1.Compare operational and analytical customer relationship management.
Operational CRM: Supports traditional transactional processing for day-to-day front-office operation or system that deal with the customers.
Analytical CRM:Supports back-office-operations and strategic analysis and includes all systems that do not deal directly with the customers.
2.Identify the primary forces driving the explosive growth of customer relationship management.
The primary forces driving the explosive growth of CRM include automotive efficiency or competitive advantages.
3.Define the relationship between decision making and analytical customer relationship management.
Analytical CRM solutions are design by dig deep into a company hystorical customer information and expose pattern in customer info collect
Sunday, 24 February 2013
Sunday, 17 February 2013
Chapter #9 - Enabling the Organization- Decision Making
What is Artificial Intelligence (AI) :
is the intelligence of machines and robots and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines."AI research is highly technical and specialised, deeply divided into subfields.
Expert System – computerized advisory programs
that imitate the reasoning processes of experts in solving difficult problems.
An example of expert system in terms of medical…this basic
tasks are carried out by medical expert system which is diagnosis, prognosis,
treatment, monitoring. In terms of treatment, the patient or physician could
access the system through internet. From here, the user could choose from the
choice of patient’s databases or patience disease database. Each database would
perform the particular task, either from diagnosis module or prediction module.
Then the user will received the feedback through internet so that the treatment
can be performed.
Neural Network – attempts to emulate the way the
human brains works – fuzzy logic – a mathematical method of handling imprecise
or subjective information.
An example of neural network which is bank loans….imagine a
highly experienced bank manager who must decide which customers will qualify
for a loan. His decision is based on a completed application form that contains
ten questions. Each question is answered by a number from 1 to 5 ( some
responses may be subjective in nature). If we had a large number of loan
applications as input, along with the manager’s decision as output, a neural
network could be ‘ trained’ on these patterns. The inner workings of the neural
network have enough mathematical sophistication to reasonably simulate the
expert’s intuition.
Sunday, 27 January 2013
Chapter #8 - Accessing Organizational information -Data Warehouse
1) Desribe the roles and purpose of data ware-houses and data marts in an organization.
The purpose of the Data Warehouse in the overall Data Warehousing Architecture is to integrate corporate data. It contains the "single version of truth" for the organization that has been carefully constructed from data stored in disparate internal and external operational databases.
The amount of data in the Data Warehouse is massive. Data is stored at a very granular level of detail. For example, every "sale" that has ever occurred in the organization is recorded and related to dimensions of interest. This allows data to be sliced and diced.
2)Explain the relationship between business intelligence and a data warehouse
Many of the tool vendors who sell their products or softwares call it business Intelligence software rather than Data warehousing software. so what is it? Business Intelligence is a term commonly associated with data warehousing. Business Intelligence is a generalized term where a company initiates various activities to gather today's market information which also includes about their competitor. Today's business Intelligence systems are contrasted to more classical way of information gathering in mining and crunching the data in the most optimal manner. In short we can say BI simplifies information discovery and analysis. In this way the company will have a competitive advantage of business and intelligently using the available data in strategic and effective decision making. it has the ability to bring disparate data under one roof with a meaningful information and ready for analysis.
Business intelligence usually refers to the information that is available for the enterprise to make decisions on. A data warehousingsystem is the backend, or the infrastructural, component for achieving business intelligence. Business intelligence also includes the insight gained from doing data mining analysis, as well as unstructured data (thus the need fo content management systems).
Let me give the path of Data warehousing. All the source data from disparate sources are used to load/Stage data. Different sources can be flat files, another database or some other process. The starting point of the Data warehouse should extract the data in order to load into its environment.This is extracting. This data may not be the expected format or size. your business demands are different or your organization business requirements are different. So the business process has to modify the data or better word is to transform the incoming data to meet requirements and objectives. This is called Transformation. Once every slicing and dicing of the data is done along with applied business rules, this data is ready for loading into the target tables. This process is called Loading. So overall till now we have done Extraction, Transformation and Loading. In short we call this ETL. There are lot of tools available in today's market which does help in achieving the ETL process. Once this data is loaded in to the database, this is ready for next processing. We call that database as Data warehouse database. The next process could be building of datamarts or directly reporting from it. There are lot of tools/software available for reporting/analysis. Some call it business reporting or analysis tool. But if you see the whole process has intelligence involved in business. we can call this or the gurus call it Data warehousing and the system involved from end to end is called business intelligence system.
