When an organization needs to make important decisions, business intelligence can help by analyzing internal and external data to generate knowledge. Business intelligence enables fact-based decisions by aggregating, enriching, and presenting data from sources like ERP systems and databases. The goals of a business intelligence implementation are to capture data from across the business to create a unified view, produce an integrated data warehouse to improve decision making, and enable ongoing analysis of data rather than just collecting it.

1. When the Business needs Intelligence…
!
DIPTI DALIA PATIL

2. Agenda
• Introduction
• Decision-making
• Data in the organisation
• What is Business Intelligence
• The evolution of analytics
• BI Strategy

5. Data
!
Data
!
Data

6. So how do I justify my
decisions?

8. ERP systems
!
The back-bone of any business

9. ERP Systems
Data$Rich,$Informa6on$Poor$ Rigid$
ERP$
Systems$
Not$Time$Variant$ Reports$are$Sta6c$
Transac6onal$Reports$

10. Spreadsheets
• Fragile
• Ownership is restricted - not suitable for
collaborative work
• Trivial Human Errors
• Not enough validation
• Difficult to test or troubleshoot
• Reorganisation and Consolidation are
daunting tasks
• Vulnerable to fraud
• Not Scalable
• Not suited for BUSINESS CONTINUITY

11. “Derive all information from the systems
and present it in a useful manner!”
… a daunting task that can haunt any organisation

13. Business Intelligence gives the
ability to analyse and interpret the
wealth of data stored in ERP
databases

14. What is Business Intelligence?
• The capability of analysing internal and external
data to generate knowledge and value for the
organisation
Data$ Informa+on$ Intelligence$ Insight$

15. What is Business Intelligence?
Business Intelligence enables the business to make
intelligent, fact-based decisions
Aggregate
Data
Database, Data Mart, Data
Warehouse, ETL Tools,
Integration Tools
Present
Data
Enrich
Data
Inform a
Decision
Reporting Tools,
Dashboards, Static
Reports, Mobile Reporting,
OLAP Cubes
Add Context to Create
Information, Descriptive
Statistics, Benchmarks,
Variance to Plan or LY
Decisions are Fact-based
and Data-driven

16. Goals of a BI Implementation
• To capture, store and rationalise data from across the
business to create a unified view of the customer and
operations
• To produce a versatile, integrated data warehouse
architecture to improve the decision-making process and
enable on-going success
• Users should spend time analysing data rather than
collecting it

17. Trends that Influence the need for
Business Intelligence
• Information Quality
• Data Governance
• Integrated View of Data
• Standards and Regulatory Compliance

18. Information Quality
• How useable is my information?
• Is my information accurate?
• How is the information impacting my business?
• BI is more about data quality, not quantity

19. Information Quality
Poor$
Informa*on$
Quality$
Makes$Regulatory$
compliance$difficult$
Promotes$
Inefficiency$
Impairs$Decision@ Stunts$Growth$
making$
Inflexible$Analysis$
Lowers$Staff$Morale$

20. Data Governance
• Setting standards and procedures to ensure that
information quality goals are achieved
• Provides strategic direction

21. Integrated View of Data
• Bring together the program and business logic
dispersed among many applications across the
organisation
• Measure and monitor performance consistently at
an organisational level
• Requires a solid Enterprise-Level Business
Intelligence Strategy

22. Standards and Regulatory
Compliance
• There is an increasing need to comply to regulatory
standards reporting
BASEL&II&
SOLVENCY&II&
Botswana&Bureau&of&Standards&
CFR&–&Protec6on&of&Environment&
Trading&Standards&
Health&and&Safety&
Manufacturing&Standards&

23. Data Warehouse
The term Data Warehouse was coined in 1990 by Bill
Inmon as:
“A warehouse is a subject-oriented, integrated,
time-variant, and non-volatile collection of data in
support of the management’s decision-making
process”

24. Data Warehouse
Subject Oriented:!
Data that gives information about a particular subject instead of
about a company’s ongoing operations
Integrated:!
Data that is gathered into the data warehouse from a variety of
sources and merged into a coherent whole
Time Variant:!
All data in the data warehouse is identified with a particular time
period
Non-Volatile:!
Data is stable in a data warehouse. More data is added, but data
is never removed. This enables management to gain a consistent
picture of the business

25. Managing the Data in the
Warehouse
Data Profiling!
Process of examining and collecting information about
organisational data to analyse data correctness and evaluate
whether it can be effectively used for reporting and decision-making
purposes.
Data Cleansing!
The process correcting incomplete, inaccurate or invalid data
records in the existing systems.
Meta Data!
Descriptive information about the data to be used for analysis
and decision-making.

