DataOps: augmenting enterprise data development and operations

Apr 25, 2024 6:50:25 PM

Data never sleeps. It’s a good thing, but can also bite you. DataOps is the answer to handling growing piles of data in a complex technological landscape, maintaining high data quality at reasonable costs, and responding to the demands of business units with data products and faster time to value.



Every business is a data business, so this touches all of us. But for enterprises, the impact of good or bad data management is ten steps up in magnitude. Want to stay firmly on the good side? Keep reading.

Get your virtual hands on our Guide to DataOps →




What is DataOps?

DataOps refers to an agile and iterative approach to data development and operations. It entails thinking, processes, and tools aimed at providing high-quality data pipelines, products, and analytics in time and with predictable costs while streamlining the data work done by engineers. ​​In essence, it’s about managing and developing data efficiently and reliably, to extract as much value from it as possible. 

The term DataOps is – for an obvious reason – easy to misunderstand as just data operations, but considering also the continuous improvement of the data development aspect is a must for successful implementation.

As a disclaimer, the concept of DataOps is still taking shape, so you will find different definitions for it. We could get pretty deep into it here, too. But we’ll stick to the definition above in hopes that it will be one you can use for getting less tech-savvy people around you on board, as well.


Time for a reality check?

Our DataOps maturity test lets you analyze your current state of data capabilities, ways of working, tech stack, culture, and more.

Take 3 minutes to answer a set of questions. Get your DataOps maturity score with our recommendations for prioritizing data investments.



Origins of DataOps – why is it a thing?

You may have noticed the amount and inherent value of data explode recently. Well, traditional data management has a hard time coping with the profound implications.

The volume and variety of data and the call for advanced analytics, AI, and machine learning have pushed organizations to move from on-premise data warehouses to cloud-based data platforms. These developments, together with a rapidly changing data landscape, raised the need for better data value chain management. DataOps was born.

Especially enterprises operating in a complex data ecosystem face a choice between two options:

1Keep managing and developing data with the existing setup, which very likely means a lot of manual work, subpar data quality and usability, and limited ability to respond to the business’ data needs.

2Start shifting towards more agile data development and operations, which helps reduce manual work, standardize and automate data processes, build in-house competences, simplify the tech stack, and deliver more value.

The change most certainly doesn’t happen with a flip of a switch, but the preferred path should be fairly obvious.

Benefits of DataOps in a nutshell

By considering DataOps principles and putting them to work in practice, you can expect the following:

  • Improved efficiency and predictability of data development
  • Consistently high quality of data products
  • Faster time to value, and overall better ROI on data
  • More accurate and useful analytics and reporting
  • Happier data engineers and data analysts
  • Stronger culture of collaboration and continuous improvement
  • Enhanced ability to innovate


DataOps vs. DevOps

Wait, all this sounds familiar? Some see DataOps as merely “DevOps for data” but you can argue that there’s more to it. 

Indeed, DataOps is meant to streamline data delivery from source to report, much like DevOps is about trimming the software development cycle and going from code to production. Both share the goal of aligning tools, processes, and people for better integration and efficiency.

So, the underlying principles are pretty much the same. The difference comes from what is being developed – software or data – and what it takes from the organization to make it work.

DataOps reaches back to extract the data from source systems, manages data pipelines, and stretches all the way to the deployment of data products, which feed insights and analytics to the business. Also, data engineers require additional skills on top of programming. These include an understanding of data modeling and data lifecycle management, as well as deeper experience in relational databases and statistics.




DataOps building blocks and principles

Now let’s take a look at the core elements of a well-oiled DataOps machine. Latest at this point, the similarities to software development will start to shine.

Agile data development

Whether developing data (DataOps) or software (DevOps), the basic idea of agile development is to develop incrementally in small iterations, deliver often, and get constant feedback to ensure you’re developing the right things. All this calls for uninterrupted communication and collaboration between individuals and teams.

Consider printing this agile manifesto on your mousepad:

  • Individuals and interactions over processes and tools
  • Working software over comprehensive documentation
  • Customer collaboration over contract negotiation
  • Responding to change over following a plan

(DevOps) Tools

What kind of skills and experience does your existing or future DataOps team have? What about preferences? Organizational requirements? How much can the tools cost? All of these guide the formation of the perfect stack of DataOps tools and technologies. And there’s one more super important question to answer: What are you trying to achieve?

DataOps inherits a lot of its tool stack, including version control, CI/CD pipelines, and automated infrastructure configuration, from the world of DevOps. On top of those, the key enablers specific to data development include ETL/ELT tools, data orchestration and monitoring, data lineage, and data modeling features.

