Business intelligence (BI) systems allow companies to gather, store, access, and analyze corporate data to aid in decision-making. These systems illustrate intelligence in areas like customer profiling, market research, and product profitability. A hotel franchise uses BI to compile statistics on metrics like occupancy and room rates to analyze performance and competitive position. Banks also use BI to determine their most profitable customers and which customers to target for new products.

1. The term business intelligence (BI) represents the tools and systems
that play a key role in the strategic planning process of the
corporation. These systems allow a company to gather, store, access
and analyze corporate data to aid in decision-making.
Generally these systems will illustrate business intelligence in the
areas of customer profiling, customer support, market research,
market segmentation, product profitability, statistical analysis, and
inventory and distribution analysis to name a few.

2. A hotel franchise uses BI analytical applications to compile
statistics on average occupancy and average room rate to
determine revenue generated per room. It also gathers statistics
on market share and data from customer surveys from each
hotel to determine its competitive position in various markets.
Such trends can be analyzed year by year, month by month and
day by day, giving the corporation a picture of how each
individual hotel is faring.
A bank bridges a legacy database with departmental databases,
giving branch managers and other users access to BI
applications to determine who the most profitable customers are
or which customers they should try to cross-sell new products
to. The use of these tools frees information technology staff
from the task of generating analytical reports for the
departments and it gives department personnel autonomous
access to a richer data source.

3. • Today's exciting BI market is ripe with opportunities to hit your strategic
business targets.
• Gaining market share, keeping customers and controlling costs remain key
objectives. Mid-market executives and big corporate department heads
rush to cost effectively meet these complex needs. How? Through
improved use of their existing database systems.
• CFOs require 'business intelligence' systems that display accurate SKU or
customer-level P&Ls, permitting reliable channel and store comparisons
over time. Improved forecasts are vital, too!
• Data warehousing and analytical skills are combined with an understanding
of industry issues, as we refine and implement your vision.
• According to Gartner survey of 1,400 CIOs, business intelligence was
ranked the top technology priority surpassing security.
• The BI and analytics market is currently valued at $8.5 Billion and is
expected to grow to $13 Billion over the next five years

4. • Reduce labor costs
• Reduce information bottlenecks
• Make data actionable
• Better decisions
• Faster decisions
• Align the organizations towards its business objectives
• New insights

5. • Subject-oriented
• Unified, centrally managed subject definitions and targets
• System guided data interaction and exploration
• Automated data collection and distribution
• System supported data documentation and validation

6. All data in the BI system must be interfaced using natural terms
corresponding to the organization's business' reality. For example,
users of the BI system must be able to access data in the BI system
using natural terms such as "Customer" and "Sales amount" rather
than for example table and field names in the database.

7. All definitions of business terms and KPIs must exist in one version
only and they must be managed from a central point to avoid
redundant definitions, reports referring to outdated definitions etc.
This requirement implies that application of data and definition of
data must be separated by the system into a so-called semantic
layer.

8. Interaction with data and data exploration are two vital features of a
BI system that allow users to answer questions fast and
autonomously. Many tools offer ways to manipulate data, but it is
important to notice the term guided interaction/exploration. A
system can only guide the user if it has some knowledge about the
data. As an example of what is notmeant by guided, consider a query
designer: It lets the user draw relations between tables and fields in
a database in order to manipulate the output.

9. In order to achieve the benefits of reduced labor costs and faster
decisions, all data collection must be automated. notice, that if the
BI system extracts its data directly from the operational systems
then the requirement of automated data collection is implicitly met.

10. • The users don't understand the contents of the report and
as a result they don't use it
• Users think they understand the contents and use the
report. But they make the wrong decisions from time to
time because the data is not what they think it is.
• When data looks wrong, one needs to investigate and
validate it. But if there is no documentation of how and
when the data was collected and aggregated then it can be
impossible for the user to validate the results. As a result
the user will resort to other sources of data.

11. • BI is neither a product nor a system.
• It is an architecture and a collection of integrated
operational as well as decision support applications and
databases that provide the business community easy
access to business data.

12. • Better decisions with greater speed and confidence
• Recognize and maximize firm’s strengths
• Shorten marketing efforts
• Improve customer relationships
• Align effort with firm strategy
• Improve revenue and profit

14. Improve Management Processes
• planning, controlling, measuring and/or changing resulting in
increased revenues and reduced costs
Improve Operational Processes
• fraud detection, order processing, purchasing.. resulting in
increased revenues and reduced costs
Predict the Future

15. Top 10 Business and Technology Priorities for 2011:
Cloud computing
Virtualization
Mobile technologies
IT Management
Business Intelligence
Networking, voice and data communications
Enterprise applications
Collaboration technologies
Infrastructure
Web 2.0

16. Making Business
Decisions is a Balance
Data Opinion

17. Business Intelligence Business Analytics
What happened? Why did it happen?
When? Will it happen again?
Who? What will happen if we change?
How many? What else does the data tell us that
never thought to ask?

18. Data Sourcing
Data Analysis
Situation Awareness
Risk Analysis
Decision Support

19. • AQL – Associated Query Logic
• Balanced Scorecard
• Business Activity Monitoring
• Business Performance Management
• Business Planning
• Business Process Re-engineering
• Competitive Analysis
• User/End-User Query and Reporting
• Enterprise Management System
• Executive Information System
• SCM – Supply Chain Management
• Demand Chain Management
• Finance and Budgeting tools.

20. The BI model must contain these parts:
Data model. A set of subject definitions and interrelations that describe the
name, purpose, construction and inter-relation of all relevant business data
terms. E.g. "Customer", "Item group", "Sales Amount" etc. This part
corresponds to a MDD and its components, Dimensions and Metrics.
Rule model. A set of rules that describe thresholds of KPIs, potential business
actions and business events. For example a rule can describe under which
threshold a KPI - say Profit Margin Percent - is not acceptable and what the
user could/should do about it. Rules can be associated with highlighting
information such as colors and icons plus a descriptive text that is displayed
in the viewing context, i.e. inside reports.
Process model. The process model relates reports to users and reports with
other reports. The process model ensures relevance by giving each user
access to exactly the data that user needs in any context, not less and not
more. For example, the process model describes which report/dashboard must
be opened automatically when the user connects to the system. And it
describes how data in one report can be linked to other reports.

21. • Data Mining, Framing & Warehousing
• (DSS) and Forecasting
• Document Warehouse & Management
• Knowledge Management
• Mapping,
• Information Visualization and Dash boarding;
• Management Information System (MIS);
• Geographic Information System (GIS);
• Trend Analysis;
• Software As A Service (SaaS)
• Business Intelligence offerings (On Demand)
• Online Analytical Processing (OLAP) and
• Multidimensional analysis
• sometimes called "Analytics"
• (based on the "hypercube" or "cube");
• Real Time Business Intelligence
• Statistics and Technical Data Analysis
• Web Mining, Text Mining and
• Systems Intelligence

22. Factors need to be considered
Goal Alignment queries
Baseline queries
Cost and risk queries
Customer and Stakeholder queries
Metrics-related queries
Measurement Methodology-related queries
Results-related queries

23. • “BI 2.0" is the recently-coined term which is part of the
continually developing BI industry and heralds the next step for
BI.
• “BI 2.0” is used to describe the acquisition, provision and
analysis of "real time" data.