Corporate data handling

UNIT III
Handling the Corporate Data
By Dr. Dhobale J V
Associate Professor
School of Engineering & Technology
RNB Global University, Bikaner
RNB Global University, Bikaner. 1Course Code - 11010700

Objectives
 Data Resource Management.
 DBMS.
 Types of DBMS.
 Data Warehousing.
 Data Mining.
 Information Quality.
 Data/Information security.
 IT Audit.
 IT Services contact Management.
 Risk Analysis.
2RNB Global University, Bikaner.Course Code - 11010700

Sources and Types of Information
 We receive and gather information from a
huge range of sources.
 In business, types of information includes:
 Customer and Employee information.
 Email, mail & faxes.
 Charts and graphs
 Monthly financial reports.
 Signs directing customers to a particular location.
 Procedures.
 Pictures & diagrams
 Industry specific.
 Demographic information 3RNB Global University, Bikaner.Course Code - 11010700

 Three basic types of information:
1. Strategic Information
2. Tactical Information
3. Operational Information

 Information comes from a variety of sources,
formal and informal:
1. Formal Sources:
1. Internal: – Computer System, Staff Record,
Accounting Records, Annual Reports, Company
Reports, Business documents, Planning
documents.
2. External: - Professional Journals, Books, Surveys,
Industry and Government reports.

2. Informal Sources: Includes conversation with
colleagues at lunch or from friends or other
associates external to your company.
 With Customer Contact Centers:
 Automatic Call Distribution reports.
 Other System reports.
 Call monitoring checklists.
 Customers.
 Emails.
 HR Department
 Budgets.
 Marketing Department 6RNB Global University, Bikaner.Course Code - 11010700

Database Management Systems (DBMS)
 Information form these sources needs to collect and
process for further processing.
 Organizations uses DBMS along with Data
Warehousing and Data Mining philosophies for
these.

Database Management Systems (DBMS)
 Database is a collection of related data
and data is a collection of facts and
figures that can be processed to
produce information.
 A database management
system stores data in such a way that it
becomes easier to retrieve, manipulate,
and produce information.

DBMS Characteristics
1. Real World Entity.
2. Relation based Tables.
3. Isolation of Data and Applications.
4. Less Redundancy.
5. Consistency.
6. Query Language.
7. Multiuser and Concurrent Access.
8. Multiple Views.
9. Security. 9RNB Global University, Bikaner.Course Code - 11010700

DBMS Users

DBMS Users
1. Administrators: Administrators
maintain the DBMS and are responsible
for administrating the database.
2. Designers: Designers are the group of
people who actually work on the
designing part of the database.
3. End Users: End users are those who
actually reap the benefits of having a
DBMS.

DBMS Architecture
 The design of a DBMS depends on its
architecture. It can be centralized or
decentralized or hierarchical.
 The architecture of a DBMS can be
seen as either single tier or multi-tier.
 An n-tier architecture divides the whole
system into related but
independent n modules, which can be
independently modified, altered,
changed, or replaced.

DBMS Architecture
 3-tire Architecture:

DBMS Architecture
 Database (Data) Tier: At this tier, the
database resides along with its query
processing languages. We also have
the relations that define the data and
their constraints at this level.
 Application (Middle) Tier: At this tier
reside the application server and the
programs that access the database.
 For a user, this application tier presents
an abstracted view of the database.

DBMS Architecture
 The application layer sits in the middle
and acts as a mediator between the
end-user and the database.
 User (Presentation) Tier: End-users
operate on this tier and they know
nothing about any existence of the
database beyond this layer.
 At this layer, multiple views of the
database can be provided by the
application.

Data Models
 Data models define how the logical
structure of a database is modeled.
 Data Models are fundamental entities to
introduce abstraction in a DBMS.
 Data models define how data is
connected to each other and how they
are processed and stored inside the
system.

Data Models
1. Entity-Relationship Model:

Data Models
1. Entity-Relationship Example:

Data Models (E-R Model)
1. Entity-Relationship Model: Entity-
Relationship (ER) Model is based on
the notion of real-world entities and
relationships among them.
 While formulating real-world scenario
into the database model, the ER Model
creates entity set, relationship set,
general attributes and constraints.

 ER Model is based on −
 Entities and their attributes.
 Relationships among entities.

 Entity − An entity in an ER Model is a
real-world entity having properties
called attributes.
 Every attribute is defined by its set of
values called domain.
 For example, in a school database, a
student is considered as an entity.
Student has various attributes like
name, age, class, etc.

 Relationship − The logical association
among entities is called relationship.
Relationships are mapped with entities
in various ways. Mapping cardinalities
define the number of association
between two entities.
 Mapping cardinalities − one to one; one
to many; many to one; many to many.

Data Models (Relational Model)
2. Relational Model: The most popular
data model in DBMS is the Relational
Model.
 It is more scientific a model than others.
 This model is based on first-order
predicate logic and defines a table as
an n-arry relation.

