The analytics team has been asked to determine if the organization should launch their highest
revenue generating product into the North American market. To date, this has only been available in
Eastern Europe. To answer this, the team formulates several research questions, including:
D
Explanation:
One of the steps in identifying the research questions for business data analytics is to assess the
feasibility and desirability of the proposed solution or change1. This involves understanding the
needs, preferences, and satisfaction of the existing and potential customers. Therefore, asking
whether the existing customers really like the product is a relevant research question for the
analytics team. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 22.
An analyst has just completed building a data model that shows the table structures including table
names, table relationships
with primary and foreign keys and column names with respective data types. What type of data
model has the analyst just built?
A
Explanation:
A physical data model is the most detailed and specific type of data model, which shows how the
data is stored, accessed, and manipulated in the database. It includes the table structures, column
names, data types, primary and foreign keys, constraints, indexes, and other physical attributes of
the data12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 542: Data Modeling
Essentials, Graeme Simsion and Graham Witt, 2005, p. 15.
The analytics team is identifying research questions to address a business problem. The business
analysis professional reminds the
team that the most important dimension to consider is the:
B
Explanation:
The quality of the data is the most important dimension to consider when identifying research
questions, as it affects the validity, reliability, and accuracy of the analysis and the results. Data
quality refers to the degree to which the data meets the requirements and expectations of the
stakeholders and the purpose of the analysis12. Poor data quality can lead to erroneous conclusions,
ineffective decisions, and wasted resources3. Reference: 1: Guide to Business Data Analytics, IIBA,
2020, p. 282: Data Quality Assessment, Arkady Maydanchik, 2007, p. 33: Data Quality: The Field
Guide, Thomas C. Redman, 2001, p. 1.
An analyst at a supermarket chain has been asked to extract data from multiple data sources to
complete a study on customer
spending habits. The analyst is going to query data from various databases. Which statement is true
about database querying?
D
Explanation:
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 lab is conducting a study on protein interactions. They have used the data to create a graph
visualization. In graph visualization, what would a layout be?
C
Explanation:
A layout is a way of arranging the nodes and links of a graph visualization to convey meaningful
information about the data. A layout is determined by a dedicated algorithm that calculates the node
positions based on certain criteria, such as minimizing edge crossings, maximizing node spacing, or
emphasizing clusters12. A layout can also be influenced by user interaction, such as zooming,
panning, or dragging3. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 642: Graph
Drawing: Algorithms for the Visualization of Graphs, Giuseppe Di Battista et al., 1999, p. 33:
Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et al.,
2015, p. 227.
An analyst at a bank is trying to identify research questions for an analytical study on top customer
issues across branches. During an interview with a branch manager, the analyst asks the manager
what their top customer concerns are relating to this branch?
After the manager's reply, the analyst asks a follow up question on how their top customer concerns
compare against the top customer concerns across all branches? Was the analyst's follow-up
question valid?
B
Explanation:
The analyst’s follow-up question is valid because it helps to refine the scope and context of the
research questions for the analytical study. By comparing the top customer concerns across
branches, the analyst can identify the common and unique issues that affect customer satisfaction
and loyalty. This can also help to prioritize the most critical or urgent problems that need to be
addressed by the bank12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 212: Business
Analysis for Practitioners: A Practice Guide, PMI, 2015, p. 43.
Interested in experimenting with analytics, a manufacturing company hires an analyst to see how the
capability can be developed within its organization. The analyst is getting started and recognizes the
need to show value from the onset of their work to gain upper management's trust and future
funding. What action will accomplish these objectives?
C
Explanation:
The best action for the analyst to show value from the onset of their work is to develop a meaningful
question that can be answered with data the company already has in its possession. This way, the
analyst can demonstrate the potential of analytics to solve relevant business problems, without
spending too much time or resources on data collection or market research. The question should also
be aligned with the organization’s strategy and goals, and provide actionable insights for decision
making12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 202: Data Science for
Business, Foster Provost and Tom Fawcett, 2013, p. 14.
A large car manufacturer is interested in comparing the number of sales for a specific model of
electric car across all 50 US states.
The data analytics team sourced and acquired the data, and the business analyst created the model
to compare sales across states.
In a meeting to review the results, the feedback received included several complaints concerning an
inability to distinguish the number of sales per state. What model would result in such confusion?
D
Explanation:
A pie chart is a circular chart that shows the proportion of each category in a whole by dividing the
circle into slices. A pie chart would result in confusion when comparing the number of sales for a
specific model of electric car across all 50 US states, because it is difficult to compare the angles and
areas of the slices, especially when there are many categories with similar values. A pie chart also
does not show the absolute values of each category, unless they are labeled or annotated12. A better
alternative would be a bar chart, which can show the number of sales for each state along a common
axis, making it easier to compare and rank the values3. Reference: 1: Guide to Business Data
Analytics, IIBA, 2020, p. 652: Storytelling with Data, Cole Nussbaumer Knaflic, 2015, p. 673: The
Visual Display of Quantitative Information, Edward R. Tufte, 2001, p. 178.
The definition of data elements is different across various data sources. The organization is looking to
improve the usability of data across the organization. Which practice would help address this
problem?
