DataOps is hype right now, a big trend for 2022, but it can be difficult to understand what it is and how it fits into modern companies.
So first, what is DataOps?
DataOps is a set of practices, cultural norms, architectural patterns and techniques for implementing data management and data engineering to provide continuous data for modern analytics to face changing circumstances. It enables:
Rapid innovation and experimentation to deliver new insights to customers at an increasing rate;
Extremely high data quality and low error rates;
Collaboration across complex people, technologies, and environments;
Clear measurement, monitoring, and transparency of results.
Now, let's hunt some myths about DataOps.
1. Traditional data integration can support DataOps
Traditional data integration was not designed for the DataOps world.
Traditional data integration would have data engineers try to keep up with the change manually, and with hundreds of applications and systems in an organisation and always changing, this can quickly become unmanageable. DataOps automates and streamlines the process as much as possible, leaving data engineers to the important work of building new data pipelines and delivering continuous data.
Any small change to any source or destination, such as a version upgrade or a data type change, can cause disruption and pose a significant accuracy risk leading to data loss or data corruption. It is necessary to anticipate unexpected changes and be able to handle them appropriately.
2. DataOps is very complex to an organisation
Actually, is more the opposite. DataOps reduces business complexity by incorporating DevOps principles for automation and monitoring throughout the productivity lifecycle. DataOps technologies are also used to build systems that can adapt to change and provide self-service to those who best understand an organisation's data needs. By implementing a data pipeline lifecycle, organisations enable data engineers to rapidly scale and integrate systems while enhancing the stability and resiliency of the data pipelines built.
By training data engineers with a DataOps mindset, it is possible to overcome the shortcomings and friction of traditional data integration, simplify processes through automation, make tasks easier, and drive better business outcomes.
3. DataOps it’s something aspirational to strive for
Many companies are already using the power of DataOps to accelerate their business. Rather than seeing DataOps as an endeavour, think of it as a way to achieve automation and performance goals.
4. Isn’t DataOps just DevOps for Data?
Almost everyone makes this assumption the first time they hear the word DataOps. A bit semantically misleading, the term "DataOps" conveys that data analytics can achieve what software development achieves through DevOps. This means that DataOps can improve quality and cycle time by an order of magnitude as data teams adopt new tools and methodologies. DevOps optimises the software development pipeline. This allows companies like Amazon, Netflix and Google to run millions of versions of the code each year. DataOps also accelerates the development of software, but at the same time must manage dynamic data operations. DataOps includes DevOps and other approaches to the unique challenges of managing mission-critical data operations pipelines.
Should we be sceptical of the hype around DataOps?
Probably, but DataOps is built on a solid foundation that includes Agile Development, DevOps, Lean Manufacturing, and Statistical Process Control. These proven methods have been added value to companies and businesses for decades.
DataOps is here, and it's time to implement it. By adopting a DataOps mindset, organisations can help remove the bottlenecks and inefficiencies of traditional data integration while empowering everyone on the team, regain business agility and trust in the data they deserve.
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