 The main highlights of this model are −
 Data is stored in tables called relations.
 Relations can be normalized.
 In normalized relations, values saved are
atomic values.
 Each row in a relation contains a unique
value.
 Each column in a relation contains values
from a same domain.

Data Schema & Instances
 A database schema is the skeleton
structure that represents the logical view
of the entire database. It defines how the
data is organized and how the relations
among them are associated.
 A database instance is a state of
operational database with data at any
given time.

Traditional File System and DBMS
 Characteristics of Traditional File
System:
1. It is a group of files storing data of an
organization.
2. Each file is independent from one another.
3. Each file is called a flat file.
4. Each file contained and processed
information for one specific function, such
as accounting or inventory.

 Characteristics of Traditional File
System:
5. As systems became more complex, file
processing systems offered little flexibility,
presented many limitations, and were
difficult to maintain.
6. Files are designed by using programs
written in programming languages such as
COBOL, C, C++.

 Limitations of Traditional File System:
1. Separated and Isolated Data.
2. Duplication of data.
3. Data Dependence.
4. Difficulty in representing data from the
user's view.
5. Data Inflexibility.
6. Incompatible file formats.
7. Data Security.
8. Concurrency problems.

Data Warehouse
 The term "Data Warehouse" was first
coined by Bill Inmon in 1990.
 According to Inmon, a data warehouse
is a subject oriented, integrated, time-
variant, and non-volatile collection of
data.
 This data helps analysts to take
informed decisions in an organization.

Data Warehouse
 A data warehouses provides us
generalized and consolidated data in
multidimensional view.
 Along with generalized and
consolidated view of data, a data
warehouses also provides us Online
Analytical Processing (OLAP) tools.
 These tools help us in interactive and
effective analysis of data in a
multidimensional space.

Data Warehouse
 Data mining functions such as
association, clustering, classification,
prediction can be integrated with OLAP
operations to enhance the interactive
mining of knowledge at multiple level of
abstraction.
 A data warehouse is a database, which
is kept separate from the organization's
operational databas..

Data Warehouse
 There is no frequent updating done in a
data warehouse.
 It possesses consolidated historical
data, which helps the organization to
analyze its business.
 A data warehouse helps executives to
organize, understand, and use their
data to take strategic decisions.

Data Warehouse
 Data warehouse systems help in the
integration of diversity of application
systems.
 A data warehouse system helps in
consolidated historical data analysis.

Data Warehouse - Features
1. Subject Oriented.
2. Integrated.
3. Time Variant.
4. Non-volatile

Data Warehouse Applications
1. Financial services.
2. Banking services.
3. Consumer goods.
4. Retail sectors.
5. Controlled manufacturing.

Types of Data Warehouse
1. Information Processing: A data
warehouse allows to process the data
stored in it. The data can be processed
by means of querying, basic statistical
analysis, reporting using crosstabs,
tables, charts, or graphs.

2. Analytical Processing : A data
warehouse supports analytical
processing of the information stored in
it. The data can be analyzed by means
of basic OLAP operations, including
slice-and-dice, drill down, drill up, and
pivoting.
.

3. Data Mining: Data mining supports
knowledge discovery by finding hidden
patterns and associations, constructing
analytical models, performing
classification and prediction.
 These mining results can be presented
using the visualization tools.
. 39RNB Global University, Bikaner.Course Code - 11010700

Data Warehousing
 Data warehousing is the process of
constructing and using a data
warehouse.
 A data warehouse is constructed by
integrating data from multiple
heterogeneous sources that support
analytical reporting, structured and/or ad
hoc queries, and decision making.
 Data warehousing involves data
cleaning, data integration, and data
consolidations. 40RNB Global University, Bikaner.

Data Warehousing
 There are decision support technologies
that help utilize the data available in a
data warehouse.
 These technologies help executives to
use the warehouse quickly and
effectively.
 They can gather data, analyze it, and
take decisions based on the information
present in the warehouse.

Functions of Data Warehouse
1. Data Extraction.
2. Data Cleaning.
3. Data Transformation.
4. Data Loading.
5. Refreshing.

Process Flow in Data Warehouse
 There are four major processes that
contribute to a data warehouse:
1. Extract and load the data.
2. Cleaning and transforming the data.
3. Backup and archive the data.
4. Managing queries and directing them to
the appropriate data sources.
. 43RNB Global University, Bikaner.Course Code - 11010700

Data Mining
 Data Mining is defined as extracting
information from huge sets of data.
 The information or knowledge extracted
so can be used for any of the following
applications:
 Market Analysis
 Fraud Detection
 Customer Retention
 Production Control
 Science Exploration

Data Mining - Applications
 Market Analysis:
1. Customer Profiling
2. Identifying Customer Requirements
3. Cross Market Analysis
4. Target Marketing
5. Determining Customer purchasing pattern
6. Providing Summary Information

 Corporate Analysis and Risk Management:
1. Finance Planning and Asset Evaluation
2. Resource Planning
3. Competition

 Fraud Detection:
 Data mining is also used in the fields of credit
card services and telecommunication to
detect frauds

IT Service Contract Management
 Bid Processing: is an offer
(often competitive) to set a price by an
individual or business for a product or
service or a demand that something be
done
 RFI, RFQ, RFT and RFP, These
processes have steadily grown in
popularity in procurement and purchasing,
especially amongst larger buying
organisations.

