Form 3 students MUST read this in order to be able to answer questions in the coming lesson. You can also refer to you textbook from pages 109 through 116.
Unit 3 - Storage & Retrieval of InformationRobbieA
Heather is tasked with assessing how information is stored at Caledonian Health & Fitness and identifying ways to improve it. Currently they use manual filing systems like sorters, trays, and cabinets. Some employees like the ease of use while others complain it is disorganized. Heather learns electronic systems offer alternatives like storing files on computers, disks, and CDs using software, scanning, and downloading. Backups are important to prevent data loss from technical issues or theft.
Unit 3 Storage And Retreival Of Informationiarthur
The document discusses different methods for storing and retrieving information, including:
1. The difference between internal and external mail, and examples of each.
2. Various filing methods like alphabetical, numerical, and chronological ordering. The advantages and disadvantages of each method.
3. Data storage methods such as floppy disks, hard drives, CDs, DVDs, and USB sticks. Microfilming is also discussed.
4. Security measures for data like passwords, ID cards, and physical locks. Backup procedures, computer viruses, and data encryption are also covered.
eFolder Partner Chat Webinar — How to Design a Business Continuity Plan for C...eFolder
Having the backup and disaster recovery conversation with your clients doesn’t have to be difficult or awkward.
In this eFolder Partner Chat, join Steven Thom, President and CEO of Thom Infotech, and Ted Hulsy, Vice President of Marketing at eFolder, as they discuss how to have a conversation about business continuity with clients and design a business continuity plan for them.
You’ll learn the different aspects of a business continuity plan that MSPs need to consider when approaching clients, how Steven made his clients understand the need for a BDR solution, why deploying BDR to his clients made Steven’s job significantly easier, and what resources eFolder provides partners who want to educate their clients on the risks of downtime.
A CIO’s Perspective: Reconciling Risk Management with Disaster Recovery Tacti...IT Network marcus evans
A CIO’s Perspective: Reconciling Risk Management with Disaster Recovery Tactics by Sanjay Verma, Global Business Process, IT Risk & Compliance Manager, Foster’s Group (SABMiller) at the Australian CIO Summit 2012
Disaster Recovery Planning: Best Practices, Templates, and ToolsZetta Inc
The document discusses best practices for building an effective disaster recovery (DR) plan. It recommends conducting a business impact analysis to identify critical applications and recovery objectives. A risk assessment should identify infrastructure dependencies and single points of failure to manage risks. DR plans must be tested to ensure they can meet recovery time and point objectives. The presentation then describes how the Zetta cloud backup and DR solution helps customers limit exposure to outages through geographic replication, high availability configurations, and browser-based recovery capabilities.
An Introduction to Disaster Recovery PlanningNEBizRecovery
This document provides an introduction to disaster recovery planning for businesses. It explains that a disaster recovery plan helps businesses anticipate, address, and mitigate the effects of a business disruption or disaster to return to normal operations. The plan has two main components: an emergency response plan to address immediate response, and a business continuity plan to address short and long-term continued performance of essential functions. Developing a disaster recovery plan can help reduce disruption, minimize chaos during an event, and protect a business, with the goal of keeping the business operational through a disaster.
Unit 3 - Storage & Retrieval of InformationRobbieA
Heather is tasked with assessing how information is stored at Caledonian Health & Fitness and identifying ways to improve it. Currently they use manual filing systems like sorters, trays, and cabinets. Some employees like the ease of use while others complain it is disorganized. Heather learns electronic systems offer alternatives like storing files on computers, disks, and CDs using software, scanning, and downloading. Backups are important to prevent data loss from technical issues or theft.
Unit 3 Storage And Retreival Of Informationiarthur
The document discusses different methods for storing and retrieving information, including:
1. The difference between internal and external mail, and examples of each.
2. Various filing methods like alphabetical, numerical, and chronological ordering. The advantages and disadvantages of each method.
3. Data storage methods such as floppy disks, hard drives, CDs, DVDs, and USB sticks. Microfilming is also discussed.
4. Security measures for data like passwords, ID cards, and physical locks. Backup procedures, computer viruses, and data encryption are also covered.
eFolder Partner Chat Webinar — How to Design a Business Continuity Plan for C...eFolder
Having the backup and disaster recovery conversation with your clients doesn’t have to be difficult or awkward.
In this eFolder Partner Chat, join Steven Thom, President and CEO of Thom Infotech, and Ted Hulsy, Vice President of Marketing at eFolder, as they discuss how to have a conversation about business continuity with clients and design a business continuity plan for them.
