Department of CSE, MIT, Manipalresource.mitfiles.com/CSE/IV year/VII sem/Program... ·...

Department of CSE, MIT, Manipal



Imagine that you are

1. Sales manager for a large retail organization.

2. Productivity manager

3. Petroleum minister

4. Supply chain manager

5. Customer retention manager

6. Customer care manager

7. Education minister

8. Chief of hospital and have

9. HR manager BI tool with you.

10. Restaurant owner What you will

11. LIC branch head do to improve

12. Printing press owner existing s/m.

13. TV channel owner

14. Film maker

15. Owner of Airline







BUSINESS INTELLIGENCE

AND ITS APPLICATIONS



Imagine

• Imagine that you are the sales manager

for a large retail organization and that you

were able, within some probability, to

predict how much money each of your

customers was going to spend and which

products each was going to purchase over

the next six months.


Imagine

• As the productivity manager, imagine that

you could figure out which production

teams within your organization build the

highest quality products.


Imagine

• imagine that, as the broker negotiating

electricity generation and delivery

contracts for a region of the Country, you

were able to predict with relative accuracy

what the demand for electricity would be

for the next 90 days.


Imagine

• imagine that you are in charge of

managing your company's supply chain

and that you could determine which of

your vendors provides the highest-quality

products the quickest.


Imagine

• let's say you are the customer retention

manager for a credit card company and

you can identify customers about to cut up

their credit card a month before they do

so.




Why Business Intelligence?

• Increased profitability

• Decreased costs

• Improved customer relationship

management

• Decreased risk


Few Questions

• How many customers do you have?

• For each product, how many were sold

over the last 12 months?

• Who are your 20 best customers?

• What is the value of any particular

customer?

• Who are your 20 best suppliers?


The Information Asset

• Although a significant amount of money has been

invested in attempts at building and launching B I

frameworks and applications, most of that money has

been spent in infrastructure, whereas very little has been

invested in managing and exploiting a valuable corporate

asset~a company's data. In fact, the concept of business

intelligence is so poorly defined that a manager's

expectations are set based on what that manager is told

by the last software tool vendor. Because of lack of focus

or the absence of clear success criteria, many data

warehousing implementations have been delayed or

scrapped altogether because the actual BI

implementations deliver far short of their expectations.


definition of BI

• a set of tools and methodologies designed

to exploit actionable knowledge

discovered from the company's

information asset.


Purpose

• Anybody involved in the BI process is

concerned about the ability to exploit

information in a way that can improve the

way a business (or any organization)

operates.


Drawbacks

• Unfortunately,

the disparate perceptions of what BI is and

how the knowledge derived through BI can

be used often leads to project failure.




Why BI fails

• The amorphous understanding of what BI

methods and products could do resulted in

an absence of a proper value proposition

on behalf of the business sponsor.

• The scope of the project was not fully

understood, causing delays in delivering to

the business sponsor.

• Insufficient technical training prevented

developers from getting software products

to do what the vendors said they do.


Why BI fails

• Poor understanding of technology

infrastructure led to poor planning and

scheduling.

• Business users were unable to trust

results due to poor data quality.

• The lack of a clear statement of success

criteria, along with a lack of ways to

measure program success, led to a

perception of failure.


BI DEFINITION(Complete)

• The processes, technologies, and tools

needed to turn data into information,

information into knowledge, and

knowledge into plans that drive profitable

business action. Business intelligence

encompasses data warehousing, business

analytic tools, and content/knowledge

management.


Conclusion from definition

• A BI practice is more than just a collection

of tools. This means that without the

processes and the right people, the tools

are of little value.

• The value of BI is realized in the context of

profitable business action. This means that

if knowledge that can be used for

profitable action is ignored, the practice is

of little value.




Data

• Data is a collection of raw value elements

or facts used for calculating, reasoning, or

measuring. Data may be collected, stored,

or processed but not put into a context

from which any meaning can be inferred.





Information

• Information is the result of collecting and

organizing data in way that establishes

relationships between data items, which

thereby provides context and meaning.


Knowledge

• Knowledge is the concept of

understanding information based on

recognized patterns in a way that provides

insight to information.


Turning Data into Information

• The process of turning data into

information can be summarized as the

process of determining what data is to be

collected and managed and in what

context. A good example is the process of

designing a database that models a real-

world set of entities, such as parties, which

is a frequently used term that refers to

people and organizations, along with the

roles taken on by those parties.


