and business stakeholders is at the core of DataOps. This shared responsibility improves communication and accelerates delivery of data products.
Definitions of DataOps
My Definition for DataOps is:
“DataOps is a methodology that integrates principles from Agile, DevOps, and Lean to streamline the end-to-end development of Data Products. It focuses on collaboration, automation, and continuous improvement to ensure efficient, high-quality data pipelines powering the rapid development of customer centric data products. DataOps is not just a process but a mindset, aligning teams across disciplines to ensure data products meet evolving business needs and deliver actionable insights.”
In literature there are mainly three perspectives on the definition of DataOps (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 [products] 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. (Comprehensive framework for data pipelines: LINK)
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, Containerization and Automation: Leveraging reusable components to reduce redundancy and improve efficiency.
Avoid fear and heroism in DataOps projects
Are you interested in learning more about DataOps best practices? Take a look on this blog post: LINK
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 for 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.
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Niels Humbeck
Niels Humbeck
I am passionate about bringing data products to life. Bridging the gap between business and technology to develop data products that makes a different. My Hobbies are: Traveling, Biking, Drone Footage and Cooking.