- Data teams do not have a mechanism to integrate with operations teams and operate in a silo.
- Significant delays in the operationalization of analytical/algorithms due to lack of standards and a clear path to production.
- Raw data is shared with end users and data scientists due to poor management of data, resulting in more time spent on integration and less on insight generation and analytics.
Our Advice
Critical Insight
- Data and analytics teams need a clear mechanism to separate data exploratory work and repetitive data insights generation. Lack of such separation is the main cause of significant delays, inefficiencies, and frustration for data initiatives.
- Access to data and exploratory data analytics is critical. However, the organization must learn to share insights and reuse analytics.
- Once analytics finds wider use in the organization, they need to adopt a disciplined approach to ensure its quality and continuous integration in the production environment.
Impact and Result
- Use a metrics-driven approach and common framework across silos to enable the rapid development of data initiatives using Agile principles.
- Implement an approach that allows business, data, and operation teams to collaboratively work together to provide a better customer experience.
- Align DataOps to an overall data management and governance program that promotes collaboration, transparency, and empathy across teams, establishes the appropriate roles and responsibilities, and ensures alignment to a common set of goals.
- Assess the current maturity of the data operations teams and implement a roadmap that considers the necessary competencies and capabilities and their dependencies in moving towards the desired DataOps target state.
Sprint Toward Data-Driven Culture Using DataOps Research & Tools
Start here – read the Executive Brief
Read our concise Executive Brief to understand the operational challenges associated with productizing the organization's data-related initiative. Review Info-Tech’s methodology for enabling the improved practice to operationalize data analytics and how we will support you in creating an agile data environment.Besides the small introduction, subscribers and consulting clients within this management domain have access to:
- Sprint Toward Data-Driven Culture Using DataOps – Phases 1-3
1. Discover benefits of DataOps
Understand the benefits of DataOps and why organizations are looking to establish agile principles in their data practice, the challenges associated with doing so, and what the new DataOps strategy needs to be successful.
- Sprint Toward Data-Driven Culture Using DataOps – Phase 1: Discover Benefits of DataOps
2. Assess your data practice for DataOps
Analyze DataOps using Info-Tech’s DataOps use case framework, to help you identify the gaps in your data practices that need to be matured to truly realize DataOps benefits including data integration, data security, data quality, data engineering, and data science.
- Sprint Toward Data-Driven Culture Using DataOps – Phase 2: Assess Your Data Practice for DataOps
- DataOps Roadmap Tool
3. Mature your DataOps practice
Mature your data practice by putting in the right people in the right roles and establishing DataOps metrics, communication plan, DataOps best practices, and data principles.
- Sprint Toward Data-Driven Culture Using DataOps – Phase 3: Mature Your DataOps Practice
Workshop: Sprint Toward Data-Driven Culture Using DataOps
Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
1 Identify the Drivers of the Business for DataOps
The Purpose
Understand the DataOps approach and value proposition.
Key Benefits Achieved
A clear understanding of organization data priorities and metrics along with a simplified view of data using Info-Tech’s Onion framework.
Activities
1.1 Explain DataOps approach and value proposition.
1.2 Review the common business drivers and how the organization is driving a need for DataOps.
1.3 Understand Info-Tech’s DataOps Framework.
Outputs
Organization's data priorities and metrics
Data Onion framework
2 Assess DataOps Maturity in Your Organization
The Purpose
Assess the DataOps maturity of the organization.
Key Benefits Achieved
Define clear understanding of organization’s DataOps capabilities.
Activities
2.1 Assess current state.
2.2 Develop target state summary.
2.3 Define DataOps improvement initiatives.
Outputs
Current state summary
Target state summary
3 Develop Action Items and Roadmap to Establish DataOps
The Purpose
Establish clear action items and roadmap.
Key Benefits Achieved
Define clear and measurable roadmap to mature DataOps within the organization.
Activities
3.1 Continue DataOps improvement initiatives.
3.2 Document the improvement initiatives.
3.3 Develop a roadmap for DataOps practice.
Outputs
DataOps initiatives roadmap
4 Plan for Continuous Improvement
The Purpose
Define a plan for continuous improvements.
Key Benefits Achieved
Continue to improve DataOps practice.
Activities
4.1 Create target cross-functional team structures.
4.2 Define DataOps metrics for continuous monitoring.
4.3 Create a communication plan.
Outputs
DataOps cross-functional team structure
DataOps metrics