When solving data quality issues we are usually dealing with important data quality dimensions as uniqueness, completeness, timeliness, accuracy, consistency and integrity.
One other data quality dimension I have been addressing lately is accessibility.
Data accessibility is a universal feature within information technology as recently emphasized in the big story about that the Spanish bank BBVA is to move +100,000 employees to the cloud (using Google Apps). The main driver for that is data accessibility.
The increasing adoption of cloud services will in my eyes contribute positively to solving many data quality issues through improved accessibility as discussed in the post Data Quality from the Cloud.
The fact that data is available and in principle accessible doesn’t however mean that the case is solved. The Achilles Heel is how to smoothly integrate accessible data into business processes, not at least how to integrate and present data from many different sources be that external and internal sources.
Data accessibility must be seen in conjunction with the other data quality dimensions. Fulfilling one dimension doesn’t make the day. Accessibility to data that isn’t satisfactory unique, complete, timely and accurate isn’t that much worth. Making data consistent across multiple sources isn’t a walkover. Securing data integrity between more and more accessible data will be paramount.
Metadata management is also closely related to data accessibility. The importance of a common understanding about what is in the accessible data can’t be overestimated.
My guess is that we will see data accessibility as an increasingly important data quality dimension in the years to follow.
Liliendahl on Data Quality 
Nice post Henrik. I was reading Richard Wang the other day and his answer to the question “What is the most important DQ dimension?” was the same as yours, Accessibility. He stressed that the quality of the data could not improve until all the potential consumers got their say as to what qualities made the data fit for their purpose.
Thanks for commenting Gordon. One challenge in data quality improvement is that different data consumers see different qualities in making the same data fit for their particular use. Improved accessibility will help here too for example as one consumer like a given set of data mashed-up with one other set of related data and another consumer will like the same core data mashed-up with another set of related data.