The ideal way to split up data and information is to look at them as a checkpoint and an endpoint. In this view, data refers to the figures, statistics and other hard facts that can be analyzed for further insight. The insights that come from processing and analyzing data are then considered information. In other words, without those insights, data is meaningless and no information can be taken from it. In short, processing data yields information.
This separation works in practice, but it does have some snags. Sometimes the information resulting from processing data can be “demoted” to the level of data and be processed again to yield more insights (more information). To make this work logically, we need to broaden the definition of data to anything that is processed or analyzed and tighten the definition of information to mean insights on the subject or problem being considered. In this sense, information is anything that provides meaningful insights to the observer at first glance, while data is anything that requires more processing or analyzing before it yields those insights.
So the difference between data and information depends on the purpose of the individual looking at it. What is merely a piece of data to one person may be a vital piece of information to another person. For example, the market value of a particular house is very likely important information for the homeowner, but it is merely another piece of data to an economist tracking housing prices in the city over time.