Management Information Systems - A.A. 2023-2024

Hedge computing is a little bit different than the strategy of having bid data centers, since it is based

on having small data centers that can react very fast for some particular tasks and operations. So,

thousands of this around a city. On the network side, it is not exactly the same, but we can see some

similarities with 5G. One of the benefits of 5G is a lower latency, which is the delay of the

information.

Convergence of Computing and Telecommunications

Voice and data traffic sharing a common network infrastructure

- Voice over IP (VoIP) IP telephony

- Video conferencing over IP

IP convergence allows various devices to communicate using IP technologies.

Green Computing

All companies today are working on sustainability and cloud computing is helping in reducing the

carbon footprint. Green computing represents attempts to use computing resources more efficiently

to reduce environmental impacts. Approaches to Green computing include: cloud computing - power

management software - educed printing - retiring obsolete hardware responsibly.

Alphabet is a global leader in renewable energy, which is transitioning to renewable energy. Indeed,

its goal is 100% clean energy by 2025.

Enhancing Business Intelligence using Big Data, Analytics and Artificial Intelligence

Enhancing Organizational Decision Making

Why organizations Need Business Intelligence and Advanced Analytics? Databases provide inputs

into Business Intelligence and Advanced Analytics.

• Business intelligence refers to tools and techniques for analyzing and visualizing past data,

and providing answers;

• Advanced analytics refers to tools and techniques used to understand why something

happened, predict future outcomes, or discover hidden patterns;

• Business analytics is used as an umbrella term for these concepts.

Why Organizations Need Business Intelligence and Advanced Analytics

Data Driven Organizations

- Organizations must have up-to-date, accurate, and integrated information

- Familiarity with data analysis and analytics tools is a required skill for every business user

Responding to Threats and Opportunities

- Business analytics helps organizations swiftly respond to external threats and opportunities

Understanding Big Data

Businesses are dealing with the challenge of “Big Data”, which are characterised by:

- High Volume

▪ Unprecedented amounts of data

- High Variety

▪ Structured data

▪ Unstructured data

- High Velocity

▪ Rapid processing to maximize value

Effective Planning is Continuous

Continuous planning process – Organizations continuously monitor and analyze data and business

processes

Databases: Providing Inputs into Business Intelligence and Advanced Analytics

Data and knowledge are among the most valuable assets an organization has. Enabling interactive

Web sites using databases:

- E-commerce makes extensive use of databases

- Product catalog data are stored in databases and available to users

- A customer’s billing and shipping information is available

- Electronic commerce applications process millions of transactions per day

Databases: Foundation Concepts

Computers make the process of storing and managing data much easier. This sample data table for

the entity Students includes 7 attributes and 10 records:

Databases: Advantages

Databases: Effective Management

The data model is a map or diagram that represents entities and their relationships: what data will be

captured and how the data will be represented?

The data type helps the DBMS organize and sort the data, complete calculations, and allocate space.

The data dictionary is a repository explaining several pieces of metadata (attribute, type, values,

rules).

Entering and Querying Data A computer-based form (typically blank) where users can enter

information into a computer.

An Ad Hoc Query

An ad hoc query (queries created because of unplanned information) is typically created using a

graphical user interface.

Ad Hoc Queries and Reports

Online Transaction Processing

An Online Transaction Processing (O L T P) system interacts with customers and helps runs a

business in real time. Once an organization has collected all data, it must design ways to get the

greatest value from its collection. The real power for an organization comes from analyzing the

aggregation of data from different systems using OLAP (online analytical processing).

Operational and Informational Systems

Data Warehouses and Data Lakes

A data warehouse integrates multiple large databases and other data sources into a single repository.

A data lake is a central repository allowing for storing structured, semi-structured, and unstructured

data in a raw format.

Data Marts

A data mart is a data warehouse that is limited in scope.

Extraction, transformation and loading are

used to consolidate data from operational systems into a data warehouse.

Intelligent Agent Systems

An intelligent agent (also called a “bot”) is a program that works in the background to provide some

service when a specific event occurs.

Open AI is the company behind ChatGPT, where only 1 investor namely Microsoft accounts for a

huge percentage of investments. So far, it is the best infrastructure and the fastest application to reach

1 million users in history. In the industry, this is very important because it creates culture. One of the

founders was Elon Musk, who later went out of the company due to incompatibilities with the

governance.

