Structure your data eco system

As long as we  do not understand the  problems created by   implementing  and sustaining silo driven solutions, we are going to face a lot of data redundancy, data inaccuracy and  erroneous  decision making systems.  It is obvious that  data needs to be holistic in its relationship, in order to achieve accuracy in  everything around data, be it machine  learning, decision systems or  automation.

In order to fix these silo problems and save on investments already made,  our primary  step would be to design  a data  architecture  that incorporates a relationship map between data attributes . This would take data isolation and  redundancy out of scope, and allow the system to scale , even when  we collect new data attributes  in the future

Such a data architecture, can allow us to employ  finite state automation and deploy  automation processes over your existing data infrastructure .  However complex might be the  big data environment,  deploying  the unified architecture can simplify  the way data is  stored and synthesized.  We employ  the following 5  steps to turn your data chaos into a structured and intelligent  utility


  1.  Understanding your data ecosystem and its relationship
    The  first step would encompass  creating a detailed list  of all available data sources and  documenting  the data attributes, functions and   business flows. This acts as the Mise en place for creating   the  unified design.
  2. Design the data schema with  hierarchies and relationship
    With the bucket list of all data attributes and its relational occurrences, we can setup the data hierarchy, state  tags and  node level rule table.  On  creation of the final design , a simple data parser can  tag data in your warehouse, as per the  design specs
  3. Machine learning to learn state  change from data patterns
    Now the data is ready for analysis,  holistic pattern detection and learning.  Using supervised learning  techniques, data can be synthesized  to understand and choose  optimal patterns that satisfies  a  given flow and  extract possible rules and sub states, to incorporate in the rule library.
  4. Enable automation rules and triggers.
    Now that the machine can  achieve a state of decision, we can  now  configure intermediary rules for automation to perform  based  on various sub states.  Using the real-time close-loop data automation,  data is  scored and staged to pertinent states,  enabling  rule based state automation
  5. Fine tune
    Now that the automation framework is ready for your data,  we can now  allow it to run on  sample  data and fine tune  all rules and weight distribution. On a successful test run, we can deploy it on a parallel  environment to understand the effects of its automation in real  business scenarios

With these 5 steps,   the  data environment  would be ready to learn and perform based on  supervised learning techniques  and  bring about a  substantial positive change in terms of effectiveness,  growth,  costs and time to action.


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