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First let's define "data" and "information" -- from that, the delineation between "data architecture" and "information architecture" will become evident. Data are the raw facts about things in our business, e.g. on a sales transaction, what product was sold and how many. Information can be defined as: high quality data + meta data + data context.
Data architecture is geared toward establishing the data environment for a particular application (e.g. ERP, CRM, Data Warehouse) and includes activities such as data modeling, database design, and data integration design.
Information architecture has more of an enterprise focus and encompasses aspects of data architecture, meta data management, and knowledge management to provide a holistic view of information assets to enable a cohesive platform for delivering information in the correct context to the right people. A key deliverable for an information architecture is the enterprise data model, comprised of a subject area model (SAM), enterprise conceptual data model (ECDM) and enterprise logical data model (ELDM). The enterprise data model is technology and application neutral, and defines how the business sees information.
Information architecture frequently requires development of enterprise taxonomies to organize data (e.g. products) in a hierarchical manner (made more difficult due to the variations between business units) as part of the master data management environment. Ontologies are becoming more common in information architecture to help to determine how to relate things (e.g. vocabulary words) so as to enable technologies such as enterprise search and the semantic Web.
Information is not just found in databases -- it is also found in our knowledge management systems (unstructured data) as well and so must be integrated with structured data to provide a complete picture of enterprise information.
Meta data is the glue that provides linkage between data resources, and provides context to our data in order to turn data into information. Business meta data is particularly crucial to providing understandability to our data, without which assumptions are often made leading to incorrect results ("bad information").
So you can see, there are significant differences between data architecture and information architecture, though there is overlap -- particularly in the realm of conceptual and logical data modeling. Having a good information architecture builds upon the data architecture.
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