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What Is DataOps?

DataOps (data operations) is a concept and method of introducing continuous data analytics into an organization's business processes. In much the same way that DevOps integrates continuous development and improvement into the software development process, DataOps relies on cooperation and communication between everyone involved to ensure that an organization's data workflow is managed as efficiently — and made available to end users as easily — as possible. Automation is a key element of the DataOps process as well.

At its core, DataOps relies on statistical process control (SPC) to monitor and control data flows and the data analytics pipeline, which continuously monitors and verifies data flowing through an operational system. If an anomaly occurs, the data analytics team is notified through an automated alert.

However, DataOps is still evolving, with principles that can be traced back through DevOps and Agile methodology and development, as well as various aspects of data science. Just like the development of the manufacturing process, data analytics and orchestration systems need to validate their processes along the way to make sure that mistakes distort the rest of the analysis. DataOps is intended to provide this data quality control.

In the following article, we’ll explore the problems DataOps solves, the benefits, business value and future of DataOps, and how you can get started with DataOps to create a data-driven enterprise.

What Is DataOps | Contents

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The DataOps lifecycle takes organizations from business requirement to business value.

What problem does DataOps solve?

The goal of DataOps is to solve problems the organization faces when it is inundated with more data — from multiple data sources — than it can process and use. Most organizations are struggling with their ability to handle basic data governance needs while at the same time taking advantage of the incredible opportunities data brings with it. DataOps is intended to bring automation to bear on the data challenge.

Specifically, DataOps is designed to enhance and improve communication and cooperation between the entire data team, including data scientists, analysts, engineers and the IT department. As data becomes more critical, organizations will increasingly be required to implement repeatable processes to ensure data quality and accessibility, while increasing productivity.

DataOps enables:

  • Collaboration among all teams responsible for data
  • Rapid delivery of data and insights for all stakeholders
  • Significantly improve data quality
  • Automation of difficult, time-consuming processes

When applied effectively, DataOps can:

  • Reduce the time it takes for analytics to answer specific questions
  • Improve the quality of data analysis
  • Cut data analytics costs by reducing the amount of required time and human interaction
  • Free data analytics teams and data scientists to work on more advanced data analysis

Ultimately, DataOps can help organizations use their data more effectively and efficiently to solve real-world business problems.

What is a DataOps platform?

“DataOps platform” is a non-specific term used to describe the tools an organization uses as part of its DataOps practice. In general, the term includes an organization’s data pipeline, any applications designed to automate the ingestion such as ETL (extract, transform, load) data management, and the necessary infrastructure to deliver that data across hybrid, multicloud and on-premises environments. A DataOps platform also includes a user interface (UI) that allows users to quickly find data they need to do their jobs without requiring a third party to gather it for them.

What is a DataOps engineer?

A DataOps engineer is someone tasked with building, maintaining and operating an organization’s DataOps infrastructure. However, the roles of data engineer and DataOps engineer are often used interchangeably.

As companies transform their infrastructure to become more digital, they need to find ways to move massive amounts of data intelligently and in a timely manner. A DataOps engineer, as part of a DataOps team, can help the company operationalize that data, overseeing the enterprise's information stack and playing a key role in data decision-making.

What are some DataOps use cases?

DataOps promises to increase the speed with which useful, high-quality data is available in a practical business context. There are dozens of potential use cases in which DataOps help organizations access and use their data more quickly and effectively. Some common scenarios involve:


  • Real-time analytics
  • Data integration
  • IoT analytics
  • Customer intelligence
  • Fraud detection
  • Increased data security
  • Increased operational efficiency
  • Automation of common data tasks
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DataOps incorporates dozens of use cases, including fraud detection, data security and customer intelligence.

How is DataOps transforming data management practices?

Along with the immense value of data comes challenges, costs and confusion associated with extracting its value.

One ongoing problem is that change management issues have become significantly more complex than technical challenges, with internal teams reluctant to share data between departments, which result in lack of trust.

That’s where DataOps comes in. DataOps is focused on making sure data assets are delivered to the right end users in the format they need to do their jobs — in other words, ready to be used in data analysis. Spending less time thinking about technology — and more time addressing people and cultural issues — may help organizations deliver data that data consumers will use, resulting in meaningful returns from their big data investments.

What is the difference between DevOps and DataOps?

While DataOps and DevOps are two distinctly different methodologies, DataOps embraces the principles of DevOps, in particular the focus on Agile development. DevOps is an approach to IT delivery that combines people, practices and tools to break down silos between development and operations teams. DataOps concentrates on the business outcomes — how the data is used and how it is delivered. DevOps employs Agile principles to help teams accelerate the development of applications and services and deploy and update IT products faster. DataOps uses the same Agile principles to make sure that data is delivered in the way end users need for them to make decisions.

DevOps bridges the gap between “dev” (software development, where the application code is created) and “ops” or IT operations (where those applications are put into production, available to end users and maintained.) DevOps emerged from two earlier trends: the Agile development movement and lean manufacturing principles. The former emphasizes short sprints of work and rapid iteration to create a more responsive IT development organization, and the latter minimizes waste and maximizes productivity in factories.

What are the benefits of DataOps?

Organizations that launch DataOps initiatives realize increased efficiency, more relevant insights, and many other benefits. Key advantages of DataOps include:

  • The ability to intelligently ingest, clean, organize and manipulate data for future consumption, regardless of where the data comes from in the organization. DataOps supports the entire data lifecycle (from data creation to destruction), which includes multiple applications throughout an organization’s IT infrastructure (ranging from edge to cloud) and makes it usable to any end user.
  • A DataOps architecture that encompasses all data structures inside an organization, from on-premises servers to edge networks to cloud storage, which helps ensure consistency in a hybrid cloud environment.
  • The ability to apply automation to repetitive data management tasks, using artificial intelligence (AI) and machine learning (ML) in a consistent, effective manner.
  • Standardization in the way data is ingested, cleansed, stored and accessed, making it available throughout the entirety of the organization. End users gain the ability to access the data they need without requiring the assistance of data analysts.

How do you get started with DataOps?

The primary way to begin an initiative is with a DataOps platform. Related vendors sell new hardware or software, provide assistance in setting up data architecture and infrastructure following DataOps practices, provide consulting and implementation services, or a combination of everything.

DataOps is a relatively new discipline, and therefore there aren’t nearly as many prescriptive ways to implement DataOps as there are for DevOps. But if you want to begin, you can follow a few simple steps.

  1. Make sure you can monitor and measure the right data, as well as understand the steps that make up your data pipeline.
  2. Build workflows to track the key steps in the flow of data and development processes, from development to testing, deployment and tracking.
  3. Look to the established guidelines for DevOps such as check in/check out repositories, version control, and continuous delivery and integration and continuous incremental deployment to help establish your DataOps processes.

Many organizations turn to the services of a DataOps consultant or data professional to help them build and establish their DataOps practices.

What is the future of DataOps?

DataOps is essentially a common-sense philosophy developed on the backbone of DevOps. The most likely outcome is that the practice of DataOps will gain traction as people see the benefits of increased collaboration, cooperation and communication across the data lifecycle. Regardless of how the concept evolves, it’s relatively safe to predict that the positive changes it has ushered in will continue.

The Bottom Line: DataOps provides a new way to manage data

Unlike many new data and IT concepts that require operational changes, new hardware, software and personnel, DataOps promotes a change in mindset, which bring DevOps concepts into data management. There is little or no downside to investigating and trialing a DataOps approach. With the potential benefits DataOps can bring, the bottom line is that any organization that relies on quick, effective use of its data to gain and keep a competitive advantage should learn more about DataOps and see if it's right for them.

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