v

Performance Measurement Capability

A Data Warehouse Business Architecture

v

Balanced Scorecard Activity Based Management

Performance Measurement Approaches

Robert S. Kaplan & David P. Norton “Mastering the Management System”, HBR, Jan 2008.

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

v

Business Measures

%Revenue by market segment

%Revenue by top 20 clients

%Revenue by client relationship

Increase key account / high margin clients Customer

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 =

v

Da

ta W

are

ho

us

e A

rch

itec

ture

Presentation Layer

Std Reports Analytics

%Revenue by market segment

%Revenue by top 20 clients

%Revenue by client relationship

ODS Data Marts

1. BI Presentation

3. Data Warehouse

4. Reconciliation Process

5. Operational Systems

QQ

QQ

QQ

QQ

QQ

QQ

T

L

E

2. Metadata Repository

v

Da

ta W

are

ho

us

e A

rch

itec

ture

ODS Data Marts

1. BI Presentation

3. Data Warehouse

4. Reconciliation Process

5. Operational Systems

QQ

QQ

QQ

QQ

QQ

QQ

BI Presentation Layer

? Ad Hoc Query Metadata Std Reports Analytics

T

L

E

2. Metadata Repository

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

v

BI: Data Quality Scorecard Specification Approach

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

v

Summarised Data Store: Modelling Approach

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

v

High Level Data Model

• List in scope entities • Object, place, resource or

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 all 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 Approach

Bill Inmon, “Information Engineering for the Practitioner”, Yourdon Press, Englewood Cliffs, N.J., 1988

v

Reconciliation Process Data Acquisition Approach

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

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

v

Contact

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