Turning Information into

Knowledge• We accumulate piles of information, which

are then analyzed in many different ways

until some critical bits of knowledge are

created. What makes that knowledge

critical is that it can be used to form a plan

of action for solving some business

problem.


Turning Knowledge into

Actionable Plans• If you are using BI for micromarketing,

finding the right customer for your product

is irrelevant if you do not have a plan to

contact that customer. If you are using BI

for fraud detection, finding the pattern of

fraud is of little value if your organization

does not do something to prevent that

fraudulent behavior.




Types of Digital Data

• Structured

• Semi structured

• Unstructured


Types of Digital Data

80

10

10

Data Division

Unstructured data

semi structured data

Structured data


Unstructured Data

• This the data which does not conform to a

data model.

• Eg:- memos, chat rooms, power point

presentations, images, videos, letters,

researches, white papers, body of an e-

mail, etc.


Semi-structured data

• This is the data which does not conform to

a data model but has some structure.

Eg:- XML, HTML etc.

Meta data for this data is available but is not

sufficient


Structured data

• This is the data which is in an organized

form.

• Eg:- Data stored in data bases.


Structured Data

Good Life Health Care Patient Index

Card

Patient ID <> Date <>

Nurse Name <>

Patient Name <> Patient Age <>

Body Temperature <> Blood Pressure <>


Data being exchanged in the

following form• Doctors or nurses notes in an electronic

report

• Email sharing information about

consultation or investigation

• Surveillance system report.

• Narrative portion of electronic medical

records.

• Investigative reports

• Chat rooms


Characteristics of Structured

data• Conforms to a data model

• Data is stored in the form of rows and

columns. Eg:-Relational database

• Data resides in a fixed fields within a

record or a file

• Definition, format, and meaning of data is

explicitly known

• Attributes in a group are the same

• Similar entities are grouped.


Sources of structured data

• Databases

• Spread sheets

• SQL

• OLTP systems


Ease with structured data

• Storage

• Scalability

• Security

• Update and delete


Ease of retrieval of structured

data• Retrieving information

• Indexing and searching

• Mining data

• BI Operations



characteristics of Unstructured

data• Does not conform to any data model.

• Cannot be stored in the form of rows and

columns as in a database.

• Not in any particular format or sequence.

• Not easily usable by a program.

• Does not follow any rule or semantics.

• Has no easily identifiable structure.


Sources of unstructured data

• Web pages

• Memos

• Videos

• Images

• Body of an email

• Word document

• Power point presentations

• Chats, reports, white papers and surveys




How to manage Unstructured

Data• Indexing

• Tag/Metadata

• Classification/Taxonomy

• Content Addressable Storage(CAS)


Challenges faced while storing

unstructured data• Storage Space

• Scalability

• Retrieve information

• Security

• Update and delete

• Indexing and searching


Solutions to storage challenges

of Unstructured Data• Changing format

• Developing new hardware

• Storing in RDBMS/BLOBS

• Storing in XML format

• Content Addressable Storage(CAS)


Changing Format

• Unstructured data may be converted into

formats which are easily managed, stored

and searched.

• IBM is working on a project which will

convert audio and video into text.


Developing new hardware

• New h/w needs to be developed to support

unstructured data. It may either

complement the existing storage devices

or may be standalone for unstructured

data.


Storing in RDBMS/BLOBS

• Unstructured data may be stored in

relational databases which support

BLOBs. Metadata about an image or video

can be stored in a RDBMS.


Storing in XML format

• When unstructured data is stored in XML

format some structure is added to it using

tags and elements.


Content Addressable Storage

• It organizes files based on their metadata

and assigns unique name to every object

stored in it. The object is retrieved based

on its content and not its location. It is

used extensively to store e-mails etc.


Challenges faced while extracting

information from stored unstructured data

• Interpretation

• Classification/taxonomy

• Indexing

• Deriving meaning

• File formats

• tags


Interpretation

• Unstructured data is not easily interpreted

by conventional algorithms.


Classification/Taxonomy

• Different naming conventions followed

across the organization make it difficult to

classify data.


Indexing

• Designing algorithms to understand the

meaning of the documents and then

tagging or indexing them accordingly is

difficult.


Deriving meaning

• Computer programs cannot automatically

derive meaning/structure from

unstructured data.


File formats

• Increasing number of file formats makes it

difficult to interpret data.


Tags

• As the data grows, it is not possible to put

tags manually.


Possible solutions

TAGS• Unstructured data can be stored in a

virtual repository and be automatically

tagged. Documentum is one such

solution.


