What Is DataOps?

What Is DataOps?

DataOps is a collection of technical practices, workflows, cultural norms, and architectural patterns that enable:

• Rapid innovation and experimentation delivering new insights to customers with increasing velocity
• Extremely high data quality and very low error rates
• Collaboration across complex arrays of people, technology, and environments
• Clear measurement, monitoring, and transparency of results

The best way to explain DataOps is to review its intellectual heritage, explore the problems it is trying to solve, and describe an example of a DataOps team or organization. Our explanations below start at a very conceptual level, but then quickly proceed into pragmatic and practical terms. We find this is the best way to help data professionals to understand the potential benefits of DataOps.

What is Agile Development?

For DataOps to be effective, it must manage collaboration and innovation. To this end, DataOps introduces Agile Development into data analytics so that data teams and users work together more efficiently and effectively. In Agile Development, the data team publishes new or updated analytics in short increments called “sprints.” With innovation occurring in rapid intervals, the team can continuously reassess its priorities and more easily adapt to evolving requirements, based on continuous feedback from users. This type of responsiveness is impossible using a Waterfall project management methodology which locks a team into a long development cycle, isolated from users, with one “big-bang” deliverable at the end.

Studies show that projects complete faster and with fewer defects when Agile Development replaces the traditional Waterfall sequential methodology. The Agile methodology is particularly effective in environments where requirements are quickly evolving — a situation well known to data analytics professionals. In a DataOps setting, Agile methods enable organizations to respond rapidly to customer requirements and accelerate time to value.

What is lean manufacturing and what does it have to do with data analytics?

Lean manufacturing is a methodology, originating in the Japanese manufacturing industry (e.g., Toyota), that focuses on the minimization of waste within a system without sacrificing productivity. Whereas Agile and DevOps relate to analytics development and deployment, data analytics also manages and orchestrates a data pipeline. Data continuously enters on one side of the pipeline, progresses through a series of steps and exits in the form of reports, models and views. The data pipeline is the “operations” side of data analytics. It is helpful to conceptualize the data pipeline as a manufacturing line with an active focus on quality, efficiency, constraints and uptime. To fully embrace this manufacturing mindset, we call this pipeline the “data factory.”

In DataOps, the flow of data through operations is an important area of focus. DataOps orchestrates, monitors and manages the data factory. One particularly powerful lean-manufacturing tool is statistical process control (SPC). SPC measures and monitors data and operational characteristics of the data pipeline, ensuring that statistical variance remains within acceptable ranges. SPC leads to remarkable improvements in efficiency, quality and transparency when applied to data analytics. With SPC in place, the data flowing through the operational system is verified at each step in the data-analytics pipeline. If an anomaly occurs, the data analytics team will be the first to know, through an automated alert.

While the name “DataOps” implies that it borrows most heavily from DevOps, it is all of the methodologies described above — Agile, DevOps, “lean” and statistical process control — that comprise the intellectual heritage of DataOps. Agile governs analytics development, DevOps optimizes code verification, builds and delivery of new analytics and SPC orchestrates, monitors and validates the data factory. Figure 1 illustrates how Agile, DevOps and statistical process control for the foundation of DataOps.

What is DevOps?

DevOps is an approach to software development that accelerates the build lifecycle (formerly known as release engineering) using automation. DevOps focuses on continuous deployment of software by leveraging on-demand IT resources and by automating integration, test, and deployment of code. This merging of software development (“dev”) and IT operations (“ops”) reduces time to deployment, decreases time to market, minimizes defects, and shortens the time required to resolve issues.

Using DevOps, leading companies have been able to reduce their software release cycle time from months to literally seconds. This breakthrough enabled them to grow and lead in fast-paced, emerging markets. Companies like Google, Amazon, and many others now release software many times per day. By improving the quality and cycle time of code releases, DevOps deserves a lot of credit for these companies’ success.

Isn’t DataOps just DevOps for Data?

Data Quality The DataOps Way

Data Quality underpins AI-driven insights and strategic decision-making, traditional methods often fall short in addressing the complex, ever-evolving landscape of data. DataOps offers a transformative approach, emphasizing continuous improvement, collaboration, and agility. DataOps empowers individuals with tools to identify, address, and measure data quality issues rapidly. By shifting focus from one-off projects to iterative processes, DataOps enhances data reliability, making it easier for teams to act on real-time data quality insights. This iterative, knowledge-driven method enables organizations to handle vast data at scale, reduce risks, and align quality metrics with business goals, ensuring data integrity without stalling innovation. Embrace data quality the DataOps way for a fast, responsive, and impactful data strategy.

