When the Business needs Intelligence…!

DIPTI DALIA PATIL

Agenda• Introduction

• Decision-making

• Data in the organisation

• What is Business Intelligence

• The evolution of analytics

• BI Strategy

Data !

Data !

Data

So how do I justify my decisions?

ERP systems !

The back-bone of any business

ERP Systems

ERP$Systems$

Data$Rich,$Informa6on$Poor$ Rigid$

Transac6onal$Reports$

Reports$are$Sta6c$Not$Time$Variant$

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

“Derive all information from the systems and present it in a useful manner!”

… a daunting task that can haunt any organisation

Business Intelligence gives the ability to analyse and interpret the

wealth of data stored in ERP databases

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$

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

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

Trends that Influence the need for Business Intelligence

• Information Quality

• Data Governance

• Integrated View of Data

• Standards and Regulatory Compliance

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

Information Quality

Poor$Informa*on$Quality$

Makes$Regulatory$compliance$difficult$

Promotes$Inefficiency$

Stunts$Growth$Impairs$Decision@making$

Inflexible$Analysis$

Lowers$Staff$Morale$

Data Governance

• Setting standards and procedures to ensure that information quality goals are achieved

• Provides strategic direction

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

Standards and Regulatory Compliance

• There is an increasing need to comply to regulatory standards reporting

BASEL&II&

SOLVENCY&II&

CFR&–&Protec6on&of&Environment&

Botswana&Bureau&of&Standards&

Trading&Standards&

Health&and&Safety&

Manufacturing&Standards&

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”

Data WarehouseSubject 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

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.

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

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

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

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

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

Goals

• Maximise Revenue

• Improve Cost-Effectiveness

• Maximise client satisfaction

• Minimise Administrative Burden

• Maximise Compliance Rates

The Continuum of Analytics

Trial&and&Error&

Standard&Business&Repor3ng&

OLAP&Applica3ons&

Advanced&Analy3cal&

Technologies&

Specula(ve*

Informa(ve*

Descrip(ve*

Predic(ve*

Intelligence*Ga

ined

*

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?”'

Soph

is'ca'o

n+of+Analy'c+Capability+

Predic#ve*Analy#cs*

Descrip#ve*Analy#cs*

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

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

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

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!

Whats the ROI of Business Intelligence?

“Its higher than the ROI of ignorance…”

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

Thank You!

dipti@patil.co.uk