Every company now has the role of Data Strategy within them, they are investing a lot in technology

and also people. More and more regulations are coming, because how data are used is a public matter.

Business Intelligence

Decision Support Systems are used to analyze structured data and support decision making. Examples

od functions of companies that use it are: the corporate level, accounting, finance, marketing, HR and

production (even more than marketing, since everything happens here nowadays).

Online Analytical Processing

Online analytical processing (OLAP) refers to the process of quickly conducting complex,

multidimensional analyses of data stored in a database. An OLAP cube allows for analyzing data by

multiple dimensions, while Sling and dicing refers to analyzing the data on subsets of the dimensions

of a cube.

Information Visualization

Visualization refers to the display of complex data relationships using a variety of graphical methods

it is very important and we can build our own dashboard.

Digital Dashboards

A digital dashboard presents information in a highly aggregated form ashboard use various

, d

graphical elements to highlight important information.

Mobile Business Intelligence

Mobile business intelligence can provide executives with relevant information regardless of location

and device.

Data Mining

Data mining provides capabilities for discovering “hidden” predictive relationships in the data.

Complicated algorithms run on large data warehouses. The types of data mining algorithms are:

Association discovery – Clustering - Classification - Text and Web content mining

Quantum computing is not on the market yet, but many companies are investing a lot on it.

Text Mining

Text mining refers to the use of analytical techniques for extracting information from textual

documents. Web content mining refers to extracting textual information from web documents.

Data Mining Results Data mining results can be delivered to users in a variety of ways.

Machine Learning

Neural networks approximate the functioning of the brain by creating common patterns in data and

then comparing new data to learned patterns to make a recommendation. Machin Learning is

replicating the way in which our brain works and learns by doing.

Enhancing Business Processes using Enterprise Information Systems

Core Business Processes and Organizational Value Chains

• Core Business Processes

• Organizational Activities Along the Value Chain

• Value Systems: Connecting Multiple Organizational Value Chains

A company’s functional areas should be interrelated.

Core Business Processes

• →

Order-to-Cash The process of selling goods or services and collecting revenue for them

8me and my clients)

• →

Procure-to-Pay The process of ordering goods or services and paying for them (me and my

suppliers)

• →

Make-to-Stock/Make-to-Order The process of manufacturing goods, either based on

forecasts or based on orders (first we sell and then we produce). The downside of this switch

is the waiting time, depending on the industry the make to order is a luxury. Instead, if you

buy an iPhone you do not want to wait but to have immediately the product (i.e., make-to-

stock).

The Order-To-Cash Process

Functional Areas in Order-to-Cash: Sales and Marketing - Accounting and Finance - Manufacturing

and Operations.

In specific cases, we can have the payment before the shipment (or a large part of the price). This

standard process can have different variations depending on the industry, this is crucial in building a

satisfying customer experience.

Procure-To-Pay

Functional Areas in Procure-to-Pay: Supply Chain Management - Accounting and Finance (because

we have some payments and financial procurements) - Manufacturing and Operations

The Purchase Order is an official document, like a document.

The Make-To-Stock and Make-To-Order

Processes An example of a make-to-stock is

the book

Supply Chain of a Book

Organizational Activities Along the Value Chain

After the core, there are the support processes. Depending on how a company is organized, there can

be changes. This is a picture of a standard company.

Value Systems: Connecting Multiple Organizational Value Chains

Three companies combine their value chains, forming a value system. A value system works in the

same way of a value chain the is the concentration of different value chains. In Italy there are

disctricts, which are value systems in a specific geographical area.

The Rise of Enterprise Systems

The real change was the introduction of these systems in the last 2 years, the last era in ISs. The Rise

of Enterprise Systems was to support Business Processes. Information flows using standalone

systems did not exist, they were very vertical applications with no intersections and no possibility to

use data mining or business intelligence.

Standalone and Enterprise Systems

The big change was to integrate all the department in one system, so to make everything more

efficient. It was a revolution in every industry.

• Standalone Applications (Legacy Systems)

– Each department had their own proprietary systems that were not designed to

communicate with other systems

– Information reentered from one system to the next manually

• Enterprise System (Integrated Suite)

– All departments are integrated into one system

– No duplication of data and more efficient

Integrated Enterprise Systems<