Download Data Mining - KV Institute of Management and Information Studies

Enterprise resource planning ( ERP)
Enterprise resource planning covers the techniques and
concepts employed for the integrated management of
business as a whole, from the viewpoint of the effective
use of management resources, to improve the efficiency
of an enterprise.
EVOLUTION OF ERP

1970- material requirement planning

1980 – Manufacturing Resource planning

1990- Enterprise- wide resource planning.

2000- Extended ERP.

2005- ERP II
NEED OF ERP SYSTEMS

Business integration

Flexibility

Better analysis and planning capabilities.

Use of latest technology.
Features of ERP

Accommodating variety

Integrated management information

Seamless integration.

Supply chain management

Resource management

Integrated data model.
Advantages of ERP

Intangible benefits.

Inventory reduction

Material cost reduction

Labour cost reduction
Tangible benefits

Effects on accounting

Effects on production and material management

Effects on MIS function
Disadvantages of ERP

Expense and time in implementation

Difficulty implementing change

Difficulty integrating with other systems

Risks in using one vendor

Risks of implementation failure
E - BUSINESS

Electronic business, commonly referred to a “EBusiness”, may be defined as the utilization of
information
and
communication
technologies
in
support of all the activities.

E- business = E–Marketing + E – Commerce+ Eoperations
Characteristics of E - Business

collaborative product development

Collaborative
planning,
forecasting
replenishment

Procurement and order management

Operations and logistics
and
E-Business framework

It consists of

Business partners, suppliers, distributors, resellers

Employees

Supply chain management

Enterprise resource planning

Customer relationship management

E – procurement

Selling chain management.
Advantages of E - Business

To organizations

Business survival

Find new customers

Grow sales to new and existing customers and
provide information

Improve customer service levels and satisfaction
To customers
•Provides
less expensive products and services by
allowing consumers to conduct quick online search
and comparisons.
•Enables
the customer to shop or make other
transactions 24 hours a day, from almost any location.
•Delivers
relevant and detailed information in seconds.
•Enables
the customer to get a customized product
from PCs to cars, at competitive prices.
To society

Enables individuals to work at home and
do less traveling, resulting in less road
traffic and lower air pollution.

Allows some merchandise to be sold at
lower prices, thereby increasing peoples
standard of living.
Disadvantages of E - Business

Technical limitations

Insufficient telecommunication networks.

Still – evolving software development tools.

Expensive
and
inconvenient
internet
accessibility for many would – be users.
Non – technical limitations

Customer resistance to changing from a real to a
virtual store.

People do not yet sufficiently trust, paperless, faceless
transactions.

Perception that EC is expensive and unsecured.

Unresolved legal issues( for quality, security and
reliability)
Applications of E - Business

Electronic banking

Electronic trading

E – learning

Employment and placement and job market.

E – tailing

Electronic auctions
E- Governance

E- governance is also seen as a multi – dimensional
concept, an IT driven methodology that improves
efficiency in administration, brings about transparency
and leads to the reduction of costs in running the
government.

It facilitates the government services to the masses
through procedural simplicity, speed, and convenience.
Objectives of E - Governance

Build service around citizen’s choice.

Make government more accessible.

Use government resources effectively.

Reduce government spending

Deliver online services.
Domains of E - Governance

E- administration: Improving process

E- citizens and E- services: connecting
citizens.

E – society : building external interactions
Advantages of E- Governance

Integration
of
various
ministries
and
departments

Documentation, monitoring, and control of
various projects.

Revenue generation by elimination of tax
evasions.
Data Mining

Generally, data
mining
(sometimes
called
as
knowledge
discovery) is the process of analyzing data from different perspectives
and summarizing it into useful information - information that can be
used to increase revenue, cuts costs, or both.

It allows users to analyze data from many different dimensions or
angles, categorize it, and summarize the relationships identified.

Technically, data mining is the process of finding correlations or
patterns among dozens of fields in large relational databases.
COMPONENTS OF DATA MINING

Graphical user interface
Pattern evaluation

Data mining engine
Database or data
warehouse server
Database
Data
warehouse
Knowledge
base
Data Mining Techniques
1.Cluster analysis
2.Induction
i.Decision tress
ii. Rule induction
3.Neural networks
4.Online analytical processing
5.Data visualization.
CRISP – DM MODEL FOR DATA MINING

CRISP – DM : Cross – Industry Standard
Process for Data Mining.