Possible solutions -Text Mining

• Text mining tools help in grouping as well

as classifying unstructured data and assist

in analyzing by considering grammar,

contexts, synonyms etc.


Possible solutions- Application

Platforms• Application platform like XOLAP help

extract information from email and XML

based documents.


Possible solutions-

Classification/Taxonomy• Taxonomies within the organization can be

managed automatically to organize data in

hierarchical structures.


Possible solutions- Naming

conventions/Standards• Following naming conventions or

standards across an organization can

greatly improve storage, retrieval, index

and search



UIMA (figure Previous slide)

• Unstructured Information Management

Architecture (UIMA) is an open source

platform from IBM which integrates

different types of analysis engines to

provide a complete solution for knowledge

discovery from unstructured data.


UIMA

• Various analysis engines analyze unstructured data

in different ways such as:

– Breaking up of documents into separate words

– Grouping and classifying according to taxonomy

– Detecting parts of speech, grammar and

synonyms

– Detecting events and times

– Detecting relationships between various elements


Characteristics of Semi

Structured data• Does not conform to a data model but

contains tags and elements(meta data).

• Cannot be stored in the form of rows and

columns.

• Data described by the tags and elements

is stored.

• Not sufficient metadata.

• Attributes in a group may not be the same.

• Similar entities are grouped.


Good Life Health Care Patient Report

Card

Patient ID <> Date <>

Nurse Name <> Doctor Name <>

Patient Name <> Patient Age <>

Body Temperature <> Blood Pressure <>

Diagnosis (notes)

Conclusion (notes)


Sources of semi-structured data

• Email

• XML

• TCP/IP packets

• Zipped files

• Binary Executable

• Mark-up languages

• Integration of data from heterogeneous

sources.


Integration of data sources


How to manage semi structured

data• Schemas

• Graph based data model

• XML


Schemas

• These can be used to describe the

structure of data. schemas define the

constraints on the structure, content of the

document etc.

• Drawbacks being as requirement change

data changes changing the schema.


Graph based data models

• These can be used to describe data. The

relationships and hierarchies are

represented in the form of a tree like

structure where the vertices contain the

object or entity and the leaves contain

data.


XML

• It allows the user to define the tags to

store data in hierarchical or nested forms.

Schemas in XML are not tightly coupled to

underlying data.


Challenges faced in storing

semi structured data• Storage cost

• RDBMS

• Irregular and partial structure

• Implicit structure

• Evolving schemas

• Distinction between schema and data.


Possible solution for storing

semi structured data• XML

• RDBMS

• Special purpose DBMS

• OEM



OEM

• IN Object Exchange Model objects are the

entities, labels are the attributes and leaf

contains the data.

• Indexing and searching a graph based

data model is easier and quicker as it is

easy to traverse to the data.


Challenges faced while extracting

information from semi structured data

• Flat files

• Heterogeneous sources

• Incomplete / irregular structure


Possible solutions for extracting information from


• Indexing

• OEM

• XML

• Mining tools


Possible solutions for extracting information from


• Indexing data in a graph based model

enables quick search.

• OEM stores data in graph based data

model which is easier to index and search

• XML allows data to be arranged in a

hierarchical or tree like structure which

enables indexing and searching

• Mining tools search data based on graphs

schemas structures etc.


Intranet

Applications

B2B

APPLICA

TIONS

BUSINESS LOGIC

RDBMSRDBMS


XML

• Standard for exchanging the data over the

web.

• It enables separation of content(XML) and

presentation(XSLT)

• Document Type Descriptors(DTD’s)

provide partial schemas for XML

document.


XML

• It is written in plain text.

• It is independent of hardware and

software.

• It is designed to store and transport the

data over the internet.

• Users can define tags to store data.

• XML has no predefined tags.

• Data can exist without schema and

schema can be added later.


Difference between semi

structured and structured dataName Email

Donald duck [email protected],

[email protected]

First name: Sachin

Last name: Tendulkar

[email protected],

[email protected]

Mickey Mouse [email protected]

mailto:[email protected]






Structured representation

First name Last name Email Id Alternate Email

Id

Donald Duck [email protected] donald@disneyla

nd.com

Sachin Tendulkar sachin@indiacrick

et.com

sachin@parliment

.in

Micky Mouse mickey@disneyla

nd.com







INTRODUCTION TO OLTP AND

OLAP


On-Line Transaction Processing

• Point Of Sale (POS)


Product master table

Column name Data type and

length

Constraint Description

PID Character,7 Primary key Not null and

unique

Product Name Character,35 Not null Name of the

product must be

specified

P description Character,50 Not null Description must

be there.

