What Is DataOps? Definition, Principles, and Benefits

What exactly is DataOps?

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations like GDPR and CCPA.

DataOps is growing as a use case, with its market size as per revenue exceeding $3.9 billion in 2023. Analysts estimate the industry will increase at a CAGR of 23 percent between 2023 and 2028, reaching a size of $10.9 billion by that time. 

So, why has it grown so popular? In essence, DataOps is a practice that helps organizations manage and govern data more effectively.

However, there is a lot more to know about DataOps, as it has its own definition, principles, benefits, and applications today – which we will cover in this article!

What is DataOps?

DataOps, not to be confused with DevOps, is a set of technologies, processes, and best practices that combine the data and the automation methods of the agile software development methodology to improve speed and quality and foster a collaborative culture of rapid, continuous improvement in the data analytics field.

Put another way, DataOps is a set of practices that combine data engineering and operations to improve the speed, quality, and reliability of data analytics. This methodology aims to streamline the entire data lifecycle from ingestion to delivery, ensuring real-time data availability and enhancing collaboration between teams.

DataOps as a term was brought to media attention by Lenny Liebmannin 2014, then popularized by several other thought leaders. Over the past few years, there has been a steady increase in interest in DataOps:

DataOps is essentially a mix of these methodologies:

• Lean manufacturing

• Agile development

• DevOps

However, some may confuse it as DevOps for data, but that’s not the case, as there are key differences between DevOps and DataOps. 

DataOps vs. DevOps: Understanding the differences

While both DataOps and DevOps aim to enhance agility and efficiency, their focus areas are distinct. 

DevOps revolves around streamlining software development and deployment, ensuring faster and more reliable releases. In contrast, DataOps is dedicated to optimizing the entire data lifecycle, from data ingestion to analysis. 

While DevOps employs practices like continuous integration and continuous delivery (CI/CD) for code, DataOps applies these principles to data pipelines, promoting collaboration and automation to ensure high-quality, timely data delivery.

DataOps, at its core, is about collaboration between data professionals and other IT roles to help increase the speed, quality, and frequency of data analytics deployments.

In order to make DataOps successful, there needs to be a shift in culture and mindset around how data is managed.

DataOps strategies share these common elements:

• Collaboration among data professionals and business stakeholders

• Easy-to-experiment data development environment

• Rapid iteration and automated deployment processes

• Automated testing to ensure data quality

• Monitoring and reporting of processes

What are the differences between DataOps and data warehousing? 

DataOps is quite different from data warehousing yet the terms are often confused. Data warehousing focuses on storing and analyzing large datasets for reporting and business intelligence, while DataOps emphasizes the entire lifecycle of data, from its creation to its use. DataOps encompasses data governance, quality, and accessibility, ensuring data is reliable and usable for various applications.

How does DataOps provide value?

There are many inefficiencies that riddle a data pipeline. DataOps aims to deal with that.

Here are 3 ways DataOps provides value:

1. DataOps encourages team collaboration

DataOps encourages better collaboration between data professionals and other IT roles.

This is done by breaking down the silos that exist in most organizations, which leads to a more streamlined and efficient process overall.

When everyone is on the same page, it’s much easier to identify issues and solve them quickly.

Teams are better able to conduct research on AI technologies, new analytics tools, and methods and share them in a DataOps environment.

2. DataOps makes processes more efficient

DataOps makes processes more efficient by automating as much of the data pipeline as possible.

This includes tasks such as data quality checks, deployments, and monitoring. Automating these tasks frees up time for data professionals to focus on more important tasks, like the analysis.

In addition, automated processes are often more accurate and reliable than manual processes, which leads to fewer errors and data that is more trusted

3. DataOps helps integrate diverse technologies

DataOps aims to integrate diverse technologies in order to provide a more comprehensive solution.

This includes everything from data storage and data catalog solutions for DataOps to artificial intelligence and analytics reporting tools that improve visibility and support data cultures. By integrating these technologies, organizations can get a complete picture of their data pipeline and identify issues more easily.

Moreover, as the highly fragmented technologies of the data industry are now ever-expanding, having a DataOps mindset and infrastructure helps data teams to build a sustainable way of adopting new technologies as a company grows.

Alation provides robust DataOps solutions that help you foster collaboration, build trusted data solutions, automate testing & monitoring, and visualize data pipelines.

How does DataOps help other data-centric enterprise teams?

Most enterprises strive to be data-driven. Since so much value is locked within enterprise data, organizations have invested heavily in building data-focused roles and teams. 

Here’s how DataOps helps many of these data-reliant teams across the enterprise:

• DataOps for Data Scientists: DataOps is revolutionizing the way data scientists work, streamlining their workflows and boosting productivity, automating data analysis, and enabling effective collaboration with other teams.

• DataOps for Business Analysts: Business analysts rely on data to make informed decisions, but traditional data analysis methods can be slow and cumbersome. DataOps offers a powerful solution by automating data access, cleaning, and analysis, enabling business analysts to focus on strategic thinking and problem-solving.

