INFOSYS 110 - Notes PDF

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Note for INFOSYS 110 Semester one 2021...

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Final Exam

Focusing on 6 Main topics (But other topics might be included too!) 1. A “Digital First” World 2. TPS, DSS, Database 3. Data Analytics 4. Data Visualization 5. Project Management Tools 6. Security & Ethics

**Module 1: A “Digital First” World

Using pen and paper

Using computer and print out if need be

we’re living in a “digital first” world transform from traditional data recording to digital data recording What are “Digital Systems”? = Information Technology that Captures Analyses Outputs digital data/information • Data captured digitally = Data collection • Info Technologies (Data Analyses) = Processing / Summarization and other advance analysis technics then transform info to Digital information Output • Digital information Output = Getting valuable information from data analysis Example: At Supermarket... • Info Tech captures customer personal information and their shopping history Then, Info Tech (system itself) will summarize and process those data (analyze) those data and try to find relationships among them • Finally, it will spit out report (output) that may highlight special spending / shopping patterns of customers who are in a certain age group **Using the digital information output, the supermarket can better manage their promotion / marketing / sale processes •

Pros and Cons? (Why Digital? – Digital makes things a lot easier!) 5 Benefits

5 Challenges

1. Digital data/info is easier to store/maintain/ transport

1. Stored data/info can be easily copied by anyone who gained access

2. Digital data/info is easier to share and make a copy

2. Personal or sensitive data can easily be shared

3. Exact copies can be made easily with consistency 4. Digital resources can be accessed by multiple users at once 5. Digital data/info can easily be encrypted for protection (password lock)

3. Digital media can be easily edited and modified; therefore, integrity of contents becomes a concern 4. It is very easy to copy digital data/info. Piracy and plagiarism become much easier 5. Since it is so easy to store data/info digitally, a flood of stored media can become difficult to organize for easy access

Module 2: Data Governance What is “Data Governance”? = A set of policies that is designed for collecting data (these following three aspects) Three things that Data Governance manages: • Data Collection – E.g. At supermarket, barcode scanner scanning barcodes • Data Handling = How the collected data is stored and processed • Data Protecting = A set of policy that is about protecting data, such as how it would be stored or used **Data Governance is important since managing data these days is quite difficult Five Key challenges about data management 1. Flood of data – for example the photos on smartphone, supermarket data collected - Easy to collect, handling and protecting is hard! Data Governance policies needed: - Collection policies = only collect necessary data/photos - Handling policies = Keep them organized, easy to recall - Protection policies = Access control, usage control **Related GDPR Principles: Principle 3,6 2. Data Compatibility among departments and organization - Everyone has their unique way to collect data, so when they start working together, it may cause data incompatibility - How is it a compatibility problem? – not being able to take data from one source to the destination directly - How data Governance help? – Ensure compatibility **The most relevant GDPR Principle is Accuracy 3. Rapid development of new forms of data Three new forms of useful data that have recently emerged: - Data from social media [collect Reactions, Comments, Posts from customers] → Data collection policy = Principle 3 – Data minimization → Data handling = Principle 2,4,5 – Purpose Limitation, Accuracy, Storage Limitation → Data Protecting = Principle 2,6 – Purpose Limitation, Integrity and Confidentiality - Wearables and Tracking data → Relevant GDPR Principles: Principle 2,3,4 – Purpose Limitation, Data minimization, Accuracy - Satellite data → Relevant GDPR principles: Principle 3,6 – Data minimization, Integrity and Confidentiality

4. Data Obsolescence - Make sure that our data is up to date and still correct / useful **The most relevant GDPR Principles are Accuracy and Storage Limitation 5. Data Security - Data that needs to be kept secure – Personal details, Item purchased - Why do they need to be kept secure? - Privacy - Who in the organization will need access? – Marketing Dept, Procurement Dept **The most relevant GDPR Principles are Integrity & Confidentiality and Purpose Limitation General Data Protection Regulations

