1. Center of Excellence for IT at Bellevue College

2. IT-enabled business decision making based
on simple to complex data analysis
processes
 Database development and administration
 Data mining
 Data queries and report writing
 Data analytics and simulations
 Benchmarking of business performance
 Dashboards
 Decision support systems

3. Make more informed business decisions:
 Competitive and location analysis
 Customer behavior analysis
 Targeted marketing and sales strategies
 Business scenarios and forecasting
 Business service management
 Business planning and operation optimization
 Financial management and compliance

4.  Through 2012, more than 35 % of the top 5,000 global companies will
regularly fail to make insightful decisions about significant changes in
their business and markets
 By 2012, business units will control at least 40% of the total budget for BI
 By 2010, 20% of organizations will have an industry-specific analytic
application delivered via software as a service (SaaS) as a standard
component of their BI portfolio
 In 2009, collaborative decision making will emerge as a new product
category that combines social software with BI Platform capabilities
 By 2012, one-third of analytic applications applied to business processes
will be delivered through coarse-grained application mashups
Gartner Research, Jan 2009, http://www.gartner.com/it/page.jsp?id=856714

5.  Database systems and database integration
 Data warehousing, data stores and data marts
 Enterprise resource planning (ERP) systems
 Query and report writing technologies
 Data mining and analytics tools
 Decision support systems
 Customer relation management software
 Product lifecycle and supply chain management
systems

6. Leveraging new Web 2.0 technologies to:
 Enhance the presentation layer and data
visualization
 Provide information on-demand and greater
customization
 Increase ability to create corporate and public
data mashups
 Allow interactive user-directed analysis and report
writing

7. BI careers cross over all industries:
 BI solution architects and integration specialists
 Business and BI analysts
 BI application developers and testers
 Data warehouse specialists
 Database analysts, developers and testers
 Database support specialists

8.  Database theory and practice
 Data mining and relational report writing
 Enterprise data and information flow
 Information management and regulatory compliance
 Analytical processing and decision making
 Data presentation and visualization
 BI technologies and systems
 Value chain and customer service management
 Business process analysis and design
 Transaction processing systems
 Management information systems

9.  Knowledge of database systems and data
warehousing technologies
 Ability to manage database system integration,
implementation and testing
 Ability to manage relational databases and create
complex reports
 Knowledge and ability to implement data and
information policies, security requirements, and
state and federal regulations

10.  Understanding of the flow of information throughout the
organization
 Ability to effectively communicate with and get support
from technology and business specialists
 Ability to understand the use of data and information in
each organizational units
 Ability to present data in a user-centric framework
 Ability to understand the decision making process and
to focus on business objectives
 Ability to train business users in information
management and interpretation

11. For rapid analysis and display of large
amounts of data:
 On-Line Analytical Processing (OLAP)
 Multidimensional/ hyper cubes
 OLAP operations: Slice, Dice, Drill Down/Up, Roll-
up, Pivot
 OLAP vendors and products

12.  Basics of data warehousing design and management
 Data warehouse architectures
 Data marts and data stores
 Data structures and data flow
 Dimensional modeling
 Extract, clean, conform and deliver
 Server management tools to package, backup and
restore
 Database server activity monitoring and performance
optimization

13. Data mining: the extraction of predictive
information from large databases.
 Data trend, connection and behavior pattern
analysis
 Data quality
 Data mining tools
 Predictive and business analytics
 Descriptive and decision models
 Statistical techniques and algorithms

14.  Data representations
 Information graphics
 Data representation techniques and tools
 Visual representation – trends and best practices
 Interactivity in data representation
 Tools and applications
 The user perspective on information presentation
http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/

15.  Capturing and documenting the business
requirements for BI solution
 Translating business requirements into technical
requirements
 BI project lifecycle and management
 Key Performance Indicators (KPIs), actions, and
stored procedures
 User education and training
 Data-based decision making
 Effective communication and consultation with
business users

16.  Business Intelligence (BI) Specialist works with
business users to obtain data requirements for new
analytic applications, design conceptual and logical
models for the data warehouse and/or data mart and
communicate physical designs to the database group.
The BI specialist also develops processes for
capturing and maintaining metadata from all data
warehousing components.

