In the fast-paced world of today, more businesses and institutes, apps are popping up. More numbers mean more data. This is tricky as it accounts for contrasts in the form of employees' skills, tools, code versions, procedures, storage facilities, data hubs, and software.

This led to the emergence of a new data management system to use available data to generate business value or deliver insights- DataOps. It integrates Agile Development, DevOps and Statistical Process controls and applies them to Data Analytics. So, it helps to improved coordination between the analysis and operation of data in a business. It can yield high speed and accuracy of analytics. That includes data access, quality control, automation, shorter cycle time, integration, model deployment, and time management. This can translate into better insights, business strategies, and higher profitability.

It is an umbrella method that utilizes technology and cultural changes to bring speed and agility to the data pipeline process, from collection to delivery. As mentioned earlier, this methodology employs three distinct processes viz. agile development, DevOps and Statistical Process Control. Agile controls analysis tools. DevOps optimizes code verification, builds and delivers new analytics. And SPC arranges and monitors the data libraries.

The advantages of implementing this system include real-time data insights, and goals. It curtails the time wasted on fixing bugs and defects. It allows data teams to quickly and effectively respond to new requests. Also it mitigates the risks of data silos. It further serves as a channel for real-time monitoring for businesses.

But what led to experts predicting the boom in this sector in the 2020?

The current evolving digital transformation across the verticals, in Artificial Intelligence, Big Data analysis, Machine learning, etc., operates on one basic component: data. And with technological advancements taking place, it is giving birth to data explosion. And these large volumes of data are coming from various sources in different formats. In big enterprises, this data grows exponentially in form bank records, CRM, consumer info, research statistics, and so on. The objective was pretty clear: create analytics, move to production, receive user feedback and advance via further iterations.

Like-wise technology overload also increased the number of users for the data. These consumers want data that would cater to their demands and purpose. And the current reliance on conditional analytics is not helping. Data-officers found that introducing DataOps enabled smooth on-boarding and off-boarding process. It provides the opportunity to apply developer-proven disciplines as a quality check for data and associated programming. And improved quality by surfacing key processes that impact the organization. DataOps was therefore viewed as a virtue of abundance of potentials. It provides quality of extreme-scale automation while stressing on the need for collaborations of platforms and operations. This eliminates error-prone manual work. For Instance, Qlik offers a Data Integration Platform which delivers faster, better insights with modern DataOps for analytics, accelerating the discovery and availability of real-time, analytics-ready data by automating data streaming (CDC), refinement, cataloging, and publishing

Although it is still a new 'jargon' DataOps can dominate the market and technical world in the future. This can be made by empowering existing technologies, having an adaptive and innovative architecture, enriching data and building a strong delivery framework, and finally converting to cross-skill communication.