COMM190 - Bus Tech Final Exam

Post Midterm - Week 6: Dashboards

Dashboards - what and why?

• WHAT is a dashboard?

  • a dashboard is a tool used to visually display data to gain deeper insight into the overall well-being of the organization, a department, or even a specific process

• WHY are dashboards important?

  • connecting dashboards to specific metrics or key performance indicators (KPIs), you gain vital business intelligence and the ability to dive deep into specific pieces of information to continually monitor success

• WHAT can you do with a dashboard?

  • provide up-to-date information and context to help inform business decisions and empower employees

    • performance measurements

    • data transparency and accessibility

    • detect changes

    • forecasting

• WHAT are the benefits of a dashboard?

4 types of data analytics

what is the data telling you?

from least to most valuable and complex

descriptive: whats happening in my business

• comprehensive, accurate and live data

• effective visualization

diagnostic: why is it happening?

• ability to drill down to the root-cause

• ability to isolate all confounding information

predictive: what’s likely to happen

• business strategies have remained fairly consistent over time

• historical patterns being used to predict specific outcomes using algorithms

• decisions are automated using algorithms and technology

prescriptive: what do I need to do?

• recommend actions and strategies based on champion/challenger testing strategy outcomes

• applying advanced analytical techniques to make specific recommendations

Data Cube

Dashboard Visual

Dashboards with AI

• manus AI example

• can also create dashboards with other AI tools

• Python is a great programming language for creating visualizations

Building a Dashboard in Excel: Pivot Table Components

Week 7: Artificial Intelligence

“because of AI, 100% of jobs will be different”

• IBM CEO Ginni Rometty (2019)

“AI is the new electricity. It will transform every industry and create huge economic value”

• Dr. Andrew Ng

“The development of full AI could spell the end of the human race”

• Stephen Hawking

“AI is highly likely to destroy humans”

• Elon Musk

“Elon Musk’s doomsday AI predictions are ‘pretty irresponsible'"

• Mark Zuckerberg

reactive maintenance vs predictive maintenance

(not on slides)

• Reactive Maintenance: This approach follows a "run-to-failure" logic, where repairs only happen after a machine breaks down, leading to high costs from unplanned downtime and emergency fixes.

• Predictive Maintenance (AI): Using IoT sensors and machine learning, this method analyzes real-time data like vibration and heat to identify failure patterns and perform maintenance just before a breakdown occurs.

• Strategic Impact: While predictive maintenance requires more upfront investment in technology, it significantly extends asset life and reduces long-term operational expenses by transforming "surprises" into scheduled, minor tasks.

Robotic Process Automation

• robotic process automation is a form of business process automation that allows anyone to define a set of instructions for a robot or ‘bot’ to perform

• these bots are capable of mimicking most human-computer interactions to carry out a ton of error-free tasks, at high volume and speed

• the “robot” in robotic process automation is software robots running on a physical or virtual machine

In the beginning - Dartmouth College (1956)

The 1956 Dartmouth Summer Research Project on Artificial Intelligence established AI as a formal academic field. Organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, it provided the discipline with its name and foundational goals.

Highlights

• The Goal: The organizers believed that every aspect of learning or intelligence could be precisely described and simulated by a machine.

• The Name: John McCarthy coined "Artificial Intelligence" to distinguish the field from cybernetics and computer science.

• The Breakthrough: Allen Newell and Herbert Simon introduced the "Logic Theorist," the first program designed to mimic human problem-solving skills.

• The Impact: Though the workshop did not achieve immediate human-level AI, it shifted computing from simple number crunching toward symbolic logic and language.

Quick Stats

• Location: Dartmouth College, Hanover, New Hampshire.

• Funding: A $7,500 grant from the Rockefeller Foundation.

• Duration: Eight weeks during the summer of 1956.

From slide:

• this conference is “to proceed on the basis of the conjecture that every aspect of learning of any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”

  • Professor John McCarthy, 1956 (convenor of the AI meeting)

What is Artificial Intelligence?

• there is much debate surrounding what is artificial intelligence

  • some feel that it is acting and thinking like a human, while others feel it is to act and think rationally

• think humanly, act humanly

• think rationally, act rationally

What is AI: Act Humanly

What does it mean to act like a human?

Need to be able to…

• natural language processing

  • communicate successfully in a human language

• knowledge representation

  • store what it knows or hears

• automated reasoning

  • answer questions and to draw new conclusions

• machine learning

  • adapt to new circumstances and to detect and extrapolate patterns

• computer vision and speech recognition

  • vision and speech recognition to perceive the world

• robotics

  • manipulate objects and move about

What is AI: Think Humanly

What does it mean to think like a human?

