A1 Revenue Assurance Risk Coverage model
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README.md

RA Risk Coverage model

This project is a fork of the Dataflow and RA Risk Coverage modeling tools which are part of the Revenue Assurance toolset used in A1 Telekom Austria Group.

It is an attempt to build a community around the idea of designing standard and open tool that supports risk based approach of doing cost and revenue assurance in telecom industry. The main purpose of the A1 RA Risk Coverage model is to provide a framework for risks modeling of individual business processess and product segments by guiding the process of risk assessment, helping to understand the risks and assisting in decisions for controlling and reducing the identified risks. It also implements easy to grasp methodology for quantifying revenue assurance risk coverage and residual risk aggregated on various levels.

The main features include:

  • Data-flow modeler (provides repository of systems, interfaces, datasources, procedures and controls)
  • Risk catalogue (provides repository of risks, root causes and generic revenue assurance controls)
  • Risk node modeler (enables risk assessment of defined product segments in context of the process and core system)
  • Risk coverage visualisation and analysis (provides means to set priorities and plan risk mitigation strategies)

The risk modeling tool comes out of the box with two (mutually exclusive) data models based on different risk catalogues, whcih are de-facto risk management standards in telecom industry:

Getting Started

Prerequisites

Set-up and run

After the prerequisites are installed:

  1. Clone this repository using git command from the terminal:

    git clone https://github.com/a1telekom/ra-risk-coverage.git

    this will create directory named ra-risk-coverage in your working directory.

  2. Change the working directory to the newly created location:

    cd ra-risk-coverage
  3. To install the server dependencies run:

    npm install
  4. To install the front-end dependencies:

    bower install
  5. Create DB user for the application:

    mysql -u root -p -h localhost
    CREATE USER 'rrc_user' IDENTIFIED BY 'rrc_password';
    GRANT ALL PRIVILEGES ON tag.* TO 'rrc_user'; 
  6. Create DB connection config file server/utils/db.js and be sure to set your MySQL server parameters for user and password created in the previous step. You can use included example file server/utils/db.js.spec or copy the following template to create new server/utils/db.js file:

    var mysql = require('mysql');
    var pool = mysql.createPool({
    	connectionLimit	: 10,
    	host : 'localhost',
    	database : 'tag',
    	user : 'rrc_user',
    	password : 'rrc_password'
    });
    module.exports = pool;

    (or rename and edit the included example file server/utils/db.js.spec)

  7. Select one of the two included data models and create a new DB schema using mysql import functionality. e.g. to import the RAG based data model into MySQL database server running on the localhost:

    mysql -u rrc_user -p -h localhost < server/db/rrc_rag.sql

    Following are the locations of the two data models:

    • RAG data model: server/db/rrc_rag.sql
    • TMF data model: server/db/rrc_tmf.sql

    Unrelated to which data model is chosen - new DB schema named tag will be created in the MySQL DB. If tag schema already exists it will be overwritten.

    It's also possible to use the tool UI to import the data model from the Settings page (http://localhost:9000/settings).

  8. To start the development server:

    grunt serve

    It should automatically open your default browser when ready. If you can - avoid IE especially versions less then IE 11.

  9. When prompted for user and password, sign-in using the pre-set user / pasword: test@example.com / password.

Build for production

  1. Run grunt build for running all tests and building production project

  2. Run grunt serve for preview in a browser.

Data model

RA Risk Coverage data model RA Risk Coverage data model detailed

Screenshots

Look and feel