1 ) Managing Data:
* The Difficulties of managing Data:
1. The amount of data increase exponentially with time.
For example: To support million of customers, large retailers such as Walmart have to manage petites of data.
2. Data are scattered and collected by many individuals using various methods and devices.
3. Data are obtained from many sources: internal sources (for example, corporate databases and company documents), personal sources (for example, personal thoughts, opinions, and experiences), and external sources (for example, commercial databases, government reports, and corporate websites).
Data also are downloaded from the web, in the form of click stream data. Click stream data are produced by visitors and customers when they visit a website and click on hyperlinks.
4. Data security, quality, and integrity are critical.
Information Systems that do not communicate with each other can result in inconsistent data.
5. Data degrades overtime.
Example: customers move to a new address, employees are hired and fired.
6. Data rot: problems with media on which the data are stored.
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* Data Governance:
- Data governance: is an approach managing information across an entire organization.
- Formal sets of business processes and policies designed to ensure that the data are collected, handled, and protected in certain, well defined fashion.
- Master data management: is a process that spans all of an organization's business processes and applications. It provides companies with the ability to store, maintain, exchange, and synchronize a consistent, accurate, and timely "single version of the truth" for the company's master data.
(http://www.ncsi.gov.om/)
- Master data: are a set of core data, such as customer, product, employee, vendor, and geographic location, that span all of the enterprise's information systems.
- Transaction data: Data that are generated and captured by operational systems, describe the activities, or transactions, of the business.
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2) The Database Approach:
- Database management system (DBMS)- provide all users with access to all the data
- DBMSs minimize the following problems:
*Data redundancy: The same data are stored in many places.
*Data isolation: Application cannot access data associated with other applications.
*Data inconsistency: Various versions of the data do not agree.
- DBMSs minimize the following strengths:
🔲 Data security: keeping the organization's data safe from theft, modification, and/or destruction.
🔲 Data integrity: Data meet certain constraints, such as no alphabetic characters in a Social Security number field.
🔲 Data independence: Applications and data are not linked to each other, meaning that application are able to access the same data.
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* The Data Hierarchy:
Data are organised in a hierarchy that begins with bits and proceeds all the way to database.
▶ Bit: a binary digit, or a "0" or a "1" the smallest unit of data a computer can process.
▶ Byte: a group of eight bits and represents a single character (e.g. letter, number, or a symbol).
▶ Field: is a group of related characters (e.g. student's name, age, mobile number).
▶ Record: a group of logically related fields (e.g. student in a university database).
▶ File (table): a group of related records.
▶ Database: a group of related files.
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* Designing the Database:
- Data model: is a diagram that represents the entities in the databases and their relationships.
- Entity: a person, place, thing, or event about which information is maintained. [ a record generally describes an entity].
- Attribute: a particular characteristic of a particular entity.
- Primary key (key field): a field that uniquely identifies a record, so that it can be retrieved, updated, and sorted.
- Secondary key: are other fields that have some identifying information but typically do not identify the file with complete accuracy.
------------------------------------------------------------------------------------
✴ Entity - Relationship Modeling
- Entity- relationship (ER) modeling: a database designers plan and create the database through a process.
- ER diagrams consists of entities, attributes and relationships.
• Entity classes: groups of entities of a certain type.
• Instance: the representation of a particular entity.
• Identifiers: attributes that are unique to that entity instance.
------------------------------------------------------------------------------------
3) Database Management Systems:
■ Database management system (DBMS): is a set of program that provide users with tools to add, delete, access, modify, and analyse data store in one location.
- Examples: Microsoft Access, Oracle.
♢ Relational database model: is based on the concept of two- dimensional tables.
🌟 Query Languages:
- Requesting Data from a database:
* Structured Query Language (SQL): allows users to perform complicated searches (request information) by using relatively simple statements or keywords.
Example: SELECT (Student Name) FROM (Student) Database WHERE (GPA)>3.4
* Query by Example (QBE): allows users to fill out a grid or template to construct a sample or description of the data he or she wants.
🌟 Data Dictionary:
- Defines the format necessary to enter the data into the database.
- Provide information on each attributes.
- Provide information on how often the attribute should be updated.
● Metadata: Data about the data.
🌟 Normalization:
- Is a method for analyzing and reducing a relational database to its most streamlined form for:
🔹 Minimum redundancy.
🔹 Maximum data integrity.
🔹 Best processing performance.
- Normalized data is when attributes in the table depend only on the primary key.
-----------------------------------------------------------------------------------
4) Data Warehouses and Data Marts:
- Data warehouse: is a repository of current and historical data to support decision makers in the organization.
✴The basic characteristics:
◾ Organized by business dimension or subject [for example, by customer, product, price and region].
◾ Consistent.
◾ Historical: can be used for identifying trends, forecasting, and making comparisons over time.
◾ Multidimensional ➡ Data cube.
✴ Benefits of Data Warehousing:
• End users can access data quickly and easily via Web browsers because they are located in one place.
• End users can conduct extensive analysis with data in way that may not have been possible before.
