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Businessintelligencebyvmoulakakis
1. PERFORMANCE MANAGEMENT
THE EVOLUTION OF
BUSINESS INTELLIGENCE &
STRATEGY
4/3/2013 Vassilis Moulakakis M.Sc. 1
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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Notas do Editor
IT-enabled business decision making based on simple to complex data analysis processesDatabase development and administrationData miningPerformance Management (Balanced Scorecards.)Data queries and report writingData analytics and SimulationsBenchmarking of Business PerformanceDashboards
Make more informed business decisions:Competitive and location analysisCustomer behavior analysisTargeted marketing and sales strategiesBusiness scenarios and forecastingBusiness service managementBusiness planning and operation optimizationFinancial management and compliance
Database systems and database integrationData warehousing, data stores and data martsEnterprise resource planning (ERP) systemsQuery and report writing technologiesData mining and analytics toolsDecision support systemsCustomer relation management softwareProduct lifecycle and supply chain management systems