• to say that a program thinks like a human, we must know how humans think

  • we can learn about human thought in 3 ways:

1. introspection

• trying to catch our own thoughts

2. psychological experiments

• observing a person in action

3. brain imaging

• observing the brain in action

once we have enough information, we have a working model

• if the program’s input-output matches corresponding human behaviour, we have evidence of human thinking

What is AI: Act Rationally

What does it mean to act rationally?

• This is the field of logical thinking that many philosophers and mathematicians have been working on

• In artificial intelligence, it is called Logicism, which hopes to build intelligent systems

• Though the systems are intelligent, it does not generate intelligent behaviour

What is AI: Think Rationally

What does it mean to think rationally?

• It is called the Rational Agent Approach

• Computer programs are expected to do something, but agents are expected to do more;

  • operate autonomously

  • perceive the environment

  • persist over a prolonged time

  • adapt to change

  • create and pursue goals

Artificial Intelligence: Broad

• AI, in the broadest term, applies to any technique that enables computers to mimic human intelligence, using logic, if-then rules, decision trees and machine learning

  • Time Magazine, 2017

Machine Learning

• Machine learning is a method of data analysis that automates analytical model building

• It is a branch of AI based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention

  • SAS (2017)

Neural Networks

• software constructions modelled after the way adaptable networks of neurons in the brain are understood to work, rather than through rigid instructions predetermined by humans

  

A simple neuron

• a perception takes several binary inputs (x1, x2, x3), and generates an output

• weights (w1, w2, w3), representing the importance of each input is identified

• the neuron’s output is 0 or 1

• the neuron’s output is determined by whether the weighted sum (w1×1 + w2×2 + w3×3) is greater than some threshold value

Types of machine learning

supervised learning

• a model uses known input and outputs to generalize future outputs

unsupervised learning

• the model doesn’t know input or outputs, so it finds patterns in the data without help

reinforcement learning

• where the model interacts with its environment and learns to take actions that will maximize rewards

Supervised learning

• Learn by identifying patterns in data that is already labelled

• Classification:

  • fraud detection

  • image recognition

  • customer retention

  • medical diagnostics

  • personalized advertising

• Regression:

  • product sales prediction

  • weather forecasting

  • market forecasting

  • population growth prediction

Unsupervised learning

• The machine must uncover and create the labels itself

• Clustering:

  • product recommendations

  • customer segmentation

  • targeted marketing

  • medical diagnostics

• Dimensionality reduction:

  • visualization

  • natural language processing

  • data structure discovery

  • gene sequencing

    

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machine learning examples

a tale of two games

• chess

• GO

Chess

• IBM Deep Blue

• vs

• Garry Kasparov, May 11, 1997

GO

• DeepMind AlphaGo

• vs

• Fan Hui (Go), 2015

IBM’s Deep Blue was programmed with decision trees or equations on how to evaluate board positions of with if-then rules

AlphaGo learned how to play Go essentially from self-play and from observing big professional games

WHY NOW? - machine learning

• increased availability: in volume, velocity, and variety of data

• as of 2025, the world generates approximately 463 exabytes of data daily, which is equivalent to 463 million terabytes

• to put this in perspective, this data generation is comparable to the storage capacity of about 212 million DVDs

• this exponential growth in data creation is driven by factors such as increased internet usage, the proliferation of Internet of Things (IoT) devices, and the expansion of cloud-based services

• significant improvements in computing power and storage (at affordable cost)

computing power improvements

• 1970s-1980s

  • early personal computers, like the Apple II (1977), operated at speeds around 1 MHz (million instructions per second)

  • limited multitasking capabilities; computations took hours or days

• 1990s-2000s

  • pentium processors (1993) reached speeds around 100 MHz - 1 GHz

  • performance improvements enabled graphical interfaces, gaming, and productivity software

• 2000s-2010s

  • multi-core processors emerged, allowing parallel processing

  • typical CPU speeds increased significantly (2-3 GHz per core became common)

  • GPUs (Graphics Processing Units) became popular for complex computations (AI, rendering, gaming)

• 2010s-today

  • high-performance GPUs and specialized AI hardware (ex// NVIDIA GPUs and TPUs) now deliver petaflops of computing power

  • modern GPUs (like NVIDIA A100 or H100) handle thousands of parallel processes, vastly accelerating AI tasks

Storage Capacity and Cost Improvements

Current Snapshot (2025):

• the ability to store massive data at affordable costs has transformed how businesses and individuals operate

• affordable SSDs (NVMe) offering storage at roughly $0.05-$0.10 per GB

• cloud providers (AWS, Azure, Google) offering virtually unlimited storage at declining prices

Big Data

• extremely large data sets that are used for computational analysis, many for neural networks to reveal patterns or trends

