When data professionals define data architectures, the focus is usually on production requirements: performance, latency, load, etc. The problem is that the specifications don’t include architecting for rapid change. A DataOps data architecture makes the steps to change what is in production a 'central idea,' so that changes over time to your code, your servers, your tools, and monitoring for errors, are simple and seamless.