DataOps Maturity Model: The Journey to DataOps Success

Avatar photo Haley Teeples December 23rd, 2020

Zaloni Founder and Chief Product Officer, Ben Sharma, has been a long time pioneer for data governance. In his webinar last week, Data Governance Framework for DataOps Success, Sharma discusses the importance of a DataOps approach to standardize data governance within your organization’s cloud data environment. The webinar provides a deep dive on the common challenges data producers and consumers face and walks viewers through the 5 stage DataOps Maturity Model. 


Solving Cloud-Based Challenges

Data ecosystems are becoming increasingly complex with numerous data types, multiple data environments, and a growing number of technology vendors. In an environment as complex as the one described, Data Sprawl becomes a common occurrence. According to a CrowdFlower report, “60% is the estimated fraction of time that data scientists spend cleaning and organizing data.” As data becomes more siloed and decentralized, data consumers face great difficulty finding, managing, and ensuring the accuracy of data sets. Additionally, creating data pipelines are time-consuming, lack automation and are often created for single-use. 

In addition to complex and inefficient pipelines, inconsistent governance across the data ecosystem is another major concern. Without proper data governance in place, data producers cannot ensure data quality, security and access. Creating a data governance framework in the cloud is a step in the right direction towards developing self-service access to data. Traditionally, data consumers must request the desired data through the data producer or owner. The data producers would embark on a timely process to find and make the data accessible. With the proper governance in place, data producers can provide their consumers with self-service access to a catalog of trusted data. 


Identifying and Planning Your Path to DataOps Maturity 

Now that we just discussed the common challenges that companies face with their transition to the cloud, the next step is how to solve them. Overcoming these challenges by implementing data management techniques that maximize efficiency at each stage of the data supply chain will help companies enable business insights, develop new products, and keep customers happy and long-term. 

As Sharma testified in the webinar the first step in developing your data management and governance approach in the cloud is to identify the stage of your company’s DataOps maturity within the model below. The model has five stages: Unmanaged, Managed, Operationalized, Governed and Augmented. 

Each stage describes specific data characteristics to depict each stage of maturity and the business impact of each stage: 

Unmanaged: Typically companies progress from an unmanaged state with limited visibility and mainly used for ad-hoc reporting or analytics. 

Managed: Once they begin implementing data management they are able to improve data visibility and understanding through metadata and cataloging.

Operationalized: Once proper data management is in place, companies may begin operationalizing many of the steps in the data supply chain, improving efficiency and reducing costs. 

Governed: With operationalized data pipelines in place, companies can begin to focus on various aspects of data governance in this stage to make trusted data accessible in a self-service manner, ultimately reducing time to insight and adding value to business use cases. 

Augmented: Enabling ML and AI techniques in the DataOps pipeline provides a frictionless and more timely delivery of trusted data to its end users. 

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DataOps to the Rescue

At Zaloni, DataOps is the guiding principle for unlocking transformative business insights through data. DataOps is a methodology that views data as a product and the data supply chain as an infinite, unified, and collaborative process. This holistic approach to data management creates much-needed visibility and control for data producers from ingestion to consumption. 

In reference to the DataOps Maturity Model, the ultimate goal is to get a data environment to the augmented stage where data processes are streamlined through use of AI and ML, data governance is standardized, and pipelines are both efficient and reusable to fulfill company use cases. Having access to clean and trusted data delivers the peace-of-mind to the data consumers while relieving the burden on IT. 

Arena, Zaloni’s end-to-end DataOps software platform is the unified approach to cloud data management. Embracing the holistic approach that is DataOps, Arena incorporates: 

  • Integrated data supply chain
  • Augmented data catalog
  • Standardized data governance
  • Self-service data provisioning and access
  • Simplified and collaborative UI for easy and understandable data management

Want to learn more about DataOps? Check out some of our resources:

In addition to these Zaloni resources, be sure to visit our website to book your custom demo with our team of data experts.

dataops maturity

about the author

Haley Teeples is a recent graduate from North Carolina State University and has been working at Zaloni for over a year. She initially joined Team Z as a Marketing Intern and then worked as a Technical Documentation Intern on the Engineering team. With a clear passion for content creation and learning in the data management space, today, Haley serves as an associate on Zaloni's Product Marketing team.