Database Answers Components in an Integrated Performance Reporting System all work together
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  Welcome to our Tutorial on Integrated Perfomance Reporting
 
Map of Cities in South-East Asia
DEMONSTRATION OF RED-AMBER-GREEN REPORTING :

This shows a map of cities served by Royal Brunei Airlines in South-East Asia, Australia and New Zealand.
A single traffic-light colour is placed close to each City. The colour is Red if something is seriously wrong, Amber (or Yellow) if slightly wrong, and Green if everything is OK.
The colours displayed are a demonstration of a Performance Reporting System for a fictitious Freight Transport operation in South-East Asia.
1) INTRODUCTION :

This is a Tutorial on Integrated Performance Reporting based on the Database Answers Road Map for Enterprise Data Management. Here's a related Tutorial on MDM and CRM which provides the next step in detailed Specifications for mapping to a commercial product such as Informatica (Sections 18 and 22).
The Scope includes these five major Components :- 1) Performance Reports 2) Data Marts 3) Data Integration 4) Data Sources 5) An Informtaion Catalogue Data Integration provides a "Single View of the Truth" Each Components has implications for Data Quality. The Tutorial concludes with an analysis of how commercial products from Informatica and Microsoft can be used to implement this Approach. I hope you find this Tutorial interesting and helpful. Please email me and let me know.
Four Tiers in Performance Reporting
Four Tiers in Performance Reporting showing More Details

2) MORE DETAILS
This diagram shows some more details.

For example, two important topics in Data Integration are a 
Common Data Model and a 'Single View of the Truth'.

Examples of Performance Indicators include Basel II and Sarbanes-Oxley.
3) FUTURE ARCHITECTURE FOR DATA QUALITY :

This Architecture shows how Data Quality will be achieved in the future. It is based on the role of a Data Quality Administrator. In the future, Web Services will be used to run Data Quality procedures against data that can be stored in a variety of locations. Results of Data Quality Test Runs will be reported locally to the Administrator. This approach has many benefits :-
  • It is possible to establish DQ functions which can be located anywhere, either internal or external to the enterprise.
  • Reference Data can be centralised in the Global HQ and accessed from anywhere around the world.
  • Future Architecture Data Quality
    Data Sources
    
    4) DATA SOURCES :
    
  • Data Sources are the point-of-entry for data from any Source.
  • These can include batch inputs, such as Spreadsheets, data extract files from Operational Systems, and third-party data.
  • They can also include real-time input such as PDA or Smartphone data, data delivered by Web Services, or Internet Forms.
  • 
    5) DATA INTEGRATION :
    
    
  • Scope of Integration
    Data Integration includes :-
  • Mapping to Common Data Model
  • Transformation
  • Clean-Up
  • Validation

    The details of all Data Transformation are defined in terms of Data Structures, Interfaces, and Data Models.
  • All Transformations also require Rules, with different syntax for different types of Transformations. When Transformations are applied, everything becomes a transition from one Model to another
  • Data Integration
    A Single View of the Truth
    
    6) A SINGLE VIEW OF THE TRUTH :
    
  • A Single View of the Customer is enabled by a Customer Master Index is used to consolidate data about a Customer from different Sources.
  • This involves matching individual Customers and mapping all Data Sources on to a common definition of a Customer.
  • This requires good quality data so that data integration is guaranteed to produce valid results.
  • This is achieved by applying Transformation algorithms to the individual data fields from the Operation Applications.
  • Definitions of these Transformations are stored in the Information Catalogue, along with Business Users who have authority to sign-off acceptable Data Quality.
  • 7) A CUSTOMER MASTER INDEX :
    	
  • A Customer Master Index is used to consolidate data about a Customer from different Sources.
  • This diagram shows the Customer Master Index (CMI) and the processes that are associated with it.
  • Customer Master Index
    SOA Architecture
    8) SOA ARCHITECTURE :
    	
  • In this diagram Levels 2 and 3 in the DQ Architecture are combined into one Data Services Layer.
  • Data Validation is applied when data is loaded in batch mode at regular intervals, such as monthly. Validation is also applied in real-time when data is loaded using PDAs or Smartphones.
  • 9) COMMON DATA MODEL :
    	
  • This Common Data Model applies to a wide range of business operations and environments.
  • It provides a standard Layer which provides a target for integration of data from various Sources, which are existing Operational Systems.
  • Father of all Data Model
    Master Data Management (MDM) Architecture
    10) MDM ARCHITECTURE :
    	
