An international strategy is much like the multinational strategy as there are autonomous local subsidiaries that operate independently from the headquarters. However, unlike the multidomestic strategy, the international strategy does not involve extensive customization of products and services to local markets.
Instead, the international strategy relies on offering a standardized product worldwide with little or no change.
The international strategy is suitable for firms that face low pressure for global integration and low pressure for local responsiveness. Examples of firms pursuing an international strategy are Harley Davidson, Starbucks, and Rolex. References: International Business Strategy EXPLAINED with EXAMPLES | B2U, 9.4 Types of International Strategies – Strategic Management

According to the CITM course outline1, there are five data collection techniques that are commonly used in business analytics. They are:
Observations: This technique involves watching and recording the behavior, actions, or events of interest. Observations can be either direct or indirect, structured or unstructured, participant or non-participant.
Interviews: This technique involves asking questions to individuals or groups of people who have relevant knowledge or experience on the topic. Interviews can be either structured, semi-structured, or unstructured, depending on the level of flexibility and standardization of the questions.
Surveys: This technique involves administering a set of questions to a sample of respondents who represent the population of interest. Surveys can be either quantitative or qualitative, depending on the type and scale of the questions.
Focus groups: This technique involves gathering a small group of people who share some common characteristics or opinions on the topic and facilitating a discussion among them. Focus groups can be used to explore attitudes, perceptions, feelings, or preferences of the participants.
Documents: This technique involves reviewing and analyzing existing documents or records that are relevant to the topic. Documents can be either primary or secondary, depending on the source and authenticity of the information.
References:
1: 7 Data Collection Methods and Techniques | SafetyCulture
2: CITM 500 Data and Information Management | The Chang School of Continuing Education – Toronto Metropolitan University
3: Data Collection Methods | Step-by-Step Guide & Examples – Scribbr
4: Data Collection – Methods Types and Examples – Research Method

Distributed databases provide several advantages over centralized databases, including improved scalability, availability, performance, flexibility, fault tolerance, and security1. Here are some of the advantages of distributed databases:
Increased availability: Distributed databases can tolerate failures of individual nodes or sites without affecting the overall system functionality. Users can access data from other sites if their local site is down or unreachable. Data replication and backup mechanisms can also enhance data availability and recovery2.
Easier expansion: Distributed databases can be scaled horizontally by adding more nodes to the network. This allows for increased capacity and performance as data and user demand grow. Adding new sites or units to the distributed system does not require significant efforts or disruption in the existing functions2.
Improved performance: Distributed databases can offer faster response and lower communication costs by locating data near the users who normally use that data. Distributed query processing and transaction management can also optimize the use of network and computing resources. Furthermore, distributed databases can exploit the parallelism of multiple nodes to execute operations concurrently13.
References: What Is a Distributed Database? {Features, Benefits & Drawbacks}, Advantage and Disadvantage of Distributed Database Management System (DDBMS) – Bench Partner, Advantages of Distributed database – GeeksforGeeks

Data warehousing and data mining are closely related technologies that support business intelligence and analytics. Data warehousing is the process of collecting, integrating, and organizing data from various sources into a centralized repository that can support complex queries and analysis. Data mining is the process of applying various techniques and algorithms to extract useful information and patterns from the data stored in the data warehouse. Data mining can help discover hidden relationships, trends, anomalies, and insights that can improve decision making and performance. One of the main reasons for building a data warehouse is to enable data mining, as data warehouses provide a consistent, reliable, and comprehensive source of data that can be mined for various purposes. Data warehouses also facilitate data mining by providing data quality, data cleansing, data transformation, data aggregation, and data indexing services that can enhance the accuracy and efficiency of data mining. Data warehouses and data mining are complementary technologies that work together to deliver business value and competitive advantage. References: Data Warehousing and Data Mining
101, Data Warehousing and Data Mining – Topcoder, Difference between Data Warehousing and Data Mining