  • Time Magazine

Computer Vision

• ability of computers to “see” and understand images and videos, just like humans do

• this technology helps computers identify objects, people, text, or even actions in images and videos

• its used in things like:

  • face recognition (unlocking your phone with your face)

  • self-driving cars (helping the car see the road and avoid obstacles)

  • checking out groceries at a store without scanning each item (the camera sees and recognizes them)

• its like teaching a computer how to see and understand the world visually

Natural Language Processing (NLP)

• capabilities within AI for a computer to interpret written or spoken language, and respond in kind

• NLP enables you to create software that can:

  • analyze and interpret text in documents, email messages, and other sources

  • interpret spoken language, and synthesize speech responses

  • automatically translate spoken or written phrases between languages

  • interpret commands and determine appropriate actions

    • Microsoft (2024)

GPT

• GPT stands for Generative Pre-trained Transformer, a type of advanced artificial intelligence model developed by OpenAI

• its designed to understand and generate human-like text based on input it receives

1. Generative

• it creates or generates content, such as answers to questions, stories, or summaries, rather than just analyzing or recognizing patterns

2. Pre-trained

• the model is trained on a cast amount of data (like books, websites, and articles) before being fine-tuned for specific tasks

• this pre-training allows it to “understand” language and context

3. Transformer

• the architecture of GPT is based on a technology called the Transformer, which is excellent at processing sequences of words and understanding relationships between them

• its the reason GPT is so good at tasks like conversation and text generation

what is generative AI

• Generative AI refers to a class of artificial intelligence systems designed to generate new content or data that is similar to what humans might produce

• These systems are capable of creating original content, such as text, images, audio, and even video, based on patterns and examples learned from large datasets

Large Language Model (LLM)

• An LLM, or Large Language Model, is a kind of super-smart computer program designed to understand and generate human language

• It’s like a virtual assistant that can read, write, and even answer questions in a way that feels natural

  • Trained on lots of text: LLMs learn from huge amounts of text, like books, websites, and articles, so they can understand the way people communicate

  • Generates responses: when you ask it a question or give it instructions, it uses what it learned to create a response that makes sense

• Popular examples include ChatGPT and Claude

• They’re used in chatbots, virtual assistants, and tools that help people with writing, customer service, or research

Tokens

• tokens are like the building blocks of language for LLMS

• a token is a piece of text that LLMs use as the smallest unit of understanding

• It can be:

  • a word: “cat”

  • part of a word: “walk” from “walking”

  • a single character: “$” or “.”

  • or even a space

• for example:

  • the sentence “Hello, world!” might break into tokens like:

    • [“Hello”, “,”, “world”, “!”]

Tokens and LLMs

• input and output:

  • when you type something into an LLM, the text is broken into tokens

  • the model processes these tokens, understands their relationships, and generates tokens as an output (ex// a response or prediction)

• training and learning:

  • LLMs are traiend on billions (or even trillions) of tokens

  • these tokens are derived from vast datasets like books, websites, and articles

  • by seeing patterns in token sequences, the model learns grammar, context, and meaning

• efficiency:

  • breaking text into tokens makes it easier for LLMs to process language

  • it allows the model to handle partial words, special symbols, or even languages with different stuctures

Tokens and GPTS

• Token limits: modern GPT models have varying limits on the number of tokens they can process in a single interaction:

• GPT-5 Context window: ~196,000-256,000 tokens (150k-200k words) of text in a single chat session

  • this is the amount of text GPT-5 can remember and reference at once in a conversation

• Cost and usage: API usage of GPT models is priced based on the total number of tokens processed, counting both input prompts and generated outputs

  • longer prompts and responses consume more tokens, increasing costs

• Prediction method: GPT models generate text incrementally, one token at a time

  • for example

    • user prompt: What is the capital of France?

    • GPT model output tokens sequentially as:

      • [“The”, “capital”, “of”, “France”, “is”, “Paris”, “.”]

Tokens & Context Window

• Token Limit and Context Windows:

  • token limit = max tokens per interaction (prompt + response)

  • Also called “context window”

• What happens after reaching the limit?

  • limit resets with new interactions

  • context doesn’t carry over automatically

  • provide summaries or key points to reconnect the context between sessions

What is prompt engineering?

• prompt engineering is a technique used in natural language processing (NLP) and machine learning to fine-tune or guide the behavior of AI language models, such as GPT (Generative Pre-trained Transformer) models

• it involves designing and crafting specific prompts or instructions that are provided to the model to achieve desired responses or outcomes

• the goal of prompt engineering is to influence the AI model’s generation in a way that produces more accurate, relevant, and context-aware responses

Training responsibility?

