The document discusses the concepts and functionalities of Business Intelligence (BI), highlighting its evolution from traditional reporting to advanced predictive and prescriptive analytics. It emphasizes the importance of data warehousing in BI, including its architecture, components, and tasks. Key features of BI output, such as reporting, dashboards, and mobile BI capabilities, are also detailed to illustrate how organizations can leverage data for better decision-making.

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Business Intelligence Workshop
Claudio Menozzi
(BI overview – reduced version for slideshare)
December 2014
Blue BI
Business Intelligence

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Agenda
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What is Business Intelligence?1
BI Market Analysis3
Enabling BI: Datawarehouse technology4
BI output samples2

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Business Intelligence Definitions
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Business intelligence (BI) is the set of techniques and
tools for the transformation of raw data into meaningful
and useful information for business analysis purposes.
- Wikipedia
The BI and analytics platform market is in the middle of
an accelerated transformation from BI systems used
primarily for measurement and reporting to those that
also support analysis, prediction, forecasting and
optimization.
- Gartner - 2014
From Data to Knowledge to Action

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Data Availability
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Capabilities
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BI historical functionalities set
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BI Evolution: Data Visualization
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BI Evolution: Prescriptive and Predictive Analysis
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Fonte: Wikipedia
Predictive analytics answers the
question what will happen. This is when
historical performance data is
combined with rules, algorithms, and
occasionally external data to determine
the probable future outcome of an
event or the likelihood of a situation
occurring.
The final phase is prescriptive
analytics, which goes beyond predicting
future outcomes by also suggesting
actions to benefit from the predictions
and showing the implications of each
decision option.
Prescriptive analytics not only
anticipates what will happen and when
it will happen, but also why it will
happen.

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From BI to Business Enabling
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Agenda
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What is Business Intelligence?1
BI Market Analysis3
Enabling BI: Datawarehouse technology4
BI output samples2

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Reporting
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Analytical Reporting: Olap Analysis
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Balance Scorecard/Dashboards
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The balanced scorecard is a strategic planning and management system that is used
extensively in business and industry, government, and nonprofit organizations worldwide
to align business activities to the vision and strategy of the organization, improve internal
and external communications, and monitor organization performance against strategic
goals.
- Balance Scorecard Institute

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Balance Scorecard
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Dashboards
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Data Visualization (1)
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Data Visualization (2)
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Agenda
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What is Business Intelligence?1
BI Market Analysis3
Enabling BI: Datawarehouse technology4
BI output samples2

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Business Intelligence functionalities
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Information Delivery
• Reporting:
Provides the ability to create highly formatted, print-ready and interactive reports
• Dashboards:
A style of reporting that graphically depicts performances measures.
• Ad hoc report/query:
Enables users to ask their own questions of the data, without relying on IT to create a report.
• Microsoft Office integration:
MS Suite acts as the reporting. Advanced integration includes cell locking and write-back.
• Mobile BI:
Enables organizations to develop and deliver content to mobile devices
Fonte: Gartner - 2014 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms

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Business Intelligence functionalities
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Information Delivery
• Reporting:
Provides the ability to create highly formatted, print-ready and interactive reports
• Dashboards:
A style of reporting that graphically depicts performances measures.
• Ad hoc report/query:
Enables users to ask their own questions of the data, without relying on IT to create a report.
• Microsoft Office integration:
MS Suite acts as the reporting. Advanced integration includes cell locking and write-back.
• Mobile BI:
Enables organizations to develop and deliver content to mobile devices
Fonte: Gartner - 2014 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms
Analysis
• Interactive visualization:
Enables the exploration of data via the manipulation of chart images,
• Search-based data discovery:
Applies a search index to structured and unstructured data sources
• Geospatial and location intelligence:
Specialized analytics and visualizations that provide a geographic, spatial and time context.
• Embedded advanced analytics:
Enables users to leverage a statistical functions library embedded in a BI server.
• Online analytical processing (OLAP):
Enables users to analyze data and navigate multidimensional drill paths.

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Business Intelligence functionalities
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Advanced BI Functionalities
• Scorecard:
Scorecards are primarily delivered by corporate performance management.
• Predictive Analysis:
Encompasses a variety of statistical techniques from modeling and data mining that analyze current
and historical facts to make predictions about future, or otherwise unknown, events.
• Prescriptive Analysis:
Enables users to ask their own questions of the data, without relying on IT to create a report.
Fonte: Gartner - 2014 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms

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What BI for end-users?
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Gartner Analysis
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Business Intelligence and Analytics Platforms Advanced Analytics Platforms

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BI Worlwide Market
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Fonte: Forbes

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BI market trends
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Fonte: Gartner

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Agenda
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What is Business Intelligence?1
BI Market Analysis3
Enabling BI: Datawarehouse technology4
BI output samples2

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Datawarehouse Definition
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Data warehousing incorporates data stores and conceptual, logical, and physical models
to support business goals and end-user information needs.
A data warehouse (DW) is the foundation for a successful BI program.
Creating a DW requires mapping data between sources and targets, then capturing the
details of the transformation in a metadata repository. The data warehouse provides a
single, comprehensive source of current and historical information.
- TDWI (The Data Warehousing Institute)

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Datawarehousen architecture overview
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Datawarehouse Components
• Data Integration
• Quality Management
• Master Data Management
DWH Tasks
• Aggregate and homogenize
Data
• Enable Historical Data
• Data Integrity
• Data Cleaning

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Datawarehouse definition by Bill Inmon
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A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of
data in support of management's decision making process.
Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For example,
"sales" can be a particular subject.
Integrated: A data warehouse integrates data from multiple data sources. For example, source A and
source B may have different ways of identifying a product, but in a data warehouse, there will be
only a single way of identifying a product.
Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data from 3
months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a
transactions system, where often only the most recent data is kept. For example, a transaction
system may hold the most recent address of a customer, where a data warehouse can hold all
addresses associated with a customer.
Non-volatile: Once data is in the data warehouse, it will not change. So, historical data in a data
warehouse should never be altered.

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Gartner analysis: Data Integrator Tools
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Data acquisition for business intelligence (BI), analytics and data
warehousing
—Extracting data from operational systems, transforming and merging that
data
Consolidation and delivery of master data in support of master data
management (MDM)
— Enabling the consolidation and rationalization of the data representing
critical business
Data migrations/conversions
—replacement of legacy applications during mergers and acquisitions.
Synchronization of data between operational applications
— ensure database-level consistency across applications, both on an
internal and an interenterprise basis
Interenterprise data sharing
— receive data from, external trading partners
Delivery of data services in a service-oriented architecture (SOA) context
— Data integration tools will increasingly enable the delivery of many types
of data services.
Integration involving cloud-based data
— Integrating a combination of data residing on-premises or in SaaS
applications
Big data initiatives
— Supporting delivery of data to, and accessing data from
Fonte: Gartner - 2014 Gartner Magic Quadrant for Data Integrator tools