Business Intelligence with Data Approach

BI
Business Intelligence
Khandakar Shojibul Alam
Sr. Executive, HR
Nitol Niloy Group
Email: shojib2603.cs@gmail.com

What is Business Intelligence ?

effective strategic,
tactical, and
operational insights and decision- making
Used to enable more

Business Intelligence Strategy
A business intelligence strategy is:
The art of planning and delivering data-driven insights to drive
sustained growth and innovation.
BI is some culmination of data and artistic ability.
Business data must be presented to the organization in a way that any person can understand
and digest the information at-hand, and thus must be designed and set-up so that it does so
effectively.

Objectives of a BI Strategy
 Assess the current BI situation
 Propose a BI Strategy in terms of Vision, Organization, Processes, Solutions
and Architecture
 Suggest a roadmap based on priority and feasibility

BI Strategy: 5 pillars in 4 steps

What do we want to achieve?

Which organization to put in
place to govern, define and run
the BI Strategy?

Which processes to put in
place to: Define the Strategy?

Which Business Intelligence
solutions do we want to
deliver?

Which components to
support the BI Strategy?
Selection? Deployment?
Support?

Where do we stand
today?

Where do we want to
go?

How to go there? Gap
& Impact analysis

When to go there?
Priorize, Budget &
Propose a roadmap

• Lack of vision?
• Technology oriented?
• Misalignment with IT and/or organization strategy?
• Who are the actual players?
• Are there some overlaps in people functions?
• Do we have coordinated approach?
• Do we face loss of productivity?
• How does BI is governed?
• How do we handle Data Governance?
• How do we deliver BI projects?
• How do we support our users?
• How do we evolve to stay aligned with business changes?
• Which BI solutions are currently deployed?
• Are they used? Why?
• Which ones are bringing real value?
• What types of users are concerned by Business Intelligence today?
• Which tools are today supporting our strategy?
• Are we maximizing the impact of those technologies?
• Is the architecture in line with business constraints and company strategy?
• Is the current licensing model adapted to our needs? What about the hosting model?
Vision
Organization
Processes
Solutions
Architecture

• Maximize data as a key asset of the organization
• Align BI Strategy to IT and organization Strategy
• Deliver BI solutions for multiple purposes
• Consider Business Intelligence trends
• Do we want to create a BICC – Business Intelligence Competency Center?
• To who the BICC should report to? IT? Business? CEO?
• Do we want to put in place a BI champion in each business department?
• What is our sourcing strategy?
• How do we want to organize BI delivery? BI support? BI Management?
• What will be the most efficient way in our organization to deliver Business
Intelligence solutions?
• Which processes do we want to put in place to support those solutions?
• How do we want to settle efficient data governance processes?
• How do we want to structure the periodic reviews of the BI strategy?
• What kind of solutions must we provide?
• Which operational processes and departments could be the most impacted by BI
solutions?
• Do we want to equip all our staff with Self-Service Dashboarding Solutions?
• Do we want to leverage BI mobility?
• Can we imagine other usages of Business solutions to support our organization growth?
• Can we leverage innovative BI technologies to better support business growth?
• Is there an opportunity with Big Data?
• Shall we move to cloud?
• Should we put in place a Disaster Recovery Plan?
Vision
Organization
Processes
Solutions
Architecture

Define the required transformations which will be
needed to move from the As-Is state to the To-Be state
Structure those transformations into clearly-defined initiatives
• Identify the foreseen impacts on your business
• Define the related investments and potential risks
• Score the value of each initiative based on the ratio
Prioritize each initiative based on its impact on business and its balance vs costs &
investments
Consider the right mix between tactical early-value deliveries and strategic long-term
initiatives
Vision
Organization
Processes
Solutions
Architecture

Define dependencies between initiatives
Define the immediate next steps
Estimate the schedule of each initiative considering dependencies and
business constraints
Build a roadmap
Define the immediate next steps
Vision
Organization
Processes
Solutions
Architecture

First Things First: Establish the Right Foundation
for a Business Intelligence Strategy
 Articulate Your Vision and Goal
 Engage and Build a Team of Subject Matter Experts (SMEs)
 Be a Revenue Generator Not a Cost Center

