Many organizations have yet to make their operations data-driven, which increasingly stands in the way of fully accomplishing their strategic goals. DataOps is an effective way to meet this challenge.
“DataOps” is the process of analyzing data, automating data insights, and distributing them to people throughout the organization. It’s the key to getting value from the data a company collects or makes an investment to acquire. DataOps provides a way to extract value from that data and make it accessible to employees throughout the company who need it to improve their job performance and business outcomes.
Another factor that DataOps addresses is that, in general, data loses its value over time. For some organizations, the window in which to use data can be months long. However, in others, minutes count. DataOps processes ensure that a company’s systems and teams have access to data when needed –sometimes in real time. This gives companies the agility to adapt to changes in their operations, markets, customer demands, and other dynamics and keep their operations on track to provide the best customer experiences, maintain efficiency, and maximize profitability.
Making DataOps Work
DataOps isn’t a one-and-done project. Outcomes improve when the data team regularly meets with data consumers to understand their challenges, learn their most critical needs, and collaborate to find solutions. Your DataOps team should include stakeholders from throughout your organization, and members should have responsibilities based on their roles and areas of expertise. Additionally, as data volumes and the scope of analyses grow, DataOps team members must work together to refine or rework processes that are sustainable as the operation scales.
Companies must equip their DataOps teams with the right tools to access, integrate, model, and visualize the data they work with. Providing analytical teams with technical environments where they can replicate the production environment can be a crucial step in keeping experimentation costs to a minimum. When teams focus on technical quality and sound, simple design can help control costs, and the results are almost always better than when designs become more complex.
It’s also important for teams with the goal of democratizing data insights to ensure that data consumers of all IT skill levels can access the information they need for informed decision-making. No-code tools or platforms leveraging generative AI for “conversational” user experiences can help bring data access within reach of more employees.
Additionally, companies need effective ways to manage data analytics tools and ensure they work together seamlessly. When DataOps processes are effective, they produce repeatable, reliable outcomes, but, more importantly, employees throughout the organization can easily build data-driven decision-making into their workflows, and performance improves.
Similarities Between DataOps and DevOps
You probably see the parallels between DataOps and DevOps. While DevOps focuses on making software delivery more efficient, DataOps does the same for data insights. Additionally, DevOps has a goal of breaking down silos and paving the way for collaboration between development and operations teams, DataOps aims to make it easier for data teams and data consumers to work together to reach business goals.
The better an organization gets at analyzing data and sharing insights in an agile and secure way, the faster it can see positive changes and improved performance at an employee and company level. Pave the way with DataOps.
