Automorphic design...  Hmmm, lots and lots of different things come to mind.  Lately I've been experimenting with "self-adapting" data models, Artificial Intelligence, learning systems, neural capacities of the brain (for learning new ideas, categorization theory), and so on...  Just a hodge-podge or mish-mash of activity centered around the capture, representation of conceptual thinking - along with the ability to "mine structures" to figure out more optimal models, or alternative points of view.

If you aren't using the Data Vault Modeling today, or you haven't heard about it, then this entry might not make a lot of sense.  You can check it out or come to one of our certification courses at: http://www.DataVaultInstitute.com

Now let's break from the traditional discussions for a moment, and think about "memories" and how humans learn new ideas.  *** PLEASE NOTE: THIS IS A HOBBY OF MINE, I AM NOT OFFICIALLY CERTIFIED IN NEURAL TECHNOLOGIES, I AM NOT A NEURAL SURGEON, NOR DO I STAKE CLAIM TO UNDERSTANDING HUMAN PSYCHOLOGY ***

These are just ideas, thoughts, and opinions.  With that, let's be on our way.

If I give you a date on a calendar, especially one that has significance in your life, then you would start recalling memories around that date.  This is representative (I believe) of a HUB or a business key.  It's also representative of a primary identifier to a base-concept or point of view.  The memories you recall may end up being interpreted differently than a person who was with you on that date, this is the notion of contextual thinking.  You have assimilated facts about that day (maybe you remember a partly cloudy sky when your friend remembers it to be sunny) that now are interpreted based on how you felt about that day.

Ok, Data Vault Models have a similar construct, note: NOT a similar function (at least not yet).  The HUB is the "key" or the unique identifier which allows us to construct or establish a base access point to a specific conceptual idea for a point in time.  The Satellites around the HUB establish the "contextual facts" or memories across multiple time-spans.  How we interpret these facts can be seen as an AI activity/application. 

How we view these facts will all depend on our "point-of-view/reference" with which we are querying these facts.

They say, that when we learn something new - we establish new dendrites/synapses that connect neurons together.  They also say, that the more we think about it, the stronger the electrical impulses, and the thicker these connections get.  They then say, at a certain point (for the most part) these memories become "permanent" as they are comitted to long-term memory.  Finally, they say we never stop learning....  Well, we also dream at night, some argue that dreaming is a form of assimilation of short-term memories (categorization, organization, and attachment) to long-term memories.  They also suggest that this is a notion of learning, but also a notion of establishment of context - your frame of reference about the way you live your life.  Considering these statements, one could suppose that this contributes to character and the way you live your life.

Back to data modeling.  How is this implemented in a data warehouse?  And what REALLY is Business Intelligence?  Esoterical questions to say the least!  Ok, we'll take a shot at it.

A data warehouse (built on the Data Vault) is like short-term and long term memory, it commits FACTS (sights, sounds, colors, smells, tastes) to long term memory, it (hopefully) organizes it according to your business point-of-view (Line of business or industry your in).  non-important facts should be tossed out.  Important and auditable facts should be committed to permanent (enterprise) memory.  They should be KEYED by business keys for quick reference and searchability.  The Hubs are then surrounded by "fact data" in satellites to determine a CONCEPT.  These CONCEPTS are then LINKED together by associations, the associations have concepts as well that describe these notions.  The resulting Data Vault model acts as a giant Ontology organizer, the POINT-OF-VIEW can be pivoted based on the HUB that someone is interested in.

Well, this is all very well and nice, but how does this relate to auto-changing data models?

The hope or plan is: that AI algorithms that "pair-up" ontologies, and optimize references.  In other words, we build a META DATA (or structural) mining algorithm which uses the data mining results to "stand-up" or "tear-down" the assumptions about the structural linkages.  At the end of the day, this "algorithm" watches queries, and load patterns and secondarily applies DATA patterns (for strength and confidence ratings) to establish new linkages, tear down old linkages, and evolve the model.

The Data Vault Model is unique in this way, that it can be applied to "dynamic changes" to structure without too much hassle, and in fact is proving very effective in this area.  The thought process is: with manual occasional correction to the AI learning algorithm, the model will self-adapt and evolve as the business changes, and needs change, and point-of-view or reference changes.

I've already had one business where I applied my knowledge against the model to create a Link table that "previously didn't exist", and the business gained a 40% revenue increase through access to additional insights that they never had before.

Ok, enough babble for now... what does this mean to my EDW?

Nothing really, at least not today.  But it does mean that you should be looking at the Data Vault Modeling components in order to achieve these dynamic benefits in the future.

As always, I'm open to your thoughts, comments and opinions - did I end up in left-field here or do you see this as important?

Thanks,

Dan Linstedt  danl@danlinstedt.com