• End users have a consolidated view of organizational data.
✴ Problems with Data Warehousing:
• Very expensive to build and to maintain.
• Incorporating data from obsolete (old) mainframe systems can be difficult and expensive.
• People in one department may be reluctant to share data with other department.
- Data Marts: a small data warehouse, designed for the end - user needs in a strategic business unit (USB) or a department.
Example: Marketing and sale data mart to deal with customer information.
✴ Benefits of Data Marts:
• Far less costly than a data warehouse.
• Can be implemented more quickly (around 3 months)
• More rapid response and easier to learn and navigate.
------------------------------------------------------------------------------------
5) Knowledge Management:
- (KM): is a process that helps organizations manipulate important knowledge that is part of the organization's memory, usually in an unstructured format.
- KM is not technology. It's a process supported by IS.
✴ Benefits of KM:
⭐KM faster innovation by encouraging the free flow of ideas, novel approaches and better ways of solving problems.
⭐KM improve customer service by streamlining response time.
⭐KM boosts revenue by getting products and services to market faster.
⭐KM enhance employee retention rates by recognizing the value of employees' knowledge.
- Knowledge: information that is contextual, relevant, and actionable.
▫Intellectual capital.
▫Intellectual assets.
▶ Explicit knowledge: codified (documented) in a form that can be distributed to other (CEPS student's handbook).
▶ Tacit knowledge: a set of insight, expertise and skills knowledge that people carry in their heads, but difficult to write down in a document.
- Best Practices: the most effective and efficient ways of doing things.
♣ Knowledge Management System (KMS): the use of information technologies to systematic, enhance, and expedite knowledge management within a single firm and among multiple firms.
♣ The Knowledge Management System Cycle:
- The reason the system is cyclical is that knowledge is dynamically refined over time.
- The knowledge in an effective KMS is never finalized because the environment changes and knowledge must be updated to reflect these changes.
- The cycle works as follows:
1. Create knowledge: knowledge is created as people determine new ways of doing things or develop know - how.
2. Capture knowledge: New knowledge must be identified as valuable and be represented in a reasonable way.
3. Refine knowledge: New knowledge must be placed in context so that it is actionable.
4. Store knowledge: Useful knowledge must then be stored in a reasonable format in a knowledge repository so that other members of the organization can access it.
5. Manage knowledge: the knowledge must be kept current.
6. Disseminate knowledge: knowledge must be made available in a useful format to anyone in the organization who needs it, anywhere and anytime.
* The Difficulties of managing Data:
1. The amount of data increase exponentially with time.
For example: To support million of customers, large retailers such as Walmart have to manage petites of data.
2. Data are scattered and collected by many individuals using various methods and devices.
3. Data are obtained from many sources: internal sources (for example, corporate databases and company documents), personal sources (for example, personal thoughts, opinions, and experiences), and external sources (for example, commercial databases, government reports, and corporate websites).
Data also are downloaded from the web, in the form of click stream data. Click stream data are produced by visitors and customers when they visit a website and click on hyperlinks.
4. Data security, quality, and integrity are critical.
Information Systems that do not communicate with each other can result in inconsistent data.
5. Data degrades overtime.
Example: customers move to a new address, employees are hired and fired.
6. Data rot: problems with media on which the data are stored.
--------------------------------------------------------------------------
* Data Governance:
- Data governance: is an approach managing information across an entire organization.
- Formal sets of business processes and policies designed to ensure that the data are collected, handled, and protected in certain, well defined fashion.
- Master data management: is a process that spans all of an organization's business processes and applications. It provides companies with the ability to store, maintain, exchange, and synchronize a consistent, accurate, and timely "single version of the truth" for the company's master data.
(http://www.ncsi.gov.om/)
- Master data: are a set of core data, such as customer, product, employee, vendor, and geographic location, that span all of the enterprise's information systems.
- Transaction data: Data that are generated and captured by operational systems, describe the activities, or transactions, of the business.
------------------------------------------------------------------------------------
2) The Database Approach:
- Database management system (DBMS)- provide all users with access to all the data
- DBMSs minimize the following problems:
*Data redundancy: The same data are stored in many places.
*Data isolation: Application cannot access data associated with other applications.
*Data inconsistency: Various versions of the data do not agree.
- DBMSs minimize the following strengths:
🔲 Data security: keeping the organization's data safe from theft, modification, and/or destruction.
🔲 Data integrity: Data meet certain constraints, such as no alphabetic characters in a Social Security number field.
🔲 Data independence: Applications and data are not linked to each other, meaning that application are able to access the same data.
* The Data Hierarchy:
Data are organised in a hierarchy that begins with bits and proceeds all the way to database.
▶ Bit: a binary digit, or a "0" or a "1" the smallest unit of data a computer can process.
▶ Byte: a group of eight bits and represents a single character (e.g. letter, number, or a symbol).
▶ Field: is a group of related characters (e.g. student's name, age, mobile number).
▶ Record: a group of logically related fields (e.g. student in a university database).
