DataOps is an automated, process-oriented methodology used by analytics and data teams to improve quality and reduce the cycle times of data and analytics. Or, as Gartner defines it:

“DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.”

Specifically, DataOps focuses on providing agile data onboarding, enhanced data quality, trusted information, and governance and security policies natively woven into the fabric of key workflows – all in an iterative process that’s constantly refined and allows teams to surface new insights effectively and efficiently.

DataOps is also about more than just data flowing, it’s about the context behind the data. For example, when there is a change in a data artifact, DataOps looks at elements like why the change occurred, who was responsible, and how users can fix downstream reports.

Importantly, DataOps doesn’t operate in isolation; it must operate in unison alongside DevOps and data science practices like MLOps.

What is DevOps? DevOps is a set of practices that combines software development and information technology operations to shorten the system development lifecycle and provide continuous delivery with high software quality. Critically, teams can’t achieve these goals if they don’t have the right data that’s well governed and trusted. Therefore, DataOps is critical to powering DevOps.

What is MLOps? MLOps was born out of the need for data science teams to more effectively operationalize machine learning models. The rise of machine learning models has compressed the need of data scientists to get access to information, operationalize that data, and surface it in production in a manner that allows DevOps teams to use it efficiently. Once again, this all starts with having regular access to real-time and trusted data, therefore relying on DataOps.

A note on DataOps versus data operations

It’s also important to note that DataOps is not necessarily the same as data operations. Data operations is much broader than DataOps and the two have different goals.

In terms of reach, data operations extend to data potential as well as quality and accessibility of data. To that end, data operations have a goal of ensuring data reaches its full potential and provides maximum value, whereas the goal of DataOps focuses more on ensuring quick and easy access to quality, real-time data.

Although DataOps and DevOps are related and have many similarities, there are also many differences that separate the two practices.

On the similarity front, both DataOps and DevOps help drive collaboration, focus on Agile methodology practices, use automation, solicit user feedback, and rely on quick iterations to deliver value faster and offer continuous delivery.

That said, DataOps is more complex than DevOps and uses (but adds onto) many DevOps principles. Key differences include:

Organizations need quality, reliable and business-ready data to compete and meet business objectives – whether that’s delivering AI initiatives, opening new business models, or optimizing growth.

Against this backdrop, CIOs are under pressure to expand DevOps practices. In turn, this demand puts pressure on real-time access to data and increased automation, which then increases the need for AI. Supporting all of this requires a modern infrastructure and data architecture with appropriate governance. Enter DataOps.

DataOps helps ensure organizations make decisions based on sound data. Previously, organizations have grabbed their full dataset across multiple environments, put it all into a data warehouse, and surfaced information from there. However, this approach was not timely or cost effective and it typically didn’t drive the desired business results—especially as the volume of data increased. That’s because by the time teams delivered any insights, the window of usefulness had already passed. With DevOps putting pressure on data and AI to operate more efficiently, teams must be able to iterate on data and surface insights in hours or days—not weeks or months.

Furthermore, without DataOps, it’s easy for teams to spend too much time on AI and ML, even though those efforts only comprise about 20% of what’s required to provide context and trusted information. When this happens, organizations risk pushing new decisions and capabilities based on bad data. Meanwhile, DataOps focuses on the other 80%—ensuring quality data, but making sure that teams can access that data efficiently rather than having to wait months for new insights.

DataOps is important because it not only supports quality data, but does so at the pace of today’s business.

Beyond the faster pace of business and increased pressure from DevOps for more real-time insights from data, several other factors have led to the rise of DataOps in recent years. These factors include:

1. Increasing volume of data: More and more data means more opportunities and insights—but only if that data is governed and handled appropriately. If teams have limited governance and context around data, it can take weeks or months to surface insights. Aside from being outdated by that time, those insights might not even be accurate. DataOps aims to solve the problems of both speed and quality, even as the volume of data most organizations has continues to increase.
2. Increasing systems and processes that rely on data: In a world where nearly everything relies on data, organizations cannot afford to have untimely or inaccurate data flowing into systems and processes. This can lead to poor business decisions that have an impact on end users, company growth, and revenue. Once again, DataOps helps ensure reliability by focusing on timeliness and accessibility.
3. Increasing number and variety of data consumers: With more users both internally and externally consuming data, organizations must find ways to make this data easily accessible to people of all kinds. Waiting for a technical team to deliver answers is no longer a feasible solution. DataOps helps solve this challenge by democratizing access to data for users of all kinds, particularly when it comes to answering data-related questions efficiently and effectively via self-service.

DataOps is based on principles from the Agile methodology, DevOps, and lean manufacturing.

First, DataOps relies on the concept of iteration from the Agile methodology to deliver insights faster.

Next, it pulls the concepts of collaboration, breaking down silos, and continuous delivery from DevOps to bring together data scientists, data analysts, data engineers, AI and ML teams, and DevOps teams to deploy new insights quickly, make those insights easily accessible, and regularly iterate as needed.

