1. PERFORMANCE MANAGEMENT
THE EVOLUTION OF
BUSINESS INTELLIGENCE &
STRATEGY
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2. WHAT IS BUSINESS INTELLIGENCE (BI)?
Database development and administration
Performance Management (Balanced Scorecards.)
Data mining
Data queries and report writing
Benchmarking of Business Performance
Dashboards
Data analytics and Simulations
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3. WHY BI?
Competitive Customer Targeted
and location behavior marketing
analysis analysis and sales
• Business • Business strategies
scenarios and service • Business
forecasting management planning
• Operation • Financial
optimization management
and compliance
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4. IT TECHNOLOGIES SUPPORTING BI
Database
systems and
database
integration
Product lifecycle Data
and supply chain warehousing,
management data stores and
systems data marts
Enterprise
Decision support
resource planning
systems
(ERP) systems
Query and report
Data mining and
writing
analytics tools
technologies
Customer relation
management
software
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5. GARTNER REVEALS FIVE BUSINESS
INTELLIGENCE PREDICTIONS FOR 2009 AND
BEYOND
 T h r o u gh 2 0 1 2 , m o r e t h a n 3 5 % o f t h e t o p 5 , 0 0 0 g l o b a l c o m p a ni e s w i l l
r e g u l a r l y f a i l t o m a k e i n s i g ht f ul d e c i s i o ns a b o u t s i g n i f i c a nt c h a n g e s i n
their business and markets
 By 2012, business units w ill control at least 40% of the total budget for BI
 B y 2 0 1 0 , 2 0 % o f o r g a n i z a t io ns w i l l h a v e a n i n d u s t r y- s p e c i f ic a n a l yt i c
application delivered via software as a service (SaaS) as a standard
c o m p o ne nt o f t h e i r B I p o r t f o l io
 I n 2 0 0 9 , c o l l a bo r a t i v e d e c i s i on m a k i n g w i l l e m e r g e a s a n e w p r o d u c t
c a t e g or y t h a t c o m b i n e s s o c i a l s o f t w a r e w i t h B I P l a t f or m c a p a b i l it i e s
 B y 2 0 1 2 , o n e - t h i r d o f a n a l yt i c a p p l i c a t i ons a p p l i e d t o b u s i n e s s p r o c e s s e s
w i l l b e d e l i v e r e d t h r o u g h c o a r s e - gr a i ne d a p p l i c a t i on m a s h u p s
G a r t n e r R e s e a r c h , J a n 2 0 0 9 , h t t p : / /www. g a r tn e r. c o m/ i t / p a g e . j s p ? i d = 8 5 6 7 1 4
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6. MOVING THE CONTROL OF BI INTO THE
HANDS OF THE USERS: BI 2.0
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
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7. BI SKILL AND KNOWLEDGE CLUSTERS
 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
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8. CRITICAL INFORMATION TECHNOLOGY
KNOWLEDGE AND SKILLS
 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
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9. CRITICAL BUSINESS AND CUSTOMER SKILLS
AND KNOWLEDGE
 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
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10. DATA WAREHOUSING
 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
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11. MULTIDIMENSIONAL ANALYSIS
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
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12. DATA REPORTING
Data Reporting: the extraction of predictive information
from large databases.
 Data quality
 AD HOC Reporting
 Executive Book report
 Delivery routing
 Online Reporting
 Consolidation reporting
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13. DATA VISUALIZATION
 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
h t t p: / / w w w.smashingma gazine. c om/20 07/0 8/02 /dat a - visualiz at ion - modern -
a ppro a c h es/
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14. DATA MINING
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
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15. BI AND PERFORMANCE MANAGER ROLE
 IT dept. ready for deploying business systems
 BI project lifecycle and management
 Collaborate with Business/Sale analysts and business
executives
 Capturing and documenting the business requirements for
BI solution
 Translating business requirements into technical
requirements
 Key Performance Indicators (KPIs), actions
 Data-based decision making
 Effective communication and consultation with
business/sales analysts and business users
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16. ROLE:
BUSINESS INTELLIGENCE DEVELOPER WITHIN
IT
 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.