Unit Price Numeric 8,2 The price per unit

of the product

Qty in stock Numeric 5 The units of the

product in stock


Possible operations in OLTP

• Insert

• Update

• Delete


Possible queries which OLTP

can answer• Search for a particular customer record

• Retrieve the unit price of a particular

product.


Advantages of an OLTP system

• Simplicity

• Efficiency


Challenges of an OLTP system

• Security: An OLTP requires concurrency

control (locking) and recovery

mechanisms (logging)

• OLTP system data content not suitable for

decision making: Detailed Current data is

not suitable for decision making.


The queries that OLTP cannot

answer• Which product to introduce?

• Which customer segments for new

products?

• How much discount should they offer?

• Should different discounts be given to

different customer segments?

• How to zero in on its most consistent sales

person?


On-Line Analytical Processing

(OLAP)• In OLAP data is held in the multi

dimensional form rather than the relational

form.




TABLE

Section Product

Category

YearQuarter SalesAmount

Men Accessories Q1 3000.50




Women Accessories Q1 1250.50





TABLE

Section Product

Category


Kid Accessories Q1 1234.50




Infant Accessories Q1 1555.50





TABLE

Section Product

Category


Men Clothing Q1 2000.50




Women Clothing Q1 4536.50





Table

Section Product

Category


Kid Clothing Q1 1000.50




Infant Clothing Q1 2345.50





Answer these question

• What is sales amount for– Men

– Women

– Kid

– Infant

– Accessories

– Clothing

– Q1

– Q2

– Q3

– Q4


One dimensional data by

sectionSection Sales Amount

Men 18313.00

Women 16941.00

Kid 34070.00

Infant 22124.00


One dimensional data

Product Category Sales Amount

Accessories 33837.00

Clothing 57611.00

Year Quarter Sales Amount

Q1 16924.00

Q2 22046.00

Q3 26663.00

Q4 25815.00


Two dimensional data

Year Quarter Accessories Clothing Sales Amount

Q1 7041 9883 16924

Q2 9680 12366 22046

Q3 9660 17003 26663

Q4 7456 18359 25815

Total 33837 57611 91448




Q1 7041 9883 16924

Q2 9680 12366 22046

Q3 9660 17003 26663

Q4 7456 18359 25815

Total 33837 57611 91448

9680




Q1 7041 9883 16924

Q2 9680 12366 22046

Q3 9660 17003 26663

Q4 7456 18359 25815

Total 33837 57611 91448

12336




Q1 7041 9883 16924

Q2 9680 12366 22046

Q3 9660 17003 26663

Q4 7456 18359 25815

Total 33837 57611 91448

22046


Three Dimensional data

Product

Category

Year

Quarter

Men Women Kid Infant Total

Accessori

es

Q1 3000.5 1250.5 1234.5 1555.5 7041

Q2 1000.5 1000.5 5678.5 2000.5 9680

Q3 3500.5 1500.5 1233.5 3425.5 9660

Q4 2556.5 1556.5 1567.5 1775.5 7456

Clothing Q1 2000.5 4536.5 1000.5 2345.5 9883

Q2 1230.5 2345.5 6789.5 2000.5 12366

Q3 1456.5 3200.5 8889.5 3456.5 17003

Q4 3567.5 1550.5 7676.5 5564.5 18359

Total 18313 16941 34070 22124 91448


Answer the question

• Which section has purchased the highest

Accessories in the second quarter. What is

the amount?


Three Dimensional data

Product

Category

Year

Quarter

Men Women Kid Infant Total

Accessori

es

Q1 3000.5 1250.5 1234.5 1555.5 7041

Q2 1000.5 1000.5 5678.5 2000.5 9680

Q3 3500.5 1500.5 1233.5 3425.5 9660

Q4 2556.5 1556.5 1567.5 1775.5 7456

Clothing Q1 2000.5 4536.5 1000.5 2345.5 9883

Q2 1230.5 2345.5 6789.5 2000.5 12366

Q3 1456.5 3200.5 8889.5 3456.5 17003

Q4 3567.5 1550.5 7676.5 5564.5 18359

Total 18313 16941 34070 22124 91448

5678.5


Typical Application of OLAP

• Budgeting

• Sales forecasting

• Sales reporting

• Business process management etc.


Queries that an OLAP s/m can

process• What will be the future sales trend for

accessories in the kids section

• Given the customers buying pattern, will it

be profitable to launch product “XYZ” in

the kid’s section

• What impact will a 5% increase in the

price of products have on the customers?