Next Steps: Planning and Delivering an
Effective Business Intelligence Strategy
 Ensure Executive Sponsorship
 Define Organizational Metrics
 Plan for Future Growth by Assessing the Current Situation (Not the Toolset)
 Conduct Interviews with Business Partners
 Question Your Business Intelligence Toolset
 Don’t Forget On-Brand Delivery
 Hold Lunch-and-Learn Training Sessions
 Deliver a Few Highly Visible, Quick-to-Market Reports
 Fail Hard, Fail Fast, and Learn Even Faster

BI TRENDS
➢ BI TRENDS #1: Moving from Core to Edge BI
➢ BI TRENDS #2: Mobile BI
➢ BI TRENDS #3: Consumer-Centric BI
➢ BI TRENDS #4: Big Data
➢ BI TRENDS #5: Cloud BI
Where organizations will invest their money in? What are the big trends on the BI market?

BI TRENDS
Traditional BI often failed in meeting business
expectations in
Edge BI or Agile BI is typically there: deliver as fast as
the market requires it, a self-reliant BI experience.
Equipping fast each business line with engaging,
intuitive and simple decision-making tool.

BI TRENDS
➢ Ability for mobile workforce to analyze key data
wherever they are located.
➢ Ability to collect new data sets from mobile devices.

BI TRENDS
➢ BI plays a key role in this transition
➢ Sentiment analysis through social medias &
blogs monitoring
➢ Identification of influencers and advocates
➢ Market Predictions
➢ Enable engaging consumer experience

BI TRENDS
The amount of data to analyze being
exponential, big data will play an important role

BI TRENDS
This will concern both BI solution delivery in a
specific business area and the actually
provisioning of external data though data store
in the cloud.

Common Functions of BI
Analytics is the discovery,
interpretation, and communication of
meaningful patterns in data

The regular provision of information to
decision-makers within an organisation
to support them in their work.

Data mining is the computational process of
discovering patterns in large data sets involving
methods at the intersection of artificial
intelligence, machine learning, statistics,
and database systems.

Process mining is a process
management technique that allows for the
analysis of business processes based on event logs

OLAP is an approach to
answering multi-dimensional
analytical (MDA) queries swiftly
in computing

Event processing is a method of
tracking and analysing (processing)
streams of information (data) about
things that happen (events), and
deriving a conclusion from them

Business performance management is
a set of performance
management and analytic processes
that enables the management of
an organization's performance to
achieve one or more pre-selected
goals

Benchmarking is used to measure
performance using a specific indicator
resulting in a metric of performance
that is then compared to others.

Predictive analytics encompasses a variety of
statistical techniques from predictive
modeling, machine learning, and data
mining that analyze current and historical facts
to make predictions about future or otherwise
unknown events.

Which goes beyond predicting future
outcomes by also suggesting actions to
benefit from the predictions and showing
the implications of each decision option.

BI are capable of handling large amount
of…
Structured and sometimes unstructured data to help
• Identify
• Develop
• Create New Strategic Business Opportunities

Types of Information Processing
Transactional Processing
• Focus on individual data item
processing: data insertion,
modification, deletion, and
transmission
Analytical Processing
• Focus on reporting, analysis,
transformation, and decision
support

General Process of BI
Results are presented and delivered
in different human comprehendible
formats, to support decisions. It also
includes data exploration and
reporting.
The organization and
transformation of data
into clean and common
models and formats.
The collection of raw
data from different
sources by different
means, and in different
formats.
Data
Gathering
Data
Cleanse
Data
Analysis
Data
Presentation
Data
Storage
The refined data will be modeled
and stored in a particular data
management systems for quality
management, easy and fast
access, and data profiling.
The process involves analytical
components, such as dimensional
analysis, statistical analysis, business
analytics, and data mining, to extract
information and knowledge.

BI System
• Query
• OLAP
• Business analytics
• Statistics
• Data mining
• Visual analytics
• Reports
• Information visualization
• Dashboard
• Scorecards
• Strategy map
• Performance management
• Benchmarking
• Market research
• CRM
• Strategic management
• Web site analytics
• Operational data
• Data warehouse
• Data modeling
• Data governance
• Data integration
• ETL
• Information manage
• Data quality
• Metadata
• Master Data
• Data virtualization
Data Storage
and
Management
Analytical
Processing
Presentation
Applications
• Website
• Reporting server
• Application server
• BI server
• Portal
• Excel services
Management
and Delivery
Users with applications
(browser, desktop app,
mobile app, email, etc.)
and devices (computer,
tablet, phone, print-
outs, etc.)

