DataOps (from ‘data operations’) is a practice with huge potential to revolutionize data management and industrial operations. In this article, we explore what it means, why it is different from other practices, and how enterprises can use it to achieve greater success.
What is DataOps?
Let’s start by defining what DataOps really is. It’s interesting to note that both of the definitions below emphasize the centrality of collaboration to the concept.
Another common thread is the focus on integrating data across different parts of an organization. DataOps is very much about breaking down silos and optimizing the broad availability and usability of data.
Ultimately, DataOps aims for predictable data delivery and change management, using technology to automate, orchestrate, and operationalize data use and value dynamically.
DataOps: not to be confused with DevOps...
It’s helpful at this point to take a step back and look at what DataOps is not. DevOps has been around for some time and, despite some superficial commonalities, is different. Both are methodologies used to enhance operational practices, but there the similarity largely ends.
The focus for DataOps is the delivery of business-ready, trusted, actionable, high-quality data, available to all ‘smart engineers’ or ‘data citizens/consumers’. Automation efforts center on data governance and integration. Another key goal is alignment between IT system support, operations, and the business.
The focus for DevOps, on the other hand, is software and application development. Automation efforts center on the development cycle, software delivery processes, and waste elimination. The alignment of developers, operations, and the business is a major aim.
What’s the current status of DataOps adoption?
DataOps is still a relatively new concept, awaiting the development of concrete best practices. Confusingly, some technology providers are opportunistically seizing on the ‘buzz’ around the term, and applying it to existing solutions.
The growing interest in DataOps stems from the recognition that many processes and procedures are broken, and there is a need for greater data collaboration, education, and tooling evolution. Currently, organizations are using parts of DataOps to begin to drive change around data and analytics, but there is a long way to go.
How will DataOps impact business?
For enterprises that implement DataOps successfully, there will be a transformational shift in the speed of delivering data management and integration solutions to the organization.
Continuous and reliable delivery of data will improve the speed and effectiveness of:
- Business intelligence (BI) initiatives
- Continuous intelligence efforts
- Operational data uses
- Data for machine learning (ML) applications
- Other data-reliant activities
In addition, the drive towards automation and more reliable pipelines of data is set to minimize risk and multiply data deployment opportunities within organizations.
The table below outlines a number of key solution features that enable DataOps:
How to achieve success with DataOps
For organizations keen to extract the benefits of the new and emerging practice of DataOps, Gartner offers the following advice for launching a project:
- Target a tight, well-defined scope.
- Ensure there is a level of executive sponsorship: ideally from the CDO or other high-level data and analytics leader. This is key as DataOps represents a new way of servicing data consumers.
- Be prepared to encounter and overcome resistance to changing existing practices as the concept is introduced.
- Exploit other emerging practices, like data literacy, to deliver on the promise of DataOps.
- Remember: DataOps is a practice, not a technology or tool. It is a cultural change that is supported by tooling.
DataOps is fundamental to driving up data literacy, the ability to read, write, and communicate contextualized data. Data literacy, in turn, is key to obtaining optimal value from data and digital. Therefore, the more organizations embrace DataOps, the better equipped they will be to truly harness the transformative potential of data.
Vice President of Product Marketing: Petteri’s professional career spans across enterprise SaaS technologies, where he has found himself at the intersection of emerging transformational technology development and its commercial applications for customers. Prior to Cognite, Petteri worked in senior product management, marketing, sales, and general management positions for companies such as at Sumea, Rovio, and Cxense. Petteri has a master’s degree in technology from Aalto University in Helsinki.