The inability of businesses to effectively interact with their data prevents them from using disruptive technologies, such as AI. ‌ ‌

DataOps’ progress may be able to solve the issue. The phrase “DataOps” first appeared seven years ago to describe best practices for obtaining accurate analytics. Research company Gartner describes it as a prominent trend involving all stages of the data lifecycle. Gartner also projects that, due to the development of AI orchestration platforms, the proportion of businesses that have operationalized their AI initiatives will jump from 8% in 2020 to 70% in 2025. ‌ ‌

DataOps trends refer to increased cooperation between various teams managing data and operations teams releasing data into apps, much like how the DevOps trend improved the workflow for coordination between developers and operations teams. 

Have a look at the top 8 DataOps trends that are redefining global industries.

What is DataOps?

DataOps: DataOps Trends
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Data management techniques known as “DataOps” regulate the flow of information from source to value, accelerating the process of extracting value from data. DataOps trends are a broader cultural shift that clarifies the intricacies between the “data backend” and the “data frontend,” not simply the tools and procedures.

DataOps comprises a set of procedures and techniques supported by suitable technology that makes use of automation to increase data and insight processing agility. It improves the quality and speed of data, information, and insights and makes sure that the workplace culture is always being improved.

If used effectively, DataOps aims to address data management issues so that users can get timely and accurate analyses. A multitude of data pipelines has risen in response to the demands of data analysis, scientists, and data-driven applications, resulting in data silos with no connection to other pipelines, datasets, or providers. Poor data quality undermines the analytics set-up as a whole and puts the entire operation at risk. DataOps aids in navigating these difficulties and complexity to offer analytics effectively.

Why Do You Need DataOps?

As we have said, data people are a heterogeneous group. Let’s see how DataOps helps you achieve your goals and makes your life simpler.

  • True Data Democratization: All organization members who could profit from the data have access to it.
  • Shorter Time to Insight: Because everyone has equal access to and visibility of the data, they can quickly conclude and make improvements.
  • Powerful Data Governance: DataOps guarantees uniform data generation, consumption, and deletion procedures, guaranteeing central data governance. 

Teams in a data-driven firm are focused on examining all data and then providing split-second information and actions through the usage of data operations (DataOps). 

For instance, this can help your business users to close a deal that would not have happened otherwise since an agent or salesman was able to respond swiftly based on an occurrence in time. Additionally, there is the advantage of immediately identifying potential bottlenecks in the network or system and avoiding them before they have a chance to do harm to your business.

Top 8 DataOps Trends

Accountability of AI

By 2027, the worldwide AI market is expected to grow at a CAGR of 33%, benefiting from the growth of linked smart devices and the strength of cloud computing applications. The issue is that algorithms may deploy at scale and absorb societal injustices related to race, gender, ethnicity, and other factors. 

Many in the data sector are aware of the significant effects of AI bias and are working to reduce it actively. The data sector is aware that AI bias is merely a quality issue, and that AI systems should be held to the same standards of process control as vehicles leaving an assembly line. Data firms are expected to implement strong automated procedures around their AI systems to increase their accountability to stakeholders.

As part of their pre-deployment testing, model developers will check for AI bias. Like every other performance indicator, “equity” will be enforced by a good test program. Biased model deployment and operation will be prevented through ongoing testing, monitoring, and observability. Because the checks that ensure equality are embedded into automated software applications that test, deploy, and monitor the model around-the-clock, we refer to this application of DataOps approaches to the issue of AI bias as “equity as code.”

Businesses Commit to Remote

Data-driven businesses are beginning to adopt methods for supporting remote work, as data storage methods and tools are increasingly being placed in the cloud. Although web conferencing is helpful, it is not likely to foster chance encounters that happen besides the water cooler. Video conferences make it extremely difficult to carry out business activities and workflows that rely on people with tribal knowledge huddling to solve issues.

Businesses will thus have to review their procedures for creating analytics and managing end-to-end data operations. Do they promote or undermine the connections and communication that are vital to your mission? Seeking and finding answers to such crucial questions is necessary.

Leading data firms will increase workflow automation to strengthen and enable communication and coordination across groups, rather than letting technology act as a barrier to collaboration. To put it another way, they will apply DataOps principles to develop a platform that develops a strong, transparent, effective, repeatable analytics process hub that integrates all processes. 

The Emergence of the DataOps Engineer

If data analytics were a factory, the DataOps Engineer would be in charge of the production line that creates a finished data and analytical product. Most businesses employ manual labor to manage their data factories.

According to polls by DataKitchen, more than half of the time that data scientists and other data professionals devote to their work is spent carrying out tasks that support data operations. To make the data team more productive and error-free, data operations engineers are in charge of the process hub that automates workflows for data generation and analytics development.

The effectiveness of the data organization can be significantly impacted by a data operations engineer. Over 950 roles were listed for applicants with DataOps experience in a recent LinkedIn job search. DevOps Engineer, a position very similar to this one, was just chosen by Linked-In as the most “in-demand” tech career.

According to DataKitchen, the DataOps Engineer will emerge as the most sought-after and well-paid member of the data analytics team, following in the footsteps of DevOps Engineers in the software sector.

