There are a slew of terms used today to identify a responsibility within a company when dealing with data. Data Management, Data Quality, Information Governance, Data Stewardship, Data Governance, Data Modeling and more. Confusion arises when these terms are used interchangeably or, worse, are used to suggest a hierarchy within the data operations that may not actually exist.
That’s the case with a scuffle over the meaning of Data Quality. Some people view Data Quality as a wholly owned subsidiary of Data Governance. I believe this is incorrect.
Both Data Quality and Data Governance have critical roles in data-driven organizations, but their responsibilities are quite different. Data Quality is charged with ensuring that all data is complete, accurate, and ready for a company’s business uses. Its importance to companies has increased as the sources, formats, and volume of collected data have multiplied. Companies now acquire data from mobile devices and social networks, sensors and RFID chips (including new streams from the Internet of Things), and deal with log data, legacy data, open data, linked data, clickstream data, and image/video or audio data along with traditional address and transaction data. Clearly, the scope of Data Quality’s responsibilities is trending way, way up.
Data Governance’s mission is to establish the framework and rules by which a company will purpose the data it receives from Data Quality. It provides the internal structure of how the data will be used – a very different assignment than Data Quality’s. In fact, even though Data Governance is still thought of as an IT-based function in some quarters, today its primary role is to make certain that the necessary data informs critical business functions.
A simple way to illustrate the difference between Data Quality and Data Governance is to see if either could be a standalone function.
Data Quality exists for one essential purpose: to collect and cleanse data and make certain that it is (as the DAMA Dictionary of Data Management states) “complete, timely, consistent with all requirements and business rules, and relevant for a given use.” Many companies, including direct mail houses, require that – and only that.
Defining those business rules and establishing what is a “given use” are done by Data Governance. But ask yourself, “Can Data Governance function without a supply of quality data?” The answer is no. Yet, Data Quality can certainly function without Data Governance. The conclusion is that these two complementary functions have fundamentally different responsibilities.
As we see, it’s inaccurate to confuse Data Quality with Data Governance. It still happens, but just as we want our data to be precise, our ways of talking about it should be, too.