• DataOps for Data Engineers: Data engineers are the backbone of any data-driven organization, responsible for building and maintaining the infrastructure that supports data analysis and machine learning. DataOps empowers data engineers to build a more efficient and reliable data pipeline, ensuring data quality and accessibility. 

• DataOps for Data Governance: Data governance is crucial for any organization that relies on data for decision-making. DataOps plays a vital role in ensuring data quality and compliance by automating data quality checks, enforcing data policies, and providing a centralized platform for data management. 

• DataOps for Data Security: Data security is paramount in today's digital landscape. DataOps plays a critical role in protecting your data by automating data access controls, monitoring data usage, and detecting potential security threats. 

• DataOps for Machine Learning: Machine learning models are becoming increasingly important for organizations seeking to gain a competitive advantage. DataOps streamlines the entire machine learning lifecycle, from data preparation to model deployment, enabling faster model development and deployment. 

• DataOps for AI/ML Operations: AI/ML operations require a robust and scalable infrastructure to support the continuous development and deployment of AI/ML models. DataOps plays a crucial role in building a sustainable AI/ML ecosystem by automating model training, monitoring, and retraining, ensuring your AI/ML models are governed and always performing at their best. 

• DataOps for Cloud Data Management: Cloud platforms offer a wealth of tools and services for data management, but managing data across multiple cloud environments can be complex. DataOps provides a framework for managing data across cloud platforms, ensuring data consistency, security, and accessibility. 

• DataOps for Agile Development: Agile development methodologies emphasize flexibility and rapid iteration. DataOps can be seamlessly integrated into your Agile process, enabling teams to access and analyze data quickly and efficiently. 

Real-world examples of successful DataOps implementations

Numerous organizations have successfully adopted DataOps to revolutionize their data practices. For instance, Netflix uses DataOps to manage its massive data streams, enabling real-time analytics and personalized content recommendations. 

Similarly, financial institutions leverage DataOps to ensure data accuracy and compliance while delivering actionable insights to their clients. 

DataOps also significantly impacts data security and privacy, reinforcing the need for robust data governance and compliance measures. Organizations are using DataOps to build trust with their stakeholders by demonstrating a commitment to data security and privacy.

These real-world examples demonstrate how DataOps can transform data management, providing tangible benefits and competitive advantages.

The role of AI and machine learning in DataOps

AI and machine learning are pivotal in enhancing DataOps practices, providing advanced capabilities for data analysis and automation. Machine learning models can automate anomaly detection, predict system failures, and optimize data workflows. AI-powered tools, and tools that support the AI journey like data catalogs, enable organizations to gain deeper insights from their data, improve decision-making, and enhance customer experiences. 

Integrating AI and machine learning into DataOps not only streamlines data operations but also unlocks new opportunities for innovation and growth.

What are the principles of DataOps?

As DataOps is a methodology, it can vary depending on its industry and application. However, the DataOps Manifesto has set forth 18 principles of DataOps.

Here are the 18 DataOps principles (summarized):

1. Continually satisfy your customer

DataOps strives to give customers the highest priority through quick and continuous delivery of data insights.

2. Value working analytics

In DataOps, data analytics performance is primarily measured through insightful analytics, and accurate data, in robust frameworks.

3. Embrace change

One key application of DataOps is customer-centricity. How can the business adapt to evolving customer needs? DataOps offers a competitive advantage by supporting real-time learning about changing customer behaviors. This requires that data engineers embrace learning and integrating new technologies, such as AI tools.

4. It’s a team sport

In DataOps, a variety of analytics & data science skills, qualifications, tools, and roles are required for increased innovation and a productive team.

5. Daily interactions

Stakeholders must work collaboratively daily for the project.

6. Self-organize

In DataOps, the best analytics products come from teams that can self-organize.

7. Reduce heroism

DataOps analytics teams should do away with heroism and work towards the sustainability and scalability of their teams.

8. Reflect

DataOps teams should do reflections on performance, be it on themselves, customers, or stats about their operations. What went well? Where can we improve? Critical thinking about providing customer value supports improvement over time.

9. Analytics is code

All analytics tools generate code that will configure and process the data to deliver insights. All code should be treated like any other application source code.

10. Orchestrate

Data, tools, environments, and the teams themselves must be well-orchestrated, from the beginning to the end, for analytic success.

11. Make it reproducible

Everything must be versioned so that the code and configurations made are reproducible.

12. Disposable environments

Technical environments and IDEs must be disposable so that experimental costs can be kept to a minimum.

13. Simplicity

In DataOps, simplicity is essential – an art of maximizing undone work.

14. Analytics is manufacturing

DataOps has analytic pipelines that are similar to that of lean manufacturing lines. A focus has to be placed on process thinking and how to continuously make it more efficient.