  • This diagram shows an expanded version of the Data Integration Layer.
  • The Enterprise Data Model is also called the Common Data Model.
  • 11) DATA INTEGRATION :
    Data Integration includes applying Rules for :-
    1. Transformation
    2. Clean-Up
    3. Validation
          
    
  • The final step is mapping to the Common Data Model.
  • The use of Rules and a Rules Engine is a very powerful technique to implement these processes.
  • Some examples of Rules are shown in the Section on the Information Catalogue.
  • Rules fall into a number of categories:
    1. Field Validation - these perform the basic data manipulation required to understand data, for example, checking that an e-mail has the right format, or checking for typing errors in fields, etc.
    2. Cross-Reference - these check two or more fields with each other, for example, that an age field is consistent with a data of birth field, or a first name matches the gender of a title, etc.
    3. Reference Data - these rules match to internal or external reference files, for example, Royal Mail's PAF File, ( Postal Address File).
    4. Deduplication Rules - these apply particularly to Customers, but are also relevant for matching and deduplication 'de-duping' functions across multiple data sources,
      for example, deduplicating Customers across Billing, CRM and Operational Systems.
  • The details of all Data Transformations are defined in terms of Data Structures, Interfaces, and Data Models.
  • All Transformations also require Rules, with different syntax for different types of Transformations.
  • When Transformations are applied, everything becomes a transition from one Model to another.
  • Enhanced Data Integration
    Data Platform
    12) DATA PLATFORM :
    	
  • The Data coming out of the Data Platform must be of good quality to provide a sound foundation for the overall Data Marts.
  • The Common Data Model is a vital Component in the Data Platform because it provides a technique for consolidating multiple Sources into one.
  • 13) STEPS IN BUILDING THE DATA PLATFORM :
    	
  • Data at each level, from the bottom to the top, builds on the lower one. The lowest ones here are Reference Data, which in this case, are Properties and Services. Then the Customer Master Index, which part of an MDM approach. Finally, Customer Services and a Data Mart.
  • The Steps in establishing good quality data at each Step :-
    1. Define the Rules for checking and improving Data Quality for each Data Set.
    2. Apply the Rules until a satisfactory result is achieved.
    3. Move on to data at the next Step.
  • Steps to the Data Platform
    Global Data Platform
    14) GLOBAL DATA PLATFORM :
    	
  • This diagram can be used to plan an Approach to a Global Data Platform.
  • It provides a focal point for design and implementation of a common approach to the consolidation of data.
  • 15) DATA MARTS :
    	
  • The data produced by the Data Integration Layer should be consistent and of a very high quality, especially where the matching join fields are required.
  • These conditions are required for the data to be loaded into Data Marts with compatible Dimensions and Facts.
  • This should be straightforward if the Data Integration Layer has been designed correctly.
  • However, it is possible that new Data Marts may be required that may, in turn, require new combinations of data that will show up incompatible or incorrect data.
  • In this case, additional Validation Rules will need to be defined, signed-off and recorded in the Information Catalogue.
  • Data Marts
    A Data Model for a Data Mart
    16) DATA MODEL FOR DATA MART :
    	
  • This is a Model for a typical Data Mart for Customers, Products and Purchases.
  • It is necessary to have good quality Data in order for it to be loaded into the Data Mart.
  • In other words, it is necessary to have a Single View of the Truth.
  • This includes successful completion of these activities :-
    1. Mapping Customer and Product data to a single definition in the Common Data Model
    2. Conversion of units to a standard Unit of Measurement
    3. Calculation of the Fact which are Amounts and Counts in line with the Key Performance Indicators (KPIs).
    The Data Model shown on the left is a generic design that can be used to define a Template for Data Marts.
  • 17) PERFORMANCE REPORTS :
    	
  • Overview
  • In recent years there has been increasing attention to the need for enterprises to report correct data in a wide range of Performance Reports.
  • This applies to Basel II in the financial world, and Sarbanes-Oxley in the business world.
  • A failure to comply with the appropriate regulations can result in a prison sentence for the senior management responsible.
  • This has meant that attention has been focussed on the critical aspect of Data Quality.
  • Metrics for Data Quality These are 'Key Quality Indicators' (KQIs) similar to 'Key Performance Indicators' (KPIs).
  • Defining metrics for data quality makes it possible to measure the quality and establish a plan to improve it.
  • Every time a rule is executed, the improvements can be measured and targeted.
  • This could be done on a daily or weekly, and graphs could be plotted to keep track of how well the data quality has improved.
  • Let's say you want to measure the quality of your account records.
  • The first step is to establish the Metrics. Step 1) in establishing Customer Data Quality as as a Metric and part of a 'Single View of a Customer' means addressing these Questions :-
    1. Are business names matched to an external source, such as Experian's National Business Database ?
    2. Is the address valid and in the UK Royal Mail Postal Address File (PAF) ?
    3. Does a valid e-mail exist ?
    4. A correctly formatted telephone exists,
      Is the Customer record is unique ?