17.  Business Intelligence Developer is responsible for designing and
developing Business Intelligence solutions for the enterprise. The
Developer works on-site at the corporate head quarters. Key functions
include designing, developing, testing, debugging, and documenting
extract, transform, load (ETL) data processes and data analysis
reporting for enterprise-wide data warehouse implementations.
Responsibilities include: working closely with business and technical
teams to understand, document, design and code ETL processes;
working closely with business teams to understand, document and
design and code data analysis and reporting needs; translating source
mapping documents and reporting requirements into dimensional data
models; designing, developing, testing, optimizing and deploying server
integration packages and stored procedures to perform all ETL related
functions; develop data cubes, reports, data extracts, dashboards or
scorecards based on business requirements.

18.  The Business Intelligence Report Developer is
responsible for developing, deploying and supporting reports,
report applications, data warehouses and business intelligence
systems. Primary responsibilities include creating and
automating quality control processes and methods, providing
maintenance and enhancement of data warehouse reports,
creating ad hoc data warehouse queries, solving data related
reporting issues and documenting all reports created. The report
developer must have experience in user facing roles (e.g.
gathering requirements, establishing project objectives, leading
meetings) and in developing, selecting and conducting user
training as needed. The Developer also participates in all
aspects of data warehouse projects including conceptualization,
design, construction, testing, selection, deployment and post-
support implementation.

19.  http://www.spscc.ctc.edu/academics/programs/business-intelligence/class-description.html
 http://bellevuecollege.edu/business/info_bus_intelligence.html
 http://www.austincc.edu/techcert/microsoftbusintell.php
 http://www.sju-online.com/programs/business-intelligence-curriculum.asp
 http://www.setfocus.com/MastersProgram/curriculum_businessintelligence.aspx
 Top 5 On-Premise CRM Software Systems
http://www.crmsoftware360.com/crmsoftware.htm

20.  Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the
amount of data doubling every three years data mining is becoming an increasingly important tool to
transform this data into information. It is commonly used in a wide range of profiling practices, such as
marketing, surveillance, fraud detection and scientific discovery.
 Dashboards: Typically, information is presented to the manager via a graphics display called a
Dashboard. A BIS (Business Intelligence System) Dashboard serves the same function as a car’s
dashboard. Specifically, it reports key organizational performance data and options on a near real time and
integrated basis. Dashboard based business intelligence systems do provide managers with access to
powerful analytical systems and tools in a user friendly environment.
 Enterprise resource planning (ERP) is a company-wide computer software system used to manage
and coordinate all the resources, information, and functions of a business from shared data stores.
 Online analytical processing, or OLAP is an approach to quickly answer multi-dimensional analytical
queries. OLAP is part of the broader category of business intelligence, which also encompasses relational
reporting and data mining. The typical applications of OLAP are in business reporting for sales, marketing,
management reporting, business process management (BPM), budgeting and forecasting, financial
reporting and similar areas. The term OLAP was created as a slight modification of the traditional database
term OLTP (Online Transaction Processing)
 Multidimensional/ hyper cubes : A group of data cells arranged by the dimensions of the data. For
example, a spreadsheet exemplifies a two-dimensional array with the data cells arranged in rows and
columns, each being a dimension. A three-dimensional array can be visualized as a cube with each
dimension forming a side of the cube, including any slice parallel with that side. Higher dimensional arrays
have no physical metaphor, but they organize the data in the way users think of their enterprise. Typical
enterprise dimensions are time, measures, products, geographical regions, sales channels, etc.
Synonyms: Multi-dimensional Structure, Cube, Hypercube
 OLAP operations: Slice, Dice, Drill Down/Up, Roll-up, Pivot
 See this site for all these definitions: http://altaplana.com/olap/glossary.html#SLICE AND DICE