What problem is DataOps trying to solve?

DataOps exerts control over your workflow and processes, eliminating the numerous obstacles that prevent your data organization from achieving high levels of productivity and quality. We call the elapsed time between the proposal of a new idea and the deployment of finished analytics “cycle time.” Many organizations require months of cycle time to deploy 20 lines of SQL. Lengthy cycle times discourage and disappoint users and hinder creativity.

Ideally, data teams work hand-in-hand with their users like a well-oiled machine, fielding new idea proposals, implementing them rapidly and quickly iterating toward higher-quality models and analytics. Unfortunately, our experience is the opposite. Data teams are constantly interrupted by data and analytics errors. Data scientists spend 75% of their time massaging data and executing manual steps. Slow and error-prone development disappoints and frustrates data team members and stakeholders. Lengthy analytics cycle time occurs for a variety of reasons:

• Poor teamwork within the data team
• Lack of collaboration between groups within the data organization
• Waiting for IT to disposition or configure system resources
• Waiting for access to data
• Moving slowly and cautiously to avoid poor quality
• Requiring approvals, such as from an Impact Review Board
• Inflexible data architectures
• Process bottlenecks
• Technical debt from previous deployments
• Poor quality creating unplanned work

What is a DataOps Engineer?

DataOps Engineers create and implement the processes that enable successful teamwork within the data organization. They design the orchestrations that enable work to flow seamlessly from development to production. They make sure that environments are aligned and that hardware, software, data, and other resources are available on demand. DataOps Engineers also manage tools that promote reuse — a big multiplier of productivity.

The rise of the DataOps Engineer will completely change what people think of as possible in data analytics. Through tools automation, the DataOps Engineer eliminates data lifecycle bottlenecks, which sap data team productivity. A DataOps Engineer who understands how to automate and streamline data workflows can increase the data team’s productivity by orders of magnitude. A person like that is worth their weight in gold. The role of DataOps Engineer goes by several different names. The skillset includes hybrid and cloud platforms, orchestration, data architecture, data integration, data transformation, CI/CD, real-time messaging, and containers

How do you prove that DataOps is really adding value?

DataOps will deliver an unprecedented level of transparency into your operations and analytics development. DataOps automated orchestration provides an opportunity to collect and display metrics on all of the activities related to analytics. Figure 6 shows a typical DataOps dashboard with metrics related to team collaboration, error rates, productivity, deployments, tests, and delivery time. We call this the CDO dashboard. The dashboard in Figure 7 contains some common metrics. DataOps easily customizes these metrics to meet an organization’s specific needs.

Figure 7: DataOps metrics report

Here’s a short explanation of the CDO Dashboard:

• Team Collaboration — Teamwork is measured by the creation of virtual workspaces, also known as “Kitchens.” Each Kitchen creation corresponds to a new project or sub-project in a team context.
• Error Rates — The graph shows production warnings at a rate of 10 per week falling to virtually zero. This is the positive result of the 100+ tests that are now operating 24×7 checking data, ETL, processing results, and business logic. As the number of tests increases, the data pipeline falls under increasingly robust quality controls.
• Productivity — Team productivity can be measured by the number of tests and analytics created. The rise in “keys” (steps in data pipelines) coupled with the rise in test coverage shows a thriving development team. Also, the number of Kitchen merges at the top right shows the completion of projects or sub-projects. The “Feature to Dev” metric shows new analytics ready for release. “Dev to Prod” merges represent deployments to production (data operations).
• On-time Delivery — Mean deployment cycle time falls sharply, meeting the target service level agreement (SLA).

What is a DataOps Automation?

