5 Ways Your Business Can Benefit From DataOps
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.
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.
