Sustainability Assessment (SA) is a complex appraisal method. It is conducted for supporting decision-making and policy in a broad environmental, economic and social context, and transcends a purely technical/scientific evaluation.

Aug 29, 2017

Water centric framework - Part 3

Darius Program Management ~ Iranian water centric framework for agriculture, Part 3
  • The dots that connects the business rules of natural environment
  • The dots that connects the business rules of built environment
  • The dots that connects the business rules of Iranian people water centric framework
Having the job of gathering business requirements done - may never ends. We have knowledge management techniques to deal with adding new requirements the way it does not need to change the layout of the system just because of change for the new business rules.

Lets get into more technical hints by having the data scientists involve in the program. You may say why them! It's too early in requirement gathering phase! They have to have first the data in order to start their work! That's the tricky part. 

A data scientist is not just a person who knows to work with related software, do some clicks here and there - make a report in different shapes - compare it with others and say something smart! Data scientist needs to have a university degree of science on which he or she is involve to analyze the data using the software, or it's just another business intelligent regular user!

Data scientists have the ability to categorize the dots of data needed to get connected by building the scientific entities and business rules together in their own boundaries. They can provide advice where and when it gets overlapped or over ridded or which features need to be defined dynamic.

Sample practices during requirements gathering for data scientist

  • Active stakeholder participation
  • Apply the right artifacts (entities and business rules)
  • Collective ownership
  • Consider testability
  • Create several requirements (model) in parallel
  • Create simple content
  • Depict requirements (model) simply
  • Display requirements (model) publicly
  • Iterate to another artifact
  • Gather requirements (model) in small increments
  • Gather requirements (model) with others
  • Prove it with code (You need programmers in hand just dedicated to your demands)
  • Single source information (Gather all scientific references as one source for a set of requirements)
  • Use the simplest tools
  • Apply gathering requirements (modeling) standards
  • Apply Patterns gently (You need software architects in hand for early consultation)
  • Discard temporary requirements (models)
  • Formalize contract requirements (models)*
  • Update only when it hurts
To make a methodology for getting the business rules and entities done correctly, we need a bottom up and water fall traditional analysis to be worked in parallel where data scientists provide the agile requirement mediatory layer of scientific rules as dots that connects the outcomes of these two traditional analysis the way, a methodology is born within an aura capable of handling the dynamic features of requirements being applied to the right entities and rules directed in the right path!

* Making "the" contracts is finalizing the requirements gathering phase before we move on with building, joining and/or developing the use cases - this is where traditional top-down and bottom-up contracts are gonna join with the work of the data scientists requirement gathering (modeling) to start creating the use case in a new paradigm.

You will find it as a continuous work for many years which are based on its values, define the basis for its practices - it would eventually turn to be a methodology.

I remembered a guy said to me once, I have a degree in psychology, perhaps I can be a teacher in literature in primary school, if I get lucky - an old friend I haven't seen for many years! Those days will be over soon! You can be a data scientist by a little bit of proper training and investment on the right IT and ICT infrastructure - I say less than three months - and you can work on the dots that connects the business rules of Iranian people water centric framework program of your region. You are hired. Engineer students in universities used to be the "top notch nerds", all good grades entries rush into engineering where science was considered 2nd hand! Remember those days! Those days will be over soon! Engineers have become nothing but just another user on a technical software designed to work with a pre-defined set of rules - no kidding!

Are you kidding link? (Rajanews)

I decided to have the principles being organized into three collections: the core principles, which a data scientist must fully adopt to be able to claim they are "agile modeling", and the supplementary principles, which support the core and the completeness that finalize the join effort with traditional top-down and bottom-up processes in one iterative or incremental cycle.

Sample principles of requirements gathering by data scientist 
  • Assume Simplicity
  • Embrace Change
  • Enabling the next effort is your secondary goal
  • Incremental change
  • Maximize stakeholder investment
  • Gather requirement with a purpose (Model with a purpose)
  • Multiple models
  • Quality work
  • Rapid feedback
  • Software is your primary goal (You're not an explorer!)
  • Travel light (That's gonna happen a lot)
  • Content is more important than representation
  • Open and honest communication
  • Work with people's instincts

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