GDPR Principles for Data Management 1. Lawfulness, Fairness, and Transparency - Collecting data legally (must be conforming to the legal requirements) - Inform and don’t cheat the source of data - Be honest about data collection and usage 2. Purpose Limitation - Be clear about the purpose of the data collected - Stick to the promise (that you made with the source of data) 3. Data minimization (related to purpose limitation) - Only collect the necessary data - E.g. Reserving a table at restaurant, only collect customer’s name, phone number, number of people in the party, and email for confirmation. 4. Accuracy - Make sure that the data collected remains accurate and up to date 5. Storage Limitation - Be specified how long the data collected would be kept / who can access - Delete all data collected after the retention period 6. Integrity and Confidentiality - This principle is all about privacy issues and security issues - All collected data must be kept safe 7. Accountability - Not directly related to Data Governance - It is related to the policies and data collection/handling which should be documented according to the regulations

Module 3: Privacy (ComLaw) Protecting information generally Information can be protected by: • Intellectual Property Law e.g. copyright (Not focus on this course) • •

Contractual agreements Duty of Confidentiality in other limited circumstances E.g. Promise not to tell secret to anyone else (but should be legal) **Privacy Information will end when the public danger begins Information Privacy Principles (IPPs)

Principle 1 “You can only collect personal information if it is for a lawful purpose and the information is necessary for that purpose” • Example Violation of Principle 1: In addition to their phone number (for its loyalty scheme) a clothing retailer asks customers for details about their income level. Principle 2 “You should generally collect personal information directly from the person it is about” • Example Violation of Principle 2: A retailer asks customers to provide the name and email of a friend or family member so the customer can go into a draw to win a prize. Principle 3 “When you collect personal information, you must take reasonable steps to make sure that the person knows: o Why it is being collected o Who will receive it? o Compulsory/Voluntary to give it? o What will happen if the information is not given” • Example Violation of Principle 3: A University intends to film and/or photograph attendees at a seminar/conference. The purposes are only to advertise future events as well as publicise past events. The footage will not be shared with anyone else. Principle 4 “You may only collect personal information in ways that are lawful, fair and not unreasonably intrusive” • Example Violation of Principle 4: A clothing retailer installs hidden cameras in its changing rooms Principle 5 “You must make sure that there are reasonable security safeguards in place to prevent loss, misuse or disclosure of personal information” • Example Violation of Principle 5: The Government agency responsible for the managed isolation facilities sends unencrypted and non-password protected spreadsheets containing details of those who have tested positive for Covid-19 to another agency.

Principle 6 “People have a right to ask you for access to their personal information” • Example Violation of Principle 6: A student asks a University for all information it holds concerning their application for a scholarship. Principle 7 “A person has a right to ask an organisation or business or business to correct their information if they think it is wrong” • Example Violation of Principle 7: A customer who is incorrectly billed requests a correction be made to their account status or for it to be recorded that the amount is disputed Principle 8 “Before using or disclosing personal information, you must take reasonable steps to check it is accurate, complete, relevant, up to date and not misleading” • Example Violation of Principle 8: An organisation uses an out of date or inaccurate CV when hiring or promoting someone (e.g. they now have a qualification it fails to record). Principle 9 “Personal information must not be kept for longer than reasonably necessary” • Example Violation of Principle 9: A retailer holds a competition for which entrants are asked to send their details. They are not told about anything else their details would be used for. However, after the competition ends the details are still stored so that promotional material can be sent to entrants on the competition’s anniversary. Principle 10 “Personal information can only be used for the purposes it is collected for Exceptions: (a) box ticked saying OK to receive info about products etc. (b) told will receive information from that company or other companies unless box ticked– and didn’t (c) somewhere on the form says that will be sent info” • Example Violation of Principle 10: Information is collected for a competition an individual enters and they do not tick a box saying they agree to receive information about products from the company. Information is sent anyway. Principle 11 “Personal information held by an organization cannot be disclosed unless this was a purpose it was collected for (see principle 3)” • Example Violation of Principle 11: A University gives contact details for its students to a company looking for graduates (students had not been advised their details would be used for this purpose). Principle 12 “You can only send personal information to someone overseas if the information will be adequately protected” • Example Violation of Principle 12: An insurance company needs to send details of its customers’ circumstances (risk profile, health, property ownership etc) to an overseas company it has links with for the purpose of re-insurance/underwriting a policy. Principle 13 “An organization cannot assign the same unique identifier that another organization has assigned to the individual” • Example Violation of Principle 13: A courier company decides to use its drivers’ licence numbers as their staff ID numbers.

