How to Get Started with DataOps

Wondering how to get started with DataOps? We have gathered the most important points and put together 10 steps to get started with DataOps.

10 Steps to Implement DataOps

If you want to implement DataOps in your organization, you can use the following steps as a starting point:

1. Understand the DataOps principles: Begin by deepening your understanding of the DataOps principles and how they can help. It is important to understand that DataOps is more than just a set of tools; it is about changing how teams work together and how they approach data.
2. Establish needs and goals: Identify the problems and challenges your organization faces with its current approach to data management. Define the goals you want to achieve with DataOps, such as faster data deliveries, improved data quality, or increased flexibility.
3. Identify key stakeholders: Identify key stakeholders, such as managers, data engineers, data analysts, and business users. It is important to get support and engagement from these stakeholders to succeed with the implementation.
4. Build a cross-functional team: Assemble a team of people with diverse skills, including data engineers, data analysts, data scientists, business experts, IT infrastructure, and IT security. Ensure that the team understands the principles of DataOps and how they apply to their work.
5. Find the first use case: The first step toward DataOps involves ingesting raw data and developing an infrastructure that makes it easily accessible for use, typically in a self-service model based on a first use case that delivers business value. Then the capabilities of the platform can be gradually expanded as the use cases require it.
6. Establish architecture principles: Establish some overarching principles for what the architecture should look like in the long term, and select the technologies required only for the first use case.
7. Implement DataOps tools: Select and implement the tools that best suit your needs. This may include tools for data collection, data integration, data validation, automated testing, monitoring, and deployment. Define semantic rules for data and metadata early on, so that you maintain control over the data.
8. Adopt DataOps methods: Use Agile methods, such as Scrum or Kanban, to manage the workflow and ensure that the team can respond quickly to changes. Use DevOps/DataOps principles and tools to automate data delivery and monitor quality.
9. Start small and scale gradually: Begin with a small, manageable part of the data landscape and expand as you become more comfortable with DataOps practices and gain experience with what works best for your organization.
10. Measure and improve: Use KPIs to measure the success of the DataOps implementation, and use this data to drive continuous improvement. This may include measurements of data quality, the speed of data deliveries, and user satisfaction.

DataOps is an iterative process, so be prepared for the need for adjustments and changes along the way. The most important thing is to build a culture of collaboration, continuous learning, and improvement.

Working in an Agile Way Requires Time, Experience, and Support

It takes time and experience to get an agile team to perform well, regardless of whether we are talking about data or other areas. It also requires new thinking and actions from the surrounding organization. If the relevant stakeholders around your product do not understand or are not able to support agile deliveries, it is difficult to succeed.

It usually pays off to hire an agile coach for at least a couple of months to get the data team on the right track. Preferably an agile coach with experience in developing data products.

Do not break up the team without a good reason: it always takes time for a new team to perform well, even when the individuals are experienced.