Center For Business Intelligence
Need to establish a center
for business intelligence excellence and want standards and best
practices for data governance accountability?
What is a Center for business intelligence excellence (COE)?
Many organizations have large
IT shops involved with legacy system development and maintenance
based on traditional software development methodologies.
As they move towards an information management discipline
and better use of business intelligence, they sometimes establish small
centers of excellence to establish standards and best practices and to
ensure that these they are adopted by the rest of the organization.
centers of excellence include skilled center of business intelligence
specialists with extensive experience in the areas of:
- Data modeling
- Database administration;
- Data integration;
- Data access;
- Business intelligence reporting and decision making;
- Data quality; and
- Business intelligence project management.
What is a structure and design center for business intelligence?
structure and design discipline
focuses primarily on data modeling.
The goal is to
direction on standards, best practices, and the use of conceptual,
logical, and physical models, naming standards, and tools.
Data modelers in this discipline will require experience with data warehouse design and data mart design.
intelligence resources in this COE require extensive experience defining
data requirements for analytic reporting and defining dimensional data modeling solutions.
What is a data storage center of excellence?
data storage discipline focuses on the management of persistent data
stores throughout the organization. It covers such topics as
database selection guidelines, backup & recovery procedures,
retention/archiving policies, and disaster recovery planning.
administrators in this discipline require extensive experience working
in a data warehousing/business intelligence environment.
They are also usually heavily involved with integration testing and with test data acquisition.
What is a data integration center of excellence?
The data movement discipline focuses
on the movement of data between
systems. In this context, “systems” include external data
sources, operational systems, and analytic data stores. Data
movement encompasses the extract-transform-load (ETL) facilities used
for bulk data movement. It also includes mechanisms
continuous movement of discrete records, rows, or messages between
integration requires developers experienced with extract, transform and
load (ET) and data movement software development lifecycle.
may be involved with extracting data from source systems and moving it
to data warehouse structures and with extracting data from data
warehouse structures and moving it to data mart structures.
What is a data access center of excellence?
The data access discipline is
concerned with the means of delivering data from data stores to the
consumers of the data. In this context, “consumers” will be the
business logic layer in an operational (not analytical)
environment. It includes standards for database drivers and
A major concern of the data
access discipline is ensuring the proper separation between data access
logic and business logic. This aspect of data access is often
considered within the scope of software architecture rather than data
Specialists working in this
discipline frequently provide technical support to other IT
sectors in addition to the business intelligence COE's
What is a data usage center for business intelligence excellence?
data usage discipline is concerned with the tools and techniques used
to enable data analysis by end-users. In this context, data analysis
- End-user customizable reports;
- Online analytic processing (OLAP); and
- Data mining.
What is a metadata management center of excellence?
is data describing or specifying other data or defining and organizing
characteristics that need to be known about data, including (but not
limited to) information about physical data representations, technical
and business processes, rules and constraints, and data
Business intelligence resources involved with metadata management practices enable information used by management in
its daily decision-making and in its long-term strategic planning to be
accurate, accessible, complete, consistent, timely, valid and relevant
and assists in maintaining high integrity over production data.
What is a data quality center of excellence?
quality is not a stand-alone discipline. Rather, data quality is
a result of adherence to a sound data management methodology.
Business intelligence resources involved in this disciple require experience with:
What is a center for business intelligence management practice?
- Data validations in operational legacy systems,
- Validation of purchased data sets,
- Quality checks on data extracts,
- Audit controls to verify integrity of bulk data movements; and
- Use of common field validations (both technical and business) across systems including the ETL environment
Business intelligence management is a roadmap of tasks, artifacts, standards, guidelines, and best practices.
As such, it provides a structured framework for delivery of business intelligence projects.
This structure is usually organized according an organizations solution development methodology (SDM) project phases.
Business intelligence resources in this discipline require experiences
with planning and directing business intelligence projects.
They define tasks and deliverables as well as the roles and responsibilities required to execute and deliver.
The business intelligence practice consists of a complete set of
documents that form the business intelligence framework or roadmap
These tasks and deliverables are organized by project phase and business intelligence discipline within each phase.
- Tasks and Deliverables;
- Guidelines and Best Practices;
- Roles & Responsibilities; and
- Work Breakdown Structure.
A center for business intelligence excellence is staffed with highly
skilled business intelligence resources specializing in the following
- Structure and design
- Data storage
- Data integration
- Data access
- Data Usage;
- Metadata management;
- Data quality; and
- Business intelligence management practice.