Information derived from processing transaction reduces uncertainty about a firm’s order backlog or financial position because it provides a quantitative and forward-looking measure of demand. Order backlog is the amount of orders that a firm has received but not yet fulfilled. It reflects the expected future revenue and cash flows of the firm, as well as its ability to meet customer needs and expectations. Order backlog can also indicate the competitive position and market share of the firm, as well as its operational efficiency and capacity utilization. Therefore, disclosing order backlog can help stakeholders such as investors, analysts, managers, and regulators to assess the firm’s performance and prospects more accurately and reliably. References: Sample Exam – GAQM, page 4; Implications of Disclosing Order Backlog, page
1-2; Backlog Definition, Implications, and Real-World Examples – Investopedia

Structured design and object-oriented design are two common approaches to design a system. Structured design is a method of breaking down a system into smaller and simpler modules, which can be independently created and tested. Structured design follows a top-down approach, where the system is divided into sub-systems, and then into modules, until the desired level of detail is reached. Structured design is suitable for systems that have well-defined inputs and outputs, and a clear hierarchy of functions. Object-oriented design is a method of modeling a system as a collection of objects, which have attributes and behaviors. Object-oriented design follows a bottom-up approach, where the system is built from reusable and interchangeable components, called classes. Object-oriented design is suitable for systems that have complex interactions, dynamic behavior, and multiple inheritance. Functional design and organizational design are not considered to design a system, but rather to describe the system’s purpose and structure. Functional design is a process of defining the functions and processes that the system performs, and how they relate to each other. Functional design focuses on the what and why of the system, rather than the how. Organizational design is a process of defining the roles and responsibilities of the people and units involved in the system, and how they communicate and coordinate with each other. Organizational design focuses on the who and where of the system, rather than the how. References: Structured Design – an overview | ScienceDirect Topics, Object-Oriented Design – an overview | ScienceDirect Topics, Functional Design – an overview | ScienceDirect Topics, [Organizational Design – an overview | ScienceDirect Topics]

In a multi-user design, one group usually develops the systems for use by another group. This is because different groups of users may have different needs, preferences, and expectations for the system. For example, a system that is designed for the accounting department may not be suitable for the marketing department, or a system that is designed for the managers may not be user-friendly for the employees. Therefore, a multi-user design involves identifying the target users, analyzing their requirements, designing the system accordingly, and testing the system with the users. A multi-user design also requires coordination and communication among the developers and the users, as well as among different user groups, to ensure that the system meets the needs and expectations of all stakeholders. References: Multi-User Operating System – GeeksforGeeks; Multiple Group Design: Definition & Examples – Study.com; Chapter 10: Information Systems Development

Explicit knowledge is the type of knowledge that can be easily articulated, expressed, and recorded in the form of text, number, symbol, code, formula, or musical note. It is the knowledge that is based on facts, data, and rules that can be shared and communicated. Examples of explicit knowledge are company policies, process documents, research reports, etc12345 References:
1: Different Types of Knowledge: Implicit, Tacit, and Explicit | Bloomfire
2: Tacit Knowledge Vs. Explicit Knowledge – Association for Intelligent Information Management
3: Tacit Knowledge: Definition, Examples, and Importance – Helpjuice
4: Explicit Knowledge: Definition, Examples, and Methods – Document360
5: Difference Between Explicit Knowledge and Tacit Knowledge – Key Differences

An Integrated Global IT approach is a strategy that aims to align the IT functions and capabilities of a firm with its global business objectives and needs. One of the benefits of this approach is that it enables the firm to provide more consistent customer service internationally, by leveraging common platforms, standards, processes, and data across different regions and markets. This can enhance customer satisfaction, loyalty, and retention, as well as reduce costs and risks associated with IT fragmentation and duplication. References: Integrated Global IT approach customer service, Toward an integrated technology operating model, Managing Global Customers: An Integrated Approach, Five steps to an integrated customer experience

A data flow diagram (DFD) is a graphical representation of data flow in any system. It uses defined symbols like rectangles, circles and arrows, plus short text labels, to show data inputs, outputs, storage points and the routes between each destination1. Data flow: data flows indicate the data movement between external entities, processes, and the data store. They’re represented as arrows in the DFD with a short text label2. Therefore, the symbol that represents flow of data in DFD is arrow. References: 1: What is a Data Flow Diagram | Lucidchart 2: What Are Data Flow Diagrams? | Baeldung on Computer Science

