DataOps: The next big thing for data-focused businesses

Data is the fuel to run almost all organizations and enterprises across the globe. While most enterprises are data-driven, every company irrespective of its size and industry relies on data for its functioning and everyday operations. To accommodate the growing need for data in the current enterprise world, a new enterprise practice has emerged: the fusing of data and operations teams within a company. Say hello to DataOps.

DataOps is the latest Agile practice that brings together the existing DevOps teams with data engineers and data scientists to support all companies that are data-focused.

DataOps can provide organizations with real-time data insights, allowing every team to collectively work toward a common goal. For example, if a company wants to increase sales, DataOps can provide them with real-time statistics and insights that show the areas where the company needs to improve for better sales.

DataOps: Origin story

DataOps is one of the many enterprise methodologies that came into being after DevOps. DevOps is used worldwide for better software development and continuous rollouts.

As we all know, DevOps brings together two separate entities in an IT industry — development and operations for better collaboration. DataOps, on the other hand, is also based upon a similar concept. It connects the teams handling data, such as data engineers, data analysts, and data scientists with the rest of the organization. With DataOps, companies can start fetching the productive aspects of data science in day-to-day operations. Moreover, DataOps also provides a collaborative work environment, wherein every employee of an organization can have real-time metrics and goals.

Advantages of DataOps

  • Provides real-time data insights
  • Improves collaboration among various teams in an organization
  • Enables teams to quickly and effectively respond to new requests
  • Enables better operations and support
  • Provides real-time goals for organizations
  • Helps to avoid disastrous scenarios by predicting them in advance using data analytics
  • Improves efficiency and overall quality through statistical process control (SPC)
  • Shortens the time to fix bugs and defects

DataOps for the enterprise

Although not every company might be data-centric, every company can certainly benefit from implementing DataOps. DataOps, in simple terms, is an Agile methodology, primarily meant to develop and deploy data-intensive applications and solutions. DataOps also serves as a means of real-time tracker or a monitor for the enterprise.


DataOps aligns data management objectives in application development, support, and other day-to-day activities. For any enterprise to successfully adapt to DataOps methodology, it first needs to have a common global data view, which defines the data journey in an organization. The structural composition of teams change and everyone in the organization must collaboratively think and work to make the most of the DataOps.

By decentralizing the data, enterprises can promote self-service data solutions and services, fostering better collaboration and productivity in the company. By adopting DataOps methodologies, enterprises can get over the traditional slow data operations and can mitigate the risk of data silos.

Most companies today struggle to achieve their goals as they fail to get accurate and detailed information about their services, processes, products, employees, customer, and market. With the advent of DataOps in the enterprise, companies will have a significant amount of analytics to give accurate insights into these aspects.

Building a DataOps team

Building a DataOps team for an enterprise doesn’t necessarily mean that you need to hire new specialists. Enterprises need to first start with small projects to understand the value and functioning of the data-intensive development and deployment environment. Companies need to focus on picking the right candidates from the existing set of employees rather than hiring new resources.


A person having domain knowledge will undeniably prove to be a valuable asset in the implementation of DataOps for an organization. The specialist needed to deal with the data-role doesn’t necessarily have to be a data scientist. They can be a data engineer or an analyst with the right skill sets.

Cross-skill communication is a key motivator that can make DataOps work much more efficiently. Cross-skill communication becomes much easier when different teams collaborate and focus on enterprise level goals rather than the team-level goals.

Collaboration and operation are among the several other advantages DataOps offers to enterprises. Although the role of a data specialist is prominent in implementing DataOps, everyone in the business has an equal role to play for a successful adoption of the methodology.

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