BI Applied to Business Purposes
 Measurement
 Analytics
 Reporting/Enterprise Reporting
 Collaboration
 Knowledge Management

Define Data Analysis
Data analysis allows one to answer questions, solve problems, and derive
important information.

DataAnalysis
Qualitative Research
Quantitative Research
Methods of Data Analysis

DataAnalysis
revolves around describing
characteristics. It does not use
numbers. A good way to remember
qualitative research is to think of
quality.

DataAnalysis
is the opposite of qualitative
research because its prime focus is
numbers. Quantitative research is all
about quantity.

Qualitative Data Analysis
 Words
 texts
 observations
 and not numbers
Data which consists -
 People
 And their activities
Data usually involve
 People said or done
The most common forms of qualitative data are what

Qualitative Data Analysis
Data Collection
Processes &
Procedures
Transform To
Explanation,
Understanding of
situation
Investigated

Qualitative Data Analysis : Features
 data also can be analyzed and interpreted for better understanding.
 There are no variables and hypotheses in qualitative analysis
 there is no one way to analyze this textual data
 Qualitative analysis transforms data into findings
 there is no fixed formula for the transformation.
Qualitative analysis is to answer the
❑ why
❑ what and
❑ how
To examine
> The meaningful and symbolic content of qualitative data.

Qualitative Data Analysis : Gathering Data
 Copy of the narration of an interview
 Field notes taken by a student doing a research or finding
 Transcript of video and audio recordings
 Interpretation of images
 Documents which may consist of reports, minutes of meeting, e-mails, and so on
Common examples of such qualitative data are explanation and information gathered from
these documents:

Process of QDA
Writing and Coding Them into Themes
Interpreting
Organizing

Process of QDA
• It involves writing detailed observations, facts, and approaches of
your findings or data you gathered . It may be some form of
summary of the data, or conclusions from observations. After
writing, to organize your fact findings you need to sort them into
themes or coherent categories.
Interpreting
Organizing

Process of QDA
Interpreting
• Review the purpose of your evaluation and identify the questions
you want your analysis to answer. Now focus on each question and
topic and see how the respondents replied to each set of queries or
topics. Make a detailed understanding to explain why things are as
you have found them.
Organizing

Process of QDA
Interpreting
Organizing
• The process of organization has become much easier. Though smaller data
can be handled manually, it may be a good idea to use word processing
software to write and annotate texts and use worksheets for analysis.
Computers can be also used for data storage and it is much easier to
categorize and sort data under different folders and retrieving of data
becomes much easier.

Pitfalls to Avoid in QDA
 Do not Reduce Data to Numbers
 Avoid Generalizing
 Address Limitations and Alternatives
do not try to reduce the
data to numbers. If you
do so, you will be
defeating the purpose of
qualitative data analysis.

You cannot generalize the
respondents’ replies. You
have to understand from
their perspective and try
to read into the meaning
of their replies. You have
to find answers to the
questions

Address the possible
alternatives and what else
might explain the results.

Quantitative Data Analysis:
Quantitative data refers to
numbers and statistics,
and is very useful in finding patterns of behavior or overriding themes.
Quantitative research can generate large amounts of data.
Quantitative Data Analysis is applied to the raw data
so that
the research can be displayed in a friendlier way,
especially to those who do not understand the area the statistics refer to.

The Process of Data Analysis
 Data Requirements
 Data Collection
 Data Processing
 Data Cleaning
 Data Analysis
 Communication

 Data Collection
 Data Processing
 Data Cleaning
 Data Analysis
 Communication
The data required for analysis
is based on a question or an
experiment. Based on the
requirements of those
directing the analysis, the
data necessary as inputs to
the analysis is identified

 Data Collection
 Data Processing
 Data Cleaning
 Data Analysis
 Communication
Data Collection is the process
of gathering information on
targeted variables identified as
data requirements. The
emphasis is on ensuring
accurate and honest collection
of data.

 Data Collection
 Data Processing
 Data Cleaning
 Data Analysis
 Communication
The data that is collected must
be processed or organized for
analysis. This includes
structuring the data as
required for the relevant
Analysis Tools.

 Data Collection
 Data Processing
 Data Cleaning
 Data Analysis
 Communication
The processed and organized
data may be incomplete,
contain duplicates, or contain
errors. Data Cleaning is the
process of preventing and
correcting these errors.

