The amount of data that businesses generate and collect continues to soar, and enterprises that want to accelerate their end-to-end processes and obtain business insight can’t keep using the manual data management processes they have relied on for decades. And the situation will only get worse in the coming years. IDC expects the amount of data to grow at a 32% CAGR to 180 zettabytes by the year 2025. Fortunately, DataOps can help.

DataOps is a relatively new and still-maturing discipline that arose about five years ago. It’s built on the foundations created by agile development and DevOps, and the goal is to bring similar principles to data analytics and data science to improve data quality and reduce the time required for obtaining actionable business intelligence.

Over the next few years, enterprises that resist adopting DataOps will spend even more time reacting to data errors and broken manual processes, and fall even further behind in their ability to provide timely, accurate information to business leaders. Meanwhile, those that embrace DataOps will create streamlined, automated data pipelines that enable data administrators and data scientists to optimize business processes, focus on higher-value tasks and support decision makers with the best possible intelligence.

In this article, we’ll review the principles and power of DataOps and five specific ways every organization can benefit from it.

What Is DataOps?

Because of the massive size of datasets, performing data analytics today requires automation to be able to do validity testing, analyze the activity and behavior of the data pipeline itself, and detect anomalies and outliers that can indicate a quality issue with the data or the pipeline. Based on the efficacy of agile development and DevOps principles to deliver high-quality results orders of magnitude faster than previous approaches, relevant principles and best practices were borrowed and codified in the 18 principles of the DataOps Manifesto to enable the same types of automation and quality-detection capabilities within the data pipeline.

A foundational concept of DataOps is that “analytics is code,” which means that everything–collection routines, ETL routines, even the analysis routines that drive metadata and business intelligence-level summaries–needs to be modular, automated and easily and instantly repeatable. As businesses work toward this goal, operational benefits will accrue, such as the ability to receive real-time alerts on streaming data, so quality issues can be resolved before bad data gets propagated or has a chance to impact decision making.

The Power of DataOps

DataOps is already enabling businesses to transform their data management and data analytics processes. For example, like DevOps, DataOps lets teams easily spin up isolated, safe and disposable testing environments that allow them to experiment and innovate (Principle 12 of the Manifesto). However, while developers typically focus on applications with small test databases, data analysts and scientists may need to spin up a sandbox environment that includes applications along with terabytes or even hundreds of terabytes of data. By easily implementing intelligent DataOps strategies such as automation, cloning, predictive analytics and more, spinning up massive disposable data environments becomes possible.

DataOps principles are also enabling businesses to act on their massive production datasets in ways that were unimaginable just a few years ago. For example, DreamWorks can now easily share the datasets of its films in development with teams of creative artists around the world, enabling rapid collaboration and dramatically shortening production times. Another example is the genomics company, WuXi NextCODE, which has developed a genome platform that can compare human DNA–millions of bits of data–and integrate the data on the fly to explore the differences or mutations that may cause cancer or rare diseases.

There are also many financial services companies, in America, EMEA and APAC, all leveraging NetApp for moving to a hybrid cloud data pipeline model. The hybrid model allows them to maintain compliance for protected and sensitive data, while taking advantage of cloud cost savings for non-sensitive data and applicaation components. This allows for a strategy that delivers both regulatory compliance and the flexibility of a hybrid data pipeline architecture.

However, you don’t have to be a film studio, genomics company or financial services firm to benefit from DataOps. Every company that needs to obtain timely actionable business intelligence will benefit in five critical ways.

Top 5 Benefits of DataOps for Any Business

1. Reducing toil: Similar to DevOps, DataOps is fundamentally about process-oriented methodologies and automation, which dramatically increases the efficiency of workers. By baking intelligent testing and observation mechanisms into the analytics pipeline, teams can stay focused on strategic tasks instead of poring over spreadsheets looking for anomalies.
2. Better-quality data: Creating automated, repeatable processes, along with automatic code checks and controlled rollouts, reduces the chance that any type of human error will get distributed to multiple servers and take down the network or produce erroneous results.
3. Faster access to actionable intelligence: Reducing toil and improving data quality leads directly to faster access to actionable business intelligence. Automated ingestion, processing and summary analytics on incoming data streams, combined with the elimination of errors, can deliver insights into customer behavior patterns, market shifts, price fluctuations, etc.–instantly instead of hours, days or even weeks later.
4. Seeing a bigger picture of dataflow: Beyond the business-critical day-to-day insights, DataOps can provide an aggregated view over time of the entire dataflow, across the organization and out to end users. This can reveal macro trends, such as adoption rates of features or services, or search pattern deltas over time. Even behavioral or geographic patterns for focused or global data sets. Creating such a view would never be possible for teams that are constantly reacting to anomalies and errors with manual processes.
5. Career enhancement: Data analytics and operations professionals who learn how to implement and manage DataOps processes will enjoy career benefits as they become the leaders of the next generation of data teams and set the standard for data practices for at least the next 10 years. The business also benefits from increased employee satisfaction and retention as repetitive and monotonous processes are replaced by an exciting and fast-moving organization focused on innovation.

Over the next five years, DataOps will become mainstream in the same way that DevOps did. The benefits are too compelling, and the consequences of ignoring it are too dire. However, as companies progress on the DataOps journey and find success in leveraging its principles to drive business intelligence and streamline processes involving large datasets, they will eventually confront the limitation of their infrastructures. As a result, they will need trusted technology partners that can help them ensure data replication, data distribution and data availability at a scale that dwarf’s today’s requirements.