Information Architecture, Data Architecture and Data Visualization : Levels of context

The nuance and practical distinctions in the use of the terms Data Architecture and Information Architecture deserve clarification. We can include with these Data Visualization. These concepts are often complementary but not always interchangeable and in professional circles, especially within UX design the roles of a Data Architect and Information Architect are distinct if partially overlapping. Professional Job Descriptions encompass differing responsibilities, methods and deliverables. This blog discussion introduces some of my understanding about the theory and usage of the terms theory and in practice. I hope to also expand the recognition of data visualization as an art producing informational content in its most elemental level. I will provide examples that demonstrate common usage of the terms and also look at some examples of formative semiotic conventions that start to build up contextual meaning and useful blocks or information.

Some attribute the foundation of theoretical IA to designer Richard Saul Wurman, who spoke about the relationships of Information Design in 1976 and is attributed with coining the phrase information Architecture.(Medium, 2020) Information Architecture (AI) is concerned with structural design or interface aspects, including labeling and organization of apps, websites and intranet software. Usability and navigation, information retrieval and classification in a descriptive sense are objectively approached distinctly from data-architecture, although AI can include aspects of both back-end and front-end service interactions. The origins of IA in fact draw as much from library science, though what Wurman emphasizes is that Information design is often more than an aesthetic consideration.

Edward Tufte 

Semiotic theorist and data visualization expert; Theo Van Leeuwen's Reading Images: The grammar of Visual Design  and Edward Tufte's Envisioning Information, meanwhile consider elements of visual vernacular as rudimentary to a syntax of meaning that begins to build information by comparing "multivariate" data in charts. Levende Diagramer og Zoobare Kart is an analysis of infographics and data visualization examples from Norwegian net-based news sites. In these examples the effectiveness of graphical content is considered in terms of discursive semiotics and formal critique. Examples are from Aftenposten, NRK, VG and Bergen's Tidende. Content Visualization is also enabled through Information Architecture.

In a straightforward parlance the IA is a blueprint of the design that can be represented as wire-frames and sitemaps. A method for depicting design functionalities is the "User Flow" diagram, which is something of a task function chart combined with wire-framing. It illustrates the interaction functionality and sequence of a set of app or web page screens.

Information Architecture in UX design refers to a repertoire of quite specific contextualized design considerations, while research data is somewhat raw and uncontextualized. Information Architecture (IA) relates to the contextualization of information via a CRM customer or service relationship (or data governance). In UX interaction design this traditionally or typically points to (implies) web or app design along with the "library conventions" and navigation interfaces (Adobe Blog, 2020). 

In specific, considerations about the structure of directories, indexes, menus and navigation tools that add value to the data are what we are talking about. IA concerns the functional organization of information. 

File and directory based systems are often based on custom software apps for preforming tasks. Relational Database Management Systems provide for storage, query and data transformation as well as simultaneous access that can be made available to relevant data stakeholders. The use of these relational tools makes content more responsible to users and adapts resources to the context of the particular user goals.

(IA) Deliverables include:

• Navigation
• Sitemaps
• User-Flows
• Wireframes
• Content: taxonomies, labeling, categories and  meta-data

“Information may be infinite, however…The organization of information is finite as it can only be organized by LATCH: Location, Alphabet, Time, Category, or Hierarchy.” — Wurman, 1996

The field of Information Architecture draws from library science and cognitive psychology. 

Directories and cataloging of information sources can today be powerfully enabled by relational databases that provide users more customizable and responsive interactions. "From the user’s perspective, a mental category is a grouping mechanism, a way to bring together items or concepts through some unifying characteristic(s) or attribute(s)."   The cognitive aspect of UX design is magnified by the powerful capabilities of searching, sorting and customizing. This can be done either by  controlling variables (with controlled vocabularies) or providing users unlimited customization or something in between. Organizing content over a digital product navigation, usage, search, filtering and indexing are the domains of IA.

Data Architecture

The Data Architect meanwhile, may be creating the database from scratch and is typically concerned with data quality and data reporting. The function here is dataflow management and data storage strategy, collection, storage and security, service oriented integration, data modelling and such. Metadata and labeling conventions are vital to back-end development and are arguably an intersection point between Data Architecture and Information architecture. Metadata enables the analysis of structured and unstructured sources. Controlled vocabulary metadata conventions are often used for indexing fields for data analysis in research.

Data Architecture Deliverables include: 

• Policies about data usage
• Guiding principles
• Statements of intent about usage
• Mechanisms for accountability  
• Quality Assurance (QA)  Quality Control and Data Standardization

Some of the tools of a data architect typically include: Python R SQL, data visualization, Tableaux, XML, Java, Data Governance, Data Warehousing and Database Architecture.

Insofar as data infrastructure is at the foundation of solid information infrastructure, proper data lifecycle management will be a key driver of the information lifecycle management process from creation and initial storage to the time when it becomes obsolete and is deleted. 

In Summary

Data Visualization, Information Architecture and Data Architecture are intersecting and distinct specializations and a part of Interaction and UX design. The degree of specialization varies from organization to organization. Many of designers work with all of these or specialize in one area of design.


References:

Adobe Blog. (2020). A Beginner's Guide to Information Architecture for UX Designers | Adobe Blog. https://theblog.adobe.com/a-beginners-guide-to-information-architecture-for-ux-designers/

BMC Blogs. (2020). Data Architecture vs Information Architecture: What’s The Difference?. [online] https://www.bmc.com/blogs/data-architecture-vs-information-architecture/

Kirk, A. (2019). Data Visualization: A Handbook For Data Driven Design. 2nd ed. SAGE Publications Ltd.

Medium. (2020). Information Architecture, making sense of information since 1976.Available at: https://medium.theuxblog.com/information-architecture-making-sense-of-information-since-1976-3b48129a6ad2

Medium. (2020). User flow is the new wireframe.Available at: https://uxdesign.cc/when-touse-user-flows-guide-8b26ca9aa36a.

Rosenfeld, L., Morville, P, Arango, J.(2015) Information Architecture for the Web and Beyond, O'Reilly, Sebastapol, CA

Kress, G. and Van Leeuwen, T. (1996). Reading Images: The Grammar of Visual Design . 2nd ed.
London: Routledge.

Withrow, J. and Withrow, J. (2003). Cognitive Psychology & IA: From Theory to Practice - Boxes and Arrows. [online] Boxes and Arrows. Available at: https://boxesandarrows.com/cognitive-psychology-ia-from-theory-to-practice/ .