DataOps has emerged as a modern and strategic approach to organizing, automating and boosting the use of data in companies, since it is increasingly crucial to make agile decisions in the corporate world.
With a focus on automation, data integration, team collaboration and continuous improvement, DataOps transforms the way data is handled – from collection to the delivery of real-time insights.
In this article, you’ll understand what DataOps is, how it differs from other methodologies such as DevOps, the principles that underpin this approach and how to apply it to your corporate data strategy.
Come along and have a good read!
What is DataOps?
Imagine your company generating thousands of data points every day, but with disorganized, disconnected and unreliable information. In this context, making the right decisions becomes a challenge.
DataOps (Data Operations) has emerged as an answer to this problem. It is a set of practices and principles aimed at optimizing the data lifecycle – from collection to analysis – with a focus on automation, collaboration between teams and fast, reliable deliveries.
With DataOps, companies can now count on continuous flows of quality data, guaranteeing faster, safer and more efficient strategic decisions.
The origin of the term and its relationship to DevOps
The term “DataOps” was first mentioned in 2014 by Lenny Liebmann, in an article published on theIBM Big Data and Analytics Hub. The proposal was to create a model similar to DevOps, but applied to the world of data.
While DevOps unites software development and operations to accelerate deliveries, DataOps focuses specifically on data orchestration – promoting integration, automation and governance at all stages of the data pipeline.
In other words, DevOps optimizes software development; DataOps transforms data management into a continuous and strategic process.
Why is DataOps gaining ground in companies?
The popularization of DataOps has a clear explanation: companies need to turn data into decisions quickly, safely and consistently.
Organizations that adopt DataOps practices succeed:
- Reduce bottlenecks in data flow
- Automate repetitive processes
- Improving the quality and reliability of information
- Increasing operational efficiency
- Empower decision making based on real data
According to McKinsey & Company, companies that apply good DataOps practices can increase their chances of competitive success in the market by up to 25%.
In other words, more than a trend, DataOps is a strategic advantage.
What are the principles of DataOps?
To understand how DataOps transforms the way data is managed within companies, it is important to know the principles that underpin this approach.
Continuous delivery of data and insights
One of the pillars of DataOps is the continuous delivery of data and insights, which seeks to ensure that up-to-date and reliable information is always available to the teams that need to make decisions.
This means creating a constant, automated flow of processed data, ready for analysis, reducing dependence on manual, time-consuming processes.
With this, companies gain agility, precision and the ability to react quickly to market changes.
Collaboration between multidisciplinary teams
Collaboration between multidisciplinary teams is one of the main pillars of DataOps.
It connects areas such as data, IT and business so that they work together, avoiding silos and accelerating the strategic use of data. This guarantees more agile deliveries, aligned with the company’s real needs.
Automation and integration of data pipelines
Automation and integration of data pipelines are essential in DataOps. This principle ensures that the entire data path – from collection to analysis – works in a continuous, automated and connected way.
This reduces manual errors, speeds up the process and ensures that the data is always ready to generate reliable insights.
Data governance and quality
Data governance and quality assurance are cornerstones for ensuring that information is reliable, secure and used ethically.
In the context of DataOps, this means establishing rules, standards and processes to validate, protect and monitor data throughout the pipeline. This practice avoids inconsistencies and strengthens data-based decisions.
Continuous monitoring and a culture of improvement
In DataOps, continuous monitoring makes it possible to track the performance of data pipelines in real time.
As a result, failures and bottlenecks are identified quickly and constant adjustments are made. This culture of continuous improvement makes processes more efficient and data operations more resilient.
DataOps in practice: how does it work?
Far beyond theory, DataOps involves the integration of people, processes and technologies. See how this approach works in the day-to-day running of companies.
The role of the DataOps engineer
The DataOps engineer is the link between software engineering and data operations. They apply DevOps principles to data flows to ensure efficiency, quality and availability.
Its main function is to master the data lifecycle – from collection to analysis – creating automated and integrated processes to support strategic decisions.
Typical flow of a DataOps-oriented pipeline
When we talk about a data pipeline, we are referring to a structured set of steps that automates the journey of data from its sources to destinations such as databases, analysis tools or even visualization systems.
The data pipeline flow ensures that information is collected, processed and delivered continuously, securely and efficiently, enabling faster decisions based on reliable data. A data pipeline consists of three fundamental stages:
- Data source: gathering information from APIs, banks, files or clouds.
- Processing: data cleaning, transformation and enrichment.
- Destination: storage in data warehouses, analytical tools or visualizations.
Most used tools in DataOps projects
To apply DataOps successfully, it is essential to use tools that automate, monitor and orchestrate pipelines. Below, we highlight the most relevant ones, divided by category:
Data pipeline orchestration tools
- Apache Airflow: ideal for automation and creating complex pipelines.