 Data Collection
 Data Processing
 Data Cleaning
 Data Analysis
 Communication
Data that is processed,
organized and cleaned would
be ready for the analysis.
Various data analysis
techniques are available to
understand, interpret, and
derive conclusions based on
the requirements.

 Data Collection
 Data Processing
 Data Cleaning
 Data Analysis
 Communication
The results of the data
analysis are to be reported in
a format as required by the
users to support their
decisions and further action.
The feedback from the users
might result in additional
analysis.

BI : Reporting
Reporting Means
 Collecting
 Presenting or Visualizing
 So That it can be
 Analyzed
 And taking decision.

BI : Reporting…
In BI :
 Reporting Strictly Defined
 Reporting taken in a more general meaning

BI : Reporting…
In BI :
Reporting is the art of collecting data from various data sources and
presenting it to end-users in a way that is understandable and ready to be
analyzed.

BI : Reporting…
In BI :
Reporting means presenting data and information, so it also includes
analysis–in other words, allowing end-users to both see and understand
the data, as well as act on it.

BI : Reporting- Classification
BI Reporting cab be classified by :
 Reporting by the role of the person
 Managed reporting is reporting prepared by technical personnel
 Ad-hoc reporting

BI : Reporting- Goal
Reporting’s goal is the first–to enable end-users to see data so that they can analyze it
and make it understandable through analysis.
Reporting deals with data, while analysis is what turns the data into information.

What is Data Mining?
we can say thatIs defined as
Extracting information
From huge sets of data. mining knowledge from data.
data mining is the procedure of

Categories of Data Mining
On the basis of the kind of data to be mined, there are two categories of
functions involved in Data Mining
Data Mining
Descriptive
Classification &
Prediction

Categories of DM: Descriptive Function
• Class/Concept Description
• Mining of Frequent Patterns
• Mining of Associations
• Mining of Correlations
• Mining of Clusters
The descriptive function deals with the general properties of data in the database.
Here is the list of descriptive functions −

Categories of DM: Classification & Prediction
Classification is the process of finding a model that describes the data classes or
concepts.
The purpose is to be able to use this model to predict the class of objects whose
class label is unknown.
Classification models predict categorical class labels; and prediction models predict
continuous valued functions.

Example:
Classification
A HR Manager wants to analyze the data
in order to know which employee are
lower satisfaction or higher satisfaction.
In both of the above examples, a model
or classifier is constructed to predict the
categorical labels. These labels are risky
or safe for loan application data and yes
or no for marketing data.
Prediction
the marketing manager needs to predict
how much a given customer will spend
during a sale at his company
In this example we are bothered to predict
a numeric value. Therefore the data
analysis task is an example of numeric
prediction

• Classification (IF-THEN) Rules
• Decision Trees
• Mathematical Formulae
• Neural Networks
The derived model can be presented in the following forms −

• Classification IF-THEN Rules
• Decision Trees
• Neural Networks
Rule-based classifier makes
use of a set of IF-THEN rules
for classification. Can be
express a rule in the following
from −
“IF condition THEN conclusion”
Precondition
Consequent

• Classification IF-THEN Rules
• Decision Trees
• Neural Networks
A decision tree is a structure
that includes a root node,
branches, and leaf nodes.
Each internal node denotes a
test on an attribute, each
branch denotes the outcome
of a test, and each leaf node
holds a class label.

 Classification
 Prediction
 Outlier Analysis
 Evolution Analysis
The list of functions involved in these processes are as follows −
It predicts the class of objects
whose class label is unknown.
Its objective is to find a derived
model that describes and
distinguishes data classes or
concepts.

 Classification
 Prediction
•It is used to predict missing or
unavailable numerical data
values rather than class labels.
Regression Analysis is generally
used for prediction. Prediction
can also be used for
identification of distribution
trends based on available data.

 Classification
 Prediction
•Outliers may be defined as the
data objects that do not comply
with the general behavior or
model of the data available.

 Classification
 Prediction
•Evolution analysis refers to the
description and model
regularities or trends for
objects whose behavior
changes over time.