• “800 million workers globally could be displaced by robotic automation by 2030”

  • McKinsey & Company

Canada: Data Privacy and AI Regulation

• Personal Information Protection and Electronic Documents Act (PIPEDA):

  • This federal law governs how private-sector organizations collect, use, and disclose personal information during commercial activities. It grants individuals rights to access and correct their personal data and mandates organizations to obtain consent for data handling.

• Artificial Intelligence and Data Act (AIDA):

  • Introduced as part of Bill C27, the Digital Charter Implementation Act, 2022, AIDA aims to establish a legal framework for the responsible development and deployment of AI systems in Canada. It focuses on mitigating risks associated with highimpact AI systems, including bias and potential harm to individuals. AIDA was proposed under Bill C-27 but has not been enacted.

• Provincial Privacy Laws:

  • Certain provinces have enacted their own privacy legislation deemed substantially similar to PIPEDA

• Privacy Act:

  • This federal statute regulates how government institutions handle personal information, ensuring transparency and accountability in public sector data management.

• Voluntary Code of Conduct for Generative AI:

  • In September 2023, Canada launched a voluntary code of conduct for organizations developing advanced generative AI systems. This initiative encourages adherence to principles of transparency, accountability, and safety in AI development.

Europe: Data Privacy & AI Regulation

• General Data Protection Regulation (GDPR):

  • Enforced since May 25, 2018, the GDPR is a robust regulation that governs the processing of personal data across the European Union (EU). It grants individuals significant rights over their personal information and imposes stringent obligations on organizations handling such data. The GDPR applies to all entities processing personal data of EU residents, regardless of the organization's location.

• Artificial Intelligence Act (AI Act):

  • Effective from August 1, 2024, the AI Act is the world's first comprehensive legal framework for AI. It adopts a risk-based approach, categorizing AI systems into different risk levels and imposing corresponding obligations. High-risk AI systems are subject to strict requirements, including transparency, accountability, and human oversight, to ensure they do not compromise safety or fundamental rights.

USA: AI Regulation (before trump)

• Executive Order on AI:

  • On October 30, 2023, President Biden issued an Executive Order aimed at ensuring the safe, secure, and trustworthy development and use of AI. This order outlines principles for AI deployment, emphasizing safety, equity, and civil rights, and directs federal agencies to establish guidelines and standards for AI technologies.

• AI Bill of Rights:

  • In October 2022, the White House Office of Science and Technology Policy released the "Blueprint for an AI Bill of Rights," which provides guidelines to protect individuals from potential harms associated with AI systems. The blueprint outlines five principles: Safe and Effective Systems, Algorithmic Discrimination Protections, Data Privacy, Notice and Explanation, and Human Alternatives, Consideration, and Fallback.

USA: Executive Orders and Federal AI Policy Shifts (2025)

• Rescinding Biden’s AI Order:

  • Immediately upon taking office, President Trump revoked former President Biden’s sweeping October 2023 executive order on AI.

    • The Trump administration, viewing these measures as overregulation, repealed the order as “hindering AI innovation”.

• “Removing Barriers” Executive Order:

  • On January 23, 2025, President Trump issued a new AI executive order, EO 14179 “Removing Barriers to American Leadership in Artificial Intelligence.” This order proclaims it U.S. policy to “sustain and enhance America’s global AI dominance” for economic and national security benefit.

    • It represents a significant shift toward deregulation and rapid innovation.

• Focus on AI Infrastructure:

  • The Trump White House has also emphasized domestic AI infrastructure and deployment.

Quote

“The ability to think outside the box, to adapt, and to think about things from unconventional perspectives are exactly what future employers are looking for. ” – Globe and Mail, 2018

Data Analytics Literacy Skills

• the ability to understand how to make better decisions using qualitative and quantitative data (in addition to experiential knowledge)

AI LIteracy Skills

• the ability to understand what AI is all about and how AI applications can be best leveraged within an organization (while taking into consideration the potential for bias)

• The biggest threat in the AI era!

Week 8: Cloud Technologies

Course Framework

• Focus: Technology

Technology Trends

SOME of the trending technologies in 2026:

1. AI & GenAI

2. Autonomous AI Agents

3. Cloud Computing

4. Internet of Things (IoT)

5. Quantum Computing

6. Extended Reality (XR) - combination of VR, AR, MR

7. Robotics & Automation

8. Cybersecurity

9. Edge Computing

10. Blockchain & Web3

Top 10 Trending Technologies in 2026: 1. AI & GenAI

• AI continues to dominate technology trends

• Examples:

  • ChatGPT-like AI assistants

  • AI copilots for coding, marketing, and finance

  • AI agents that automate tasks

  • AI-generated images, video, and music

• Impact:

  • Automation, productivity, decision-making, and new business models

Top 10 Trending Technologies in 2026: 2. Autonomous AI Agents

• The next step beyond chatbots

• Examples:

