There are more aspects to data quality than just numbers. How easy the data is to understand, interpret, use and format is often as relevant as data accuracy. It would do you little good to get 100,000 records of 100% accurate data if you were unsure what you actually bought.
Listed below are a few of the other important parameters you should be aware of when designing a data test.

Data interpretation and representation: Is a data dictionary available for the data elements? Is it readable and understandable? Does it provide you with an accurate representation of the data and where it came from (e.g., survey, warranty card, etc.)?

Data quantity: Was the match rate in each element high enough for you to use that element in modeling or list processing? What is the minimum number of returned elements that you will need for your modeling or lists? It is possible to buy data based upon overall match rates and price and not get what you need to fulfill your job.

Delivery of the data: Data delivery quality refers not only to how the data was delivered physically but also to the turnaround time and customer service involved. Five million records or less should have a 24-hour turnaround time. For 100 million records you should expect a two to four day turnaround. It is also important to measure qualities such as customer service, responsiveness to questions, and how the company handles problems.