Dexcent IDS

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Introduction to DataOps Part 3: Getting Started with DataOps: Key Principles and Best Practices

Welcome to the final part of our three-part blog series on DataOps. In the previous posts, we explored what DataOps is and why it matters, as well as the benefits of adopting DataOps practices in today's industrial landscape. In this post, we will delve into the key principles and best practices that can help you successfully implement DataOps in your organization.

Embrace Cross-functional Collaboration:

DataOps emphasizes the importance of collaboration and communication between different teams involved in the data lifecycle. Break down silos and encourage cross-functional collaboration among data engineers, data scientists, business analysts, and operations teams. Foster a culture of knowledge sharing and teamwork, enabling a holistic approach to data operations. Regular meetings, shared documentation, and collaborative tools can facilitate effective communication and collaboration.

Automate Data Pipelines and Workflows:

Automation plays a vital role in DataOps. Automate data pipelines and workflows to streamline data processes, reduce manual errors, and improve efficiency. Leverage technologies like workflow orchestration tools, data integration platforms, and cloud-based services to automate data movement, transformation, and analysis. This automation not only saves time but also ensures consistent and reliable data processing. At Dexcent IDS we pride ourselves in being vendor agnostic when it comes to using software to automate processes. We are familiar with the top automation platforms on the market today.

Implement Continuous Integration and Deployment:

Continuous integration and deployment (CI/CD) practices borrowed from the software development world can be applied to DataOps. Implement CI/CD pipelines for data projects, enabling frequent and automated testing, validation, and deployment of data assets. This ensures that changes and updates to data pipelines can be quickly and reliably incorporated, reducing time-to-insights and facilitating agility in data operations.

Prioritize Data Quality and Governance:

Data quality and governance are crucial in DataOps. Establish robust data quality assurance processes, including data profiling, cleansing, and validation. Implement data governance frameworks to ensure compliance, security, and privacy of data assets. Establish clear data ownership and accountability, and document data lineage to track data from its source to its destination. Regularly audit and monitor data to maintain its quality and integrity.

Monitor and Measure Key Metrics:

DataOps relies on monitoring and measuring key metrics to identify bottlenecks, optimize processes, and drive continuous improvement. Define relevant metrics such as data availability, data processing time, error rates, and data usage patterns. Utilize monitoring tools and dashboards to track these metrics in real-time, enabling proactive identification and resolution of issues. Regularly review and analyze the metrics to identify areas for optimization and innovation.

Foster a Culture of Learning and Adaptation:

DataOps is not a one-time implementation; it is an ongoing journey of learning and adaptation. Encourage a culture of learning and experimentation within your organization. Embrace failure as an opportunity to learn and iterate. Promote professional development and training to enhance the skills and knowledge of your data teams. Stay updated with emerging technologies, industry trends, and best practices in DataOps to ensure continuous growth and innovation.

Implementing DataOps principles and best practices can transform your organization's data operations, enabling agility, collaboration, and data-driven decision-making. By embracing cross-functional collaboration, automation, and continuous improvement, you can harness the full potential of your data assets. Remember to prioritize data quality, governance, and monitoring while fostering a culture of learning and adaptation. Embrace DataOps as a mindset and a framework for sustainable success in the data-driven era.


Chen, M., Mao, S., & Liu, Y. (2018). Big data: a survey. Mobile networks and applications, 19(2), 171-209.
Marz, N., & Warren, J. (2015). Big data: principles and best practices of scalable real-time data systems. Manning Publications Co.

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Table of Content

1. Purpose
1.1. Purpose and Goals
1.2. Why The Industrial DataOps Process Is Needed?
1.3. Industrial DataOps Practitioner Engagement
1.3.1. Oversee An Existing Industrial DataOps Program
1.3.2. High Data Secrecy Organizations
1.3.3. Full Engagement
1.4. Principles
1.4.1. Know Your Data
1.4.2. Curate Your Data
1.4.3. Unify Your Data
1.4.4. Analyze Your Data
1.4.5. Hardware, Software, and People Working Together
1.5. Lifecycle
2. Intention
2.1. Scope
2.2. Assumptions
3. Terminology & References
3.1. Definitions
3.2. Acronyms and Abbreviations
3.3. Industry References, Standards, Regulations and Guidelines
3.4. Site Related References, Standards, Regulations and Guidelines
4. Expectations and Responsibilities
4.1. Roles
4.2. Role Job Description
4.3. Role Assignment
5. Opportunity Identification
5.1. Need Initiated
5.2. Improvement Initiated
7. Baselining
7.1. Data Rationalization
7.2. Data Justification
7.3. Data Impact
7.4. Data Flow
7.4.1. Data Producer
7.4.2. Data Path
7.4.3. Data Consumer
7.5. Data Good State
7.5.1. Failure Conditions
7.5.2. Warning Conditions
7.5.3. Abnormal Conditions
7.6. Data Processing Team
8. Target Confidence Factors
9. Critical Success Factors
10. Risk Analysis / Mitigation Plan
10.1. Risk Analysis
10.2. Mitigation Plan
11. Technology Selection
11.1. Hardware
11.2. Software
11.3. People
12. Project Execution
12.1. Project Synergy
12.2. Project Synergy
12.3. Resource Acquisition
12.4. Scheduling
12.5. Implementation
12.6. Training
12.7. Maintenance
12.8. Contingency
13. Evaluation Vs Baseline
14. Calibration & Sustainment
14.1. Training
14.2. Maintenance
14.3. Obsolescence
15. Continuous Improvement Process
15.1. Continuous Process Documentation
15.2. Audit
16. Management Of Change (MOC)
16.1. Applicability
16.2. Methodology