  • AI that can plan tasks

  • AI that completes workflows automatically

  • AI assistance managing emails, research, and scheduling

• Impact:

  • Knowledge work automation

Top 10 Trending Technologies in 2026: 3. Cloud Computing

• Still one of the most important technologies

• Examples:

  • AWS

  • Microsoft Azure

  • Google Cloud

• Trends within cloud:

  • AI infrastructure

  • Serverless computing

  • Multi-cloud strategies

Top 10 Trending Technologies in 2026: 4. Internet of Things (IoT)

• Smart devices (tangible) connected through the internet

• Examples:

  • Smart homes

  • Industrial sensors

  • Smart sensors

  • Smart cities

  • Connected cars

• Impact:

  • Real-time data collection and automation

• What are Smart Devices?

  • Electronic devices that can connect to the internet, collect data, and interact with users or other devices automatically

  • They usually have:

    • Sensors (to collect information)

    • Software/AI (to process information)

    • Internet connectivity (WiFi, Bluetooth, cellular)

    • Automation capabilities (they can perform actions without manual control)

  • In simples terms:

    • A smart device is a device that can sense, connect, process, and act

  • Smart devices are a major part of the Internet of Things (IoT), where physical objects communicate and exchange data through the internet

Top 10 Trending Technologies in 2026: 5. Quantum Computing

• Still early but advancing rapidly

• Companies involved:

  • IBM

  • Google

  • Microsoft

• Potential uses:

  • Drug discovery

  • Cryptography

  • Financial modeling

• What is Quantum Computing?

  • a type of computing that uses the principle of quantum mechanics (the physics of very small particles) to process information

  • unlike traditional computers that use bits (0 or 1), quantum computers use quantum bits (qubits)

    • classical computer: bit = 0 or 1

    • quantum computer: qubit = 0 or 1, or both at the same time

  • this ability allows quantum computers to solve certain extremely complex problems much faster than traditional computers

Top 10 Trending Technologies in 2026: 6. Extended Reality (XR) - combination of VR, AR, MR

• Combination of:

  • Virtual Reality (VR)

    • a technology that creates a completely digital environment that users can explore and interact with using devices like VR headsets

  • Augmented Reality (AR)

    • a technology that overlays digital elements (images, text, or objects) onto the real world through devices like smartphones or AR glasses

  • Mixed Reality (MR)

    • a technology that blends the real world and digital objects so they can interact with each other in real time

  • Examples:

    • Apple Vision Pro

    • Meta Quest

    • Industrial AR training

Top 10 Trending Technologies in 2026: 7. Robotics & Automation

• Robots are becoming smarter due to AI

• Examples:

  • Warehouse robots

  • Delivery robots

  • Surgical robots

  • Humanoid robots (Tesla Optimus)

Top 10 Trending Technologies in 2026: 8. Cybersecurity

• Increasingly important due to AI-driven attacks

• Trends:

  • AI-powered security

  • Zero-trust architecture

  • Identity security

  • Cloud security

Top 10 Trending Technologies in 2026: 9. Edge Computing

• Processing data closer to where it is generated instead of sending everything to the cloud

• Examples:

  • Autonomous vehicles

  • Smart factories

  • IoT devices

• Benefit:

  • Lower latency

  • Faster processing

Top 10 Trending Technologies in 2026: 10. Blockchain & Web3

• Blockchain is a secure digital system that records transactions across many computers so the information cannot easily be changed or hacked

• Web3 refers to a new version of the internet that uses blockchain to give users more control over their data, identity, and digital assets

• Think of blockchain like a shared digital ledger (record book) where every transaction is permanently recorded and visible to everyone in the network

Applications of Blockchain and Web3

Applications of Blockchain and Web3

1. Cryptocurrencies

• digital money that operates without banks

  • ex// Bitcoin, Ethereum

2. Smart Contracts

• programs stored on a blockchain that automatically execute agreements when conditions are met

  • ex// automatic payment when goods are delivered

3. Supply Chain Tracking

• companies track products from the manufacturer to the customer

  • ex// tracking food safety in grocery supply chains

4. Digital Identity

• people can control and verify their identity online without relying on large tech companies

  • ex// secure login systems

5. NFTs (digital ownership)

• blockchain can verify ownership of digital items

  • ex// digital art, gaming items, music rights

6. Decentralized Finance (DeFi)

• financial services that operate without traditional banks

  • ex// lending, trading, payments

Emerging Technologies

Computer Hardware - Group Exercise

Computer hardware

• what are the hardware components of a laptop?

• as a group, identify 5 key hardware components of a laptop

Computer software

• what software comes pre-installed on a laptop?

