From the Open Data Handbook we get the following definition…

Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share-alike.

Here are the most important features of open data:

• Availability and Access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form.
• Re-use and Redistribution: the data must be provided under terms that permit re-use and redistribution including the intermixing with other datasets.
• Universal Participation: everyone must be able to use, re-use and redistribute - there should be no discrimination against fields of endeavor or against persons or groups. For example, ‘non-commercial’ restrictions that would prevent ‘commercial’ use, or restrictions of use for certain purposes (e.g. only in education), are not allowed.

Why is being so specific important?

Providing a clear definition of openness ensures that when you get two open datasets from two different sources, you will be able to combine them together, and it ensures that we avoid our own ‘tower of babel’: lots of datasets but little or no ability to combine them together into the larger systems where the real value lies.