anchor.diff:  become invalid every year.    
   --
   
  +* Which parts of the data are subject to (what kind of) changes?
  +* Are parts of the data known to be invalid or contain invalid portions?
  +* Does the System handle potentially wrong or invalid data appropriately?
  +* Are there (organizational or technical) processes in place that deal with 
  +data inconsistencies or faults?
  +
  +====== Analyze Data Access Protection
  +
  +* Is there an overview what kinds of data need which level of (access) protection?
  +* Is there a security concept in place covering protection against unauthorized access?
  +** How are users/roles/organizations _allowed_ to access data managed? 
  +** Is there a process in place to revoke access for outdated users/roles/organizations?
  +* Is there a plan what shall happen in case of security breaches or data theft?
  +* How is data theft recognized?
  +
  +
  +====== Analyze Backup
  +
  +* Is there a universal backup strategy in place, covering all areas of data storage?
  +* Does the backup strategy match the business criticality of the data?
  +* To what extend has the backup been verified?
  +* Does a fallback scenario exist in case of (partial or complete) data loss?
  
positive.code: ====== Analyze Data Size
  
  * Are some parts of the data especially large?
  * Does the relation between record-size (_how large is a single record_?)
  and record-volume (_how many records exists_?) plausible?
  
  
  ====== Analyze Data Validation
  
  
negative.code: ====== Analyze Data Actuality and Correctness
  Especially in data concerning dynamic entities like people, organizations,
  markets, commodities etc., facts are very likely to change over time.
  Such data (_stored facts_) might become invalid sooner or later. 
  Other types of information (like tax records, invoices or bookings on bank accounts) are created once and remain valid forever).
  
  
  [TIP]
  