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.

Feb 292016
 

Cancer MoonshotOn January 12, 2016 in his State of the Union address, President Obama called for America to become “the country that cures cancer once and for all” As he introduced the “Moonshot” initiative that will be guided by Vice President Joe Biden.

Dr. Tom Coburn, former Republican Senator from the state of Oklahoma and three time cancer survivor, in his January 14th article in the Wall Street Journal ‘A Cancer ‘Moonshot’ Needs Big Data ; indicates that “harnessing that information (“big data”) would allow us to personalize prevention and treatment based on the genetic characteristics of a patient’s tumor, family history and personal preferences, while minimizing unwanted side effects.”

On February 5, 2016 on CNN’s Global Public Square show: Big data could be a health care game-changer author and doctor, David Agus tells Fareed Zakaria how using big data and examining thousands of cases might increase how long we live and our quality of life.

At this time in our history, with the continuing electronic capture of patient information from intake to discharge, the opportunity could not be brighter to cure cancer. The Obama administration’s 2010 initiative to capture electronic health records has enabled the opportunity to improve patient care, increase patient participation, improve diagnostics and patient outcomes, improve care coordination, as well as create practice efficiencies and cost savings.

The electronic capture of patient information has created medical big data repositories. One such repository is the American College of Surgeons/American Cancer Society’s National Cancer Database – NCDB. Resources such as these will benefit by utilizing knowledge management and information architecture techniques to identify and unlock knowledge patterns contained within these big data sources. In several of my blog post dating back to January 2013, I wrote about the advantages of applying KM to big data. From understanding Contextual Intelligence KM and Big Data ; to devoting a chapter on KM and Big Data in my upcoming book KM in Practice; I believe when executed the right way KM, powered by information architecture will provide the essential ingredient when applied to big data. This will enable researchers to discover better treatments and possible cures for many diseases including cancer and we will realize the dream presented by the Moonshot initiative!

Jan 312015
 

HiatusAfter a year hiatus, I am back facilitating the flow of knowledge through the Knowledge Management Depot. During my absence we had concluded 2013 in KM which presented, an increase of social media tools being incorporated in the workplace, the rise of analytics and BIG Data to tie KM to actionable business results,  and knowledge related tools on mobile applications; while in the year just concluded (2014) we experienced more enterprise collaboration, a rise in search related tools and functionality (incorporating Information Architecture) within mobile and enterprise applications to improve findability and to respond to customer inquiries more effectively and efficiently. Now as we enter 2015, I see several opportunities where KM will make an impact.

In 2015 KM will impact M&A transactions specifically when it comes to understanding who the key knowledge holders are and to properly give a valuation to a firms knowledge, the legal community is experiencing success with KM and more legal entities will be leveraging KM in 2015,  BIG Data continues to make noise in the industry and how KM will be positioned to gleam knowledge from all of this proliferation of content will be critical to organization (NASA-KM-meeting-Big-Data-Strategy) and interacting with the customer will continue to leverage KM to provide organizations with a competitive edge to not only attract new customers but also to retain and provide more interaction with the  current  customer base (Forrester’s Top Trends in Customer Service)

Although I have been absent… I have been busy!! I am concluding my next book on KM “Knowledge Management in Practice” as well as a two (2) day class in Information Architecture for Knowledge Management Systems. I look forward to your comments and to participating in knowledge management as 2015 unfolds!