Advantages of an OLAP system

• Multidimensional data representation

• Consistency of information

• Provides single platform for all information

and business needs – planning,

budgeting, forecasting, reporting and

analysis.

• Fast and interactive ad hoc exploration.



Different OLAP Architecture

• Multidimensional OLAP

• Relational OLAP

• Hybrid OLAP


Multidimensional OLAP

• In MOLAP, data is stored in

multidimensional cube.

• The storage is in proprietary format and

not in the relational database.


Advantages of MOLAP

• Fast data retrieval

• Optimal for slicing and dicing

• Can perform complex calculations. All

calculations are pre-generated when the

cube is created.


Disadvantages of MOLAP

• Limited in the amount of data that it can

handle. The reason being as all

calculations are pre–generated when the

cube is created , it is not possible to

include a large amount of data in the cube

itself. The cube however can be derived

from the large amount of data.


Disadvantages of MOLAP

• Additional investment in human and

capital resources may be required as the

cube proprietary and might not exist in the

enterprise.


ROLAP

• In ROLAP data is stored in relational

database. In essence, each action of

slicing and dicing is equivalent to adding

“where” clause in the SQL statement.


ROLAP Advantages

• Can handle large amount of data.(limited

only by the data size of the underlying

database).

• Can leverage functionalities inherent in the

relational database.


Disadvantages

• Difficult to perform complex calculations

using SQL.

• Performance can be slow. As each

ROLAP report is essentially an SQL query

in the relational database.


HOLAP

• It combines the advantages of MOLAP

and ROLAP. Additional operation called

drill through is supported.


OLTP vs. OLAP


Feature OLTP OLAP

Focus Data in Data out

Source of data Operational/transactional

data

Data extracted from

various operational data

sources , transformed

and loaded into the data

warehouse

Purpose of data Manages(controls and

executes) basic business

tasks

Assists in planning,

budgeting, forecasting

and decision making

Data contents Current data. Far too

detailed- not suitable for

decision making

Historical data. Has

support for

summarization and

aggregation. Stores and

manages data at various

level of granularity, there

by suitable for decision

making

Inserts and Updates Very frequent updates

and inserts

Periodic updates to

refresh the data

warehouse


Feature OLTP OLAP

Queries Simple queries, often

returning fewer records

Often complex queries

involving aggregations

Processing speed Usually returns fast Queries usually take a

long time(several hours)

to execute and return

Space requirements Relatively small,

particularly when

historical data is either

purged or archived

Comparatively huge

because existence of

aggregation structures

and historical data.

Data base design Typically normalized

tables. OLTP s/m adopts

ER model

Typically de-normalized

tables; uses star or snow

flake schema

Access Field level access Typically aggregated

access to data of

business interest

Operations Read/write Mostly read


Feature OLTP OLAP

Back up and recovery Regular backup of

operational data are

mandatory. Requires

concurrency

control(locking) and

recovery mechanisms.

Instead of regular

backups, data

warehouse is refreshed

periodically using data

from operational data

sources.

Indexes Few Many

Joins Many Few

Derived data and

aggregate

Rare Common

Data structures Complex Multi dimensional


OLTP and OLAP

• OLTP helps in the execution of day-to-day

operations of an organization.

• OLAP helps in decision making


Data Models for OLTP and

OLAP• An OLTP s/m usually adopts an ER model

whereas an OLAP system adopts either a

star or snowflake model.


Data Model for OLTP

• Refer to pdf


ER DIAGRAM

• The company you work for wants to

digitize their time cards. You have been

asked to design the database for

submitting and approving time cards. Draw

the database ER diagram with the

following information:


ER DIAGRAM

• A timecard should have hours worked and

date submitted

• Each timecard is associated with exactly

one employee

• Each timecard should have a unique id

• Each timecard has a status: it is either

approved, not approved, or pending

• Each employee has a unique id


ER DIAGRAM

• Each employee has a name and address.

• Each employee submits a time card every

pay period. i.e. In 1 year, they will submit

multiple time cards

• Each employee either has direct deposit or

physical check as their method of payment

• Each employee is associated with exactly

one manager


ER DIAGRAM

• Each manager has a unique id and a

name

• Each manager is in charge of multiple

employees

• Each manager approves time cards for

multiple employees



Data Model for OLAP

• A multidimensional model can exist in the

form of star schema, snow flake schema

or galaxy schema.


Dimensions and facts

• Dimension is a perspective or entity with

respect to which an organization wants to

keep records.

• Facts are numerical measures/quantities

by which we want to analyze relationships

between dimensions.