The purpose of the Data Warehouse in the overall Data Warehousing Architecture is to integrate corporate data. It contains the "single version of truth" for the organization that has been carefully constructed from data stored in disparate internal and external operational databases.
The amount of data in the Data Warehouse is massive. Data is stored at a very granular level of detail. For example, every "sale" that has ever occurred in the organization is recorded and related to dimensions of interest. This allows data to be sliced and diced.
2)Explain the relationship between business intelligence and a data warehouse
Many of the tool vendors who sell their products or softwares call it business Intelligence software rather than Data warehousing software. so what is it? Business Intelligence is a term commonly associated with data warehousing. Business Intelligence is a generalized term where a company initiates various activities to gather today's market information which also includes about their competitor. Today's business Intelligence systems are contrasted to more classical way of information gathering in mining and crunching the data in the most optimal manner. In short we can say BI simplifies information discovery and analysis. In this way the company will have a competitive advantage of business and intelligently using the available data in strategic and effective decision making. it has the ability to bring disparate data under one roof with a meaningful information and ready for analysis.
Business intelligence usually refers to the information that is available for the enterprise to make decisions on. A data warehousingsystem is the backend, or the infrastructural, component for achieving business intelligence. Business intelligence also includes the insight gained from doing data mining analysis, as well as unstructured data (thus the need fo content management systems).
Let me give the path of Data warehousing. All the source data from disparate sources are used to load/Stage data. Different sources can be flat files, another database or some other process. The starting point of the Data warehouse should extract the data in order to load into its environment.This is extracting. This data may not be the expected format or size. your business demands are different or your organization business requirements are different. So the business process has to modify the data or better word is to transform the incoming data to meet requirements and objectives. This is called Transformation. Once every slicing and dicing of the data is done along with applied business rules, this data is ready for loading into the target tables. This process is called Loading. So overall till now we have done Extraction, Transformation and Loading. In short we call this ETL. There are lot of tools available in today's market which does help in achieving the ETL process. Once this data is loaded in to the database, this is ready for next processing. We call that database as Data warehouse database. The next process could be building of datamarts or directly reporting from it. There are lot of tools/software available for reporting/analysis. Some call it business reporting or analysis tool. But if you see the whole process has intelligence involved in business. we can call this or the gurus call it Data warehousing and the system involved from end to end is called business intelligence system.
Chapter #7- storing Organizational Information -database
1) Define the fundamental concepts of the relational database model.
A database can be understood as a collection of related files. How those
files are related depends on the model used. Early models included the
hierarchical model (where files are related in a parent/child manner,
with each child file having at most one parent file), and the network
model (where files are related as owners and members, similar to the
network model except that each member file can have more than one
owner).
The relational database model was a huge step forward, as it allowed files to be related by means of a common field. In order to relate any two files, they simply need to have a common field, which makes the model extremely flexible.
The relational database model was a huge step forward, as it allowed files to be related by means of a common field. In order to relate any two files, they simply need to have a common field, which makes the model extremely flexible.
2) Evaluate the advantages of the relational database model.
The popularity of the relational database
approach has been apart from access of availability of a large variety
of products also because it has certain inherent advantages.
1. Ease of use: The revision of any information as tables consisting of rows and columns is quite natural and therefore even first time users find it attractive.
2. Flexibility: Different tables from which information has to be linked and extracted can be easily manipulated by operators such as project and join to give information in the form in which it is desired.
3. Precision: The usage of relational algebra and relational calculus in the manipulation of he relations between the tables ensures that there is no ambiguity, which may otherwise arise in establishing the linkages in a complicated network type database.