A
Explanation:
Data governance is the practice of establishing and enforcing policies, standards, roles, and
responsibilities for the management and use of data across the organization. Data governance helps
to address the problem of inconsistent data definitions across various data sources by ensuring that
data is properly defined, documented, classified, and aligned with the business objectives and
requirements12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 292: Data
Governance: How to Design, Deploy and Sustain an Effective Data Governance Program, John Ladley,
2012, p. 3.
Insights based on the data collected indicate that a multi-national company could increase its sales of
a mature product by reducing its price by 20% which would result in increased revenues of 2% over a
6-month period. The team recommends this as an appropriate goal for its organization. This is
considered a good goal because:
A
Explanation:
A well-defined objective is one that is specific, measurable, achievable, relevant, and time-bound
(SMART)1. The goal of increasing sales of a mature product by reducing its price by 20% which would
result in increased revenues of 2% over a 6-month period meets all these criteria, as it clearly states
what the desired outcome is, how it will be measured, whether it is realistic and attainable, how it
aligns with the organization’s strategy, and when it will be achieved2. Reference: 1: Guide to
Business Data Analytics, IIBA, 2020, p. 192: SMART Goals: How to Make Your Goals Achievable,
MindTools, 2021, 1.
The marketing department for a major restaurant chain is interested in testing a Kids Eat Free
campaign to determine if it will help to increase sales. They are interested in piloting the campaign
to determine which day of the week will improve sales the most.
The campaign is launched across 7 cities with each city promoting a different day of the week. The
sales data is collected and provided to a team for analysis. What concern might the analytics team
have regarding data quality across cities?
D
Explanation:
Variation is the degree to which the data values differ from each other or from a central tendency
measure, such as the mean or median. Variation can affect the data quality across cities, as it can
indicate the presence of outliers, errors, noise, or inconsistency in the data collection or processing
methods. Variation can also influence the statistical analysis and interpretation of the results, as it
can affect the significance, confidence, and validity of the findings12. Reference: 1: Guide to Business
Data Analytics, IIBA, 2020, p. 302: Statistics for Business and Economics, David R. Anderson et al.,
2014, p. 83.
A call center has requested to review their sales conversion data for the month. The analyst working
on this request is trying to identify the chart that will effectively present the data, which includes: the
number of leads, the number of calls made, the number of calls completed, the number of
customers interested and the number of sales. What chart should the analyst use to show the values
across each stage of the pipeline?
B
Explanation:
A funnel chart is a type of chart that shows the values of different stages of a process, such as a sales
pipeline, where each stage represents a subset of the previous one. A funnel chart is useful for
showing the conversion rate, the drop-off rate, and the potential revenue or profit at each stage12. A
funnel chart would be an effective way to present the data requested by the call center, as it would
show the number of leads, calls, customers, and sales, as well as the percentage of change between
each stage. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 662: Data Visualization: A
Practical Introduction, Kieran Healy, 2018, p. 233.
A government agency is conducting a study on the performance of 12th grade students' in
mathematics across the country. In particular, they want to understand if there is a relationship
between intelligence and scores, as well as the difference in performance between various locations.
Which combination of inferential statistics procedures should be used?
C
Explanation:
A correlation co-efficient is a measure of the strength and direction of the linear relationship
between two variables, such as intelligence and scores. A correlation co-efficient can range from -1
to 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a
perfect positive relationship12. An analysis of variance (ANOVA) is a procedure that tests whether
the means of two or more groups are significantly different from each other, such as the
performance of students across various locations. ANOVA can compare the variation within each
group and the variation between groups to determine if there is a statistically significant difference
among the group means34. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 582:
Statistics for Business and Economics, David R. Anderson et al., 2014, p. 7133: Guide to Business Data
Analytics, IIBA, 2020, p. 594: Statistics for Business and Economics, David R. Anderson et al., 2014, p.
849.
An organization's customers are categorized based on the amount of purchases completed over the
last 12 months. The analytics team would like to ensure the accuracy of their survey results and
decide to randomly select 500 customers to participate in a survey from this large pool of customers.
This is an example of:
A
Explanation:
Stratified sampling is a technique that divides the population into homogeneous subgroups (strata)
based on a relevant characteristic, such as the amount of purchases, and then randomly selects a
proportional number of elements from each subgroup to form the sample. Stratified sampling
ensures that the sample is representative of the population and reduces the sampling error and
bias12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 312: Statistics for Business and
Economics, David R. Anderson et al., 2014, p. 262.
The results of the data analytics work led to some clear and strongly supported outcomes and the
analytics team is very confident in their recommendations; particularly given that the payback on the
required changes are a short 3 months. However, there is concern because the organization operates
in a highly regulated environment and some new regulatory changes are being considered with
announcements and implementation in the next 6 months. Under these conditions the team decides
to:
C
Explanation:
The best option for the team under these conditions is to identify and carefully document the
assumptions for their recommendation, such as the expected impact of the regulatory changes, the
risks and benefits of implementing the changes before or after the announcements, and the
sensitivity of the results to different scenarios. This way, the team can communicate their findings
and recommendations clearly and transparently, while also acknowledging the uncertainty and
limitations of their analysis. This can help the decision makers to evaluate the trade-offs and make
informed choices12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 242: Data-Driven
Decision Making: A Primer for Beginners, Anand Rao, 2018, 1.