• Example:-total sales amount, quantity,

discount.











Pivot


Drill Across


Drill through


ERP



ERP Benefits

• Consistency and reliability of data across

various units of the organization

• Stream lining the transactional process

• A few basic reports to serve the

operational needs.



BI Applications

• Customer Analytics

• Human Capital Productivity Analytics

• Business Productivity Analytics

• Sales Channel Analytics

• Supply Chain Analytics

• Behavior Analysis








Customer Analytics

• A common, overused term is customer relationship

management (CRM), which has become a buzzword

implying an all-encompassing magic bullet to turn all

contacts into customers and all customers into great

customers. The magic of CRM is actually based on a

number of customer analytic functions that together help

people in a company better understand who their

customers are and how to maximize the value of each

customer. The results of these analytics can be used to

enhance the customer's experience as well.


Aspects of Customer Analytics

• Customer profiling

• Targeted marketing

• Personalization

• Collaborative filtering

• Customer satisfaction

• Customer lifetime value

• Customer loyalty

– These aspects benefit the sales, marketing,

and service organizations as they interact with

the customers.


Customer profiling

• The bulk of marketing traditionally casts a wide

net and hopes to capture as many individuals as

possible. Companies are realizing that all

customers are not clones of some predefined

market segment but are thinking individuals. To

this end, customer analytics encompass the

continuous refinement of individual customer

profiles that incorporate demographic,

psychographic, and behavioral data about each

individual.


Targeted marketing

• Knowledge of a set of customer likes and

dislikes can augment a marketing

campaign to target small clusters of

customers that share profiles. In fact,

laser-style marketing is focused directly at

individuals as a by-product of customer

analytics.


Personalization

• As more business moves online, the browser acts

as a proxy for the company's first interface with the

customer.

• Personalization, which is the process of crafting a

presentation to the customer based on that

customer's profile, is the modern-day counterpart to

the old-fashioned salesperson who remembers

everything about his or her individual "accounts."

Web site personalization exploits customer profiles

to dynamically collect content designed for an

individual, and it is meant to enhance that

customer's experience.


Collaborative filtering

• We have all seen e-commerce Web sites that suggest

alternate or additional purchases based on other

people's preferences. In other words, the information on

a Web page may suggest that "people who have

purchased product X also have purchased product Y.“

These kinds of suggestions are the result of a process

called collaborative filtering, which evaluates the

similarity between the preferences of groups of

customers. This kind of recommendation generation

creates relatively reliable cross-sell and up-sell

opportunities.


Customer satisfaction

• Another benefit of the customer profile is

the ability to provide customer information

to the customer satisfaction

representatives. This can improve these

representatives' ability to deal with the

customer and expedite problem resolution.


Customer lifetime value

• How does a company determine who their

best customers are? The lifetime value of

a customer is a measure of a customer's

profitability over the lifetime of the

relationship, which incorporates the costs

associated with managing that relationship

and the revenues expected from that

customer. Customer analytics incorporates

metrics for measuring customer lifetime

value.


Customer loyalty

• It is said that a company's best new

customers are its current customers. This

means that a company's best opportunities

for new sales are with those customers

that are already happy with that company's

products or services. Customer analytics

help.


Human Capital Productivity

Analytics• Call center utilization and optimization.

• Production effectiveness.


Call center utilization and

optimization• When a company's management realizes

that inbound calls are likely to be from

unsatisfied customers, making them stew

on the phone is not going to improve

customer satisfaction. In the more

advanced cases, quick access to

customer profile information may also

affect the level of support provided to each

customer


Production effectiveness

• This includes evaluating on-time

performance, labor costs, production yield,

etc., all as factors of how staff members

work. This information can also be

integrated into an information repository

and analyzed for value.


Business Productivity Analytics

• Defect analysis

• Capacity planning and optimization

• Financial reporting

• Risk management

• Just-in-time

• Asset management and resource planning


Sales Channel Analytics

• Marketing

• Sales performance and pipeline


Marketing

• Both the ability to fine-tune a marketing program and the

ability to determine marketing effectiveness can be

derived through sales channel analytics. A typical

iterative process would be to identify a marketing

strategy based on an analysis of a clustering of

customers by profile and then to implement that strategy.

The effectiveness of the strategy will ripple through the

sales channel data, which can then be used to compare

the actual results with expectations. The degree to which

those expectations are met (or exceeded) can be fed

back into the analytical processing to help determine

new strategies.


