Lean manufacturing is a methodology, originating in the Japanese manufacturing industry (e.g., Toyota), that focuses on the minimization of waste within a system without sacrificing productivity. Whereas Agile and DevOps relate to analytics development and deployment, data analytics also manages and orchestrates a data pipeline. Data continuously enters on one side of the pipeline, progresses through a series of steps and exits in the form of reports, models and views. The data pipeline is the “operations” side of data analytics. It is helpful to conceptualize the data pipeline as a manufacturing line with an active focus on quality, efficiency, constraints and uptime. To fully embrace this manufacturing mindset, we call this pipeline the “data factory.”
In DataOps, the flow of data through operations is an important area of focus. DataOps orchestrates, monitors and manages the data factory. One particularly powerful lean-manufacturing tool is statistical process control (SPC). SPC measures and monitors data and operational characteristics of the data pipeline, ensuring that statistical variance remains within acceptable ranges. SPC leads to remarkable improvements in efficiency, quality and transparency when applied to data analytics. With SPC in place, the data flowing through the operational system is verified at each step in the data-analytics pipeline. If an anomaly occurs, the data analytics team will be the first to know, through an automated alert.
While the name “DataOps” implies that it borrows most heavily from DevOps, it is all of the methodologies described above — Agile, DevOps, "lean" and statistical process control — that comprise the intellectual heritage of DataOps. Agile governs analytics development, DevOps optimizes code verification, builds and delivery of new analytics and SPC orchestrates, monitors and validates the data factory. Figure 1 illustrates how Agile, DevOps and statistical process control for the foundation of DataOps.