MIS ISOM 3263

Management Info Systems

Fact

The confirmation or validation of an event or object. Example: A coffee shop’s daily sales log shows that exactly 320 cups of coffee were sold yesterday.

Information Age

The present time, during which infinite quantities of facts are widely available to anyone who can use a computer. Example: A retail store replaces paper receipts and manual inventory counts with digital point-of-sale systems that update stock levels in real time across all locations.

Digital Age

Refers to the modern era where digital technology, the Internet, and data driven innovations shape how people live, work, and communicate. Example: e-commerce platform like Amazon.

The Fourth Industrial Revolution

Represents the blending of the digital, physical, and biological worlds with rates of changes operating at hypervelocity. •The Fourth Industrial Revolution is when technology stops being a tool and starts becoming a partner. Example: A smart factory utilizing the Internet of Things (IoT) and predictive maintenance.

The Fourth Industrial Revolution

Sensors attached to factory assembly-line machinery (robotic arms) to continuously collect real-time operational data on temperature, vibration, and performance.

Digital Age

Example: A fitness brand launches a mobile app offering on-demand workouts, live classes, and community challenges, allowing users to access training anytime

Information Age

E-commerce platforms like Amazon track customer licks and purchase histories to manage inventory and offer personalized recommendations

Digital Age

Netflix using algorithms to recommend shows. A company moving from filing cabinets to cloud storage, A social media app connecting users globally.

Fourth Industrial Revolution

Hospitals using Ai to predict patient risks

(IoT) Internet of things

Any device connected to the Internet with the goal of enhancing performance with human intervention

Machine-to-Machine (M2M)

Two or more connected devices interacting via wireless or wired connections with the goal of data sharing and analytics without human intervention.

IoT

Devices talk to each other and the Internet, creating data, insight, and smarter systems. IoT requires humans

M2M

Devices talk directly to each other. M2M do not require humans.

IoT example:

A logistics company equips its delivery trucks with sensors that track location, fuel efficiency, and cargo temperature.  Managers can monitor the entire fleet in real time through a dashboard, receiving alerts if a shipment overheats or a vehicle requires maintenance.

M2M example

A factory machine detects that a part is wearing out and automatically sends a maintenance request to the supplier’s system. A replacement part is shipped before the machine ever breaks down

M2M

A vending machine detects it’s out of soda and automatically sends a message to the supplier’s system to restock

IoT

3.A smart home system connects thermostats, cameras, and lights to an app so users can monitor and control everything remotely—and the system learns usage patterns over time.

The Core Drivers of the Information Age

1. Data, 2. Information, 3. Business Intelligence, 4. Knowledge

Data

Raw facts that describe the characteristics of an event or object.

Structured Data

Has a defined length, type, and format and includes numbers, dates, or strings such as Customer Address format.

Machine-Generated Structured Data

Created by a machine without human intervention

Human-Generated Structured Data

Data that humans, in interaction with computers, generate.

Unstructured Data

•Not defined, does not follow a specified format, and is typically free-form text such as emails, text messages, and voicemail.

Machine-Generated Unstructured Data

Satellite images, scientific atmosphere data, and radar data.

Human-Generated Unstructured Data

Text messages, social media data, and emails.

Structured data examples

sensor data, weblog data, financial data, click-stream data, point of sale data, accounting data.

Unstructured Data

Satellite images, Photographic data, video data, social media data, text message, voice mail data.

Big Data

•Large, volumes of data—both structured and unstructured—containing greater variety, increased veracity, and more velocity.

Variety

Different forms of structured and unstructured data

Veracity

The uncertainty of data, including biases, noise, and abnormalities

Volume

the scale of data

Velocity

The analysis of streaming data as it travels around the Internet

Veracity of data example:

A clothing retailer analyzes social media comments to forecast trends, but bots and sarcasm skew the sentiment analysis. Acting on this unreliable data leads to poor inventory choices

volume of data example

A food delivery app processes thousands of new orders, driver updates, and customer locations every minute. Real-time data flow is critical to keep deliveries on schedule

Variety of data

A hotel chain gathers reservation data, customer feedback emails, and voice recordings from call centers. The mix of data types complicates analysis but provides a fuller picture of guest satisfaction

Volume of data example:

A streaming platform stores petabytes of user activity data—what people watch, skip, and rewatch—to improve content recommendations

Velocity of data

A ride-sharing company continually receives GPS data from drivers and passengers. The system must process this fast-moving data instantly to match rides and calculate prices

Variety of data example

A sports organization tracks player stats, video footage, and fan tweets. Combining these diverse data sources helps improve both performance and fan engagement

Volume of data example

A bank processes millions of financial transactions every hour, producing vast amounts of data that must be archived for compliance and fraud detection

Veracity

A marketing team relies on website analytics, but duplicate clicks from automated traffic distort engagement numbers, misleading campaign performance evaluations

Environmental Data

Captures environmental data including annual carbon emissions and energy consumption, water usage, waste, pollutions output, and so on.

Social Data

Captures statistics related to workforce diversity, gender equity, human rights, and so on.

Governance Data

Captures company data regarding corruption, labor practices, gender composition of the board of directions, and so on.

Environmental Data example

Ocean buoys measuring temperature, pH levels, and rising sea levels to monitor climate change

Governance Data example

A company publishes its annual corporate report detailing board member tenure, executive compensation, and shareholder voting results. A city government tracks tax-collection rates, budget allocations, and audit findings in its public finance dashboard

Social Data example

1. Employee engagement scores and internal satisfaction polls used to assess company culture. 2. Social media posts, hashtags, and comments reflecting public sentiment toward a brand or campaign. 3. Community impact reports tracking volunteer hours, donations, and partnerships with local organizations.

Environmental Data example

Smart sensors recording noise pollution and traffic emissions in major cities. Satellite imagery tracking deforestation, urban expansion, and changes in vegetation over time

Information

Data converted into a meaningful and useful context.

Foundation of MIS

Taking raw data like individual snack sales and transforming it into information that helps a business act smarter.

Variable

A data characteristic that stands for a value that changes or varies over time.

Report

A document containing data organized in a table, matrix or graphical format allowing users to easily comprehend and understand information.

Dynamic Report

Changes automatically during creation

Static Report

Created once based on data that doesn’t change

Business Intelligence

Information collected from multiple sources such as suppliers, customers, competitors, partners, and industries that analyzes patterns, trends, and relationships for strategic decision making

Analytics

The systematic computation analysis of data for the discovery, interpretation, and communication of meaningful patterns

Algorithm

A step-by-step procedure or rules for solving a problem or accomplishing a particular task

Business Analytics

The scientific process of transforming data into information for making data driven business decision.

Business Analytics example

A marketing firm uses analytics to determine which ad campaign generates the highest return on investment

Analytics

A researcher uses analytics to study temperature data over 20 years to identify climate change trends

Business Analytics

Amazon uses an algorithm that analyzes your past purchases and browsing behavior to recommend products you’re likely to buy next

Algorithm

Google Maps uses routing algorithms that factor in distance, traffic speed, and road closures to calculate the fastest route in real time

Business Intelligence

A social scientist applies analytics to survey responses to understand shifts in public opinion

Business Analytics

A retail chain uses analytics to study sales data across regions, discovering that certain products sell best before holidays and adjusting inventory accordingly

Analytics

1.A public health team analyzes COVID-19 case data to detect infection spikes by region.