Applying DataOps requires a combination of new methods and automation that augment an enterprise’s existing toolchain. Some organizations build DataOps capabilities from scratch, but the fastest way to realize the benefits of DataOps is to adopt an off-the-shelf DataOps Platform. The DataKitchen DataOps Platform unifies the end-to-end workflow and processes related to data analytics planning, development and operations into a single, common framework, improving overall collaboration. It incorporates your existing tools into automated orchestrations that drive analytics creation and the transformation of raw data to insights. DataOps Automation accomplishes this goal by managing the creation, deployment and production execution of analytics. The DataKitchen DataOps Automation software offers four fundamental capabilities:

• Spins up safe and synchronized workspaces – Using virtualization, DataOps separates and harmonizes your production and development environments. Aligning the two technical environments avoids unexpected errors during deployment. Access control secures each workspace and domain. When it’s time to start a new project, data scientists spin up self-serve development sandboxes in minutes – this includes test data, validation tests, tools, a password vault – in short, everything they need. No more waiting months for IT.
• Automates deployment – New analytics pass extensive validation tests and seamlessly move from development to production engineering and then operations, with a few clicks. Verification tests replace your impact review board, minimizing the time and effort required to deploy.
• Orchestrates, tests, and monitors the data pipeline – Data flows in from hundreds or thousands of sources and is integrated, cleaned, processed, and published in analytics. As millions of data points flow through the pipeline, tests distributed throughout the data pipeline monitor work in progress and check data for anomalies. Virtually zero errors reach user analytics. When errors are found, DataOps takes appropriate action based on severity: warnings, alerts, or even suspension of a data source. Dashboards that summarize test results and activity provide unprecedented visibility into operations and development. our DataOps Automation software provides quality and productivity metrics, showing the progress of your DataOps initiative.
• Fosters collaboration – DataOps automates workflows to coordinate tasks and improve teamwork. Workspace environments provide the structure to move analytics through the development workflow, from person to person, eventually reaching production. Sandboxes feature reusable analytics components saving time and enforcing standardization. Coupled with source control, workspaces branch and merge, providing centralized control of artifacts. With a DataOps Platform, everyone has a common view of the development and operations pipelines.

With an orchestrated data operations pipeline, quality controls, and an automated development workflow, our DataOps Automation software minimizes unplanned work. Task coordination between team members and groups leads to a more transparent and robust workflow. DataOps tests virtually eliminate data errors. The DataKitchen DataOps Automation software enables data professionals to develop and deploy new analytics at lightning speed, delighting users and delivering insights that meaningfully impact the enterprise’s goals and initiatives.

Every tool vendor claims to do DataOps. Has the term lost its meaning?

Since DataOps awareness spiked in 2018, marketers have started to hijack the term and bend it toward whatever technology they are selling. Many of these tools can contribute to DataOps. No tool delivers DataOps by itself. An excellent place to get a synthesized, publicly vetted view of DataOps is Wikipedia. You can also read and sign the DataOps Manifesto. The Eckerson Group has published several excellent reports about DataOps and our recent blog post, A Guide to Understanding DataOps Solutions, helps cut through all the marketing spin.

It’s important to remember that DataOps is a combination of methodologies and tools. Stay focused on the goals: improving data and analytics quality, reducing the cycle time of creating new analytics, and increasing the productivity of the data organization by orders of magnitude. You can’t go wrong serving these aims.

Figure 5a: Data Journey First DataOps

Figure 5b: Five Pillars of Data Journeys

What is DataOps Observability?

DataOps Observability monitors, tests, alerts, and analyzes your data estate in real-time. It provides a view of every data journey from data source to customer value, from any team development environment into production, across every tool, team, environment, and customer so that problems are detected, localized, and understood immediately.

DataOps Observability makes your Data Journeys come alive!

Should I be skeptical of the hype around DataOps?

Probably, but DataOps is based upon a solid foundation that includes Agile development, DevOps, lean manufacturing and statistical process controls. These mature methodologies have added value in enterprises and businesses for decades.

Where can I learn more about DataOps?

You’ve come to the right place! A great place to start is to read The DataOps Cookbook. Other useful resources include:

• [White Paper] DataOps is NOT Just DevOps for Data
• [White Paper] The Seven Steps to Implement DataOps
• [Blog] For Data Team Success, What You Do is Less Important than How You Do It
• [Blog] A Guide to Understanding DataOps Solutions
• [Webinar] Why Do DataOps?
• [BOOK] Recipes for DataOps Success
• [FAQ] What is DataOps Observability? 
• [Manifesto] Data Journey Manifesto
• [Blog] Data Journey First DataOps
• [Latest Link Collection] DataKitchen Resource Guide To Data Journeys & Data Observability & DataOps