A DBMS is software that monitors and maintains accurate, quality data in a data structure. It helps organizations optimize, store, retrieve and manage data in a database. A DBMS consists of a group of programs that manipulate the database and accept requests for data from an application or user. A DBMS can also provide security, redundancy, abstraction, and multiple views of the data. A DBMS that stores data in tables and uses relationships between them is called a relational DBMS or RDBMS. Some examples of popular DBMS software are PostgreSQL, Oracle Database, Microsoft SQL Server, MySQL, SQLite, IBM Db2, etc. References: What is DBMS? Database Management Systems, Explained, What Is a DBMS? – LearnSQL.com, Database management system (DBMS) | Definition & Facts.

According to the Certified IT Manager (CITM) course outline, there are nine stages in a systems design life-cycle. They are: 1. Initiation, 2. Concept Development, 3. Planning, 4. Requirements Analysis, 5. Design,
6. Development, 7. Integration and Test, 8. Implementation, and 9.Operations and Maintenance. Each stage has its own objectives, deliverables, and activities that ensure a systematic and effective approach to system development. References: CITM Course Outline, System Development Life Cycle – GeeksforGeeks, Systems development life cycle – Wikipedia

Interpreting information means understanding its meaning and implications in a given context. Two factors that are important while considering interpreting information are external and situational. External factors refer to the sources, reliability, validity, and timeliness of the information. Situational factors refer to the purpose, audience, and expectations of the information. These factors help to evaluate the relevance, accuracy, and usefulness of the information for a specific situation or problem. References: Certified Information Technology Manager (CITM) – gaqm.org, page 7; Certified Information Technology Manager (CITM) – GAQM, Module 2 – Understanding and Interpreting Information.

A field is a unit of data that can store one or more characters, such as a name, a date, or a number. Fields are used to organize and store data in databases, spreadsheets, and other applications. A byte is a unit of digital information that consists of eight bits, which are binary digits that can have two values: 0 or 1. An octet is another term for a byte, especially in the context of network protocols and data transmission. A decimal is a number system that uses ten symbols: 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. Decimals can also have a decimal point to represent fractions, such as 3.14 or 0.25. References: Certified IT Manager (CITM) Course Outline, Grouping Symbols and Order of Operations, A group of characters is called as a

A relation is a type of association that describes how two or more entities are related to each other. A relation can have different properties, such as cardinality, optionality, and directionality, that specify the nature and constraints of the association. A relation can also have attributes that store information about the association.
For example, a relation between Employee and Project entities can have an attribute called Role that indicates the role of each employee in each project. A relation can be represented by a line connecting the entities in an entity-relationship diagram (ERD). References: Association and Associative entity – IBM, When to use Associative entities? – Stack Overflow, [Entity-Relationship Model – Tutorialspoint].

Important decisions may require more care in analyzing data because they have higher stakes, greater uncertainty, and more complexity. Data analysis can help managers to identify patterns, trends, correlations, and causal relationships that can inform their decision making. Data analysis can also help managers to evaluate alternatives, test hypotheses, and predict outcomes. However, data analysis is not a substitute for judgment, intuition, and creativity. Managers should also consider the quality, reliability, and validity of the data, as well as the ethical and social implications of their decisions. References: CITM Course Outline, Sample Exam – GAQM, TEST 1 2020, questions and answers – CITM 102 TEST BANKS … – Studocu