It is a data mining process model that describes
commonly used approaches that expert data miners
use to tackle problems.

It is the leading methodology used by data miners.

CRISP – data mining model are developed by a
consortium of several companies.
CRISP – DM MODEL FOR DATA MINING
Six phases of CRISP - DM

Phase 1: Business understanding

Phase 2: Data understanding

Phase 3: Data preparation

Phase 4: Modeling

Phase 5: Evaluation

Phase 6: Deployment
Business Understanding

This initial phase focuses on understanding the
project objectives and requirements from a
business perspective, and then converting this
knowledge
into
a
data
mining
problem
definition, and a preliminary plan designed to
achieve the objectives.
Data Understanding

The data understanding phase starts with an initial
data collection and proceeds with activities in order
to get familiar with the data, to identify data quality
problems, to discover first insights into the data, or
to detect interesting subsets to form hypotheses
for hidden information.
Data Preparation
•The data preparation phase covers all activities to construct the final dataset
(data that will be fed into the modeling tool(s)) from the initial raw data.
•Data preparation tasks are likely to be performed multiple times, and not in any
prescribed order.
•Tasks include table, record, and attribute selection as well as transformation and
cleaning of data for modeling tools.
Modeling

In this phase, various modeling techniques are selected and
applied, and their parameters are calibrated to optimal values.

Typically, there are several techniques for the same data mining
problem type.

Some techniques have specific requirements on the form of
data. Therefore, stepping back to the data preparation phase is
often needed.
Evaluation

At this stage in the project you have built a model (or models) that
appear to have high quality, from a data analysis perspective.

Before proceeding to final deployment of the model, it is important to
more thoroughly evaluate the model, and review the steps executed to
construct the model, to be certain it properly achieves the business
objectives.

A key objective is to determine if there is some important business issue
that has not been sufficiently considered.

At the end of this phase, a decision on the use of the data mining results
should be reached.
Deployment

Creation of the model is generally not the end of the project. Even if the purpose of the model
is to increase knowledge of the data, the knowledge gained will need to be organized and
presented in a way that the customer can use it.

Depending on the requirements, the deployment phase can be as simple as generating a report
or as complex as implementing a repeatable data mining process.

In many cases it will be the customer, not the data analyst, who will carry out the deployment
steps.

Even if the analyst deploys the model it is important for the customer to understand up front
the actions which will need to be carried out in order to actually make use of the created
models.
Disadvantages of data mining

Privacy issues

Security issues

Misuse
of
information.
information/
inaccurate
Advantages of Data Mining

Automated predictions of trends and behaviors.

Automated discovery of previously unknown
patterns.

Databases can be larger in both depth and
breadth.
Business Intelligence

Business intelligence is a technology based on customer and
profit oriented models that reduces operating costs and
provide increased profitability by improving productivity, sales,
service and helps to make decision making capabilities at a
least time.

Business intelligence models are based on multi dimensional
analysis and key performance indicators (KPI) of an enterprise.
COMPONENTS OF BUSINESS INTELLIGENCE
These system allow a company to gather, store, access and analyze corporate data to aid in decision –
making.
These systems will illustrate business intelligence in the areas of customer profiling, customer
support, market research, market segmentation, product profitability and inventory.
Advantages of business intelligence

Authorize employee

Simplify team work and allocation

Examine and increase insight.

Enhance association

Lessen training requirements.

Transport refined investigation and reporting.
Disadvantages of business intelligence

Piling of historical data

Cost

Complexity

Muddling ( disordering) of commercial settings.

Limited use

Time consuming implementation
Pervasive Computing

The word pervasive and ubiquitous means ‘existing everywhere’.

Pervasive computing is a rapidly developing area of information and
communication technology.

The term refers to the increasing integration of ICT into peoples lives
and environments, made possible by the growing availability of
microprocessors with inbuilt communications facilities.
Pervasive computing principles

Decentralization

Diversification

Connectivity

Simplicity.
Pervasive computing technologies

Computing

i)
sensors
: Input
device
that
deduct
environmental changes and user behaviors.

ii)
processors
: Electronic
system
that
interpret and analyze input- data.