Analytics & Data Suffers From The Great Resignation

Over 24 million people have left their employment since April 2021. The number of resignations as a percentage of all employment was 3%, which is the largest figure ever. The draw is there for data professionals as it is for many other economic areas.

DataKitchen conducted a survey to learn more about how 600 data engineers are doing and how they feel about their work, including 100 supervisors. Overall, 97 percent of data engineers reported feeling burned out. More than 70% of the data engineers polled said they expected to quit their present employer in the upcoming year. What’s more startling is that 79 percent of respondents said they had thought about quitting the area of data engineering altogether.

Over the past five years, there has been a rising lack of data experts. The absence of a work-life balance that the data team employees face must be addressed by hiring managers. Employing data specialists is already challenging for businesses. 

Data Meshes Up

Enterprise designs that use modular components, such as data mesh, will gain more traction. The data mesh divides the system into distinct domains handled by smaller, cross-functional teams in order to solve issues common to big, complicated, monolithic data systems. Each domain consists of a group of connected microservices that may be independently deployed and can communicate with users or other domains via modular interfaces. Every domain has a significant task to perform and a dedicated team of five to nine individuals who would get totally aware of the data sources, data consumers, and functional aspects.

In principle, having a variety of interconnected domains sounds fantastic, but in practice, a decentralized organizational and architectural structure presents a number of problems that need to be resolved. For instance, managing shared infrastructure, inconsistent processes, inter-domain connectivity, and ordered data dependencies. Decentralized teams also frequently duplicate efforts, such as in horizontal infrastructure that crosses numerous domains.

A decentralized organizational structure/architecture, such as a data mesh, presents issues that are addressed by data operations. A DataOps Platform unifies all of an enterprise’s domains into a single superstructure by spanning multiple toolchains, teams, and data centers. 

Data mesh and DataOps work well together to synchronize domain activities in a seamless end-to-end pipeline of operations, enabling innovation through decentralization. While DataOps covers global orchestration, common infrastructure, and inter-domain relationships, and allows policy enforcement, DataMesh promotes independence. Domain-specific infrastructure requirements may be combined into a self-service platform that is controlled by a DataOps superstructure. DataOps and data mesh make the ideal combination.

Data Observability

Business executives might not be able to address issues and seize opportunities if data and dashboards are wrong. Every second counts when there is a crucial data outage. By viewing a system’s external outputs, you may determine the state of the system thanks to data observability

You can more readily identify the cause of a problem with a system that is more visible. Granular DataOps instrumentation connected to high-level dashboards and alarms is necessary for visibility. Using tests, metrics, logs, and other artifacts, data pipelines are tested and monitored. In order to decrease mistakes, get rid of unwanted work, and shorten the cycle time of problem resolution, businesses are expected to introduce DataOps observability to data factories.


Data scientists that are extremely busy may overlook governance. The business lexicon, data catalog, and data lineage are not always updated and maintained due to the strain to manage a constant stream of analytics updates and the exponential development of data. 

Data quality evaluations are crucial, but they take a lot of time and resources, and when finished, they only provide an old picture of the situation. The next area prime for DataOps automation is data governance. DataGovOps (governance-as-code), the concept that governance may be deployed as a sequence of automated procedures that operate as part of analytics continuous deployment, will start to gain traction with businesses.

DataOps “Kitchens” are self-service analytics development environments produced on demand with integrated background processes that monitor governance. They serve as a notable example of DataGovOps. An automatic warning can be sent to the relevant data governance team member if a user breaches regulations by adding a table to a database or exporting sensitive data from the sandbox environment. 

Source control is used to store development-related code and logs, creating a complete audit trail. DataGovOps actively encourages responsible use of data through automation that enhances governance and relieves data scientists and analysts of tedious manual labor. 

Self-Servicing Sandbox

Eliminating bottlenecks in data scientists’ development workflows is essential to optimize their output. For instance, when a project starts, it could take a data scientist 10–20 weeks to set up a development environment. Today, we see a trend toward self-service sandbox automation, which will shorten the time it takes to create an environment from months to days or hours. 

These on-demand environments, which are referred to as Kitchens, have all the components a data scientist or analyst needs to build analytics, such as a full toolchain, reusable microservices, security, prepackaged data sets, and workflow integration, continuous deployment automation, observability, and governance. 

The fact that any authenticated user may instantly establish the environment makes it “self-service.” In the future, businesses that obstinately persist in building environments manually will discover that they are unable to match the agility of rivals.


DataOps’ all-encompassing procedures and data make it a buzzword. Data ecosystems may now be streamlined in ways never before possible thanks to the advent of DataOps. Bringing various data management responsibilities under one coherent roof is one of the most convenient approaches.

New DataOps trends will become more prevalent in the future. Keep an eye on these developments to ensure your company’s eventual success and to avoid getting caught off guard by advancing technology. 

Akshaan Sehgal
Former Marketing Content Analyst, Hevo Data

Akshaan is a data science enthusiast who loves to embrace challenges associated with maintaining and exploiting growing data stores. He has a flair for writing in-depth articles on data science where he incorporates his experience in hands-on training and guided participation in effective data management tasks.

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