Why DataOps Is Here to Stay

Why DataOps Is Here to Stay

With DataOps, data engineers and data scientists can work together, bringing a level of collaboration and communication, with a common goal of producing valuable insight for the business.

The emergence of AI and machine learning in the past decade has forever transformed the data landscape. It is estimated that businesses worldwide will spend more than $1.8 trillion annually by 2021 on big data and AI-driven digital transformation efforts. In response to the COVID-19 pandemic, network providers like Netflix, Zoom and Dropbox have recognized that automation is a critical factor in keeping up with demand while being able to scale their IT operations and infrastructure. A recent report from Bain and Company projects that the number of companies scaling automation technologies will at least double in the next two years. As businesses rush to embrace these new technologies, the vast amount of data and interconnected systems will continue to grow and become significantly more complex.

With this increasing complexity, businesses will be forced to rethink their approach to data with new tools and techniques. As businesses continue to pursue more advanced data analytics and AI initiatives across their organization, DataOps holds the potential to keep pace with accelerating data development and innovation lifecycles. With DataOps, data engineers and data scientists can work together, bringing a level of collaboration and communication, with a common goal of producing valuable insight for the business.

Unlocking the Potential of DataOps

DataOps is by no means a new term or methodology; however, businesses have begun adopting DataOps practices at an increasing rate to be able to scale and deliver on their investments in data, analytics and machine learning. DataOps brings both agility and stability to the world of data and analytics, enabling data teams to not only build quickly, but to do so safely as their data complexity skyrockets.

DATA COMPLEXITY = DATA SYSTEMS x DATA WORKERS x DATA PRODUCTS

DataOps consists of a set of tenets, philosophies, and practices that unifies builders and consumers of data products, with an explicit focus on delivering speed, quality, and flexibility. DataOps takes an integrated approach to better drive collaboration between separate data teams, and automate as much as possible to minimize manual bottlenecks and errors.

Nick Heudecker, VP analyst at Gartner, defined DataOps in a recent blog post as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and consumers across an organization. The goal of DataOps is to create predictable delivery and change management of data, data models, and related artifacts. DataOps uses technology to automate data delivery with the appropriate levels of security, quality, and metadata to improve the use and value of data in a dynamic environment.”

At its core, DataOps aims to reduce the end-to-end time of development lifecycles for data pipelines, utilizing automation technology to streamline projects and drive faster time to value. Traditional data pipeline development requires manual and repetitive processes prone to human error and oversight, resulting in brittle pipelines and high maintenance costs. Applying DataOps to the data development lifecycle frees data engineers to focus on new data products critical to the business, and brings stability to data architectures that can withstand the inevitable changes ahead.

Additionally, applying DataOps to the data development lifecycle can reduce the time data scientists are spending to extract and produce meaningful insights that transform the business. For example, automating data pipeline technology gives data teams, including data engineers and data scientists, scalable solutions that ultimately creates business value across the entire organization.

From DevOps to DataOps: Strategies for the Coming Decade

DataOps is rapidly maturing and becoming a mainstream practice. However, there are still many common misconceptions in the industry around DataOps and how it can be applied within an organization.

One of the most common DataOps misconceptions is that it is simply “DevOps for data.” By leveraging DevOps methodologies, companies and teams have achieved speed, quality, and flexibility in creating and maintaining software products. DataOps has those same goals in mind for teams building data products — enabling more developers to build increasingly complex systems, faster, and safely. However, data pipelines do have fundamental differences with unique requirements to deliver on this ambitious goal.

Similar to how DevOps changed the way software is developed, DataOps is changing the way data products are created. A core tenant of DevOps is having automated systems that ensure the right code is running in production, with safety checks to ensure it is working properly. With DataOps, it is essential that every person working with data receives similar guarantees — namely, that the data they are working on is correct, timely, and in sync with other systems. As much of the focus in DevOps moved to smaller systems, such as microservices, and well-defined APIs, we similarly see the focus of DataOps moving towards the key asset, data, with simple, incrementally defined, automated, and tested data sets.

The broad implementation of DataOps will be an extremely transformational shift for companies in the coming years. With shorter development cycles, increased iteration frequency, zero maintenance, and inclusive data-driven innovation, DataOps will bring agility to the world of data and analytics.DataOps is a changing concept that will continue to evolve in the years ahead, but one thing is for sure: DataOps is here to stay.