Example – Using ILAC to answer a short problem-type question X, a bank worker, prints out a number of documents relating to a customer including her bank account details, X leaves the documents on a desk and another customer Z picks up the documents. Has the Privacy Act been breached? First, when answering a question, never give the answer at the beginning (the answer is the conclusion!) X leaves the documents including a customer’s personal information on a desk. Section 5 of the Privacy Act states that reasonable security safeguards must be put in place to prevent loss, misuse or disclosure of personal information. Leaving a document on a desk is not considered safe, therefore it is not safe enough in accordance to Principle 5. This is a clear breach of Principle 5 as the documents were lost and therefore disclosed to Customer Z who picked up the documents.

**Module 4: Intro to Databases Example data collected daily (Data --> Storing --> Retrieving) • • •

Photos --> Photos App --> Search by tags/faces Note taken in class --> File System on Device --> Folders/Search by file names Phone number --> Contacts App --> Search by names Data bases in Organisation

Data collected at supermarket - Customer Name - Item purchased - Amount charged Information generated for supermarket - Customer’s shopping pattern - Customer’s monthly spending - Recommendations for customer

Garbage In Garbage Out (GIGO) (Putting Garbage into system, the outcome would be Garbage as well) Example of GIGO From time to time, errors may occur in calculations of assessment marks. For example, a test on Canvas may have been set to be out of 100, when the actual test is out of 50 marks. When a marker posts a student’s raw mark (e.g. 48 out of 50) onto Canvas, instead of showing 96%, it would’ve shown 48% instead. The student will get a failing grade, instead of an excellent grade! Using the dimension of quality data/info (to minimise the impact of GIGO) 1. Completeness = Having 100% of all the expected data/info 2. Uniqueness = The data is only recorded once 3. Timeliness = The data represents the reality at the specific point in time and is available at the required time 4. Validity = Data conforms to the defined syntax (format, type, range) 5. Accuracy = The data correctly records reality 6. Consistency = All occurrences of the same data should show no differences

**Module 5: TPS & DSS

→ TPSs = Transaction Processing Systems – for capturing useful data from transaction → DSSs = Decision Support Systems – for analyzing data to generate useful information that supports decision-making Example TPS: Online Booking/Reserving systems • Capturing Customer Personal Information Example DSS: Algorithm used by a platform (Netflix and YouTube) • recommending shows/videos that user may be interested in, based on viewing history

