A Glimpse into the Future of Modern Data Architectures with DataOps at its Core

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Click to learn more about author Itamar Ankorion.

Enterprises are building out modern Data Architectures to accommodate new analytic requirements, larger data volumes and take advantage of newer technologies such as cloud, data lakes and data streaming. These platforms make it possible to support digital transformation with modern analytics, delivering new services empowered by data and AI, as well as advanced data analysis and science. And as much as these new infrastructures assist with leveraging data more readily across the organization, they do not replace the need to change the process in which IT makes data available. This process that delivers higher agility, faster time to value, and makes it seamless to have real-time data available in an efficient manner, is DataOps.

This year we will see DataOps begin to gain popularity amongst data teams. It will be a time in which IT educates itself on how to put together this collaborative Data Management process and approach, as enterprises seek to make data seamlessly and continuously available, with faster initial delivery and rapid improvement cycles.

What is DataOps, more specifically?

DataOps is a collaborative Data Management practice introduced in an official manner in mid-2018 on the Gartner 2018 Hype Cycle and is emerging as a coverage topic and area of interest with other analyst groups who recognize the upcoming shift in the industry.

DataOps is not a technology solution, but a process for managing data, people and technology in a manner that improves efficiency and ways in which data is used across a company. DataOps will start to gain importance as part of the internal digital transformation as it allows IT leaders to focus more on improving communication, integration and automation of data flows enterprise-wide. This methodology aims to help enterprises implement an approach that facilitates managing and using their increasing data volumes effectively while reducing the cycle time of data analytics.

DataOps can be likened to the DevOps movement that occurred a few years back. It breaks down the silos in IT development and operations by leveraging agile automation and continuous data integration technologies (such as change data capture (CDC) and autonomous schema evolution). As a result, DataOps modernizes the consumption of data and helps managers improve the speed and accuracy of their analytics with automation and integration and provides analytics-ready data. DataOps is the foundation of a more streamlined process that helps enterprises make data available faster so that they can operate at the speed of market changes and make decisions based on their most recent data.

How does DataOps fit into the IT infrastructure?

The key point to remember is that DataOps is a process – not a solution that can be purchased and installed on the network or in the cloud. As such, enterprises and industry analysts will work together in 2019 to figure out what this principles-based practice should be, how it can be best implemented and what technologies will be essential solutions for success.

To that point, enterprises should first start the adoption process by outlining agile practices they want to emulate – similar to what was done with DevOps when it was first introduced – as agility is at the core of the DataOps process. As part of these practices, enterprises will also need to select corresponding technology solutions that can enable automation of real-time data movement. Some of these technologies may include data integration solutions that help optimize this process and bring together data in real-time from various heterogenous platforms including databases, data warehouses, cloud, streaming, IoT/AI technologies and data lakes for a single, more comprehensive view of the business with improved analytics.

Why Is DataOps Important for a Modern Data Architecture?

Businesses today need to quickly deliver data to support more analytic applications, Data Science and AI, and microservices, used by their line of business personnel as well as their customers.

Agile operations are the cornerstone of modern Data Architectures and in the coming year, enterprises will focus on modernizing them to include automation and real-time data integration technologies.  The most successful companies will leverage these methodologies that provide the agility and framework needed to meet the ever-changing requirements of the business. Some are already ahead of the curve, giving them a competitive edge. DataOps also includes fluid procedures for sharing and using data in the chosen architecture to enable every business user to deliver and provision data with ease and speed.

This shift to a more collaborative and streamlined Data Management practice is happening now, at businesses all around you. However, DataOps will start to gain greater market interest and importance as IT leaders focus more on improving communication, integration and automation of data pipelines for analytics. In a nutshell, you can expect that DataOps will be the secret sauce that enables organizations to stay competitive by automating processes and moving data at the speed of change.

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