• as a group identify 5 pieces of software that comes pre-installed on your computer

• as a bonus try to identify WHY they come pre-installed on a laptop

Cloud software

• what is software?

• what is cloud software and what makes it cloud software?

• as a group identify the top 5 cloud software that you use regularly

What is the cloud?

The higher you go, the more the cloud provider handles for you

1. Software as a Service

2. Platform as a Service

3. Infrastructure as a Service

What is the Cloud: How much is managed? - Customer vs. Provider

Cloud: Pizza Analogy

Think of making pizza

• Depending on how much help you want, you can do it 4 ways:

1. On-Premises

• like a homeade pizza, made from scratch, you do everying yourself

  • ex// datacentre

2. Infastructure as a Service

• you share a kitchen with others. the utilities and oven are provided, but you make and cook the pizza yourseld

  • ex// EC2

3. Platform as a Service

• you order a pizza for delivery, the pizzeria makes and cooks the pizza using their facilities

  • ex// app engine

4. Software as a Service

• you go to someone’s house for a party, they provide the pizza and invite others around for you to meet. conversation with the guests is still your responsibility

  • ex// Gmail

Cloud: Car Analogy

1. On premise

• you own your own car

  • you can drive yourself anywhere you want, but are responsible for all repairs and maintenance, and upgrading means buying a new car

2. IaaS

• leasing a car

  • you can pick any car you want and drive it anywhere you want, but at the end of the day the car is never yours

3. PaaS

• like taking a taxi

  • you tell the driver exactly where you want to go, and then sit back and enjoy the ride by yourself

4. SaaS

• like taking the bus

  • a bus has an assigned route and schedule, so you’ll eventually get to where you want to go, but you will have to ride with other passengers along the way

Cloud Platforms

Cloud: Iaas, Paas (AWS, Azure, Google Cloud)

Iaas

• compute

• storage

• networking

Paas

• integrated development environment

• elastic app containers

Cloud Considerations: What should you consider before moving to the cloud?

1. Cost

2. Security/Privacy

3. Control

4. People

Cloud Considerations: Cost

• The key insight on cost: On-premises means buying and owning everything — expensive upfront, but cheap to run later. The problem is that hardware gets old (value lowers), and leaving early is painful. Cloud flips this — low entry, but you pay continuously. No depreciation headache.

Cloud Considerations: Security/Privacy

increased attack surface

• due to its size, cloud environment have become a large and highly attractive attack surface for hackers

lack of visibility and tracking

• in the IaaS model, the cloud providers have full control over the infrastructure layer and do not expose it to their customers

• the lack of visibility and control is further extended in the PaaS and SaaS cloud models

• cloud customers often cannot effectively identify and quantify their cloud assets or visualize their cloud environments

  • like renting an office and not knowing where all the doors are

complex environments

• there are multiple ways of leveraging the cloud

  • Hybrid (some on-premises, some cloud) and Multi-cloud (using AWS + Azure + Google at once)

  • Single vs. Multi-tenant (do you share infrastructure with other companies, or have dedicated resources?)

  • Public vs. Private cloud (internet-accessible vs. restricted access)

Cloud Considerations: Control

• DATA control

• FUNCTIONALITY control

• ASSET control

• ACCESS control

Cloud Considerations: People

• Moving to the cloud doesn't just change technology — it changes what skills and roles your organization needs

• The mental model: you used to need someone who could build a car from parts. Now you need someone who can pick the right car from a dealership and keep it fuelled.

Are you ready for the Cloud? - Readiness Assessment

• Not every application should move to the cloud, and not all at once. The Gartner framework from the slides gives organizations a systematic way to decide.

The process has 3 steps:

1. Enumerate all your applications (list everything you have)

2. Assess each application on two dimensions

3. Plot them on a matrix and prioritize migration

The two assessment dimensions:

• Cloud Readiness → How technically compatible is the app with cloud architecture? Can it migrate easily, or does it need major code changes?

• Business Impact → How much value does migrating this app actually create for the business?

Top outcomes Achieved by Adopting Cloud

Cloud Readiness and Business Impact Application (Matrix)

Cloud Readiness

• How compatible is the application, its dependencies and its technology stack with the cloud architecture and design patterns? This aspect also helps you understand the challenges, approach and effort of migrating it to the cloud.

Business Impact

• What is the degree of positive business impact of migrating the application to the cloud? This aspect helps identify the business motivation and rationale for migrating it to the cloud. Consult and engage business stakeholders during this assessment.

Quadrants

Initial Focus

• Each of the applications in this quadrant has been deemed a reasonable to good technical fit for cloud migration. There is a strong business motivation to migrate them to the cloud. This is where you should place your initial focus, and you can migrate applications in this quadrant to the cloud with the least amount of time and effort, as compared with others in the portfolio.