Multiprocessing is a feature that supports more than one processor in a computer system. It allows multiple processes to run concurrently on multiple CPUs (or cores), thus increasing the performance and throughput of the system12. Multiprocessing can be achieved by using either multiple physical processors or a single processor with multiple cores3. Multiprocessing can also be classified into symmetric multiprocessing (SMP) and asymmetric multiprocessing (AMP), depending on how the processors are coordinated and share resources4.
Multiprocessing is different from other features such as multiprogramming, multitasking, and multithreading. Multiprogramming is the ability of an operating system to keep multiple programs in the main memory at the same time, ready for execution5. Multitasking is the ability of an operating system to switch between multiple tasks (applications) on a single CPU, giving the illusion of parallelism6. Multithreading is the ability of an application to create multiple threads of execution within a single task, sharing the same memory space and resources7. These features aim to maximize the utilization of CPU and memory, but they do not require more than oneprocessor. References: 1: CITM Study Guide, Chapter 2: Computer Architecture, Page 24-25. 2: Multiprocessing 3: What is Multiprocessing? 4: Symmetric vs. Asymmetric Multiprocessing:
What’s the Difference? 5: CITM Study Guide, Chapter 1: Operating Systems, Page 9. 6: CITM Study Guide, Chapter 1: Operating Systems, Page 10. 7: CITM Study Guide, Chapter 4: Object-Oriented Analysis and Design, Page 64.

When deciding on system alternatives, the IT manager has to consider the following points1:
Technological feasibility: The IT manager has to assess whether the proposed system can be implemented with the available technology, resources, and skills. The IT manager has to evaluate the technical risks, challenges, and benefits of each alternative.
Organizational impact: The IT manager has to analyze how the proposed system will affect the organization’s structure, culture, processes, and performance. The IT manager has to consider the stakeholders’ needs, expectations, and resistance to change. The IT manager has to ensure that the system aligns with the organization’s vision, mission, and goals.
Financial constraints: The IT manager has to estimate the costs and benefits of each alternative, including the initial investment, operating expenses, maintenance costs, and return on investment. The IT manager has to compare the alternatives based on their financial viability and value for money. The IT manager has to secure the necessary funding and budget for the system. References: 1: EXIN EPI Certified InformationTechnology Manager, Module 6: IT Project Management, Section 6.3: System Analysis and Design, Page 6-11.

System analysis is the process of understanding problems and needs and arriving at solutions that meet them.
It involves identifying, defining, and specifying the requirements of a system, as well as designing, developing, testing, and implementing the system. According to Wikipedia1, system analysis can be broken into five phases: scope definition, problem analysis, requirements analysis, logical design, and physical design. However, according to MasterStart2, there are seven stages of system development life cycle (SDLC), which include planning, requirements analysis, designing, development and testing, implementation, documentation, and evaluation. Therefore, depending on the perspective and the methodology, system analysis can involve three to seven stages, but the most common number is three: requirements analysis, logical design, and physical design. References: Wikipedia; MasterStart

An analytic decision maker looks for quantitative information, or data that can be counted or measured in numerical values. An analytic decision maker uses logic, facts, and statistics to make decisions, and prefers objective and structured data. A heuristic decision maker, on the other hand, looks for qualitative information, or data that is descriptive and not expressed numerically. A heuristic decision maker uses intuition, experience, and judgment to make decisions, and prefers subjective and unstructured data. References: CITM Study Guide, page 8, section 2.3; What is Quantitative Data?, paragraph 2.

The conceptual level is a high-level description of the data and its relationships in the database, without specifying how the data is physically stored or implemented. It defines the main entities, attributes, and constraints for the entire database, and is independent of any specific database management system or application. The conceptual level is also known as the logical level, and it is usually represented by an entity-relationship diagram (ERD). References: Conceptual schema – Wikipedia, Navigating the Three Levels of Database Design: Conceptual, Logical, and Physical

A worm is a type of malicious code that can distribute itself without using having to attach to a host file.
Unlike a virus, which needs to infect an existing program or file to spread, a worm can create copies of itself and send them to other devices through a network. A worm can consume network bandwidth, slow down the system performance, or deliver a payload that can damage or compromise the security of the infected device.
According to the CITM study guide, a worm is “a self-replicating program that does not alter files but resides in active memory and duplicates itself” (p. 70). Some examples of worms are Stuxnet, Conficker, and Blaster. References:
CITM Study Guide, Chapter 5: Information Security, pp. 69-71
12 Types of Malware + Examples That You Should Know1
What is Malicious code?2
Cyber Awareness Challenge 2022 Malicious Code3
Malicious Code-What is it and How to Prevent it?4