26. Data Profiling
• Also known as Data Discovery
• Using analytical techniques to examine current data for
completeness, consistency, accuracy and integrity
• Essential for effective data cleansing and transformation
• Helps improve data quality by deciphering and validating
data patterns and formats
• Reaffirms whether meta data (when available) accurately
describes the values across all datasets
• Identify redundant data across data sources
• Key in identifying data sources for the enterprise data
warehouse

27. Data Cleansing
• Process of correcting incomplete, inaccurate, or duplicate
records in the systems
• Necessary to ensure effective reporting and analysis
• Incorrect data —> Incorrect Analysis —> Incorrect Decisions
• Strict Cleansing - rejecting any inaccurate or inconsistent
data
• Fuzzy Cleansing - correcting records that partially match
existing, known values

28. Meta Data
• Meta Data is descriptive information about the data in the
warehouse
• Describes the data in business terms that the users can
identify with
• Avoids conflicts in data definitions and interpretations
• Prevents the “silo-effect” of creating non-integrated pockets
of data

29. Why Business Intelligence?
• Program and business logic dispersed among many
applications in the organisation
• Increased data volumes and demand result in
performance deterioration on the transactional system
• Transactional systems are not time-variant
• Transactional Data is volatile
• Get increased insight into the business processes and
marketplace
• Departmental Objectives vary and it is easy to lose sight
of the core organisational objectives

30. Why Business Intelligence?
• Business Intelligence can tap into the vast silos of data in
the organisation
• Business Intelligence provides a flexible means to
analyse and interpret data in different ways
• Trend Reporting and Predictive Analysis
• Turn data into meaningful business decisions
• Monitor performance and promote growth

31. Goals
• Maximise Revenue
• Improve Cost-Effectiveness
• Maximise client satisfaction
• Minimise Administrative Burden
• Maximise Compliance Rates

32. The Continuum of Analytics
Predic(ve*
Descrip(ve*
Informa(ve*
Trial&and&Error&
Standard&
Business&
Repor3ng&
OLAP&
Applica3ons&
Advanced&
Analy3cal&
Technologies&
Specula(ve*
Intelligence*Gained*

33. Descriptive vs. Predictive
Analytics
Op#misa#on* “Whats'the'best'that'can'happen?”'
Predic#ve*Modelling* “What'will'happen'next?”'
Forecas#ng* “What'if'these'trends'con7nue?”'
Sta#s#cal*Analysis* “Why'is'this'happening?”'
Alerts* “What'ac7ons'are'required?”'
Query/Drill*Down* “What'exactly'is'the'problem?”'
Ad*Hoc*Reports* “How'many,'how'o?en,'where?”'
Standard*Reports* “What'happened?”'
Sophis'ca'on+of+Analy'c+Capability+
Predic#ve*
Analy#cs*
Descrip#ve*
Analy#cs*

34. Common Misconceptions about
Business Intelligence
• BI = Reports + Dashboards
• BI is only needed when there is a lot of data in the organisation,
or, More data = Better BI
• BI is an IT issue
• BI is a software product
• Implementing BI is a one-off activity
• BI is a data warehouse
• “Self-service BI will give me any report I want”
• BI Software = BI Strategy

35. The BI Strategy
• Before evaluating BI technology, it is essential to have a BI
strategy and vision in place
• Strategy should:
• Align with enterprise strategy/goals from both, a technical
and business standpoint
• Be driven by business objectives
• Benefit the business by making optimal use of information
• Ensure a data-driven environment for decision-making
• Define how people, processes and technology will work
together

36. Key Considerations of a BI Strategy
• C-Level Sponsor (ideally not the CIO)
• Create a business case with expected benefits
• Assess BI readiness of Strategic, Tactical and Operational Users
• All Business Intelligence components (e.g. metrics, meta data, data
presentation, etc)
• Frameworks, standards and methodologies
• Common definitions
• Gap analysis
• Build or buy?
• Incremental approach - actionable, baby-steps
• Prioritise - start with the high-value, simple components
• Change Management
• Policies and Procedures
• Critical Success Factors

37. Some DON’Ts
• Do not plan on a big-bang implementation approach - iterative works the
best
• Do not be biased for a specific technology without solid reason
• Do not limit scope - prioritise instead
• Do not adopt an inflexible approach
• Do not overlook the process of data collation, data profiling and data
cleansing
• BI is not just an added feature of your ERP, it is a business enabler that
supports the ERP systems
• A Waterfall implementation lifecycle will not work - think Agile
• Do not leave BI to IT
• Don’t define BI requirements as a list of reports
• Don’t keep using Excel!

38. Whats the ROI of Business Intelligence?
“Its higher than the ROI of ignorance…”

39. http://www.everyonemakesdecisions.com/comic/

40. Thank You!
dipti@patil.co.uk