Sales performance and pipeline

• Data associated with the sales staff can be

analyzed to identify variables that affect

the efficiency of the sales cycle, such as

individual sales staff member, region,

industry, contact people, contact times,

and contact frequency.


Supply Chain Analytics

• Supplier and vendor management

• Shipping

• Inventory control

• Distribution analysis


Supplier and vendor

management• Many organizations are unable to identify

who their vendors are or how many vendors

are supplying products or services. Supply

chain analytics allow a company's

management to track performance and

reliability by supplier, evaluating and rating

the quality of the products supplied, as well

as help to optimize supplier relationships with

respect to spending, procurement, and risk.


Shipping

• There are different methods by which a company delivers

its products to its customers, each with its own cost

schedule. For example, it may be more expensive to ship

products by air than by truck, but the products will arrive at

the destination faster if shipped by air. A company can

minimize its delivery costs by being able to select the most

efficient delivery method for any specific business

arrangement, but knowing whether the products can be

available within the right time schedule is a difficult

problem, especially if your production depends on external

suppliers. Therefore, merging supplier and inventory

information with productivity data lets management

accurately determine the best way to move product.


Inventory control

• Maintaining an inventory of commodity products that

exhibit volatile pricing and limited useful life creates a

market risk if those products cannot be used before their

obsolescence. Alternatively, we would not want to keep

the shelves empty, because parts are needed to build

the products that are in the order and fulfillment cycle.

Between the sales channel information, the productivity

data, and the supply chain data, it is possible to make

more precise predictions about inventory requirements. It

is also possible to determine the best way to quantify

and mitigate risk, especially through the development of

financial products (such as barrier options) to limit

financial losses.


Distribution analysis

• Imagine that your company has a large number of retail outlets, a

smaller number of regional warehouses, and a very small

number of factories. The optimal distribution model would

arrange for the delivery of the exact number of products from

each factory to its closest warehouses so that each warehouse

could deliver the exact number of products to each of the retail

stores. Unfortunately for both companies and customers, this

optimal distribution is pretty rare. If a company can predict

demand for specific products within certain areas, though, the

managers cannot only distribute the product to the right locations

in the right quantities, but also minimize shipping costs by

ramping up product creation at the factories most economically

geographically located at a rate that matches the consumer

demand.


Behavior Analysis

• Purchasing trends

• Web activity

• Fraud and abuse detection

• Customer attrition

• Social network analysis


Purchasing trends

• Behavior analytics can be used to identify

purchasing patterns that indicate a

growing trend that can be used to adjust a

company's reaction to customer trends.

• toy sales around winter holiday time.


Web activity

• In the world of e-commerce, the ability to

draw and maintain customers to a Web

site and then encourage them to commit to

purchasing products is not only critical to

success, but also much more difficult than

doing the same in a brick and mortar

environment.


Fraud and abuse detection

• Fraudulent (or abusive) behavior frequently

is manifested in patterns. For example,

there are many popular health insurance

fraud schemes involving making claims with

inflated charges or practitioners prescribing

expensive medications or procedures that

may not be necessary. Behavior analytics

can be used to seek out patterns of

suspicious behavior by provider,

geographical region, agent, etc.


Customer attrition

• Another serious problem for many

businesses is customer attrition, when a

company's customers decide they no

longer want to remain affiliated with that

company.


Social network analysis

• Sometimes it is important to identify relationships

between specific entities within a system and to analyze

their behavior as a group. For example, a component of

criminal intelligence is finding collections of individuals

whose individual behavior may be nondescript yet who

act suspiciously as a group. This kind of analytical

processing is valuable to law enforcement, regulatory

compliance (think of insider trading), marketing (consider

viral marketing, which is a strategy that encourages

individuals to pass your marketing message to all of their

contacts), as well as sales optimization (by finding a

contact path of people to find the right audience).



Information Sources before

dawn of BI• Marketing research

• Statistical data

• Management reporting

• Market survey


Marketing research

• It is about understanding the customers,

the competitors, the products, the

changing market dynamics etc.


Statistical data

• This is essentially about unraveling hidden

pattern, spotting trends, etc. through

proven mathematical techniques for

understanding raw data.


Management reporting

• IT team prepares the report.


Market Survey

• Enterprises also employ third-party

agencies to conduct consumer surveys

and competitive analysis. They also use

benchmark data to understand their

strength, weaknesses and specific market

opportunities they could exploit as well as

risks that might reduce their revenue or

market share.


Important features of Business

Intelligence• Fact based decision making

• Single version of truth

• 360 degree perspective on your business

• Virtual team members on the same page.





