Everyone starts out blaming IT. However, data is created by people outside IT, and is used by people outside IT. IT is responsible for the quality of the systems that move the data and store it. However, they cannot be held completely responsible for the content. Much of the problem lies outside IT, through poorly articulated requirements, poor acceptance testing of systems, poor data creation processes, and much more.
Data quality problems are universal in nature. In just about any large organization the state of information and data quality is at the same low levels.
The fact that data quality is universally poor indicates that it is not the fault of individually poorly managed organizations but rather that it is the natural result of the evolution of information system technology. There are two major contributing factors. The first is the rapid system implementations and change that have made it very difficult to control quality. The second is that the methods, standards, techniques, and tools for controlling quality have evolved at a much slower pace than the systems they serve.
Virtually all organizations admit that data quality issues plague their progress. They are all aware of the situation at some level within the enterprise. Quality problems are not restricted to older systems either. Nor are they restricted to particular types of systems. For example, practitioners intuitively assume that systems built on a relational database foundation are of higher data quality than older systems built on less sophisticated data management technology. Under examination, this generally turns out not to be true.
Information technology evolution is at a point where the next most important technology that needs to evolve is methods for controlling the quality of data and the information derived from it. The systems we are building are too important not to address this important topic any later than now.