DataOps is as much about managing people as it is about tools. The resources for dataops will be data scientists, engineers, analysts who just want to analyze the data and build models.
DataOps: Best Data Management Practices:
Any DataOps implementation can adopt and follow these best practices.
Start small and build incrementally
Agile methodology is the inspiration for the whole DataOps philosophy.
The objective of the data ops is to steer the function of the data analytics team towards business goals and objectives. Lay down business priorities for the data team and review them fortnightly or monthly to kickstart the process.
To achieve this, insist on a cross-functional team structure and work on improving collaboration.
Building incrementally is the core principle of agile. Agile data processes focus on starting quickly with the data subsets and then focusing on incremental value delivery while incorporating feedback from the end-users. The agile data mastering process needs to be incremental, automated, and collaborative to streamline seamless formation of data pipelines.
Build operationally supportive applications:
Data analytics teams usually source huge amounts of data analyzed by machines mostly.
Consider cases when these data sources can be directly mapped with operational teams that use insights from this data. Empower your data developers to build apps that support multiple internal operations.
The new apps developed must be treated and built like software development projects to make sure that data always stays updated. You need to allocate one resource or more within your data teams who can take data from its source, analyze it, and bring it to a point where internal teams can make use of it.
Create business data glossaries and catalogs
A glossary helps in answering various questions about the data itself. These are mostly questions such as the technical name, definition, and function of a particular type of data in different systems within the organization.
Catalogs are like supersets that go beyond glossaries. They provide more metadata about the structure of the data. The creation of catalogs presents unique collaborative opportunities with teams that are the end consumers of data.
Cataloging helps users understand deeper aspects of the data such as its locations, its users, and best practices for leveraging it.
Enable self-service mechanisms for using data
Most data analytics teams tend to do their own data preparation when they have no data available for a specific use case. They self-sourced the data and use whatever tools they can find, internally or externally, to prepare it for their use case.
This kind of self-service data prep needs to be an organization-wide initiative. This initiative provides business users with the capabilities to explore, manipulate, and merge new data sources.
A proactive data analytics team goes beyond past and current data use cases and predicts data needs for frequently and rarely utilized use cases. This can be achieved with the timely collaboration between the business teams and data teams.
Use automation to anticipate source changes to avoid downtime
When a data source changes its format or becomes unavailable, affecting apps that use that data, it becomes a key problem for DataOps teams. Thus, causing downtime and these apps are often not ready to handle the changes.
For enterprise DataOps teams, handling of source changes in the least disruptive way and is non-negotiable. Downtime caused by one source can disrupt multiple systems and affect multiple teams.
Smart DataOps systems have apps that can work with updating data sources and detect the changes automatically. Mechanisms are built in such a way that they safely propagate change information to affected apps with zero (or minimal) downtime.
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