You’ll learn the different aspects of a business continuity plan that MSPs need to consider when approaching clients, how Steven made his clients understand the need for a BDR solution, why deploying BDR to his clients made Steven’s job significantly easier, and what resources eFolder provides partners who want to educate their clients on the risks of downtime.
A CIO’s Perspective: Reconciling Risk Management with Disaster Recovery Tacti...IT Network marcus evans
A CIO’s Perspective: Reconciling Risk Management with Disaster Recovery Tactics by Sanjay Verma, Global Business Process, IT Risk & Compliance Manager, Foster’s Group (SABMiller) at the Australian CIO Summit 2012
Disaster Recovery Planning: Best Practices, Templates, and ToolsZetta Inc
The document discusses best practices for building an effective disaster recovery (DR) plan. It recommends conducting a business impact analysis to identify critical applications and recovery objectives. A risk assessment should identify infrastructure dependencies and single points of failure to manage risks. DR plans must be tested to ensure they can meet recovery time and point objectives. The presentation then describes how the Zetta cloud backup and DR solution helps customers limit exposure to outages through geographic replication, high availability configurations, and browser-based recovery capabilities.
An Introduction to Disaster Recovery PlanningNEBizRecovery
This document provides an introduction to disaster recovery planning for businesses. It explains that a disaster recovery plan helps businesses anticipate, address, and mitigate the effects of a business disruption or disaster to return to normal operations. The plan has two main components: an emergency response plan to address immediate response, and a business continuity plan to address short and long-term continued performance of essential functions. Developing a disaster recovery plan can help reduce disruption, minimize chaos during an event, and protect a business, with the goal of keeping the business operational through a disaster.
This document discusses database systems and file-based systems. It defines key terms like data, information, knowledge, wisdom. It provides examples to illustrate database concepts like maintaining inventory and processing credit card transactions. It describes problems with traditional file-based systems when trying to cross-reference or analyze information across files. Finally, it uses a real estate example to demonstrate how a file-based system with separate departmental files worked and the role of data processing staff.
The document discusses database management systems and file-based systems. It describes some common uses of databases like tracking inventory and processing credit card transactions. It outlines the problems with traditional file-based systems, like the difficulty answering queries that require data from multiple files. A database management system aims to solve these problems by storing all related data together and allowing complex queries across multiple tables.
A database is a collection of logically related data sets or files. Each files may contain different type of information and are used for specific purposes.
Data mining involves extracting useful patterns and knowledge from large amounts of data. It is the process of discovering hidden patterns in large datasets. Key techniques of data mining include classification, clustering, association rule learning, and prediction. Data mining has various applications such as customer relationship management, fraud detection, market basket analysis, education, manufacturing, and healthcare. Knowledge discovery is the overall process of discovering useful knowledge from data, where data mining is one important step that analyzes and extracts patterns from data.
Data mining concept and methods for basicNivaTripathy2
This document provides an overview of data mining concepts and techniques. It discusses why data mining is useful given the massive amount of data being collected. Data mining involves extracting patterns from large datasets and can be used for applications like market analysis, risk analysis, and fraud detection. The document outlines the key steps in the knowledge discovery process including data preprocessing, data mining, and pattern evaluation. It also describes different types of patterns that can be mined, such as associations, classifications, and clusters. Factors that determine whether patterns are interesting to users are discussed. Finally, the document introduces the concept of a data mining query language to allow interactive exploration of patterns.
The document discusses databases and database management systems. It provides examples of common database applications like banking, universities, sales, and airlines. It defines what a database is, the role of a database management system, and examples of DBMS software. It also compares the advantages and disadvantages of using a database system versus a traditional file system to store data. Key benefits of a DBMS include supporting complex queries, controlling redundancy and consistency, handling concurrent access from multiple users, and providing security and data recovery.
The document discusses strategies for planning and resourcing digital archives and recordkeeping over the long term. It emphasizes understanding information needs, designing systems to support records, using open formats, applying metadata, managing migration, educating staff, and securing funding for projects through building support and linking to popular ideas. Free tools and resources are also mentioned.
Data science involves extracting knowledge and insights from structured, semi-structured, and unstructured data using scientific processes. It encompasses more than just data analysis. The data value chain describes the process of acquiring data and transforming it into useful information and insights. It involves data acquisition, analysis, curation, storage, and usage. There are three main types of data: structured data that follows a predefined model like databases, semi-structured data with some organization like JSON, and unstructured data like text without a clear model. Metadata provides additional context about data to help with analysis. Big data is characterized by its large volume, velocity, and variety that makes it difficult to process with traditional tools.