As the agile manifesto on your new mousepad says, people trump tools. However, you don’t want to be caught up in a situation where available technology limits your team’s ability to fulfill the other elements of successful data operations.

Total Quality Management

Total Quality Management (TQM for our acronymaholics) is a methodology for – surprise surprise – taking care of quality management in DataOps. The leading idea of TQM is to involve everyone in the quality improvement process by making it a continuous, always-present part of the data product's lifecycle. 

In quality management, continuity means eliminating the unnecessary throughout the entire process from design to delivery – not just picking & choosing parts of the process to clean up. So make it total.


At the core of Lean development is a strong drive to cut all excess waste from the development process and strive for highest possible efficiency. Besides the development processes, also data products and pipelines should be eyed with lean goggles on.

Of course, you need to keep quality in mind. Much of data development and operations is about strween  Read more about how quality and lean go hand-in-hand in DataOps →

DataOps management principles

The combo of agile data development, DevOps-inspired tools, continuous quality management, and lean processes enable DataOps success. 

In addition, you may want to improve your odds of achieving top-of-the-class status for your data teams and ops by appending the following principles to your daily mantra. Consider these as our DataOps management manifesto:

  1. From project to product thinking
  2. Make it business-driven
  3. Design for team productivity
  4. Design for constant change

These guiding thoughts should help in calibrating your people and efforts correctly when taking steps on your DataOps journey.




Kesko x Agile Data Engine

A renewed data platform and a cross-functional DataOps team enable leading Nordic retailer Kesko to turn months into weeks and weeks into days in data development.





Implementing DataOps in enterprise data management

It’s one thing to talk about a data wonderland and an entirely different thing to get there. The enterprise ship turns slowly – and that is exactly why you may want to start wrestling with the rudder today.

Pains that DataOps can remedy

Let’s first find an answer to the question: Should you even bother? Here’s a hand-picked shortlist of headaches you can heal with DataOps:

  • Data development is slow and error-prone
  • Analytics reports need to be built from scratch, time after time
  • Data team’s time is heavily consumed in troubleshooting data load failures
  • The data lake has become a data swamp
  • Data is not trusted and therefore not used
  • Data teams and individuals work in deep silos
  • Data integrations and pipelines keep breaking under change
  • Resources are not enough to meet business’ demand for data

How many did you get? We’re here to listen.

Recommended steps for implementing DataOps

Ok. You’re starting to feel that maybe there’s room for improvement in how that sweet enterprise data translates to business insights?

Each case is unique, so there’s unfortunately no golden recipe to follow.  That being said, we believe that covering these action points will put you on the right path and help you to make the move to enterprise DataOps:

1. Break dev and ops silos

Collaboration and communication between data development and operations teams is a must. Cross-functional teams, shared responsibility, unified workflows, and agile methodology all help foster the cooperation and understanding needed to achieve DataOps success.

2. Start treating data as a product

Shift the organization’s perspective from data pipelines to data products. Business and product management thinking direct focus to how data products can be used as valuable assets. With this shift, data lifecycle management, end-users’ needs, and feedback loops for continuous improvement get the attention they deserve.

3. Implement continuous delivery

Predictable and reliable delivery of data products is at the heart of DataOps. You’ll want to introduce automated processes for deploying changes in data pipelines. Continuous integration and deployment (CI/CD) practices ensure that changes can be tested, validated, and released efficiently.

4. Standardize and automate “as much as possible”

Make the most of team and workflow alignment by establishing standard practices along the data product lifecycle. Minimize manual inefficiencies and reduce errors by automating repetitive data ingestion, cleansing, transformation, and monitoring tasks.

5. Continuously measure & improve data operations

When it feels like you have things figured out, don’t stop improving. Shift to DataOps management mode. Use metrics linked to data quality, pipeline efficiency, and delivery frequency to identify improvement areas and iterate to enhance your DataOps machine.

Hear answers to the 'why' and 'how' of DataOps in this webinar →

Common headaches

When done right, DataOps migration cuts through to the culture of your organization: technology and processes alone are not enough if the people and their ways of working don’t follow suit. At some point on your journey, you may feel like going nowhere. During those times, it can help to acknowledge that challenges like these are part of the exercise:

  • Everything’s a mess: Let’s not downplay the magnitude of this change. It’s important to consider high-level data governance and architecture principles, like security and scalability, before starting agile data development.
  • Legacy tools can’t keep up: It’s quite common to notice that existing data solutions don’t match the heightened requirements for data quality and speed of development. DataOps migration is a great moment to weed out technological debt.
  • You’re missing critical capabilities in your team: Whilst the move to agile data development can help expand your in-house capabilities over time, the start of the journey may surface skill gaps in your existing team.
  • The agile team isn’t performing up to expectations: New teams or teams with new responsibilities have a learning curve, and you can’t really skip past it. But you can, for instance, hire an agile coach to get on the right track faster.
  • The rest of the organization doesn’t “get” you: DataOps doesn’t only concern your data team, but calls for new thinking and ways of working from the surrounding people. Again: it’s a cultural change, too.




