Performance management business architecture, describing the process, data, organisation and data warehouse architecture required to deliver this capability.

1.  v
Performance Measurement Capability
A Data Warehouse Business Architecture

2.  v
Balanced Scorecard Activity Based Management
Performance Measurement Approaches
Robert S. Kaplan & David P. Norton “Mastering the
Management System”, HBR, Jan 2008.

3.  v
Performance Management Capability
The performance management domain defines the set of capabilities supporting
the extraction, aggregation, and presentation of information to facilitate decision
analysis and business evaluation
Capability Description
Analysis
& Statistics:
Defines the mathematical and predictive modelling and simulation capabilities that
support the examination of business issues, problems and their solutions
Business
Intelligence
Defines the forecasting, performance monitory, decision support and data mining
capabilities that support information that pertains to the history, current status or future
projections of an organization.
Visualization: Defines the presentation capabilities that support the conversion of data into graphical or
pictorial form.
Reporting: Defines the ad hoc, standardised and multidimensional reporting capabilities that support
the organization of data into useful information.
Data
Management:
Defines the set of capabilities that support the usage, processing and general
administration of structured and unstructured information.
FEA Consolidated Reference Model Document v 2.3

4.  v
Business Measures
%Revenue by market segment
%Revenue by top 20 clients
%Revenue by client relationship
Increase key account
/ high margin clientsCustomer
Perspective
£Sales revenue by market segment
Number of new projects by top 20 clients
Revenue by top 20 clients (client value)
Product
Time Period
Region
Employee
Customer
£ Sales Income / Revenue
Calc. = quantity  price
Target =
Alert Threshold =

5.  v
DataWarehouseArchitecture
Data Marts
QQ
QQ
QQ
QQ
QQ
QQ
BI Presentation Layer
Analytics
1. Presentation
3. Data Warehouse
4. Reconciliation Process
5. Operational Systems
2. Meta Repository
T
L
E
Business
Rule
Validation
ODS
% Revenue by market segment
% Revenue by top 20 clients
% Revenue by client relationship
Standard
Reports

6.  v
DataWarehouseArchitecture
Data Marts
QQ
QQ
QQ
QQ
QQ
QQ
BI Presentation Layer
Metadata Analytics
T
L
E
Business
Rule
Validation
ODS
Standard
ReportsAd Hoc Query
1. Presentation
3. Data Warehouse
4. Reconciliation Process
5. Operational Systems
2. Meta Repository

7.  v
Reference Architecture Components
Component Description
Business Intelligence
Presentation Layer
The presentation layer is responsible for providing tools for delivering ad hoc, standard and
analytical reporting. The reporting tools available fall under the business intelligence umbrella
(BI). These tool support access to and analysis of information to improve and optimize
decisions and performance, i.e. data mining, analytical processing, reporting & querying data..
Information Catalogue The information catalogue (data dictionary) component is responsible for maintaining the
definition of data and its lineage from the source systems through to the data warehouse. This
incudes data definitions, data mapping and transformations conducted on the data.
Data Warehouse
Data Mart
The data mart component is responsible for delivering line of business, departmental and
individual information needs and key performance indicators. These information needs are
reported as facts, allowing the data to be reported against standard dimensions, such as,.
Customer segment, product, organisation structure, location and time.
Data Warehouse
Operational Data Store
The operation data store (ODS) component is responsible for holding historic atomic data
extracted from operational systems. This data is held in non-redundant third normal form
arranged by subject area. It contains static near current data which is refreshed on a regular
basis from the source operational systems, e.g. daily, weekly or monthly. It is used to support
all decision support reporting needs.
Data Acquisition
Extract, Transform & Load
Data reconciliation component is responsible for data acquisition and resolving consistencies
and discrepancies between common data elements stored across the source systems, e.g.
reference codes, spelling & field lengths. The reconciliation process is conducted in a separate
staging area where the extracted data is reformatted, transformed and integrated into an agreed
common data model.
Operational Systems The transactional processing systems used to support the business operations of the
enterprise. These operational systems provide the primary data used for decision support and
reporting. This data is dynamic and constantly changing with each business transaction.
Bill Inmon and Gartner

8.  v
BI: Data Quality Scorecard
Business Measure - Information Need
Business Measure: Data Quality
Types
1. Actual
2. Target ± tolerance
Dimensions:
Agency Data Item Location
Channel Attribute Post code
Segment Entity Statistical Area
Organisation Data Collection
Outlet
Calculations:
% Master data duplication
% Collection submission data completeness
% Data item accuracy
% Consistency across data sets
Statutory timeline aging of collection receipts
Time Dimension:
Weekly
Monthly
Year to date
Atomic Data:
Agency
Agent Collection
Data Item
Attribute
Entity
Reporting Period
Data Submission
Validation Result
Rule

9.  v
Summarised Data Store Modelling
Business Measure
Data Model
• Identify business measure (fact)
• Define measure formulae
• Identify measure dimensions
• Identify measure source data
• Entity
• Attributes
• Maintain measure dimension
affinity matrix
Business Measure
Database Design
• Design summarised database
• Star Schema
• Snowflake Schema
• Prepare use case specification
Ralph Kimbal

10.  v
High Level
Data Model
• List in scope entities
• Party, place, resource, event
• All entities at the same
level of abstraction
• Entity relational model
structured by subject
areas
• Defines scope of
integration
Mid Level
Data Model (DIS)
• Third normal form ERD
• Remove repeating groups
• All attributes are dependant
on the primary key
• Resolve M:M relationships
• Add sub types where
relevant
• Includes all data elements
(data item set)
• Primitive data elements
only, no derived data
Low Level
Physical Model
• Derived from the DIS
• Identify primary keys
• Add alternate keys
• Define physical fields
• Desc, field type & size
• Default values
• Value constraints
• Null value support
• Identification of system of
record for all fields (data
mapping)
• Definition of access
method (sequential or
random)
• Process data mapping
(frequency & fields used)
Operational Data Store Modelling
Bill Inmon, “Information Engineering for the Practitioner”,
Yourdon Press, Englewood Cliffs, N.J., 1988

11.  v
Data Acquisition Reconciliation
Data Mapping
• Identify source system fields
• Map source fields to target data model
• Define data transformation rules
• Determine interface services
• Prepare use case specification
Data Quality
• Determine quality grading scheme, e.g.
• Platinum
• Gold
• Silver
• Define data quality measures
• Define quality measure formulae
• Identify quality measure dimensions
• Identify quality measure source data
• Entity
• Attribute

12.  v
Data Validation ETL Use Cases
The Solution
Data Collection
Custodian
Monitor
Data Quality KPIs
Maintain
Reference Data
Assign Agency
Collection
Maintain Agency
Map Entity
Collection Data
Define
Validation Rule
Load Data
Submission
Validate Data
Submission
Notify Late
Collection
Submission
Assign Data
Item Rules
Turn Off
Agency Rule
Agency
Submission
Due Date
Agency
Record
Submission
Exemptions
Help Desk

13.  v
Contact
Technology architecture & solutions are justified at a strategic and
financial level by preparing a business case.