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!

Oct 312017
 

Data-Science-IA-Big-DataInformation Architecture is an enabler for Big Data Analytics. You may be asking, why would I say this, or how does IA enable Big Data Analytics. We need to remember that Big Data includes all data (i.e., Unstructured, Semi-structured, and Structured). The primary characteristics of Big Data (Volume, Velocity, and Variety) are a challenge to your existing architecture and how you will effectively, efficiently and economically process data to achieve operational efficiencies.

In order to derive the maximum benefit from Big Data, organizations must be able to handle the rapid rate of delivery and extraction of huge volumes of data, with varying data types. This can then be integrated with the organization’s enterprise data and analyzed. 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 Big Data. In addition, 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.

                                                            Big Data – Component

Information Architecture Element Volume Velocity Variety
Content Consumption Provides an understanding of the universe of relevant content through performing a content audit. This contributes directly to volume of available content. This directly contributes to the speed at which content is accessed by providing initial volume of the available content. Identifies the initial variety of content that will be a part of the organization’s Big Data resources.
Content Generation Fill gaps identified in the content audit by Gather the requirements for content creation/ generation, which contributes to directly to increasing the amount of content that is available in the organization’s Big Data resources. This directly contributes to the speed at which content is accessed due to the fact that volumes are increasing. Contributes to the creation of a variety of content (documents, spreadsheets, images, video, voice) to fill identified gaps.
Content Organization Content Organization will provide business rules to identify relationships between content, create metadata schema to assign content characteristic to all content. This contributes to increasing the volume of data available and in some ways leveraging existing data to assign metadata values. This directly contributes to improving the speed at which content is accessed by applying metadata, which in turn will give context to the content. The Variety of Big Data will often times drive the relationships and organization between the various types of content.
Content Access Content Access is about search and establishing the standard types of search (i.e., keyword, guided, and faceted). This will contribute to the volume of data, through establishing the parameters often times additional metadata fields and values to enhance search. Contributes to the ability to access content and the speed and efficiency in which content is accessed. Contributes to how the variety of content is access. The Variety of Big Data will often times drive the search parameters used to access the various type of content.
Content Governance The focus here is on establishing accountability for the accuracy, consistency and timeliness of content, content relationships, metadata and taxonomy within areas of the enterprise and the applications that are being used. Content Governance will often “prune” the volume of content available in the organization’s Big Data resources by only allowing access to pertinent/relevant content, while either deleting or archiving other content. When the volume of content available in the organization’s Big Data resources is trimmed through Content Governance it will improve velocity by making available a smaller more pertinent universe of content. When the volume of content available in the organization’s Big Data resources is trimmed through Content Governance the variety of content available may be affected as well.
Content Quality of Service Content Quality of Service focuses on security, availability, scalability, usefulness of the content and improves the overall quality of the volume of content in the organization’s Big Data resources by: – defending content from unauthorized access, use, disclosure, disruption, modification, perusal, inspection, recording or destruction – eliminating or minimizing disruptions from planned system downtime making sure that the content that is accessed is from and/or based on the authoritative or trusted source, reviewed on a regular basis (based on the specific governance policies), modified when needed and archived when it becomes obsolete – enabling the content to behave the same no matter what application/tool implements it and flexible enough to be used from an enterprise level as well as a local level without changing its meaning, intent of use and/or function – by tailoring the content to the specific audience and to ensure that the content serves a distinct purpose, helpful to its audience and is practical. Content Quality of Service will eliminate or minimize delays and latency from your content and business processes by speeding to analyze and make decisions directing effecting the content’s velocity. Content Quality of Service will improve the overall quality of the variety of content in the organization’s Big Data resources through aspects of security, availability, scalability, and usefulness of content.

The table above aligns key information architecture elements to the primary components of Big Data. This alignment will facilitate a consistent structure in order to effectively apply analytics to your pool of Big Data. The Information Architecture Elements include; Content Consumption, Content Generation, Content Organization, Content Access, Content Governance and Content Quality of Service. It is this framework that will align all of your data to enable business value to be gained from your Big Data resources.

Note: This table originally appeared in the book Knowledge Management in Practice (ISBN: 978-1-4665-6252-3) by Anthony J. Rhem.