Refactor/Revise

• Applications in this quadrant are less cloud -ready from a technical perspective, but they have been evaluated as supporting a high business motivation for cloud migration. They will likely require more code refactoring or other reengineering than those in the Initial Focus group. Other prioritization aspects aside (see the next section), these applications will be the next to migrate.

Hold Off

• This quadrant contains applications that are a good technical fit for the cloud, but for which there is no strong business directive or motivation to move them from their current environment. These applications will require minimal refactoring, as opposed to the applications to their left in the graph, but their migration is not a priority at present.

Don’t Migrate - Yet

• Applications in this quadrant score lower on both the technical and business assessments than other applications in the portfolio. They are not a good technical fit for migration in their current form. There also isn’t a strong business driver to move them. The recommendation is to leave these applications as they are for the time being or to wait until they are replaced.

What each quadrant means — in plain English:

• Initial Focus (top-right): These apps are technically ready AND have strong business value. Migrate these first. Least effort, most benefit. Think of these as "low-hanging fruit."

• Refactor/Revise (top-left): High business value but not very cloud-ready. Worth migrating, but they'll need significant code rework first. Do these second.

• Hold Off (bottom-right): Technically easy to migrate, but there's no strong business reason. They'll require minimal refactoring — but migration isn't a priority right now.

• Don't Migrate — Yet (bottom-left): Poor technical fit AND low business value. Leave them alone or wait until they're replaced.

When prioritizing within quadrants, also consider: (Refactor/Revise)

• Business Priority

  • Present the quadrant to business stakeholders and discuss it with them to check the business impact assessment and how their priorities relate to the applications. What gaps do they see in their business capabilities? What opportunities do they see, and what impediments do they have, that could benefit from cloud migration?

• Business Strategy

  • Discuss with business stakeholders whether, and how, applications are related to strategic programs and what milestones should be taken into account.

• Cost of delay

  • This is the money that would be lost by delaying the migration for a period.

• Cost of migration

  • A high -level appreciation of the expected effort, time and cost of migration. When business impact and cloud readiness are comparable, lower cost lifts priority.

• Return on investment

  • Is the investment of migrating to the cloud in -line with the business value of the application or the anticipated benefits?

• Risk of migration

  • The business risk associated with the migration. Lower risk lifts priority.

• Part of an application cluster

  • It may be more efficient to bundle applications together based on their dependency on a value stream, business capability or technology platform.

Enterprise Resource Planning (ERP)

• Enterprise Resource Planning (ERP) is a type of business management software that integrates various core business processes into a unified system

• ERP systems enable organizations to streamline operations, improve efficiency, and facilitate data-driven decision-making by centralizing information from different departments

Key Functions of ERP Systems

ERP systems typically include modules for:

1. Finance & Accounting

• manages budgets, payroll, expenses, and financial reporting

2. Human Resources (HR)

• handles employee records, payroll, performance management, and recruitment

3. Supply Chain & Inventory Management

• tracks raw materials, inventory levels, procurement, and logistics

4. Customer Relationship Management (CRM)

• manages customer interactions, sales, and marketing efforts

5. Manufacturing & Production

• helps with production planning, quality control, and workflow automation

6. Procurement & Vendor Management

• automates purchasing processes and supplier management

7. Project Management

• tracks project progress, costs, and resource allocation

Benefits of ERP Systems

• Efficiency & Productivity

  • automates repetitive tasks and reduces manual data entry

• Data Integration

  • provides a single source of truth for all business data

• Scalability

  • supports business growth by adapting to increased complexity

• Improved Decision-Making

  • delivers real-time analytics and reporting

• Regulatory Compliance

  • ensures adherence to industry and legal standards

Popular ERP Software Solutions

• SAP ERP

• Oracle ERP Cloud

• Microsoft Dynamics 365

• NetSuite

• Infor ERP

• Odoo (Open-source ERP)

Best of Breed vs. Best of Suite

Best of Breed vs. Best of Suite: Who is going to win?

Best of Breed

• Word processing —> Google Docs

  • (software, templates, data)

• Spreadsheets —> Microsoft Excel

  • (software, templates, data)

• Presentations —> Prezi

  • (software, templates, data)

• Notes —> Evernote

  • (software, templates, data)

Best of Suite: all (templates and data)

• Word processing —> Microsoft Office 365 (Word)

  • (software)

• Spreadsheets —> Microsoft Office 365 (Excel)

  • (software)

• Presentations —> Microsoft Office 365 (PPT)

  • (software)

• Notes —> Microsoft Office 365 (Notes)

  • (software)

or

• Word processing —> Google Apps (Docs)

  • (software)

• Spreadsheets —> Google Apps (Sheets)

  • (software)

• Presentations —> Google Apps (Slides)

  • (software)

• Notes —> Google Apps (Notes)

  • (software)

Back Office Systems

What really makes you tick?

