Dec 182017
 

Big-data-analytics-solutions: http://hpc-asia.com/japans-nec-opens-10mn-centre-for-big-data-analytics/The use of Information Architecture (IA) covers the spectrum from Big Data Analytics to Content Visualization. In my previous post “Information Architecture and Big Data Analytics” I indicated how IA is an enabler for Big Data Analytics and that Big Data includes all data (i.e., Unstructured , Semi-structured, and Structured). Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Over 90% of any organizations 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.

Big Data Analytics

The Big Data Analytics process consist 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 models (Big Data Analytic Models), 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 in order 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 information architecture to exploit relationships and synergies between your data. This infrastructure enables organizations to make decisions utilizing the full spectrum of your big data sources.

Content Visualization

Content Visualization is also enabled through Information Architecture. Content Visualization’s primary goal is to present content in an appealing and effective way. Content visualization takes content and makes is easily consumable by the user. This is an essential outcome of effectively applying IA to your content. The IA content model component identifies the content types and the relationships between content. Content modeling is the process that provides a visual representation of content in its appropriate context through the identification of content types and their relationships through the construction of content models. This also allows for the representation of content in a way that translates the intention, stakeholder needs, and functional requirements that can be translated into the user experience design and into something that can be built by developers. Content modeling is a critical portion of the implementation of your website, CMS and/or KMS. The content model is your initial exposure on what are the content components that will be displayed to a user.

The other essential IA component for content visualization is building the content taxonomy, which will lead to the navigation scheme on the user interface displaying the content. The taxonomy will provide a conceptual framework for content retrieval. Incorporating a consistent taxonomy structure will classify and name the content in an orderly manner that will produce usable content visualization elements on any software solution providing content.

Information Architecture provides the methods and tools for organizing, labeling, building relationships (through associations), and describing (through metadata) your unstructured content. Information Architecture is not only important for producing usable systems that provide content, making content more consumable for users and improving search and findability. IA is also necessary on the front end to provide the structure to enable big data analytics to be effective for your organization. It is extremely important when deciding to implement IA that you examine all the benefits it will bring to the organization and ROI you can achieve from big data analytics to content visualization!

(Visited 16 times, 1 visits today)

 Leave a Reply

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>

(required)

(required)