DataOps tools and tech stack

So, we talked about how DataOps borrows some of the tooling from DevOps. In short, DataOps tools make it possible to streamline data delivery and improve productive efficiency through integrations and automation. These tools play a role in providing a connected experience to data managers and data consumers.

How? From the data engineer’s perspective, the primary function of DataOps tools is to help manage data work and improve workflows, such as production deployment, iteratively. Reduced friction in data development and operations translates to value for the end-users.  

Here’s a brief overview of the cornerstone tools and capabilities that contribute to efficient data management, development, and operations.

Enterprise data warehouse or data platform

An enterprise data warehouse (EDW) is the core repository of data integrated from various source systems. It is used to store data and make it available for data consumers’ reports across the enterprise.

These days, it’s becoming more common to refer to data platforms, which hints at the expanding use cases and capabilities of modern, cloud-based enterprise data warehouse tools, such as Snowflake, Databricks, or Azure Synapse Analytics.

Data platform capabilities include, for instance, built-in governance solutions that ensure data security, compliance, and privacy, AI & ML models for scalable data processing, and cloud-based tools for developing data-intensive applications.

Does your data warehouse support the move to DataOps?
If not, what should you do next?

Find out in this guide to a modern data warehouse →

Data warehouse automation tool

Let’s take a step back from what happens in the enterprise data warehouse, and consider how that’s possible in the first place. Data warehouse automation (DWA) refers to the process of building a data warehouse. 

DWA tools sit between source data systems and the data warehouse as an organizing filter, if you will. The main purpose is to automate the standardization of data and models from various sources, and to integrate these for better data usability.

In other words, data warehouse automation is a very important part of enabling data teams to do their work more efficiently and making sure data consumers are able to extract value from data in the form of accurate and reliable analytics reports.

DataOps platform

A fresh way of covering necessary DataOps capabilities is to look into a dedicated DataOps platform (shameless plug) like Agile Data Engine. The rise of DataOps platforms is a result of data managers’ growing interest in reducing the complexity of having several custom solutions in their stack. 

As we see data warehouses becoming data platforms, also other tools feel the pressure of handling multiple data jobs and covering a wider range of tasks in the data lifecycle. DataOps platforms answer this call by combining several features that help extract the most value possible from the data platform.

Agile Data Engine, for instance, is built to handle data modeling and transformations, CI/CD pipelines, workflow orchestration, monitoring, and testing – all in one package – rather than just covering the data-in part of data warehouse automation. (/shameless plug)

Continuous and faster delivery of data product value is a core capability of a DataOps platform. Also, the broader coverage of DataOps tools helps organizations increase their maturity in agile data development and DataOps practices. In other words, they support the cultural change that’s necessary for more complete adoption of DataOps.

Core capabilities needed in DataOps

Regardless of what’s in your data tool stack, you will want to make sure to cover certain bases – here’s a short list (in no particular order): 

  • Data orchestration, including connectivity, data lineage, data modeling, workflow automation, scheduling, logging, alerting, and more.
  • Deployment automation, including CI/CD, version control, approvals, release pipelines, rollback, recovery, and more.
  • Test automation, including test data management, test script management, rule validation, and more.
  • Observability, including workflow monitoring, performance and cost insights, impact analysis, and more.
  • Environment management, including infrastructure as code, repository templates, credential management, resource provisioning, and more

Tech stack considerations

None of your data tools run in a vacuum. Therefore, an important part of tech stack decisions is to consider the fit of single tools with your existing stack and enterprise architecture. The parts must play well together, and  

Sometimes the needed solution can be found in your existing DevOps or DataOps tools, so take a critical look at what you already have to see whether it can be dropped or developed to fulfill new use cases.

Keep in mind, though, that tools are only part of a strong data team. And that more tools mean more complexity, which can turn your data maturity hike into a decline. There’s also the question of buy vs. build? 

At the end of the day, your ideal DataOps tech stack will be a unique mix that takes into account focus areas, people, busine                  assembling the ideal data tech stack.


What’s next?

We seem to be approaching a tipping point, where DataOps becomes the standard way of organizing enterprise data management and development.

The migration away from traditional data management promises a lot in terms of productivity and business value. But, as with any profound transformation, achieving DataOps success calls for a solid internal engine to push the change through.




Ready to start the journey?

Let’s chart your course to DataOps success →




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