15. Quality is paramount

In DataOps, analytic pipelines should incorporate automated abnormality detection (jidoka) and provide continuous feedback to avoid any errors (poka-yoke), which help to achieve quality.

16. Monitor quality and performance

Quality must be monitored continuously to catch unexpected variation cases and produce statistics on its operation. Collaboration between data and IT teams will help to resolve the root cause of any quality issues.

17. Reuse

Previous work done should not be repeated, as it reduces efficiency.

18. Improve cycle times

The time taken to solve a customer’s need, develop analytic ideas, release it in a reproducible manner, and refactor, and reuse a product must always be minimized.

Benefits of Adopting DataOps

There are a number of benefits of adopting a DataOps solution:

1. DataOps Helps Improve Data Quality

DataOps improve the quality of data by automating many of the tasks that are traditionally manual and error-prone, such as data cleansing, transformation, and enrichment.

Data quality is especially important in the healthcare industry, where data has to be accurate for clinical decision-making.

In addition, DataOps provides visibility into the entire data lifecycle, which can help identify issues early on and prevent them from becoming bigger problems down the line.

The end result is that organizations can better decisions faster and with more confidence, thanks to higher data quality.

For example, Accenture and Alation provide a pre-engineered DataOps platform that can be implemented in a cost-effective serverless cloud environment that works right away.

It has governance capabilities including automated classification, profiling, data quality, lineage, stewardship, and deep policy integration with leading cloud-native databases like Snowflake.

2. Faster Analytics Deployment

According to IDC’s DataOps survey in 2021, successful DataOps implementation has led to a whopping 49% decreased frequency of late delivery of data analytics products.

Another key purpose of DataOps is to help improve the speed of analytics deployment.

This is done by automating the tasks involved in provisioning, configuring, and deploying data analytics applications.

In addition, DataOps helps reduce or eliminate the need for manual coding by providing pre-built components that can be easily assembled into complete data analytics solutions.

These traditional manual coding solutions are very prone to errors that could have been easily avoided if they were automated.

Moreover, it gives IT professionals, data engineers, data scientists, and data analysts the knowledge of the results of their tests, which allows them to rapidly iterate possible solutions for a product.

As a result, organizations are able to get their data analytics applications up and running much faster than before, which can help them gain a competitive edge.

DataOps is critically dependent on robust governance and cataloging capabilities. This is exactly the role that Alation, the industry leader in both, plays in the Intelligent Data Foundation.

These features give data engineers the ability to explore data, understand its quality, trace lineage for root cause analysis, and enforce policies like encryption & masking.

As such, analysts see a boost in efficiency and accuracy in analytics; this, in turn, increases user confidence in the data supplied which powers better data-driven decision-making.

3. Improved Communication and Collaboration Between Teams

DataOps can help to establish better communication and collaboration between different teams within an organization.

In DataOps, the flow of data is centralized in one place, where individual stakeholders can come to one place to find all the information that they need.

This helps different teams to cross-collaborate with each other, as they work on the same DataOps architecture and the same methodology.

In addition, DataOps can help improve the efficiency of releasing new data analytics developments, as many data-related tasks are automated, leaving teams to perform higher-order tasks, like innovation and meaningful collaboration.

As a result, organizations are able to make better use of their data and analytics resources, which can help to improve their overall performance.

4. More Reliable and Faster Data Pipeline

One of the lesser-known benefits of DataOps is that it helps to create a more robust and faster data pipeline.

This is done by automating the tasks involved in data ingestion, warehousing, and processing.

When these tasks are automated, there is less chance for human error and for poorly written code to cause large problems that break the data pipeline.

In addition, DataOps helps improve the efficiency of data pipelines by providing tools and best practices for managing and monitoring them.

This is so that DataOps engineers can then jump in and rectify the issues when they receive alerts when something is amiss.

This works in a flywheel, where stability in the DataOps infrastructure can lead to speed in the long run, and that can build stability over time.

5. Easier Access to Archived Data

DataOps can help make it easier for organizations to access archived data.

By providing a centralized repository for all data, a data catalog makes it easy for people to access and query data compliantly.

DataOps can also help automate the process of archiving data, which can further improve efficiency and reduce costs.

Organizations that implement DataOps are able to realize these benefits and more.

Cloud-based DataOps services such as an Intelligent Data Foundation (IDF), are integrated into Alation at an API level.

This presents a huge time and energy saver for the analytics teams at large!

Final Thoughts

In summary, DataOps is a relatively new concept, but one that is quickly gaining traction in the world of data and analytics.

DataOps can help organizations to improve their overall performance by automating tasks, improving communication and collaboration, establishing a more reliable and faster data pipeline, and providing easier access to archived data.

Needless to say, DataOps is not a silver bullet – it will not magically fix all of your organization’s data problems.

However, the right implementation of a DataOps solution can help to improve your organization’s overall performance and maintain its competitive edge against others.

Author bio

Justin Chia is the founder of Justjooz. He seeks to educate everyday people about crypto, analytics, and home tech.