▶ File (table): a group of related records.
▶ Database: a group of related files.
* Designing the Database:
- Data model: is a diagram that represents the entities in the databases and their relationships.
- Entity: a person, place, thing, or event about which information is maintained. [ a record generally describes an entity].
- Attribute: a particular characteristic of a particular entity.
- Primary key (key field): a field that uniquely identifies a record, so that it can be retrieved, updated, and sorted.
- Secondary key: are other fields that have some identifying information but typically do not identify the file with complete accuracy.
------------------------------------------------------------------------------------
✴ Entity - Relationship Modeling
- Entity- relationship (ER) modeling: a database designers plan and create the database through a process.
- ER diagrams consists of entities, attributes and relationships.
• Entity classes: groups of entities of a certain type.
• Instance: the representation of a particular entity.
• Identifiers: attributes that are unique to that entity instance.
------------------------------------------------------------------------------------
3) Database Management Systems:
■ Database management system (DBMS): is a set of program that provide users with tools to add, delete, access, modify, and analyse data store in one location.
- Examples: Microsoft Access, Oracle.
♢ Relational database model: is based on the concept of two- dimensional tables.
🌟 Query Languages:
- Requesting Data from a database:
* Structured Query Language (SQL): allows users to perform complicated searches (request information) by using relatively simple statements or keywords.
Example: SELECT (Student Name) FROM (Student) Database WHERE (GPA)>3.4
* Query by Example (QBE): allows users to fill out a grid or template to construct a sample or description of the data he or she wants.
🌟 Data Dictionary:
- Defines the format necessary to enter the data into the database.
- Provide information on each attributes.
- Provide information on how often the attribute should be updated.
● Metadata: Data about the data.
🌟 Normalization:
- Is a method for analyzing and reducing a relational database to its most streamlined form for:
🔹 Minimum redundancy.
🔹 Maximum data integrity.
🔹 Best processing performance.
- Normalized data is when attributes in the table depend only on the primary key.
 |
| Non- Normalized Relation |
 |
| Normalizing the Database |
4) Data Warehouses and Data Marts:
- Data warehouse: is a repository of current and historical data to support decision makers in the organization.
✴The basic characteristics:
◾ Organized by business dimension or subject [for example, by customer, product, price and region].
◾ Consistent.
◾ Historical: can be used for identifying trends, forecasting, and making comparisons over time.
◾ Multidimensional ➡ Data cube.
 |
| Multidimensional Database |
✴ Benefits of Data Warehousing:
• End users can access data quickly and easily via Web browsers because they are located in one place.
• End users can conduct extensive analysis with data in way that may not have been possible before.
• End users have a consolidated view of organizational data.
✴ Problems with Data Warehousing:
• Very expensive to build and to maintain.
• Incorporating data from obsolete (old) mainframe systems can be difficult and expensive.
• People in one department may be reluctant to share data with other department.
- Data Marts: a small data warehouse, designed for the end - user needs in a strategic business unit (USB) or a department.
Example: Marketing and sale data mart to deal with customer information.
✴ Benefits of Data Marts:
• Far less costly than a data warehouse.
• Can be implemented more quickly (around 3 months)
• More rapid response and easier to learn and navigate.
------------------------------------------------------------------------------------
5) Knowledge Management:
- (KM): is a process that helps organizations manipulate important knowledge that is part of the organization's memory, usually in an unstructured format.
- KM is not technology. It's a process supported by IS.
✴ Benefits of KM:
⭐KM faster innovation by encouraging the free flow of ideas, novel approaches and better ways of solving problems.
⭐KM improve customer service by streamlining response time.
⭐KM boosts revenue by getting products and services to market faster.
⭐KM enhance employee retention rates by recognizing the value of employees' knowledge.
▫Intellectual capital.
▫Intellectual assets.
▶ Explicit knowledge: codified (documented) in a form that can be distributed to other (CEPS student's handbook).
▶ Tacit knowledge: a set of insight, expertise and skills knowledge that people carry in their heads, but difficult to write down in a document.
- Best Practices: the most effective and efficient ways of doing things.
♣ Knowledge Management System (KMS): the use of information technologies to systematic, enhance, and expedite knowledge management within a single firm and among multiple firms.
♣ The Knowledge Management System Cycle:
- The reason the system is cyclical is that knowledge is dynamically refined over time.
- The knowledge in an effective KMS is never finalized because the environment changes and knowledge must be updated to reflect these changes.
- The cycle works as follows:
1. Create knowledge: knowledge is created as people determine new ways of doing things or develop know - how.
2. Capture knowledge: New knowledge must be identified as valuable and be represented in a reasonable way.
3. Refine knowledge: New knowledge must be placed in context so that it is actionable.
4. Store knowledge: Useful knowledge must then be stored in a reasonable format in a knowledge repository so that other members of the organization can access it.
5. Manage knowledge: the knowledge must be kept current.
6. Disseminate knowledge: knowledge must be made available in a useful format to anyone in the organization who needs it, anywhere and anytime.
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