Combining Data Mining and Machine Learning for Effective User ProfilingCodePolitan
Slide presentasi ini dibawakan oleh Anne Regina pada Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDIO pada tanggal 14 Mei 2016.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
1) Database management systems were created to address issues with storing information in file processing systems, such as data redundancy, difficulty accessing data, data isolation, and integrity and security problems.
2) A DBMS allows for centralized control of data, consistent definitions and storage, reduced data redundancy, data independence from programs and applications, and support for multiple user access.
3) Some key applications of database systems include banking, airlines, universities, manufacturing, online retailers, and telecommunications.
APPLICATION OF COMPUTER IN PHARMACY.pdfNeelamparwar
The document discusses the application of computer in pharmacy. It covers several topics:
1. Computers are now integral to pharmacy for functions like database management, order entry, billing, purchasing, and dispensing.
2. The advantages of computer use in pharmacy include increased accuracy, reduced time and manpower needs, and ability to perform multitasking.
3. Computers are used in retail pharmacy for accounting, management, purchasing, inventory control, and drug information. They are also used in hospital pharmacy for patient record maintenance, purchasing, and inventory control.
This document provides an introduction to big data, including defining big data, discussing its history, importance, types, characteristics, how it works, challenges, technologies, and architecture. Big data is defined as extremely large and complex datasets that cannot be processed using traditional tools. It has existed for thousands of years but grew substantially in the 20th century. Companies use big data to improve operations and increase profits. The types include structured, semi-structured, and unstructured data. Big data works through data collection, storage, processing, analysis, and visualization. The challenges include rapid data growth, storage needs, unreliable data, and security issues. Technologies include those for operations and analytics. The architecture includes ingestion, batch processing, analytical storage
Business Analytics and Data mining.pdfssuser0413ec
Business analytics involves analyzing large amounts of data to discover patterns and make predictions. It uses techniques like data mining, predictive analytics, and statistical analysis. The goals are to help businesses make smarter decisions, identify trends, and improve performance. Data mining is the process of automatically discovering useful patterns from large data sets. It is used to extract knowledge from vast amounts of data that would otherwise be unknown. Data mining helps businesses gain insights from their data to increase sales, improve customer retention, and enhance brand experience.
This document provides an overview of key concepts in data science and big data, including:
- Data science involves extracting knowledge and insights from structured, semi-structured, and unstructured data.
- The data value chain describes the process of acquiring data, analyzing it, curating it for storage, and using it.
- Big data is characterized by its volume, velocity, variety, and veracity. Hadoop is an open-source framework that allows distributed processing of large datasets across computer clusters.
This document discusses the impact of automation and computers on manufacturing and jobs. It describes how industrial automation through CAD/CAM has reduced the need for human workers in factories while creating new technology jobs such as software programmers, network administrators, and call center professionals. While automation has eliminated many assembly line jobs, retraining displaced workers for skilled technology careers can help address unemployment issues. Overall, the text examines how the integration of information technology has transformed modern manufacturing.
Database system applications are used in many sectors including railway reservation systems, library management, banking, education, credit cards, social media, broadcasting, online accounts, online shopping, human resource management, manufacturing, and healthcare. They store information about tickets, books, financial transactions, students, credit card purchases, social connections, broadcast content, financial instruments, products, employees, inventory, and patient medical records. Database management systems provide functionality for data retrieval, manipulation, security, backup/recovery, multi-user access, and reporting/analysis.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This document discusses database systems and file-based systems. It defines key terms like data, information, knowledge, wisdom. It provides examples to illustrate database concepts like maintaining inventory and processing credit card transactions. It describes problems with traditional file-based systems when trying to cross-reference or analyze information across files. Finally, it uses a real estate example to demonstrate how a file-based system with separate departmental files worked and the role of data processing staff.
The document discusses database management systems and file-based systems. It describes some common uses of databases like tracking inventory and processing credit card transactions. It outlines the problems with traditional file-based systems, like the difficulty answering queries that require data from multiple files. A database management system aims to solve these problems by storing all related data together and allowing complex queries across multiple tables.
A database is a collection of logically related data sets or files. Each files may contain different type of information and are used for specific purposes.
Data mining involves extracting useful patterns and knowledge from large amounts of data. It is the process of discovering hidden patterns in large datasets. Key techniques of data mining include classification, clustering, association rule learning, and prediction. Data mining has various applications such as customer relationship management, fraud detection, market basket analysis, education, manufacturing, and healthcare. Knowledge discovery is the overall process of discovering useful knowledge from data, where data mining is one important step that analyzes and extracts patterns from data.
