Advantages and applications of Apparo Flow

 

sources

Data sources
kapitelpfeil_li Stammdatenpfl_Datenqualitaet_01_klein kapitelpfeil_re Excel-Import_email_klein

Excel import via e-mail
 

 

Monitor data quality



Datenqualitaet


There are several ways to ensure data quality.
You can define required input fields and add hint texts to show the user the expected format.

 

 

Nevertheless, there may be false entries.
To prevent this, there are options for data validation. Once configured, the data is checked for all types of input, whether manually or imported from Excel.

 

Validation on widget level

A widget can be e.g. an input field or drop-down.

Number types

If a widget is defined as 'Number', you can choose:

  • An absolute interval 'from-to', e.g. 1 to 10 is valid, other values are not.
  • A relative interval 'from-to', e.g. 80% - 120 % of the old value are valid.
  • Using a custom validator java class.

 

 


Date types

If a widget is defined as 'Date', you can choose:

  • An absolute interval 'from-to', e.g. 01/10/2010 to 10/12/2020 is valid, other values are not.
  • Using a custom validator java class.

 

minmax_date

 


Text types

If a widget is defined as text, you can choose:

  • Regular Expressions, that lets you define the allowed characters (e.g. no special characters, only A-Z and 0-9)
  • Using a custom validator java class.

 

regex

 

Validation on row level

Using the Data Row Validator you can interdependently validate the widgets.


rowvalidator

 

Validation after entry

Sometimes it is important to save all data and to validate later.
With the functionality 'Post Execution' you can call database functions and scripts that check the data after entering and possibly correct or mark them for further processing.