4. Security: Security control and authorization can also be implemented more easily by moving sensitive attributes in a given table into a separate relation with its own authorization controls. If authorization requirement permits, a particular attribute could be joined back with others to enable full information retrieval.
5. Data Independence: Data independence is achieved more easily with normalization structure used in a relational database than in the more complicated tree or network structure.
1. Ease of use: The revision of any information as tables consisting of rows and columns is quite natural and therefore even first time users find it attractive.
2. Flexibility: Different tables from which information has to be linked and extracted can be easily manipulated by operators such as project and join to give information in the form in which it is desired.
3. Precision: The usage of relational algebra and relational calculus in the manipulation of he relations between the tables ensures that there is no ambiguity, which may otherwise arise in establishing the linkages in a complicated network type database.
4. Security: Security control and authorization can also be implemented more easily by moving sensitive attributes in a given table into a separate relation with its own authorization controls. If authorization requirement permits, a particular attribute could be joined back with others to enable full information retrieval.
5. Data Independence: Data independence is achieved more easily with normalization structure used in a relational database than in the more complicated tree or network structure.
6. Data Manipulation Language: The possibility
of responding to ad-hoc query by means of a language based on
relational algebra and relational calculus is easy in the relational database
approach. For data organized in other structure the query language
either becomes complex or extremely limited in its capabilities.
3) compare relational integrity constraints and business- critical integrity constraints
There
are two types of integrity constraints: (1) relational integrity
constraints and (2) business-critical integrity constraints.
Relational integrity constraints are rules that enforce basic and fundamental information-based constraints.
Business-critical integrity constraints
enforce business rules vital to an organization’s success and often
require more insight and knowledge than relational integrity
constraints.
4) describe the benefits of a data- driven website
the benefits of a data-driven websites is .....
- Enabling many (potentially non-technical) users to provide content for the website. Users can publish articles on the website without needing to FTP them to a web server.
- Shopping cart
- You can provide advanced search functionality that enables users to filter the results based on a given field. They can then sort those results by a field - say "Price" or "Date".
- Customized homepage
- You can allow your users to perform tasks such as registering for a newsletter, post questions to your forums, provide comments on a blog, update their profile, etc.
- Integration with corporate applications such as CRM systems, HR systems etc
- Much more
Chapter #5 the differentiation of CIO,CTO, CSO,CPO,CKO
1. Chief Information Officer (CIO)
is
responsible for overseeing all uses of information technology and
ensuring the strategic alignment of IT with business goals and
objectives. The CIO often reports directly to the CEO. Functions usually
include Manager, Leader, and Communicator.
2. Chief Technology Officer (CTO)
is
responsible for iensuring the throughput, speed, accuracy, availability,
and reliability of and organizations information technology. CTOs have
direct responsbility for ensuring the efficiency of IT systems
throughout the organization. They have knowledge of all aspects of IT,
Including hardware, software, and telecommunications.
3. Chief Security Officer (CSO)
is
responsible for ensuring the security of IT systems and developing
strategies and IT safeguards against attacks from hackers and viruses.
CSOs possess detailed knowledge of networks and telecommunications
because hackers and viruses usually find their way into IT Systems
through networked computers.
4. Chief Privacy Officer (CPO)
is
responsible for ensuring the ethical and legal use of information within
an organization. CPOs are the newest senior executive position in IT.
5. Chief Knowledge Officer (CKO)
is
responsible for collecting, maintaining, and distributing the
organizations knowledge. The CKO designs programs and systems that make
it easy for people to reuse knowledge. CKO must continuously encourage
employee contributions to keep the systems up to date.
The greatest
problem between business personnel and IT personnel is that there is a
gap of effective communication between both departments. A
communications gap often exists between both departments because
business personnel usually have their own vocabulay based on their
experience and expertise and IT personnel have there own vocabulary
which consist of acronyms and technical terms.
Sunday, 13 January 2013
Chapter #4 -The efficiency and effectiveness using Facebook in IT metric
Doing Business online using Facebook is the most efficiency and effectiveness way, this is because it was the easy way for the customer to view about our products detail just by liked our page.The product also can be purchase by reserved the product and bankin the money.
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