What is meant by Information Architecture (IA)?
Information architecture (IA) focuses on organizing, structuring, and labeling content in an effective and sustainable way. The goal is to help users find information and complete tasks.
There must be a common consensus, an understanding of each data point collected, and the appropriate labeling and cataloging of the Information Asset. Information assets may have a score attributed to the asset and leveraged in a multitude of ways, such as guidelines for the purging of archives, sensitivity of the information, and the levels of trust.
For each data point collected, correlations/relationships can be added either manually, or through an Induction Engine (AI) leveraging a history of relationships. The definition of hierarchical relationships between data points, and link types (e.g. processor, successor, child, or generally related) further to bolster a larger lexicon.
What are Information Assets?
For example, your phone number is an information asset. Your phone number is provided to everyone you know and is a primary point of reference to contact you. Traditionally, the “phone companies” manage that resource for you. However, in this “new” day and age, we see companies like Google providing a phone number, and as a result providing features not generally available, such as Google Voice, with Call Forwarding, and obfuscation.
Common, Consumer, Information Assets Include:
- Documents of ALL Types, e.g. text, spreadsheets, presentations, etc.
- Domain Names and Email Addresses are Information Assets.
- Twitter, Facebook, Instagram, and Other Social Media Platforms Assets, such as User Names, Post Text, Images, Video, and Profile details.
- Skype, WhatsApp, and other VoIP Info Assets such as Phone Number, User Profile information
- Windows Teams, Slack, and other Team Collaboration, Information Assets, such as the historical, ongoing posted information in the Team Chat, including the integration of 3rd party apps, such as Whiteboard collaborative drawings.
- Passwords, Passwords, Passwords
Common, Corporate, Information Assets Include:
- All of the Consumer, Information Assets PLUS
- Documents of ALL Types, e.g. Solution Architecture docs, Database Models, HR Policies, Org Charts, Corp. Network Topography, etc.
Disaster Recovery for Information Assets
What happens when the technology managing information assets become “unavailable”? What is your impact assessment? Is there a centralized data/information catalog or repository that contains a partial or complete set of Information Assets?
Information Assets are also passwords, and we have a plethora of “secure” password managers, such as Norton Antivirus provides a mechanism to hold passwords in a virtual “safe”.
Insurance Policies for [digital] Information Assets
What is the cost of securing these Information Assets, verse the payment of recuperating the information assets, if even possible?
What about Hackers that “hold your data/information” hostage?
How to price out “Insurance” for your information, just like safeguarding any other personal articles insurance policies today? Are there “Personal Articles, Insurance Policies” that can currently add a rider to your existing policies? Need to price out “Information Assets”, and the recuperation values?
Norton Life Lock [Personal / Business]
Norton LifeLock reimburses funds stolen due to identity theft up to the limit of the plan total not exceeding $1 Million USD.
Notepads like Notepad++, Microsoft OneNote, and Google Keep are tools that allow their authors to quickly take notes and organize them. A wide array of Information Assets are contained within these applications, such as text, and photos with some data describing the information captured (i.e. metadata). Gathering and exporting this information to reference Information Assets could be a lengthy and laborious process without automation, rules for sorting, and tagging info.
AI Induction and Rules Engines
Dynamically labeling Information Assets as they are “discovered”, an auto curation process. For example, the Microsoft Outlook rules engine has a robust library of canned AI rules for sorting, forwarding, formatting as emails arrive in your inbox, as well as a host of other rules “triggers”. An Induction engine is a predictive instrument that “observes” behavior over time, and then creates/suggests new rules on the basis of the history of user behavior. For example, if MS Outlook had an AI Induction engine, and observed a user ‘almost’ always moving an email with the same subject to folder N, the AI Induction engine could create the rule to anticipate the user’s behavior.
Data Lakes or Sea of Information Assets
- Structured, Semi-Structured, and Unstructured data.
- Labeling/tagging Information Assets in a consistent fashion.
- Retrieval of data, and cross-referenced data types
Tool: Alation Data Catalog
Description: Alation is a complete repository for enterprise data, providing a single point of reference for business glossaries, data dictionaries, and Wiki articles. The product profiles data and monitors usage to ensure that users have accurate insight into data accuracy. Alation also provides insight into how users are creating and sharing information from raw data. Customers tout the product for its expansive partner ecosystem, and Alation has focused on increasing data literacy when metadata is distributed across business and IT.
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