Example Potential Business Intelligence: • Customer’s potential preferred items Example Resulting Competitive Advantage: • Knowing Customer’s potential preferred items gives the business a competitive advantage because the business could provide better promotion and marketing for customers Three Basic DSS data analysis techniques 1. What-if Analysis: If we invest more, what would be the impact 2. Goal-seeking Analysis: If we want to generate more profit, what should we do 3. Data Mining: Predicting trends and behaviours, Identifying previously unknown pattern Mini case: Air New Zealand uses BI to gain insight into popular holiday destinations which affect the current flight listings. The airline gathers both primary and secondary data to explore such trends. The data includes basic demographic items such as gender, age and income levels as these are factors which affect the places people decide to travel to. Additionally, the data is collected through questionnaires and surveys, as well as the qualitative and quantitative data obtained from observing the places and destinations people commonly search up or 'like’ and comments on social media. This can be done through making targeted posts and polls of different places (e.g Queenstown or London) on platforms such as Facebook or Instagram. Air New Zealand could also make posts that are more specific and targeted to groups by promoting suitable deals for the respective demographics (e.g cheap student flights or Around-the-World flights) and then see which posts get more likes and comments. The BI project allows the airline to better understand the holiday preferences of people from different demographics and socioeconomic statuses, and the likely holiday destinations and flights people book. Their approach also supports their successful marketing campaigns in targeted demographics (with suitable promotions for suitable groups – students, couples). As a result, the visibility, sustainability, and strategic directions of the airline's operations are promoted and maintained. Data mining could be used to carry out the analysis and generate business intelligence, which would help the business gain a competitive advantage. People would be more likely to travel with Air New Zealand as they would be promoting the most popular destinations people want to travel to. This process searches for valuable business information in a large database, data warehouse, or data mart. The results help predict the trends and behaviours of people and analyse the popular holiday destinations. It also can identify previously unknown patterns, which further increase the opportunity of Air New Zealand.

Module 6: Collaboration & Enterprise Systems Three forms of collaboration 1. Communication – Information transferred from one party to another 2. Cooperation – Multiple parties work together in order to achieve an objective 3. Coordination – sequential tasks carried out by multiple parties are managed (may begin when the preceding task is complete)

**Module 7: Business Analytics Adding Value to Data/Info with Analysis (From low difficulty, value -> high difficulty, value) 1. Descriptive Analytics: An analysis of what happened in the past? 2. Predictive Analytics: An estimation/prediction of what will happen in the future. 3. Prescriptive Analytics: An analysis of actions should be taken in order to achieve the goal Data Mining Segmentation: Cluster Analysis • Cluster analysis is a technique to group similar observations into a number of clusters based on the observed values of several variables for each individual • Example: Category Names High achieving students Passing

Failing

Nature of Students Assistance to be Offered Engaging in class and ask questions. Being prepared to answer the questions of these students. Barely attend classes, and not so Make the students more focus in engage in class. classes by let them answer questions. Not attending classes, and have not Sending email and warns them to done any quizzes. attend classes.

What goes together: Association Detection • Reveals the degree to which variables are related and the nature and frequency of these relationships in the information •

Example: The baby and beer are NOT related

**Module 8: Data Visualisation Reasons for creating data visualisations • • • •

Record Information Explore data Prove hypotheses Communicate ideas Tufte’s Design Principles

1. The visualization should tell a story 2. The visualization should have integrity, need to be consistent 3. The visualization should minimize graphical complexity Example: Your group’s evaluation of this graph, according to the Principles –Explain your evaluation. 1. Does tell a story of the Total Revenue of each month for 2017, however could have used a trend or line graph to portray the story better 2. Does not have integrity, as the proportion is misleading. The 13%sector seems larger than the 15% one. 3. It is 3D, which messed up all the perspective of each slice and make it misleading. The colour is quite similar, it is hard to distinguish.

**Module 9: Project Management Tools Project Management Process

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Initiation: Determines the nature and scope of the project. Planning: Plan time, cost, and resources adequately to estimate the work needed and to effectively manage risk during project execution. Execution: Involves coordinating people and resources, as well as integrating and performing the activities of the project in accordance with the project management plan. Monitoring and controlling: Consists of those processes performed to observe project execution so that potential problems can be identified in a timely manner and corrective action can be taken, when necessary, to control the execution of the project. Closing: Includes the formal acceptance of the project and the ending thereof. ** The Project Management Triangle **

These all three constraints of time, scope, and cost are interconnected, and any changes in one will affect the other two.

For example: • • •

If you increase the scope of a project (e.g., add more features), you’ll have to also increase the cost or push back the deadline. If you decrease the project’s budget, you’ll have to decrease the scope or extend the delivery deadline. If the project’s deadline is non-negotiable, then you’ll have to either descope...