Contemporary organizations are slowly but steadily understanding the potential value of data to their enterprise. As a result of this, companies are trying to build a brand-new generation of human behavioral norms and data infrastructure that complement their traditional legacy infrastructure, as well as data culture.

While several businesses today focus on finding ways to effectively derive value from data with the help of which they desire to acquire certain business outcomes, doing so would be no cakewalk. Mining data for the purpose of carving analytics applications with the aim of driving competent decision-making and innovation takes a lot of expertise and resources. To make this process smoother and more efficient, businesses are choosing to replace the traditional data management with a gradually emerging set of practices known as DataOps.

DataOps involves a distinct approach towards agile data management that comprises the combination of three critical elements – people, processes, and tech. It is a junction of analytics delivery practices, as well as advanced data governance that include the data life cycle. Much like DevOps that focuses on speeding up software development, DataOps accepts continuous and agile delivery development systems and is typically supported by distinguished on-demand IT resources.

DataOps has the potential to have incredible transformative effects on data processes. Its key aim lies in enabling companies to implement a process that effectively manages and uses their ever-increasing data stores in a competent fashion, ultimately reducing the overall time of data analytics.

The collaborative data management practice of DataOps was first introduced in an official manner in the Gartner 2018 Hype Cycle sometime around mid-2018. It is a process for managing people, technology and data in a fashion that improves efficiency, as well as the ways in which data is used across a firm. It can be considered to be the application of DevOps practices to data integration and management combined with AL/ML to minimize the cycle time of data analytics while putting a good level of focus on monitoring, collaboration, and automation.

While DataOps has not yet become mainstream when it comes to building data solutions, it essentially has raised a great amount of interest among people following the market evolution actively. In 2019 Gartner estimated the adoption rate of DataOps as less than 1% of the addressable market. Even though this is a relatively new domain, various proven technologies and competent practices involved in it are gradually becoming quite popular. These practices are known to possess expansive applicability for operational agility, data governance, as well as data analytics. DataOps is considered to be especially suitable for driving ML, AI, and deep learning methods, as these data-hungry technologies majorly require progressive improvements for the purpose of remaining effective.

To truly understand DataOps and its increasing relevance to the data environment of today it is crucial to acquire a good insight on the practice it originates from, which is DevOps. DevOps has over the years emerged as one of the key methodologies related to software development and has changed the way of thinking in regard to the delivery of new fixes and features in applications frequently, while also making sure of high quality.

DevOps has transformed the way how applications are built in the industry by applying agile practices to the process of product development and testing, and in a similar fashion, DataOps is concentrated on revolutionizing how data is integrated, shared and made available to the people.

With the rapid and exponential increase in the amount of data being gathered every passing day, an increasing number of companies are expected to turn to DataOps as a means to both capture and manage their evolving data in a highly flexible and efficient manner. This technology would ultimately improve how data loads are automated, integrated and shared, subsequently enabling companies to move data at the speed needed to remain competitive.

In addition to aiding company managers to improve the accuracy and speed of their business insights with the help of automation analytics-ready data pipelines, the practice of DataOps would also eventually transform how data is consumed across a firm as a whole. This solution would ideally break down the silos in IT operations, ultimately paving the way to build superior speed and accuracy of data analytics. By efficiently leveraging real-time integration technologies, like CDC or change data capture as an element of its overall set of principles, DataOps is known to be expected to disrupt and eventually transform the data processes prevalent across the industry.

The new cultural practices, solutions, and processes that are at the core of DataOps tend to allow firms to eliminate the manual processes that are associated with data delivery, making the system more agile and secure. These processes majorly include data versioning, provisioning and aligning database code with the relevant application code. DataOps subsequently helps in the fast provisioning of data production with masked data, while also synchronizing the database schema.

In the coming years, it is expected that more and more companies would educate themselves on the practice of DataOps, as it would gradually start its transition journey from an abstract idea to a tangible practice. In the future, one might expect to see this practice further revolutionize the usage of information in the data era.

With increasing awareness about the advantages of DataOps, a number of companies have come up in the market that offers DataOps as a service to discerning organizations. While many contemporary firms do have inbuilt facilities to efficiently support data operations, a number of them opt to outsource it as well. Well-established software development firms like HashedIn typically provide DataOps-as-a-Service or DevOps XaaS as a combination of managed services and cloud-based big data management platform. Many such firms provide purpose-built and scalable big data platforms that adequately adhere to the best practices when it comes to data security, privacy, and governance by opting to make use of DataOps components.