Finally, it relies on the process-oriented nature of lean manufacturing to improve management of data pipelines and processes, which helps ensure quality alongside efficiency.

Altogether, these elements enable DataOps to turn bottlenecks into opportunities. From sourcing data to surfacing valuable business insights from it, DataOps powers workflows and collaborations that make for more seamless handoffs between departments, provide context to data, and ensure timeliness. All this drives higher quality outcomes that better align with business priorities.

Another important element of DataOps is the AI ladder, which offers a simple way to look at all the steps required to achieve economic gain from data. Essentially, the AI ladder helps create a better prediction model to reduce risk, increase productivity, and automate mundane tasks so teams can focus on more strategic elements of AI and ML that require deeper creative thinking. The AI ladder is critical to DataOps and it looks as follows:

• Collect: Make data simple and accessible. This step of the ladder falls on the DevOps side of the business.
• Organize: Create a business-ready analytics foundation. This is where DataOps comes in, bringing together data from disparate systems.
• Analyze: Scale insights with AI everywhere. Once again, DataOps plays a critical role here by surfacing insights to provide trusted data so other teams, like data science teams, can build effective models. This step of the ladder includes efforts like master data management and data integration.
• Infuse: Operationalize AI with trust and transparency. This final step focuses on making sure teams working with data can access information quickly and effectively through data virtualization, for example by enabling data scientists to quickly identify relevant data and surface it to the organization.

At a time when data observability is essential to understanding what’s going on with data in real-time and being able to fix issues proactively to get ahead of what could easily become bigger challenges, DataOps observability is just as important.

DataOps observability is, similarly, about monitoring data pipelines in real-time and issuing alerts when certain issues or abnormalities arise. This level of observability is important to understanding the end-to-end journey of data throughout the organization across various environments and states. In doing so, it allows teams to identify problems faster and, therefore, deliver solutions faster.

As a result, DataOps observability is an essential part of maintaining high quality data and ensuring that data practices are as efficient and effective as possible.

There are several roles that might be involved in a DataOps team in any given organization. Most importantly, those who are part of a DataOps team should have expertise around data governance, data policy development, data integration, data security and privacy, data orchestration, databases, and process development.

Some of the key roles typically involved in a DataOps team include:

• Data specialists for development and data best practices, including modeling and visualizing data
• Data engineers who build and maintain the data infrastructure and offer analytics, BI, and system support
• Data scientists working on advanced analytics and AI/ML models
• Data stewards who build and manage the data governance policies
• Data analysts focusing on data quality and reliability

DataOps teams should also have an executive sponsor that drives buy-in across the organization and guarantees outcomes like high quality, efficiency, and reliability.

Of course, those involved in DevOps are also involved in DataOps teams in many organizations.

DataOps delivers several benefits for organizations when it comes to speed, quality, and access to data, as well as the processes around managing data and surfacing insights. Key benefits include:

• Improved collaboration across data teams and fewer process bottlenecks due to better defined processes
• Higher quality end results in data delivery thanks to stronger data governance covering everything from data creation and usage to data integration and more
• Faster time to insight from data as a result of the ability to surface key information more efficiently
• Improved decision-making based on higher quality data flowing into systems and faster time to insight
• Easier, more universal access to data for a variety of users throughout the organization

At the highest level, DataOps allows for “more, better, faster” by freeing up time associated with data processes. It eliminates the world in which teams spend all their time on one project, 80% of which is data prep, and complete one experiment only for it to fail and for them to have to justify that work to the business. Instead, it introduces a world where teams can run hundreds of experiments, quickly iterate, and find the one diamond in the rough success that justifies the work.

Knowing the benefits of DataOps is one thing, actually measuring those benefits in practice is quite another. So, what does it look like to measure the value of DataOps? Consider the following two scenarios from real-life organizations:

Leading retailer reduces time to update from weeks to minutes

For one leading retailer, changing data in the source system that powered its database took three weeks. That meant a several week delay to updating critical information like inventory levels, which created a major lag in the customer experience, since shoppers won’t wait around for weeks to make a purchase.

Implementing DataOps had an incredible impact for the retailer, reducing the time to update inventory data in systems from three or more weeks to less than two minutes. Additionally, it allowed the retailer to speed up other processes as well, for example reviewing customer affinity data in less than a day rather than weeks and determining the results of various A/B tests in minutes instead of hours.

Together, these improvements have allowed the retailer to operate at the speed of the consumer and grow their business to deliver on the goal of “more, better, faster.”

International bank gains ability to update customer records in real time

For one international bank, a slow turnaround in updating customer records began to cause problems. The lag time in processing transactions through accounts meant that customers would unknowingly bounce checks or overdraw their accounts since previous activity wasn’t logged yet. This situation could create serious customer trust issues.

In response, the bank introduced DataOps to streamline their processes and deliver higher quality results to customers. Specifically, they were able to reduce the time it took to update account records from 13 updates per hour to 50 updates per minute by switching from a manual to an automated process.

In doing so, the bank increased their data quality score by 15x and upped their net promoter score for two consecutive years – all evidence of a better customer experience.