    Step 2) is to assign a weighting to each Metric - for example, 80%, 20%, 15%, 5% and 5% of the respective rules.
  • This is averaged to create a unique value that indicates the level of Customer Data Quality.
  • The value of subsequent data improvement would re-calculate these values and provide historical set of metric data that truly shows the improvement in data quality.
  • Performance Reports
    Informatica Dashboards
    18) EXAMPLE OF A DQ DASHBOARD :
    	
  • Key Performance Indicators ('KPIs') would be defined as metrics for Data Quality.
  • This screenshot shows an example of a Dashboard produced by Informatica, which is a major supplier of Data Integration software. This diagram shows a Dashboard with KQIs which are Failure Percentages for Accuracy, Completeness and Conformity of Data.
  • 19) THE INFORMATION CATALOG :
    	
  • Overview
    The Catalogue is typically published over the Intranet and stores descriptive information about the five major Components and their relationships.
  • Globalisation
  • Data Quality on a Global scale requires that data is stored in a manner that is compatible with the character sets in all the appropriate languages.
  • The best solution to this is to use Unicode to hold character information so that European and international languages are supported.
  • Information Catalog



    Information Catalog




    20) THE ROLE OF THE INFORMATION CATALOG :
    	
  • Overview This diagram shows the Role that the Information Catalogue plays in planning and controlling the Steps involved in measuring and improving Data Quality.
  • 21) EXAMPLE OF THE INFORMATION CATALOG :
    	
  • This example shows how details of Data Models would be recorded in an Information Catalog.
  • Category

    Data Model Name

    Description

    Corporate

    Corporate Data Model

    Contains Customers, Services, Property and Finance.

     

     

     

    Subject Area

    Contracts

    Applies to Council Tax Payments, Rent Arrears Repayment Schedule, etc..

    Subject Area

    Customer

    Shows Person as Super-Type, & Customers, Youth Offenders, etc. as Sub-Types

    Subject Area

    Data Warehouse - Debtors

    Useful for the Debtors Pilot Project.

    Subject Area

    Data Warehouse - Service Usage

    To be determined

    Subject Area

    Finance

    Shows Accounts and Transactions.

    Subject Area

    Maintenance

    Applies to Housing Repairs, Grass-cutting, Street Lights,Vehicle Maintenance.

    Subject Area

    Parties, Organisation and People

    Shows Party as Super-Type, with Organisations & People as Sub-Types.

    Subject Area

    Property

    Shows Property as Super-Type, with Hostels, Parks, etc. as Sub-Types.

    Subject Area

    Reference Data

    Shows Reference data from all Application Data Models

    Subject Area

    Services

    Also called a 'Generalised Customer Data Model (GCDM)'

    Subject Area

    Single Sign-On

    Shows cross-references from Customer ID to other Applications

     

     

     

    User View

    Customer Problems and Haringey Solutions

    Simple but powerful Introductory Model.

    User View

    Customers and Addresses

    To be determined

    User View

    Customers and Events

    To be determined


    Implementation  of MDM with Informatica
    22) IMPLEMENTATION OF MDM WITH INFORMATICA :
    	
  • Overview This diagram shows in blue the Informatica Components that would be used to implement the Generic Architecture.
  • 23) IMPLEMENTATION OF MDM WITH MICROSOFT :
    	
  • Overview This diagram show how Microsoft SQL Products, specifically Server Analytical Services (SSAS) and Integration Services (SSIS) could be used to implement the Architecture.
  • Implementation  of MDM with Microsoft
    Four Tiers in Performance Reporting
    24) SCOPE OF THIS TUTORIAL :
    
      During this short Tutorial,we have covered the following Topics :-
      1. Master Data Management
      2. Performance Reports
      3. Data Marts
      4. Data Integration
      5. Data Source
      6. Information Catalogue
  • If you would like to see other Topics included, please let me know.

  • 25) PLEASE EMAIL ME
  • I hope you have found this Tutorial interesting and useful.
  • Please email me with your questions or suggestions so I can improve this first draft Tutorial.
    I look forward to hearing from you !

    Barry Williams
    Principal Consultant
    Database Answers Ltd.
    London, England


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