 RFI – Request for Information
An open enquiry that spans the market
seeking broad data and understanding.
 RFQ – Request for Quotation
An opportunity for potential suppliers
to competitively cost the final chosen
solution(s).

 RFT – Request for Tender
An opportunity for potential suppliers to
submit an offer to supply goods or services
against a detailed tender.
 RFP – Request for Proposal
Sometimes based on a prior RFI; a
business requirements-based request for
specific solutions to the sourcing
problem.

IT Audit
 In general an IT audit is the process of
examining information systems to ensure
they are storing, processing, controlling
and reporting organizational data
according to internal and external
procedures and regulations.

IT Audit
 The results of an IT audit can help an
organization learn about exposure to
different threats, highlight areas of non-
compliance with industry and government
standards and regulations, and can help
organizations improve efficiency and
strategic planning by identifying redundant
or inefficient processes.
 In general IT Auditors tend to either work
as “external auditors” or “internal auditors”.

Role of IT Auditors
1. Planning the Audit.
2. Interviews/Research.
3. System Interrogation.
4. Reporting.

SLA
 A service-level agreement (SLA) is
defined as an official commitment that
prevails between a service provider and
the customer.
 Particular aspects of the service – quality,
availability, responsibilities – are agreed
between the service provider and the
service user.

SLA
 The most common component of SLA is
that the services should be provided to
the customer as agreed upon in the
contract.
 Service level agreements are also defined
at different levels:

SLA
 Customer-based SLA.
 Service-based SLA.
 Multilevel SLA – Corporate Level,
Customer Level, Services Level.

SLA - Components
1. Type of service to be provided.
2. The service's desired performance level,
especially its reliability and
responsiveness.
3. The steps for reporting issues with the
service.
4. Response and issue resolution time-
frame.
5. Monitoring process and service level
reporting.
6. Repercussions for service provider not
meeting its commitment.

Arbitration clause
 An arbitration clause is a clause in
a contract that requires the parties to
resolve their disputes through
an arbitration process.
 Although such a clause may or may not
specify that arbitration occur within a
specific jurisdiction, it always binds the
parties to a type of resolution outside of
the courts, and is therefore considered a
kind of forum selection clause.

Exit / Escape clause
 An escape clause is any clause, term, or
condition in a contract that allows a party
to that contract to avoid having to perform
the contract.
 If an agreement was drawn up for the sale
of a house, for example, the purchaser
could include some kind of escape clause
in the contract, which will allow him to
"escape" from the contract without being
liable for breach of contract.

Information Quality
 Information is a vital resource for the success of any
organization.
 Future of an organization lies in using and
disseminating information wisely.
 Good quality information placed in right context in right
time tells us about opportunities and problems well in
advance.
 According to Wang and Strong, following are the
dimensions or elements of Information Quality.

Information Quality
 Intrinsic: Accuracy, Objectivity, Believability, Reputation.
 Contextual: Relevancy, Value-Added, Timeliness,
Completeness, Amount of information.
 Representational: Interpretability, Format, Coherence,
Compatibility.
 Accessibility: Accessibility, Access security.

Information Quality
 Most essential characteristic features for information
quality are:
1. Reliability - It should be verifiable and dependable.
2. Timely - It must be current and it must reach the users
well in time, so that important decisions can be made in
time.
3. Relevant - It should be current and valid information and
it should reduce uncertainties.
4. Accurate - It should be free of errors and mistakes, true,
and not deceptive.

Information Quality
5. Sufficient - It should be adequate in quantity, so that
decisions can be made on its basis.
6. Unambiguous - It should be expressed in clear terms.
In other words, it should be comprehensive.
7. Complete - It should meet all the needs in the current
context.

Information Quality
6. Unbiased - It should be impartial, free from any bias. In
other words, it should have integrity.
7. Explicit - It should not need any further explanation.
8. Comparable - It should be of uniform collection,
analysis, content, and format.
9. Reproducible - It could be used by documented
methods on the same data set to achieve a consistent
result.

Information Security
 Three basic security concepts important to
information on the internet are
confidentiality, integrity, and availability.
 Concepts relating to the people who use
that information are authentication,
authorization, and nonrepudiation.

Review
 Data Resource Management.
 DBMS.
 Types of DBMS.
 Data Warehousing.
 Data Mining.
 Information Quality.
 Data/Information security.
 IT Audit.
 IT Services contact Management.
 Risk Analysis.

Thank You!
RNB Global University, Bikaner. 67Course Code - 11010700

Corporate data handling

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