Essential Topics in Information Architecture by: Anthony J. Rhem

Information Architecture provides the methods and tools for organizing, labeling, building relationships (through associations), and describing (through metadata) your unstructured content, adding this source to your overall pool of available data for analysis. Information architecture enables data analytics to rapidly explore and analyze any combination of structured and unstructured sources. Big Data requires IA to exploit relationships and synergies between information, aligning unstructured and structured data. This infrastructure enables organizations to make decisions using the full spectrum of your big data sources.

The use of information architecture principles, practices, and procedures has expanded enormously in recent years. this expansion has also brought about the proliferation of knowledge management systems in its many forms, contact center knowledge repositories, expertise locators, content management, document management, knowledge repositories and libraries, social media applications, and decision support systems, to name a few.

Big data analytics examines large amounts of data to uncover hidden patterns, correlations, and other insights. Over 90% of any organization’s data is either unstructured or semi-structured. Therefore, creating a consistent structure for this data is extremely important for the success of any big data analytics effort. Aligning information architecture (IA) elements to big data components provides a direct mapping on how to effectively use IA to address big data. The chart below Information Architecture Elements Aligned to Big Data Components provides details in a graphical illustration of the alignment between IA elements and big data components.

Information Architecture Elements Aligned to Big Data Components

Big Data Analytics

The big data analytics process consists of identifying the big data repositories, building the statistical/mathematical algorithm, building analytics model which could be one or a combination of descriptive, diagnostic, predictive and/or prescriptive, and execution by passing the data through the model. The result is that the model will provide insights into the data and will in turn be analyzed by an expert to take some action, (big data analytics: collecting, organizing, and analyzing large sets of data to discover patterns to provide actionable insights).

To understand the data, the data must first be organized and understood. Since the majority of big data is unstructured and semi-structured, from various systems and repositories with no relationship to each other, applying IA through content modeling, metadata, and taxonomic analysis will provide the structure necessary for big data analytics and its various tools (such as, Hadoop, Spark, and MongoDB) to be effective.

Information architecture enables big data to rapidly explore and analyze any combination of structured, semi-structured, and unstructured sources. Big data requires IA to exploit relationships and synergies between your data. This infrastructure enables organizations to make decisions using the full spectrum of your big data sources.

Look for more about IA in my next book: Essential Topics in Information Architecture available on Amazon.com in January 2023!

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