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Sprint Toward Data-Driven Culture Using DataOps

“Culture eats strategy for breakfast.” - Peter Druker

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Contributors

  • Pablo Manzanera, Operations Manager, ALLEGIS Group
  • Chris Maughan, Sr. Vice President, Marketing Data & Analytics, SunTrust Bank
  • Amar Murad, Vice President, Business Technology, Ceridian
  • Tony Facca, CIO, NASA GRC
  • Nick Harvey, Lead Developer Advocate, Pachyderm

Your Challenge

  • 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.

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.

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.

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.

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.

Guided Implementations

This guided implementation is an eight call advisory process.

Guided Implementation #1 - Discover benefits of DataOps

Call #1 - Learn about the concepts of DataOps and the common use cases.
Call #2 - Understand what drives the business to improve data operations.

Guided Implementation #2 - Assess your data practice for DataOps

Call #1 - Determine the technical requirements for the integration solution.
Call #2 - Recognize the gaps your organization must overcome to effectively implement your DataOps initiative.
Call #3 - Create an actionable roadmap to mature DataOps within your organization.

Guided Implementation #3 - Mature your DataOps practice

Call #1 - Define DataOps teams, roles, and responsibilities.
Call #2 - Establish metrics to measure your data initiatives performance.
Call #3 - Create a communication plan to share next steps.

Onsite Workshop

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Onsite 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 onsite 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

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.

  • Organization's data priorities and metrics
1.3

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

2.1

Assess current state.

  • Current state summary
2.2

Develop target state summary.

  • Target state summary
2.3

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

3.1

Continue DataOps improvement initiatives.

3.2

Document the improvement initiatives.

3.3

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

4.1

Create target cross-functional team structures.

  • DataOps cross-functional team structure
4.2

Define DataOps metrics for continuous monitoring.

  • DataOps metrics
4.3

Create a communication plan.

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