Evolution of BI and role of DSS, EIS, MIS, and

digital dashboards

• In olden days whenever a question is

asked management information team used

generate a new report. The new report

involves all phases of SDLC.


Drawback of management

information system• Long delay between the request for and

delivery of reports.

• Inaccurate figures as the data would have

been copied to a new schema while IT

applications could be updating there own

databases resulting in multiple versions of

truth.

• The report copy can answer only one

question


Drawback of management

information system• Executives requirements would have

changed by the time the report is taken to

them, resulting in dissatisfaction of the

service delivered.


BI solutions

• Ad hoc reporting( for individual decision

makers)

• Decision Support System(DSS) (run day

to day operations)

• Executive Information system(EIS)


Executive Information

system(EIS)• It supports decision making at senior

management level i.e. strategic decisions.

• It provides easy access to internal as well

as external data.

• EIS focuses on KPI.


ERP VS. BI

ERP BI

OLTP S/m OLAP

Data warehouse not present Data warehouse is part of BI

Static reports Dynamic reports


BI for past, present and future


BI Component Framework


BI component framework

• BI component framework is divided into

three major layers

– Business layer

– Administration and operation layer

– Implementation layer


Business layer

• This layer consists of four components

– Business requirements

– Business value

– Program management

– Development


Business requirements

• Business requirement consists of

– Business drivers

– Business goals

– Business strategies


Business drivers

• These are the impulses that initiate the

need to act. A few examples of business

drivers are changing workforce, changing

labour laws, changing economy, changing

technology etc.


Business goals

• These are the target to be achieved in

response to business drivers.

• Examples:- increased productivity,

improved market share, improved profit

margins, improved customer satisfaction,

cost reduction etc.


Business strategies

• These are the planned course of action

that will help achieve the set goals.

• Examples:-outsourcing, partnerships,

customer retention program, employee

retention program, competitive pricing etc.


Business Value

• When a strategy is implemented against

certain goals, then certain costs are

involved. The business value can be

measured in terms of

– Return On Investment(ROI)

– Return On Asset(ROA)

– Total Cost of Ownership(TCO)

– Total Value of Ownership(TVO)


Program Management

• Program management ensures smooth

functioning of the entire program. It

involves people, projects and priorities.


Development

• The process of development consists of

– Database/data warehouse development

– Data integration system development

– Business analytics development


Administration and operation

layer• This layer consists of four components

– BI architecture

– BI and DW operations

– Data resource administration

– Business applications


BI architecture components

• Data

– Should follow design standards

– Must have a logically apt data model

– Metadata should be of high standard



• Integration

– Follow processing standards

– Data must be consistent

– Performed according to business semantic

and rules



• Information

– Usable and findable as per requirements



• Technology

– Must be accessible.

– It should have good UI.

– Should support analysis, decision making and

storage management.



• Organization

– Consists of different roles and responsibilities

like management, development, support and

usage roles.


BI and DW operations

• Security

• Configuration management

• Database management


Data Resource Administration

• Data governance

• Metadata management


Data governance

• It is technique for controlling data quality. It

is used to assess, improve, manage and

maintain information.


Meta data management

• Meta data is data about data.

• It is further subdivided into

• Business Metadata

• Process metadata

• Technical metadata

• Application metadata


Technical Metadata

• Data locations

• Data formats

• Data sizes

• Data types

• Indexing

• Data Structures


Process Metadata

• Transformation rules

• Data cleansing rules

• ETL audit trail

• Data quality audit


Application Metadata

• Data access history


Business Metadata

• Structure and hierarchy of data

• Ownership of data

• Definition of business metrics


Business Applications

• Decision Support System(DSS)

• Executive Information System(EIS)

• OLAP

• Data mining


Implementation layer

• Data Warehousing

• Information services


Who is BI for?

• BI for management

• Operational BI

• BI for process improvement

• BI for performance improvement

• BI to improve customer experience


BI Users

• Casual Users

• Power Users


BI ROLES

Program Roles Project roles

BI program manager Business manager

BI data architect BI Business Specialist

BI ETL architect BI Project manager

BI technical architect Business requirement analyst

Meta Data Manager Decision support Analyst

BI administrator BI designer

ETL specialist

Data administrator


References

• David Loshin, “Business Intelligence”, Morgan Kaufmann

Publishers, 2003

• Mike Biere, “Business Intelligence for the Enterprise”,

2nd edition, IBM Press,2003.

• R N Prasad, Seema Acharya, “Fundamentals of

Business Analytics”, Wiley India, 2011

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