Linear programming is a business analytics technique that can lend itself to supporting these types of

business decisions. Linear programming is a mathematical method that optimizes the allocation of

limited resources to achieve a desired objective, subject to a set of constraints1. Linear programming

can help the operations manager to determine the optimum number of vans to purchase, the most

efficient routes and schedule to follow, and the minimum cost or time to shuttle guests to/from the

airport, by formulating a linear objective function and a system of linear inequalities that represent

the relevant variables, parameters, and restrictions2.

The other options are not correct business analytics techniques for these types of business

decisions. Factor analysis is a statistical method that reduces the dimensionality of a large set of

correlated variables into a smaller set of uncorrelated factors that explain the underlying structure or

patterns of the data3. Factor analysis can help the operations manager to identify the key factors

that influence the guest satisfaction or loyalty, but it cannot help to optimize the resource allocation

or efficiency. Regression is a statistical method that estimates the relationship between one or more

independent variables and a dependent variable. Regression can help the operations manager to

predict the demand or revenue of the hotel based on the variables such as season, price, or location,

but it cannot help to optimize the resource allocation or efficiency. K-means clustering is a machine

learning method that partitions a set of data points into a predefined number of clusters based on

the similarity or distance between the data points. K-means clustering can help the operations

manager to segment the guests into different groups based on their characteristics or p

,

but it cannot help to optimize the resource allocation or efficiency.

Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 532: Introduction to Business Data

Analytics: A Practitioner View, IIBA, 2019, p. 93: Guide to Business Data Analytics, IIBA, 2020, p. 54. :

Guide to Business Data Analytics, IIBA, 2020, p. 54. : Guide to Business Data Analytics, IIBA, 2020, p.

55. : Guide to Business Data Analytics, IIBA, 2020, p. 53. : Introduction to Business Data Analytics: A

Practitioner View, IIBA, 2019, p. 9. : Guide to Business Data Analytics, IIBA, 2020, p. 54. : Guide to

Business Data Analytics, IIBA, 2020, p. 54. : Guide to Business Data Analytics, IIBA, 2020, p. 55.

The chart shows the personal rating of the manager and the average team rating on different areas

of training opportunities. Both the manager and the team rated “Negotiation & Conflict Resolution”

and “Facilitation” highly, indicating a shared interest in these areas. These areas are also relevant for

business analysts, as they involve skills such as communication, collaboration, problem-solving, and

stakeholder management12 Reference: 1: 6 Charts You Can Use to Create Effective Reports |

SurveyMonkey 2: Business Analysis Core Concept Model™ (BACCM™) - IIBA BABOK Guide v3

According to the Guide to Business Data Analytics, a change strategy is a document that outlines the

approach and plan for managing the change resulting from the data analysis and the proposed

solution. A change strategy should include the following elements: the vision and objectives of the

change, the scope and impact of the change, the stakeholders and their roles and responsibilities,

the communication and engagement plan, the training and development plan, the transition and

implementation plan, the risk and issue management plan, and the evaluation and measurement

plan. A change strategy can help ensure that the change is aligned with the business goals, that the

stakeholders are informed and involved, that the risks and issues are identified and mitigated, and

that the benefits and outcomes are realized and sustained.

Reference: Guide to Business Data Analytics, page 84-85; CBDA Exam Blueprint, page 8; [Introduction

to Business Data Analytics: A Practitioner View], page 26.

Querying is a technique that allows analysts to access, filter, join, aggregate, and transform data from

various databases using a specific syntax and logic1. Querying can be used for different purposes,

such as data exploration, data preparation, data analysis, and data visualization2. Querying is not

limited to creating predictive data models, nor does it always produce tabular results. Moreover,

querying languages may vary depending on the type and structure of the database, such as

relational, hierarchical, or document-based3. Reference: 1: Guide to Business Data Analytics, IIBA,

2020, p. 552: Data Analysis Using SQL and Excel, Gordon S. Linoff, 2016, p. 33: Database Systems:

Design, Implementation, and Management, Carlos Coronel and Steven Morris, 2019, p. 17.

A data strategy is a long-term plan that defines how the enterprise will identify, store, manage,

share, and use its data to achieve its business goals and objectives1. A data strategy aligns the data

vision, mission, principles, and policies with the business strategy, and guides the data governance,

data quality, data architecture, data security, data integration, data analytics, and data culture of the

organization2. A data strategy helps the organization to leverage its data as a strategic asset, to

create value, to improve performance, and to gain competitive advantage3.

A data roadmap is a document that outlines the specific actions, milestones, deliverables, and

timelines for implementing the data strategy. A data roadmap is a tactical tool that helps the

organization to prioritize, coordinate, and communicate its data initiatives, and to track its progress

and outcomes. A data roadmap is not a long-term plan, but a dynamic and flexible plan that can be

updated and revised as the data strategy evolves.

A business strategy is a high-level plan that defines how the enterprise will achieve its vision,

mission, and goals in a competitive market. A business strategy sets the direction, scope, and value

proposition of the organization, and guides its decisions on resource allocation, product

development, customer segmentation, pricing, marketing, and differentiation. A business strategy is

not a plan that defines how the enterprise will identify, store, manage, share, and use its data, but a

plan that defines how the enterprise will create and sustain value for its stakeholders.

