By 2025, the total amount of data created, captured, copied, and consumed globally is projected to reach more than 180 zettabytes. With this rapid growth, the ability to harness data for business impact is even more vital. To keep up with the exponential data growth and resulting challenges, data teams must adjust the way they operate. Best practices like continuous integration/continuous deployment (CI/CD) have made their way into the data engineering world to help data teams handle the speed and scale at which organizations are running analytics.
We have reached an inflection point where teams need to make their data systems more resilient in order to deliver data in a timely manner. To do this, many are borrowing DevOps practices from the domain of software engineering to scale and automate data projects like never before.
As such, a new Data Management practice is taking center stage in the enterprise: DataOps. Born out of DevOps, DataOps takes common practices in modern software development and applies them to data projects. These can include concepts like continuous integration, version management, unit testing, integration testing, and automated deployment. It involves a data-specific focus on automation and requires continual measurement that can help organizations glean more insights from their data.
However, many companies conflate DataOps with DevOps. Despite the fact that they share similar principles, like automation, the two are not synonymous. Enterprises that mistakenly view DataOps as simply “DevOps for data” miss out on the full range of benefits that DataOps can deliver.
For example, in databases we often talk about “primary keys.” Primary keys help data engineers match different sources of data to allow richer analytics. It might be an email address, a social security number, or product code that helps match different datasets together, thus allowing the sales team to know which marketing leads are the most likely to convert to paying customers. What makes DataOps different from DevOps is that the tools have an understanding of the inherent relationships that exist in data. It knows that dates in March can be one of 1 to 31 days, whereas in September it’s 1 to 30.
Under the guise of DataOps, many organizations take the DevOps automation practice and use it to move pieces around. However, they leave out a few key additional components of DataOps – testing, measurement, and a data-specific lens. This is ultimately unhelpful for organizations because they are not fully identifying the relationships in data that DataOps helps to uncover, and therefore not actually utilizing the practice of DataOps.
While DataOps shares similarities with DevOps, it also has its own specific data use cases. For example, DataOps can be applied to identify relationships, encryption, errors or anomalies, or any other business-specific logic an organization may code for in the automation process. It takes the automation process found in DevOps a step further through a data-specific lens and continual testing to the process. This ensures that at every single stage, the date is improving or trending in a particular direction or not violating a specific set of rules.
The Benefits of DataOps
With a strong grasp of what DataOps actually entails, organizations will be able to unlock its full potential.
On a personnel level, successful DataOps can help to foster a sense of community within a company. Data teams will gain a clear understanding of the impact their work has on different stakeholders, and, as a result, feel more connected to the rest of the business. By seeing the benefits they are producing, it creates a circuitous relationship that can inspire data teams to innovate. This helps create an organizational culture in which teams across the company can use data insights to quickly identify processes that can be optimized or otherwise fixed to better the business.
Best Practices to Know
The benefits of DataOps are profound, and while some organizations are still working to differentiate it from their DevOps processes, there are a few best practices that enterprises should follow in implementation.
1. Use a data-specific lens on automation: Without taking a data-specific lens in the automation process, an organization is not using DataOps. The data lens focuses on identifying relationships within the data, which can help to determine whether something is a problem for the organization. While automation alone may detect that something is not encrypted, the data lens will detect what type of information is not encrypted.
2. Measure – and work backwards: Another key differentiator for DataOps is the focus on consistent and constant metrics and measurement. The metrics measured will vary depending on the enterprise’s goals, but to maximize the benefits DataOps can provide, it is necessary to measure these metrics at certain stages to ensure the right work is being done. It is also helpful for data teams to work from the destination back toward the source. By starting with the problem, identifying the baseline, and figuring out how and where they want to improve, organizations can stay focused on their priorities.
3. Build data literacy within the organizational culture: In order to feel the full benefits of DataOps, organizations should invest in establishing a data-oriented culture. Building and encouraging data literacy ensures that the organization understands the importance of data and how to interpret what the data says, which permits more people across the business to identify organizational issues faster, thus leading to quicker and more efficient solutions.
It is vital for enterprises to understand what DataOps is and how to correctly implement it to improve time to value, data quality, and other facets of enterprise operations – ranging from organizational spend to the customer experience.
Although it is an early claim for organizations to say that they are fully implementing DataOps, it is certainly a great sign that they recognize the value DataOps can have on their business. Working to better understand DataOps and how to properly leverage it will be key to staying agile and remaining competitive.