Data mining concept and methods for basicNivaTripathy2
This document provides an overview of data mining concepts and techniques. It discusses why data mining is useful given the massive amount of data being collected. Data mining involves extracting patterns from large datasets and can be used for applications like market analysis, risk analysis, and fraud detection. The document outlines the key steps in the knowledge discovery process including data preprocessing, data mining, and pattern evaluation. It also describes different types of patterns that can be mined, such as associations, classifications, and clusters. Factors that determine whether patterns are interesting to users are discussed. Finally, the document introduces the concept of a data mining query language to allow interactive exploration of patterns.
The document discusses databases and database management systems. It provides examples of common database applications like banking, universities, sales, and airlines. It defines what a database is, the role of a database management system, and examples of DBMS software. It also compares the advantages and disadvantages of using a database system versus a traditional file system to store data. Key benefits of a DBMS include supporting complex queries, controlling redundancy and consistency, handling concurrent access from multiple users, and providing security and data recovery.
The document discusses strategies for planning and resourcing digital archives and recordkeeping over the long term. It emphasizes understanding information needs, designing systems to support records, using open formats, applying metadata, managing migration, educating staff, and securing funding for projects through building support and linking to popular ideas. Free tools and resources are also mentioned.
Data science involves extracting knowledge and insights from structured, semi-structured, and unstructured data using scientific processes. It encompasses more than just data analysis. The data value chain describes the process of acquiring data and transforming it into useful information and insights. It involves data acquisition, analysis, curation, storage, and usage. There are three main types of data: structured data that follows a predefined model like databases, semi-structured data with some organization like JSON, and unstructured data like text without a clear model. Metadata provides additional context about data to help with analysis. Big data is characterized by its large volume, velocity, and variety that makes it difficult to process with traditional tools.
Combining Data Mining and Machine Learning for Effective User ProfilingCodePolitan
Slide presentasi ini dibawakan oleh Anne Regina pada Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDIO pada tanggal 14 Mei 2016.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
1) Database management systems were created to address issues with storing information in file processing systems, such as data redundancy, difficulty accessing data, data isolation, and integrity and security problems.
2) A DBMS allows for centralized control of data, consistent definitions and storage, reduced data redundancy, data independence from programs and applications, and support for multiple user access.
3) Some key applications of database systems include banking, airlines, universities, manufacturing, online retailers, and telecommunications.
APPLICATION OF COMPUTER IN PHARMACY.pdfNeelamparwar
The document discusses the application of computer in pharmacy. It covers several topics:
1. Computers are now integral to pharmacy for functions like database management, order entry, billing, purchasing, and dispensing.
2. The advantages of computer use in pharmacy include increased accuracy, reduced time and manpower needs, and ability to perform multitasking.
3. Computers are used in retail pharmacy for accounting, management, purchasing, inventory control, and drug information. They are also used in hospital pharmacy for patient record maintenance, purchasing, and inventory control.
This document provides an introduction to big data, including defining big data, discussing its history, importance, types, characteristics, how it works, challenges, technologies, and architecture. Big data is defined as extremely large and complex datasets that cannot be processed using traditional tools. It has existed for thousands of years but grew substantially in the 20th century. Companies use big data to improve operations and increase profits. The types include structured, semi-structured, and unstructured data. Big data works through data collection, storage, processing, analysis, and visualization. The challenges include rapid data growth, storage needs, unreliable data, and security issues. Technologies include those for operations and analytics. The architecture includes ingestion, batch processing, analytical storage
Business Analytics and Data mining.pdfssuser0413ec
Business analytics involves analyzing large amounts of data to discover patterns and make predictions. It uses techniques like data mining, predictive analytics, and statistical analysis. The goals are to help businesses make smarter decisions, identify trends, and improve performance. Data mining is the process of automatically discovering useful patterns from large data sets. It is used to extract knowledge from vast amounts of data that would otherwise be unknown. Data mining helps businesses gain insights from their data to increase sales, improve customer retention, and enhance brand experience.
This document provides an overview of key concepts in data science and big data, including:
- Data science involves extracting knowledge and insights from structured, semi-structured, and unstructured data.
- The data value chain describes the process of acquiring data, analyzing it, curating it for storage, and using it.
- Big data is characterized by its volume, velocity, variety, and veracity. Hadoop is an open-source framework that allows distributed processing of large datasets across computer clusters.
This document discusses the impact of automation and computers on manufacturing and jobs. It describes how industrial automation through CAD/CAM has reduced the need for human workers in factories while creating new technology jobs such as software programmers, network administrators, and call center professionals. While automation has eliminated many assembly line jobs, retraining displaced workers for skilled technology careers can help address unemployment issues. Overall, the text examines how the integration of information technology has transformed modern manufacturing.
