The expectation to achieve faster results continues to rise. Businesses everywhere are looking for ways to improve their operational efficiency and effectiveness to enable the best decision-making. The need to optimize typically comes to a head with the reality that there are many silos within any company. These two factors lead business leaders to seek a way to optimize for people, process, and technology within a single framework to solve their biggest challenges.
To date, the most widespread adoption of a holistic practice has been with DevOps. The focus on high velocity, iterative development has led to measurable impact in productivity, collaboration, and revenue. With the success DevOps has had in getting products to market faster, leaders are looking to tackle the most critical challenge today, trusted, high-quality data, in a similar way.
95 percent of businesses have experienced the negative impacts of bad data. So it is no surprise leaders across the market are sharpening their focus on automating business-ready data for use. This is where the DataOps practice comes into play. In a previous blog post, we defined what DataOps is and the core components that support a successful practice. To summarize: DataOps helps drive an efficient, self-service data culture by making business-ready data available to the right people and processes to make better business decisions through automation.
However, as leaders aim to drive adoption of DataOps, they need to be prepared to answer one of the biggest questions: what is the difference between DataOps and Devops?
While both are methodologies to drive operational best practices, each has their unique place in a business. For example, we have all accessed an in-house application to look up performance metrics or customer information. In the best-case scenario, the application is easy to use, navigate and find what you need. This is DevOps at work, ensuring that the end user is satisfied with an application that continuously optimizes the user experience.
Let’s keep that best-case scenario in mind and delve into a second component. Now that you found what you need, is it accurate? In this perfect world, the metrics and data you are looking at are in fact trusted, governed and pass the quality check. This would be DataOps at work, ensuring all data is ready to use, in real-time, and actionable for insights and big strategic projects—like AI.
The above was a simple example, but it shows how the two practices can work together. DataOps can stand alone, however, as the toolchain involved prioritizes self-service. This includes getting data sets directly in the hands of business analysts and users through a modern data catalog and to QA testers with a virtual test data pipeline. The guide below shows how DataOps and DevOps compare when it comes to the objectives, automation points, and benefits of each.
Like I mentioned at the start of this post, leaders will continue to look for ways to improve operational efficiency and effectiveness. New practices are emerging, like AIOps and MLOps, to accelerate model creation and lifecycle management of AI and ML respectively. These practices will intersect with DataOps – especially due to the emphasis in areas like data governance and regulatory measures – to ensure the models leverage business-ready data. DataOps becomes critical to support many of the emerging practices.
The shift to adopt DataOps is real. According to a recent survey, 73 percent of companies plan to Invest in DataOps. Accelerate your DataOps learning and dive into the methodology by reading the whitepaper Implementing DataOps to deliver a business-ready data pipeline.