Defining DataOps

Defining DataOps, Data Pipelines and Their Impact on Analytics Success 

DataOps is one of those data buzzwords that has come and gone over the past few years, but now it’s here to stay as many companies have attributed the success of their analytics initiatives to the innovative approach that goes beyond data management technology. In this blog post, we’ll be defining DataOps, how it’s helping companies overcome their data challenges, and why a DataOps platform is the key to success.

Defining DataOps

DataOps has come even more into focus today, as companies grapple with increasingly complex data ecosystems. The definition of DataOps may vary, but essentially the term is used to describe the discipline of managing the data supply chain. DataOps is a data management methodology vs a piece of automation or technology that companies use to control and have a transparent view of their data across every data pipeline from ingestion to end-user consumption. 

DataOps combines the principles of agile development, made famous by DevOps, with operations management. DataOps is agile, process-oriented, and delivery-focused to improve efficiency, reduce costs, and deliver analytics-ready data to analysts and data scientists. 

Gartner describes DataOps as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.”  

The DataOps methodology takes into consideration people, processes, and tools. Executing effectively requires extensible technologies that offer collaboration, automation, and self-service capabilities.  

DataOps and the Data Pipeline: Going beyond data management technology

Traditionally data pipelines were seen as workflows needed for data ingestion or ETL processing. Today the term is used more broadly to describe any process where data is moving from one function to another, these include things like data quality checks, matching and mastering for golden records, provisioning to an analytics tool, or performing a data transformation.  This means that enterprises will now have 100s if not 1000s of pipelines between their IT, engineering, analytics, and data science teams.

The following diagram illustrates Zaloni’s Arena DataOps Pipeline: 

In the center, you see the data pipelines flowing from source to business user, with the typical roles involved at each stage. Surrounding the pipeline, you see all of the various data management and governance tasks that are involved with managing the larger data pipeline. 

The reality for many companies is that they have not only data but also data pipelines sprawled across their organization in various environments and siloed in lines of business. This fragmented environment creates a lack of visibility and control, which can be a risk for data security and regulatory compliance. It also hinders automation, productivity, and collaboration, which are critical elements of a successful DataOps approach. 

Streamline Data Pipelines with a DataOps Platform 

At Zaloni, our approach is to provide a unified view and control of all data pipelines within an organization, a meta-pipeline if you will, to streamline DataOps in a way that’s governed and secure.   

Zaloni’s DataOps platform, Arena, provides management and governance of data pipelines from source to consumer accelerating the time to analytics insights while reducing costs through process improvements and automation. 

Visibility and control over the data pipeline activates end-to-end data governance, mitigating risk, and ensuring regulatory compliance. 

Arena’s extensible platform connects to any data source wherever it resides, whether in the cloud or on-premises. The platform’s extensibility helps companies overcome challenges related to data sprawl and makes it quick and easy to add or connect to new data sources, improving data agility. 

Arena provides a collaborative, self-service data catalog where users can quickly find relevant data. Once a data set is found, a user can annotate, tag or share the data, improving data reliability and productivity. Finally, a user can provision the data to a sandbox environment or analytics tool. 

Having a DataOps platform in place lays the foundation for agile and governed data pipelines that serve today’s most business-critical use cases. 

Contact us today to see our DataOps platform, Arena, in action!