Iii) actuators : It refers to the device which
deliver information, rather than altering the
environment physically.
User interfaces

It represent the point of contact between
ICT and human users.

Three different forms of human- computer
interaction are

Active

Passive

coercive
Applications of pervasive computing

1.Smart homes

i. lighting

ii. Energy management

iii.Water control

iv.Home security and communications

V.Home theaters

2.Smart Appliances

3.Smart Cars

4.Smart “Things”

i. Barcodes

Ii.Auto -ID
Advantages of pervasive computing

Invisible

Socialization

Decision- making

Emergent behavior

Information processing

Enhancing experience

convergence
Cloud computing

In cloud computing, the word cloud is used as
a metaphor for "the Internet,“

so the phrase cloud computing means "a type
of Internet-based computing," where different
services — such as servers, storage and
applications
—
are
delivered
to
an
organization's computers and devices through
the Internet.
Cloud Computing Services

Software as a Service (SaaS)-End Users

Platform as a Service (PaaS)-Application
Developers

Infrastructure as a Service (IaaS)-Network
Architects
Cloud Service Layers - Characteristics
Software as a
Service (SaaS)
• Sometimes free; easy to use; good consumer
adoption; proven business models
• You can only use the application as far as what it is
designed for
Platform as a
Service (PaaS)
• Developers can upload a configured applications and it
“runs” within the platform’s framework;
• Restricted to the platform’s ability only; sometimes
dependant on Cloud Infrastructure provider
Infrastructure
as a Service
(IaaS)
• Offers full control of a company’s infrastructure; not
confined to applications or restrictive instances
• Sometimes comes with a price premium; can be
complex to build, manage and maintain
Cloud Service Layers - Example
Software as a
Service (SaaS)
Platform as a
Service (PaaS)
Infrastructure
as a Service
(IaaS)
Modes of Clouds
Public Cloud

Computing infrastructure is hosted by cloud vendor at the vendors
premises.

and can be shared by various organizations.

E.g. : Amazon, Google, Microsoft, Sales force.
Private Cloud

The computing infrastructure is dedicated to a particular organization
and not shared with other organizations.

more expensive and more secure when compare to public cloud.

E.g. : HP data center, IBM, Sun, Oracle, 3tera.
Hybrid Cloud

Organizations may host critical applications
on private clouds.

where as relatively less security concerns on
public cloud.

usage of both public and private together is
called hybrid cloud.
Disadvantages of Cloud Computing

Cloud computing is impossible if you cannot connect to the Internet.

Since you use the Internet to connect to both your applications and
documents, if you do not have an Internet connection you cannot access
anything, even your own documents.

A dead Internet connection means no work and in areas where Internet
connections are few or inherently unreliable, this could be a deal-breaker.

When you are offline, cloud computing simply does not work.
Capability Maturity Model (CMM)

The Capability Maturity Model (CMM) is a service mark owned by Carnegie Mellon
University (CMU) and refers to a development model elicited from actual data.

The data were collected from organizations that contracted with the U.S. Department of
Defense, who founded the research, and they became the foundation from which CMU created
the Software Engineering Institute (SEI).

Unlike many that are derived in academia, this model is based on observation rather than on
theory.

When it is applied to an existing organization's software development processes, it allows an
effective approach toward improving them.

This gave rise to a more general concept that is applied to business processes and to
developing people.
Capability Maturity Model structure

The Capability Maturity Model involves the
following aspects:

Maturity levels.

Key process area.

Goals.

Common features.

Key practices.
Levels of the Capability Maturity Model

There are five levels defined along the continuum of the CMM, and, according
to the SEI:

Level 1 - Initial (chaotic, ad hoc,)- the starting point for use of a new process.

Level 2 - Repeatable- the process is managed according to the metrics
described in the Defined stage.

Level 3 - Defined - the process is defined/confirmed as a standard business
process,

Level 4 - Quantitatively managed.

Level 5 - Optimized - process management includes deliberate process
optimization/improvement.