A data management plan is a document that describes the data that will be collected, generated, or

used in a specific project, and how the data will be handled, stored, preserved, shared, and reused

during and after the project. A data management plan is a operational tool that helps the project

team to comply with the data policies, standards, and best practices of the organization, and to

ensure the quality, integrity, security, and accessibility of the dat

a. A data management plan is not a long-term plan, but a project-specific plan that can be modified

and updated as the project progresses.

Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 392: Introduction to Business Data

Analytics: An Organizational View, IIBA, 2019, p. 143: Data Strategy: The Definitive Guide, Tableau, . :

Data Strategy: The Definitive Guide, Tableau, . : Data Roadmap: The Definitive Guide, Tableau, . :

Business Strategy: The Definitive Guide, Tableau, . : Business Strategy: The Definitive Guide, Tableau,

. : Data Management Plan: The Definitive Guide, Tableau, . : Data Management Plan: The Definitive

Guide, Tableau, . : Data Strategy: The Definitive Guide, Tableau, . : Guide to Business Data Analytics,

IIBA, 2020, p. 39. : Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 14.

: Data Strategy: The Definitive Guide, Tableau, . : Data Roadmap: The Definitive Guide, Tableau, . :

Business Strategy: The Definitive Guide, Tableau, . : Data Management Plan: The Definitive Guide,

Tableau, .

Database normalization is the process of organizing the data in a database to reduce redundancy and

improve integrity, consistency, and performance1. Database normalization rules are based on the

concept of normal forms, which are levels of database design that meet certain criteria2. One of the

most common normal forms is the third normal form (3NF), which states that a table should not have

any transitive dependencies, meaning that a non-key attribute should not depend on another non-

key attribute3. In the CustomerOrder entity, the set of attributes that will need to be normalized to

avoid redundancies are ProductName and ProductQuantity, as they are non-key attributes that

depend on another non-key attribute, Orderld. This means that the same product information may

be repeated for different orders, which could lead to data inconsistency, duplication, or update

anomalies. To normalize this set of attributes, a separate table should be created for the OrderDetails

entity, which would have Orderld, ProductName, and ProductQuantity as its attributes, and Orderld

and ProductName as its composite primary key.

The other sets of attributes do not need to be normalized to avoid redundancies, as they do not

violate the 3NF. CustomerPhone and ProductName are non-key attributes that depend on the

primary key, Customerld and Orderld respectively, which is allowed by the 3NF. Orderld and

ProductName are part of the composite primary key of the OrderDetails entity, which is also allowed

by the 3NF. Customerld and OrderDate are both primary keys of the Customer and Order entities

respectively, which are also allowed by the 3NF.

Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 442: Introduction to Business Data

Analytics: A Practitioner View, IIBA, 2019, p. 93: Database Normalization: The Definitive Guide,

Tableau, . : Database Normalization: The Definitive Guide, Tableau, . : Guide to Business Data

Analytics, IIBA, 2020, p. 44. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019,

p. 9. : Database Normalization: The Definitive Guide, Tableau, . : Database Normalization: The

Definitive Guide, Tableau, .

According to the Guide to Business Data Analytics, a narrative is a way of communicating the results

of data analysis in a clear, concise, and compelling manner. A narrative should include the following

elements: the purpose of the analysis, the main findings and insights, the implications and

recommendations, and the evidence and reasoning. A narrative should also use appropriate

language, tone, and style for the intended audience and medium. A narrative can enhance the

impact and value of the data analysis by providing context, explanation, and interpretation of the

data, as well as by highlighting the key messages and actions. A dashboard that lacks narrative may

not be able to convey the full meaning and significance of the data, and may not be able to engage

the audience or influence their decision-making.

Reference: Guide to Business Data Analytics, page 81-83; CBDA Exam Blueprint, page 8; [Introduction

to Business Data Analytics: A Practitioner View], page 25-26.

According to the Guide to Business Data Analytics, brainstorming is a technique used to generate a

large number of ideas or questions in a short period of time1. The purpose of brainstorming is to

encourage creativity and divergent thinking, not to evaluate or judge the ideas or questions.

Therefore, participants should avoid critiquing suggested questions raised by the group, as this could

inhibit the flow of ideas and discourage participation. The other options are not consistent with the

principles of brainstorming, as they could limit the quantity or quality of the questions generated.

Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 32.

Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an

overview of what has happened or is happening, such as the attrition rates and the personality test

scores of the employees12. The team has been asked to perform descriptive analytics to explore

possible relationships between the data variables, without making any predictions or prescriptions

for the future. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics:

Data Analysis & Decision Making,

S. Christian Albright and Wayne

L. Winston, 2015, p. 5.

Logistic regression is a technique that can be used to model the probability of a binary outcome, such

as choosing to watch or not watch a movie, based on one or more predictor variables, such as the

characteristics of the movie and the customer’s viewing history. Logistic regression can help the

business analysis professional to identify the factors that influence the customer’s decision and to

estimate the likelihood of each customer’s preference. Logistic regression can also be used to test

hypotheses and to evaluate the performance of the predictive model.

Reference: [Guide to Business Data Analytics], page 55; [Business Data Analytics: A Practical Guide],

page 93; [Introduction to Business Data Analytics: A Practitioner View], page 14.