Category DataOps
dataops trends

Eight Top DataOps Trends for 2022

dataops trends

As a perfect combination of social, economic, and technical considerations urge organizations to embrace process-driven innovation, DataOps usage continues to rise. We present an annual prediction of trends that most greatly affect the DataOps enterprise software sector as a whole, based on our unique personal vision in the progression toward DataOps automation. Keep an eye out for the eight significant trends listed below, which we predict will have a large impact in 2022.

Accountability of AI

The worldwide AI market is expected to develop at a CAGR of 33% from 2017 to 2027, thanks to strong cloud computing applications and the proliferation of linked smart devices. The issue is that algorithms can absorb and perpetuate racial, gender, ethnic, and other social inequities at scale. Many in the data business are aware of the significant consequences of AI bias and are working to mitigate it.

The data industry understands that AI bias is a quality issue, and AI systems should be subjected to the same level of process control as a car coming off the assembly line. To make AI systems more responsive to stakeholders, data firms will implement sophisticated automated processes around them by 2022.

As part of their pre-deployment testing, model developers will look for AI bias. Like any other performance indicator, quality test suites will enforce “equity.” Biased models will not be deployed or continue to run without continuous testing, monitoring, and observability.

Organizations Commit to Remote Work

Data businesses are discovering methods to allow remote work as data and technologies become increasingly cloud-based. Web conferencing is helpful, but unexpected meetings at the water cooler are rare. Video conferences make it practically hard to perform procedures and workflows that rely on individuals with tribal knowledge huddling to solve problems.

As a result, businesses will analyze their data operations and analytics generation workflows from end to finish. Are they strengthening or weakening crucial communication and connections for your mission? Rather than allowing technology to become a barrier to collaboration, leading data businesses will extend process automation in 2022 to improve and streamline communication and coordination between departments. To put it another way, they’ll create using DataOps concepts that creates a robust, transparent, efficient, repeatable analytics process hub that unifies all workflows.

Data Becomes Meshier

Modular corporate architectures like data mesh will gain even more traction in 2022. By separating the system into distinct areas controlled by smaller, cross-functional teams, the data mesh tackles the challenges that plague big, complicated, monolithic data infrastructures. Each domain is a cluster of connected microservices that may be deployed separately and communicate with users or other domains via modular interfaces.

The issues of a decentralized organizational structure/architecture, such as a data mesh, are addressed by DataOps. A DataOps Platform integrates all of an organization’s domains into a single superstructure by spanning toolchains, teams, and data centers.

Data mesh and DataOps work together to enable decentralized innovation while harmonizing domain activities in a unified end-to-end workflow pipeline. Data mesh promotes autonomy, whereas DataOps is in charge of global orchestration and shared data.

Security and Data Governance

According to, cybercrime has surged by 600 percent since the start of the COVID-19 epidemic, including ransomware, malware, and phishing attempts. Hackers not only penetrate business firewalls to steal PII and credit card details from customers, employees, and suppliers to sell on the Internet, but they also use ransomware to lock company data until a ransom is paid.

Furthermore, the emergence of national and international frameworks controlling data usage (such as GDPR and CCPA) is pushing the need for data governance and data security policies to guarantee that data is managed and safeguarded.

The Ascension of DataOps Engineer

If data analytics were a factory, the DataOps Engineer would be in charge of the assembly line that produces data and analytics. The data factory is often run by hand in most enterprises. According to polls, data scientists and other data professionals spend more than half of their time carrying out procedures that support data operations. DataOps Engineers oversee the processing hub that automates data production and analytics development workflows, allowing the data team to be more productive, creative, and error-free. A DataOps Engineer may have a substantial influence on the data organization’s productivity. A recent LinkedIn job search revealed over 950 roles available for DataOps professionals. DevOps Engineer, a comparable position, was just named the number one job on LinkedIn.

Hub-Spoke Enterprise Architectures

Due to a scarcity of data scientists, businesses are looking for methods to put data in the hands of business users who can do self-service analytics. The emergence of distributed data architectures such as Data Mesh, along with DataOps automation, will result in Hub-Spoke designs that neatly mix the benefits of centralization and decentralization. A Hub-Spoke design, for example, may merge data from a variety of sources into a data lake. The data lake generates analytics platforms that are utilized by data scientists and business users to provide on-demand insights by orchestrating pipelines that span teams, toolchains, data centers, and organizational boundaries.

Observability of Data

Business executives may be unable to address problems and exploit opportunities if analytics and dashboards are wrong. When essential data is unavailable, every second matters. Data observability allows you to determine a system’s status by looking at its external outputs. You may more readily find the cause of an issue with a more visible system. Observability necessitates granular DataOps instrumentation linked to high-level dashboards and alarms. It involves utilizing tests, metrics, logs, and other artifacts to test and monitor data pipelines. Enterprises will integrate DataOps observability into data factories in 2022 to decrease mistakes, remove unwanted work, and cut error resolution cycle time.

XOps is Cool

XOps has developed as a significant component of business transformation operations, because of the increased usage of Artificial Intelligence and Data Analytics. DevOps, which combines development and operations, is where XOps got its start. Its goal is to improve company operations, efficiency, and customer experiences by implementing DevOps best practices. Its purpose is to ensure reliability, reusability, and repeatability while eliminating duplication of technology and processes. Overall, XOps’ main purpose is to help enterprises achieve economies of scale and drive commercial value by combining flexible design and agile orchestration with other software disciplines.


These are our top eight DataOps trends for 2022. They are not, however, the only trends that will increase and grow in 2022. Driving the composable business and adopting a Single Source of Truth to produce, manage, and maintain all the many aspects of the data ecosystem are equally critical as the other trends discussed in this article.


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