There is no shortage of frameworks for thinking about data quality . There is, however, a significant shortage of practical guidance for actually building one from a standing start. This is especially true for teams that don't have years to spend on the project and need to show value quickly while building toward something sustainable. This guide is written for that. It assumes you have organizational support for a data quality initiative, some existing data infrastructure, and people who care about getting this right, but not necessarily a large dedicated team or a clear playbook. It is sequenced so that each step produces something useful before the next begins. Step 1: Define What "Quality" Means for Your Organization Before you measure anything, answer this question: quality for what purpose? Data quality is not an abstract property of data; it is always relative to a use case. Data that is perfectly adequate for internal trend analysis may be inadequate for regulatory reporting.…