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17. RESOURCES
 h t t p : // www. c i o . c o m/ a r ti c l e / 6 7 1 5 7 3 /4 _ P e r s o n a s _ o f _ t h e _ N e xt _ G e n e r a t i o n _ C I O ? t a xo
n o m yI d =3 1 7 4
 h t t p : // www. c i o . c o m/ a r ti c l e / 4 0 2 9 6 /B u s i n e ss _ I n t e l l i g e n c e _ D e f i n i t i o n _ a n d _ S o l u t i o n s
 h t t p : // www. c i o . c o m/ a r ti c l e / 1 4 8 0 0 0 /1 0 _ K e ys _ t o _ a _ S u c c e s sf u l _ B u si n e s s _ I n t e l l i g e n c
e _ S t r a te g y
 h t t p : // www. s ap. c om/ gr eec e/ c a mp a i g n / 2 0 1 0 _ 0 3 _ C RO S S _ B I _ R C / i n dex. epx? U R L_I D =
C R M - G R 11 - O N L - S R C _ A A A _ 0 1 & c a mp a i g n c o d e = CR M - G R 11 - O N L -
S R C _ A A A _ 0 1 & d n a = 11 7 8 1 2 , 8 , 0 , 9 4 3 4 6 2 4 9 , 7 7 8 7 5 5 8 5 6 , 1 2 9 9 0 9 5 4 6 0 , CI O + A N D +B U S I
N E S S + I N TE L L I G E N C E , 3 2 7 4 0 0 8 0 , 6 6 6 2 4 1 7 8 8 5
 h t t p : // www. yo u t u b e . c o m/ wa t c h ? v= yf Q d a H u t a 5 Q & f e a t u r e = r e l a t e d
 h t t p : // www. q l i k vi e w. c o m/ u s / e xp l o r e / e xp e r i e n c e / p r o d u c t - t o u r
 h t t p : // www. yo u t u b e . c o m/ wa t c h ? v= AW xg S X X B b B A & f e a t u r e = r e l a t e d
 h t t p : // www. yo u t u b e . c o m/ wa t c h ? v= g q e f _ F - f X G 4 & f e a t u r e =r e l a t e d
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18. DEFINITIONS
 Data mining is the process of extracting hidden patterns from data. As more data is gathered,
with the am ount of data doublin g ever y thr ee year s data m ining is bec om ing an inc r eas in g l y
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.
 Dash b o ard s : T ypic a l l y , inf or m ation is pr es ented to the m anager via a gr aphic s dis pla y c alled a
Das hboar d. A BIS ( Bus ines s Intell i ge nc e Sys tem ) Das hboar d s er ves the s am e f unc tion as a c ar ’s
dashboard. Specifically, it reports key organizational performance data and options on a near
r eal tim e and integr ate d bas is . Das hboar d bas ed bus ines s intel l ig enc e s ys tem s do pr ovide
m anager s with ac c es s to power f u l anal yt i c a l s ys tem s and tools in a us er f r iendl y envir onm en t.
 En t erp rise reso u rce p lan n in g ( ERP) is a c om pany- w id e c om puter s of twar e s ys tem us ed to
manage and coordinate all the resources, information, and functions of a business from shared
data stores.
 O n l i n e a n a l yt i c a l p ro c e s s i n g , o r O L AP is a n a p p r o a c h t o q u ic k l y a n s we r m u lt i - d im e n s i o n a l
analytical queries. OLAP is part of the broader category of business intelligence, which also
enc om pas s es r elat ion a l r epor ti ng and data m ining. T he typ i c a l applic at i ons of O LAP ar e 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 dim ens iona l ar r a ys have no phys i c a l m etaphor , but they or gani ze the data in the wa y
us er s think of their enter pr is e . T ypi c a l enter pr is e dim ens ions ar e tim e, m eas ur es , pr oduc ts ,
geogr aphic a l r egions , s ales c hannels , etc . Synon y m s : Multi - d i m ens io na l Str uc tur e, Cube,
Hyper c ub e
 O L AP o p e r a t i o n s : Sl i c e , D i c e , D r i l l D o wn / U p , R o l l - u p , Pi vo t
 See this site for all these definitions: http://altaplana.com/olap/glossary.html#SLICE AND DICE
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