Database system applications are used in many sectors including railway reservation systems, library management, banking, education, credit cards, social media, broadcasting, online accounts, online shopping, human resource management, manufacturing, and healthcare. They store information about tickets, books, financial transactions, students, credit card purchases, social connections, broadcast content, financial instruments, products, employees, inventory, and patient medical records. Database management systems provide functionality for data retrieval, manipulation, security, backup/recovery, multi-user access, and reporting/analysis.
Similar to Cel 2 Data Management (Form 3) dated 4.8.14 (20)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
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Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
2. Data Management
• All of us perform data
management in our daily lives.
Simple activities require data
management.
• Suppose you want to buy
clothes. Firstly, you recall what
clothes you already have
(retrieve data). Next, you
decide what type of matching
clothes to buy (process data)
3. Data management is work that involves the
following 4 parts
• Collecting
• Storing
• Retrieving
• Analysing
4. Data collection
• Data collection can be done by
input devices such as
• Barcode reader
• Card reader
• OMR reader
• MICR reader • For example, when you move in or out of a
country, your passport is used as a form of
identification.
• With new biometric passports, you will be
identified through other means such as
scanning your thumbprint. Such a system will
need to capture data concerning your
thumbprint.
6. Example 1
• As a pupil, records are kept of your personal details,
education history and examination results. This
makes it very efficient to retrieve and update your
details.
• For instance, if you had lost your examination
certificate, all you need to do is pay a small
administrative fee and have it reprinted in a very
short time.
7. Example 2
• Your school’s library may also make
use of data processing to manage
records.
• The details of the books in your
library and loan records are stored
in a database.
• This makes it very easy to perform
functions like checking if a book is
available or on loan.
Retrieving your
record alone
would probably
take hours.
8. • With a digital library system, you are able to use self-service counters
for borrowing and returning books.
9. IDENTITY THEFT
• Data processing has also brought
about new problems like identity
theft.
• For example, when you call a bank,
the bank officer identifies you by
asking for your identification number,
D.O.B. and address. So, anyone
having this information can call the
bank and pretend to be you. This is
known as identity theft.
10. What are the 4 parts of data management?
• Collecting
• Storing
• Retrieving
• Analysing
11. Name the input devices used to collect data
from the following:
• Barcodes of products
Answer: Barcode reader
• Student’s multiple-choice answers on OMR forms
Answer: OMR reader
12. What kind of data can a biometric passport
contain?
• Capturing data concerning your (scanning) thumbprint
13. How does a self-checkout system in a library
benefit customers?
• With a digital library system, you are able to use self-service
counters for borrowing and returning books.
14. What is identity theft?
• Identity theft is anyone having your personal information and
calls the bank and pretend to be you.
16. Data Storage
• The most popular method of storing data is using paper. However,
paper has advantages as well as disadvantages.
17. Data Storage
• ADVANTAGES
• paper does not need electricity to work,
• it is easily portable,
• inexpensive; and
• does not require special equipment to retrieve written data.
• DISADVANTAGES
• Paper is not good at storing large amounts of data,
• Not a good media for searching information,
• When you need to copy data, paper is not a good media to use,
• Paper also does not stand the test of time. Pages yellow over time
and will fall apart.
18. Digital media
• Allows for large-scale storage and
retrieval of data.
• The hard disk is a popular form of
digital media because of its high
speed, reliability and low cost.
• Data is usually stored in databases.
• Database management software is
used to manage databases. For
example, Web-based e-mails use
databases to store e-mails.
19. Databases
• Database is a collection of data that is organised so that its contents
can easily be accessed, managed and updated.
20. • Do you know that databases are widely used in
schools? We can use databases to store information
such as:
• Staff data
• Student records
• Library book catalogue
21. Microsoft Access is an example of a Database software.
• An electronic database is a
database saved in a
computer. It is very
important as it allows data to
be stored and retrieved
quickly, reliably and securely.
• It can be used by companies
to derive useful information
about their business.
22. Data Value
• Data is valuable to many organisations. Some people
spend a lot of time looking at data and trying to make
sense of data.
• Data is valuable on 2 levels:
• Valuable inferences and predictions can be made from
data through data analysis.
• Data may be valuable to other people
for dishonest purposes. This include:
• Credit card numbers
• Social security numbers
• Other personal details
Social Security Number (SSN):
A nine-digit number issued to US
citizens, permanent and temporary
(working) residents.
Purpose: to keep track of individuals
for social security purpose.