- AWS Glue: Amazon’s managed ETL service.
- Apache NiFi: visual interface for data integration.
Data integration and transformation tools
- Talend: data integration with high scalability.
- dbt (data build tool): transformation focused on engineering and SQL.
CI/CD tools applied to DataOps
- Jenkins: pipeline automation and continuous delivery.
- GitLab CI: native integration for versioning and collaboration.
Tools for data management and automation
- DataKitchen: integrates automation, governance and monitoring.
- StreamSets: creating pipelines in real time.
- Databricks: unites engineering, analysis and AI in a single platform.
Benefits of DataOps for companies
Adopting DataOps practices offers companies competitive advantages such as agility, precision and data governance. See the main benefits:
More agile decision-making
With DataOps, companies can significantly speed up access to reliable and up-to-date data, which has a direct impact on the speed of decision-making.
Reduction of rework and manual errors
This is one of the differentials and benefits of DataOps, the automation of repetitive tasks and the standardization of data handling processes.
With manual activities replaced by automated and integrated flows.
Greater data security and control
By adopting practices that prioritize governance, continuous monitoring and versioning, companies gain greater security and control over their data.
Integration between technology and business strategy
DataOps promotes effective integration between technology and business strategy by aligning technical teams (such as data engineering and IT) with other strategic areas of the company.
How to implement DataOps in your company
Adopting DataOps requires a structured plan. Check out the steps to get started:
Data maturity diagnosis
Even before starting to implement DataOps, it is essential to carry out a diagnosis of the company’s data maturity.
In this way, it is possible to understand the level of organization, integration, quality and strategic use of data in the current scenario. This mapping makes it possible to identify bottlenecks, define priorities and choose the most appropriate tools and practices to work with.
Team building and choice of tools
In order for the data flow to be efficient and reliable, it is necessary to integrate data engineering professionals, analysts, data scientists, DevOps and representatives from the business areas.
This collaboration ensures that the strategic objectives are aligned with the technical solutions.
Along with this, choosing the right tools – such as orchestration, integration, versioning and monitoring platforms – must take into account the company’s level of maturity, the scalability of the projects and the ease of adoption by the teams involved.
How Wevy can support your DataOps journey
Wevy can be the ideal strategic partner to drive your journey towards DataOps.
With Data & AI services, the company automates data collection, processing and integration, reducing manual efforts and enabling intelligent and timely decisions.
What’s more, with a cloud infrastructure – secure and scalable – it guarantees flexibility to orchestrate pipelines and keep up with the growing requirements of the DataOps project.
Wevy’s ongoing support and governance practices strengthen data reliability, monitoring and compliance, creating a technological foundation aligned with the business strategy.
Frequently asked questions about DataOps
Is DataOps the same as DevOps?
Although they have similar names and share principles such as automation, collaboration and continuous delivery, DataOps and DevOps are not the same thing.
DevOps focuses on integrating software development and operations, with the aim of accelerating the application lifecycle.
DataOps, on the other hand, applies these same concepts to the world of data, seeking to optimize the collection, processing, quality and delivery of data for analysis and strategic decisions.
Which areas benefit from DataOps?
DataOps benefits various areas within an organization, especially those that depend on data to make strategic decisions.
BI and Analytics teams gain agility in delivering more reliable insights. Marketing and sales departments can identify opportunities more quickly and accurately. Finance and controlling departments benefit from consolidated and consistent data for performance analysis and projections.
On the other hand, IT and data engineering gain operational efficiency through automation and reduced rework.
Is DataOps only for large companies?
Although it is more common in large corporations with massive volumes of data, DataOps is not exclusive to large companies.
Medium-sized and even small businesses can also benefit from this approach, especially as they grow and face challenges with data quality, integration and governance.
By adopting scalable and accessible tools, it is possible to apply the principles of DataOps – such as automation, collaboration between teams and continuous improvement – gradually and in proportion to the company’s maturity.
Conclusion
DataOps as a pillar of data-driven transformation
In a world where quick and accurate decisions make all the difference, DataOps is becoming more than a trend – it’s a game changer for companies that really want to be data-driven.
By combining technology, automation and collaboration between teams, it transforms stagnant processes into agile and intelligent flows.
If you want to see real results, it’s time to stop just collecting data and start using data with purpose. Don’t wait for the market to race ahead of you – deploy DataOps and turn your data strategy into a competitive advantage.
Next steps: how to get started with Wevy
If your company is ready to transform data management with agility, automation and intelligence, Wevy is the ideal partner for this journey.
With specialized Data & AI solutions and a results-oriented approach, Wevy helps your team structure efficient pipelines, adopt DataOps practices and create a truly data-driven culture.
The next step is simple: get in touch with our experts and find out how we can design the best strategy for your business together. Start now and take the first step towards data maturity.
