As a software methodology, DevOps is widely considered to be quite a trailblazer. It set an example of the kind of wondrous improvements in productivity and product quality that teams can achieve through close interdepartmental collaboration. It inspired many other approaches that also had effective collaboration at their crux and aimed to revolutionize creations. A prominent example of such offshoot approaches is DataOps.

IBM suggests that DataOps can help enterprises reach their business outcomes confidently while also delivering gains in the productivity of data workflows. By bringing in elements like intelligent automation and effective collaboration, teams can utilize DevOps to deliver cutting-edge data products. It directly addresses all the different aspects of working with data and tackles everyday challenges.

This article puts forth the fundamentals of DataOps and how practitioners bring it to life. It will also discuss the potential obstacles you may face when implementing DataOps and what benefits you can expect from it.

Bringing The Magic Of DevOps To Data

The relationship between data and the world has become much more complex, especially in recent years. Data has been one of the significant factors involved in executive decision-making for companies and is extensively shaping the roadmap to the future. Therefore, enterprises are putting in heavy investments in procuring relevant and high-quality data for their purposes. 

However, effectively managing this data and utilizing it is still a challenge.

Enterprises often have trouble trusting or even correctly understanding the data. Their success can only be achievable when their business goals inform the data strategy, and a well-constructed data pipeline executes this strategy. Additionally, quality data analyses and processing cannot come at the cost of excessive amounts of time. These aims need to be achieved with speed and efficiency.

Such aims are the primary motivation behind the creation of DataOps. It took inspiration from DevOps and how it brought the often siloed development and operations departments into one collaborative workflow. Only this time, the focus was on data workflows and bringing insights to consumers quickly. With DataOps, enterprises can wrangle data much better and reap the advantages much more rapidly.

Processes And Challenges Involved With The DataOps Pipeline

Much like DevOps, DataOps also has three key elements that form the basis of all its practices and strategies: people, processes, and technology. Additionally, you can also include data since it is the main focus of the whole approach in the first place. The aim is to establish the right synergy between these elements and create a methodology that gets the best out of them. Such an observation is apparent when one takes a look at the DataOps workflow.

The workflow is best understood if viewed from the point-of-view of the data and tracking how its nature changes. We go through the three stages based on this approach below:

• Raw Data: This is the beginner stage where the data would be unstructured, unclean, and hardly usable. Before sourcing this data, we need to have built the infrastructure for the whole workflow. Afterward, the data needs to be organized and structured correctly by the appropriate data engineering practices. Once some semblance of arrangement begins forming within the data, an analyst can explore it to develop the right questions. Between all the above stages, feedback can flow back and forth. It can help the data architect build better infrastructure. Similarly, information from the analyst can guide the data engineer to better structure the data in a certain way.
• Polished Data: After further cleaning and structuring from the analyst, the data arrives at a much more understandable form. The analyst and engineer can continue transforming the data as required before sending it to a data scientist for modeling. Here, AI and ML help make and fine-tune powerful predictive models based on the data.
• Business-Ready Data: The data has passed significant processing by now and is ready to deliver business outcomes. A data architect is responsible for constructing pipelines that operationalize the data and help govern it as it moves further. Finally, the data reaches the end consumer, and further feedback can flow back to better the workflow.

Unsurprisingly, the DataOps pipeline has an architecture where each stage directly addresses one of the main challenges. The aim also includes utilizing automation to the best extent to move one step closer towards better efficiency. Automation also helps achieve the more crucial goals like continuous delivery of data and integrating feedback for constant improvement of the pipeline.

Implementing DataOps on an enterprise level is not simply a technical endeavor. While it does involve adapting the right technological tools and bringing a workflow to life, it also depends on cultivating a mindset. All stakeholders who either work under DataOps or use it need to learn about the culture it requires. Therefore, a successful implementation shall require the enterprise to become truly data-driven, and in some cases, this might be the biggest challenge.

The Benefits of DataOps In A World Increasingly Defined By Data

With DataOps by your side, you can expect to see significant progress in your daily dealings with data. However, there are several long-term advantages that come into view when you start looking into establishing DataOps. Such benefits make the challenges and obstacles a worthy investment for the enterprises.

Some of the main benefits that come with DataOps are briefly summarized below: 

• Getting Better Quality Data: With automated processes that effectively clean the data and get better with consistent feedback, we get access to much better data in much less effort and brainpower. With feedback helping to improve the workflow, the quality keeps getting better too. The improved quality can make all the difference in achieving business outcomes efficiently.
• Viewing The Data Flow From A Zoomed-Out POV: As the data passes through several processes that may change its structure completely, it can get hard to keep track of its nature. DataOps can bring an aggregated picture of the data flow across organizations to the end-user. With such an overarching viewpoint, you can easily spot the bigger trends and utilize such insights to your advantage as well.
• Better Analytics Through Collaboration: By emphasizing constant data collaboration, you get better insights and overcome common analytic obstacles. Analysis forms an integral part of the workflow and also informs other stages of the data. It also leads to better AI/ML outcomes that translate to more robust models.
• Improved Data Literacy In Stakeholders: One of the main requirements for effective implementation of DataOps is that all technical and non-technical stakeholders need to be data literate. Business users especially require effective data communication skills if they hope to get the most out of DataOps. Overall, it will help the enterprise push towards an overall more data-driven mindset necessary to survive in today's age.
• Easier Deployment Of Pipelines Into Real-World Applications: Most real-world problems present themselves quite suddenly and don't necessarily wait around for you to create an effective data pipeline to tackle them. With DataOps, building and deploying pipelines customized to directly address the problem becomes much more accessible. With the infrastructure already set up, the solution takes much less time to form.

Such benefits explain why many enterprises are interested in adapting to the DataOps culture. The tools involved are constantly improving too and DataOps will mature accordingly. In a few years, we can expect to witness DataOps as the norm in any enterprise wanting to harness the true power of data and its insights.