The document discusses the history and concepts of business intelligence (BI). It was first introduced in 1958 as a way to provide current information to engineers and scientists. BI is now defined as processes and technologies to gather, store, analyze and provide access to data to help users make informed decisions. The main concepts are collecting data from different sources, integrating and cleaning the data, and providing business analysis for managers. Key aspects of BI include data warehousing, online transaction processing, online analytical processing, relational and multidimensional databases, metrics/KPIs, dashboards, and data mining.

1. Business Intelligence

2. History
● Business Intelligence term first apparition on
1958 by Hans Peter Luhn, an IBM researcher
● Authomatic method to provide current
awareness services to scientists and engineers
● Current definition of Business Intelligence as a
combination of processes and technologies for
gathering, storing, analyzing and providing
access to informations to help enterprise users
to make conscious decisions
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3. Main concept
● Collect data from different sources
● Integrate and clean up data in a common, easy
to analyze repository
● Provide business related analysis for managers
and decision makers
● Focus on business, data integration, data
presentation
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4. Datawarehouse
● Bill Inmon: A collection of data in support of
decisional process
● End-user oriented
● Collected from different sources
● Time dependence
● Data is not editable
● In theory means a group of processes
● In the real world is often used for the database
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5. OLTP: On-Line Transaction Processing
● Commonly used in ERP, CRM systems and
database applications
● Focuson transaction level (one invoice, one
sales order, a search query, etc.)
● Updates and insertions are frequent
● Relational model with many tables, using
normalization rules
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6. OLAP: On-Line Analytical Processing
● A system designed for analysis prouposes
● Focused on the data exploration on the whole
● Data once added changes a lot less frequently
● 13 (12+0) rules of Dr. Codd (1993)
● Multidimensional view
● Intuitive data manipulation
● Dimensions, Facts, Hierarchy levels, Cardinality
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7. On-Line Analytical Processing
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8. Relational OLAP
● Uses relational database schemas and SQL to
store and access OLAP cubes
● Reuse of RDBMS technology
● Many tools and vendors available
● SQL can be used directly by many tools
● Scalability
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9. Star schema
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10. Memory OLAP, Hybrid OLAP
● Memory OLAP uses optimized multidimensional arrays
● Requires pre-computation and storage of the cube
(processing)
● Often better in performances than ROLAP, better
caching, multidimensional indexing
● Compression techniques, statistical indexes
● Less scalable than ROLAP on high volume of data,
less tools and vendors available
● Hybrid OLAP (HOLAP) is the combination of ROLAP
and MOLAP
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11. Slowly Changing Dimensions
● In some Business Intelligence implementations data is
always added and almost never modified
● This makes possible to go back in the timeline
● For example if an employer was hired in a time period
you can analyze data as being in that period, counting
exactly the number of employes
● A common approach to ensure Slowly Changing
Dimesions is to add some special fields to the
database records, giving a time-related validity for
each record
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12. MDX
● Multidimensional Expressions (MDX) is a query
language for OLAP databases
● MDX is to OLAP as SQL queries are to OLTP
databases
● Powerfull on computing indexes and navigating
through OLAP dimensions
● SELECT
{[Measures].[Store Sales]} ON COLUMNS
{[Date].[2002], [Date].[2003]} ON ROWS
FROM Sales
WHERE ([Store].[USA].[CA])
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13. Features for a BI platform
● Data storage, data management
● Data Integration, process schedulement
● Querying and reporting
● On Line Analitycal Processing (OLAP)
● Documents management, versioning
● Statistical computations
● Microsoft Office or Open Office support
● Easy to use and end user self creation of
documents (indipendence from developers)
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14. Dashboards, KPIs
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15. Geoanalysis
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16. Data Mining
● Requires a strong preparation in computational statistics
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17. What-if analysis
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18. Open Source offers
● Reporting
● OLAP
● Charts
● Portal containers
● Data integration tools
● Libraries, CMS,
scheduler
● Databases
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19. SpagoBI (BI Suite)
● Engineering
Informatica (Italy)
● Integration of
components using
drivers
● Comprehensive
● Full Open Source
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20. Pentaho (BI Suite)
● Pentaho (USA)
● Acquisition instead of
integration
● Strong marketing
● Commercial and
Open Source
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21. JasperServer (BI Suite)
● JasperSoft (USA)
● Famous for
JasperReports
● Easy to use
● Commercial and
Open Souce
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22. Palo (In memory OLAP)
● Jedox (Germany)
● Interesting technology
(M-OLAP, GPU)
● Excel and OpenOffice
plugins
● Web spreadsheet and
reporting
● Open Source and
Commercial support
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23. Talend (Data Integration)
● Talend (France)
● „Cool Vendor“
Gartner for Data
Integration
● Data Integration, Data
Quality, Data
Management, ESB
● Open Source and
Commercial support
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