• Back Office Systems:

  • technology system(s), that, in conjunction, enable an organization to manage their business operations that are not directly related to the sales of products or services

• Includes:

  • Accounting

  • Supply Chain Management (SCM)

  • Customer Relationship Management (CRM)

  • Human Capital Management (HCM)

Back Office Systems: best of suite? breed?

Best of Suite?

• cloud

• hybrid

• on-premise

Best of Breed?

• cloud

• hybrid

• on-premise

Back Office Systems: Best of Breed

Enterprise Resource Planning (ERP)

• Accounting —> Oracle NetSuite

  • general ledger

  • accounts receivable

  • accounts payable

  • tax management

  • fixed assets management

  • cash management

• Human Capital Management (HCM) —> WorkDay

  • HRM

  • workforce planning

  • recruiting

  • talent management

  • compensation

  • benefits

  • payroll management

  • time and absence

  • expenses

• Supply Chain Management (SCM) —> SAP

  • supply chain planning

  • supply chain logistics

  • manufacturing

  • R&D/engineering

  • asset management

• Customer Relationship Management (CRM) -> Salesforce

  • sales

  • service

  • marketing

  • commerce

  • platform

Back Office Systems: Best of Suite

• Microsoft Dynamics ERP

• SAP

• Oracle - PeopleSoft

SAP

• best for:

  • finance

  • HCM

  • SCM

  • CRM

Software: process

Platform: technology

Data: data

Week 9: Project Management

Project Management

• scope

• cost

• risk

• procurement

• quality

• HR

• communication

• time

Learning Objectives

• describe the Project Management Landscape in terms of People, Processes, Methodologies

• recommend and justify the appropriate methodologies to apply to a variety of real-world Digital Projects

Project Management Landscape

• Projects

  • there are a variety of different types of projects

• Methodologies

  • there is more than one way to manage a project

• People

  • there are many different types of people required for a project to be successful

Projects - Technical vs. Business

What is a project - Technical

What is a project - Technical

• a project is a sequence of unique, complex and connected activities that have one goal or purpose and that must be completed by a specific time, within budget and according to specifications

Triple Constraint Triangle

Scope Triangle

• Changes in cost, time, and specifications affect the other constraints

• As a project’s scope changes, the project work extends beyond what was originally planned (in other words, “scope creep”)

Scope Triangle: COVID-19 Example

ex// Developing a COVID-19 Vaccine

Time

• research & development

• clinical trials (quality control)

• manufacturing

• urgency of pandemic

Budget

• funding

• resources

Specifications

• safe (product and processes)

• effective

• transportable

RISKS

• safety

• reputation

Why Projects Fail - The FBI Virtual Case File

• Goal: develop new software system to manage FBI case documents

• Outcome: abandoned after 5 years while still in development, costing $170 million

• Reasons for failure:

  • lack of planning and personnel training

  • overly ambitious schedule (22 months)

  • too many system requirements and changes (scope creep)

Projects - Technical vs. Business

What is a project - Business

• A project is a sequence of finite dependent activities whose successful completion results in the delivery of expected business value that validated doing the project

What is business value?

• New product or service

  • new operational capabilities

• Improve existing products or services

  • improve existing operational capabilities

Project Definition Variables

Clear, Unclear

Goal

• What do we want the project to achieve?

+

Solution

• how will we achieve the solution?

—>

Project

Goal vs. Solution

Goal

• a high-level statement that gives an overall context of what the project will accomplish

Solution

• the project output for achieving the goal

What is an unclear goal?

• What is a solution that does not meet a specific goal?

Types of Projects

Traditional (clear goal, clear solution)

• low complexity

• few scope challenges

• predictive

Agile (clear goal, not clear solution)

• complex

• multiple scope challenges

• adaptive

Extreme (not clear goal, not clear solution)

• chaotic

• continual scope challenges

• extreme planning

Emertxe (not clear goal, clear solution)

• chaotic

• continual scope challenges

• extreme planning

Project Methodologies

What is a Project Methodology?

• A methodology is a system of practices, techniques, procedures and rules used by those who work in a discipline

Traditional Project Methodology

• Traditional project management is a linear approach to delivering a project throughout its life cycle

• Traditional project management does not easily adapt to change and is reliant on having a well-defined solution and goal

• The focus is primarily on scope and quality with resources, schedule, and budget estimated

Tell me what you want, and I will tell you how long, how much and what resources I need

Traditional Project Methodology: Principles

PREDICTIVE

• low complexity

• well understood technology

• few scope changes

• low risk

• experiences and skilled teams

• plan driven

• linear approach