- 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.
Member Testimonials
After each Info-Tech experience, we ask our members to quantify the real-time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this blueprint and what our clients have to say.
9.0/10
Overall Impact
$10,399
Average $ Saved
9
Average Days Saved
Client
Experience
Impact
$ Saved
Days Saved
CIBC Caribbean
Guided Implementation
9/10
$10,399
9
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.
Module 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
Outputs
Explain DataOps approach and value proposition.
Review the common business drivers and how the organization is driving a need for DataOps.
- Organization's data priorities and metrics
Understand Info-Tech’s DataOps Framework.
- Data Onion framework
Module 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
Outputs
Assess current state.
- Current state summary
Develop target state summary.
- Target state summary
Define DataOps improvement initiatives.
Module 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
Outputs
Continue DataOps improvement initiatives.
Document the improvement initiatives.
Develop a roadmap for DataOps practice.
- DataOps initiatives roadmap
Module 4: Plan for Continuous Improvement
The Purpose
Define a plan for continuous improvements.
Key Benefits Achieved
Continue to improve DataOps practice.
Activities
Outputs
Create target cross-functional team structures.
- DataOps cross-functional team structure
Define DataOps metrics for continuous monitoring.
- DataOps metrics
Create a communication plan.