Issues of Data Mining
 the algorithms used can get very complex and data is not always
available at one place
 It needs to be integrated from various heterogeneous data sources.
We will discuss the major issues regarding −
➢ Mining Methodology and User Interaction
➢ Performance Issues
➢ Diverse Data Types Issues

Knowledge Discovery
Steps involved in the knowledge discovery process −
• Data Cleaning
• Data Integration
• Data Selection
• Data Transformation
• Data Mining
• Pattern Evaluation
• Knowledge Presentation

Knowledge Discovery
• Data Cleaning
• Data Selection
• Data Mining
The noise and inconsistent data is
removed

Knowledge Discovery
• Data Cleaning
• Data Selection
• Data Mining
Multiple data sources are combined

Knowledge Discovery
• Data Cleaning
• Data Selection
• Data Mining
Data relevant to the analysis task
are retrieved from the database.

Knowledge Discovery
• Data Cleaning
• Data Selection
• Data Mining
Data is transformed or consolidated
into forms appropriate for mining
by performing summary or
aggregation operations

Knowledge Discovery
• Data Cleaning
• Data Selection
• Data Mining
intelligent methods are applied in
order to extract data patterns.

Knowledge Discovery
• Data Cleaning
• Data Selection
• Data Mining
data patterns are evaluated

Knowledge Discovery
• Data Cleaning
• Data Selection
• Data Mining
knowledge is represented

Data Mining Systems
Data mining systems may integrate techniques from the following −
❑ Spatial Data Analysis
❑ Information Retrieval
❑ Pattern Recognition
❑ Image Analysis
❑ Signal Processing
❑ Computer Graphics
❑ Web Technology
❑ Business
❑ Bioinformatics

Data Mining Systems-Classification
❖ Database Technology
❖ Statistics
❖ Machine Learning
❖ Information Science
❖ Visualization
❖ Other Disciplines
A data mining system can be classified according to the following criteria −

Data Mining Systems
a data mining system can also be classified based on the kind of
(a) databases mined,
(b) knowledge mined,
(c) techniques utilized and
(d) applications adapted.

Define Process Mining
With data mining we often mean a
process of analyzing data
from several perspectives
and summarizing it
into useful information.
With the support from this information we can then make decisions that affect the success of a company.

Define Process Mining…
In process mining we take the data that
Almost all IT systems store data in databases and create logs that can be described in process
mining terms as event data.
how they are executed in real life.
exists in the information systems of a company,
use that to visualize what is actually happening in the company’s processes,

Process Mining - Goal
To be able to see the big picture of a company’s business processes
One of the main goals in process mining is -
With process mining we don’t have to settle for averages but dig out the reasons behind
unwanted behavior. To bring this closer to organizations and industries.
Be able to drill down to the root causes of e.g. deviations, bottlenecks, or
process variations.

Define Event Processing
Event processing is a method of
tracking and analyzing (processing)
streams of information (data) about things
happen (events),and deriving a conclusion from them.

Define Event Processing…
Complex event processing, or CEP, is event processing that
The goal of complex event processing is to identify meaningful events such as opportunities or
threats and respond to them as quickly as possible.
combines data from multiple sources to infer events or patterns
that suggest more complicated circumstances.

Predictive and Prescriptive Analysis
BI

Predictive analytics encompasses a variety of
In business, predictive models exploit patterns found in historical and transactional data to identify
risks and opportunities.
Define Predictive & Prescriptive Analytics
statistical techniques from of
predictive modelling,
machine learning, and
data mining
that analyze current and historical facts
to make predictions about
future or otherwise unknown events.

Define Predictive & Prescriptive Analytics…
Prescriptive analytics is related to both descriptive and predictive analytics
prescriptive analytics seeks to determine the best solution or outcome among various choices, given the
known parameters.
While descriptive analytics aims to provide insight into what has happened
and
predictive analytics helps model and forecast what might happen,

Predictive Analytics Process
Define the project outcomes, deliverable,
scope of the effort, business objectives,
identify the data sets that are going to be
used.

Data mining for predictive analytics
prepares data from multiple sources for
analysis. This provides a complete view of
customer interactions.

Data Analysis is the process of inspecting,
cleaning and modelling data with the
objective of discovering useful
information, arriving at conclusion

Statistical Analysis enables to validate the
assumptions, hypothesis and test them
using standard statistical models.

Predictive modelling provides the ability
to automatically create accurate
predictive models about future. There are
also options to choose the best solution
with multi-modal evaluation.

Predictive model deployment provides
the option to deploy the analytical results
into everyday decision making process to
get results.

Models are managed and monitored to
review the model performance to ensure
that it is providing the results expected.

Relation Between Predictive & Prescriptive

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