Description
Module 06: Critical Thinking Assignment
Apply IT Concepts to Business Decisions (15 Marks)
Qlik offers a complimentary e-book entitled “Turn your Excel Reports into Stunning Dashboards.” Download the e-book. Write a report about what you learned.
Visit the website of software provider Microstrategy at https://www.microstrategy.com
Click on “Solutions.”
Click “By Industry.”
Scroll down to choose an industry that interests you and click on that Industry.
Choose a business application that interests you such as Distribution Center Operations, Digital Loyalty Card, Vendor Portal or Customer Analysis.
Click “Watch the Video.”
Write a report describing what you learned.
Your well-written report should be 4-5 pages in length, not including the title and reference pages. To make it easier to read and therefore grade, make sure you clearly delineate each section of your answer so it can be matched with the relevant question. Use Saudi Electronic University academic writing standards and APA style guidelines, citing at least two references as appropriate. Review the grading rubric to see how you will be graded for this assignment.
Unformatted Attachment Preview
Beyond Excel:
5 Steps to a visually
persuasive dashboard
Many professionals use Excel
to store data that they want
to visualize and turn into
dashboards.
The trouble is, creating
a visually persuasive
dashboard isn’t always
simple.
Beyond Excel | 2
Excel dashboards may add
unnecessary complexity
because users often spend
more time trying to understand
the data instead of learning the
answers to their questions.
Many charts also lack interactivity
which limits the ability to explore
data and uncover meaningful insights.
Beyond Excel | 3
What if you could turn this
around and start creating
visually persuasive
dashboards?
Beyond Excel | 4
Visually persuasive
dashboards aren’t just
about making beautiful
charts and graphs.
They enable you to:
Communicate the most important
information about a given subject and
draw attention to any anomalies
Convey your findings rapidly with all the
information at your fingertips
Engage users with interactive
visualizations
Beyond Excel | 5
5 steps for creating
visually persuasive dashboards:
1.
Identify
key metrics
2.
Develop
a layout that guides
users through the
data
3.
Provide
a comparison
for your KPIs
4.
Design
the dashboard
to maximize
comprehension
5.
Drive
engagement
Beyond Excel | 6
1
Identify key metrics
This is a classic case of less is more. Go beyond
identifying whether a metric is relevant to your
dashboard and instead ask yourself, “is this data point
imperative to this dashboard?” If not, leave it out.
Establish common ground by using metrics
that users immediately recognize such as capital
expenditure, change in sales, or time to hire.
Integrate metrics from credible sources to help you
gain buy-in.
Beyond Excel | 7
2
Develop a layout that guides
users through the data
Try to anticipate users’ questions and organize
metrics accordingly, so your dashboard is ready to
deliver answers. Group related metrics together to
show how they form the big picture.
Users generally tend to look at content on the top
and left sides of a page or screen first. Place the
most important metrics in these areas.
Consider a grid layout which lets you space metrics
evenly across the dashboard to maintain visual
cohesion.
Beyond Excel | 8
3
Provide a comparison
for KPIs
Absolute measures don’t give context, which reduces
users’ ability to understand and act on data. Instead,
include comparisons and trends to show how your
organization is progressing toward its goals.
For instance, if 500 prospects visited your booth
on the first day of an industry conference, so what?
Show how this compares to the traffic on the other
days of the show and to that of last year’s show.
Highlight metrics that are moving in the wrong
direction to amplify awareness, so corrective action
can be taken.
Beyond Excel | 9
4
Design the dashboard to
maximize comprehension
Use charts that are user-friendly and easy to
interpret, such as bar and line graphs. Avoid
charts that require users to spend extra time
comprehending the data.
Eliminate fancy shading, outlines, and icons
when creating charts (also known as “chart junk”).
It distracts users and can reduce the impact of your
visualizations.
Use color to add meaning. For instance, use similar
hues for objects that are related to one another.
Use red or orange to alert users to a critical point.
Beyond Excel | 10
5
Drive engagement
Consider how users will want to interact with the
data. Star and snowflake schemas make it easier for
users to configure their view of the data.
Invite users to explore the data more deeply by including filters that allow them to view the data in a
variety of ways. Provide ways to drill into data, such
as separate pages or windows of analysis.
Give users action items based on the results they just
viewed. For instance, you can recommend next steps,
revised business goals, or a person to contact for
more information.
Beyond Excel | 11
Now that you know some
recommended best practices
for creating stunning dashboards
make sure it passes the
visual persuasion test:
It answers users’ primary and
secondary questions
Users are able to explore
their data freely
Someone viewing the dashboard
for the first time can immediately
understand what it’s showing
Go Beyond the Dashboard | 12
Ready to learn more?
data
visualization
pitfalls
(and how to avoid them)
Check out the 5 Visualizations
Pitfalls (and How to Avoid
Them) e-book to learn
visualization tips and tricks
Visit the Qlik Design Blog
for more tips and best
practices to best present
your data
Beyond Excel | 13
A B OUT QLIK
Qlik’s vision is a data-literate world, where everyone can use data and analytics
to improve decision-making and solve their most challenging problems. Qlik
offers real-time data integration and analytics solutions, powered by Qlik Cloud,
to close the gaps between data, insights and action. By transforming data into
Active Intelligence, businesses can drive better decisions, improve revenue and
profitability, and optimize customer relationships. Qlik serves more than 38,000
active customers in over 100 countries.
© 2022 QlikTech International AB. All rights reserved. All company and/or product names may be trade names, trademarks and/or registered trademarks of the respective owners with which they are associated.
MKT0004213 101922
IT for Management: On-Demand Strategies for
Performance, Growth, and Sustainability
Twelfth Edition
Turban, Pollard, Wood
Chapter 6
Business Intelligence, Data
Science, and Data Analytics
Learning Objectives (1 of 4)
Business
Intelligence
and Data
Science
Predictive and
Prescriptive
Data Analytics
Methods
and
Techniques
Big Data and
Advanced Data
Analytics
Descriptive
Data Analytics
Methods and
Techniques
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Business Intelligence and Data Science (1 of 2)
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Business Intelligence and Data Science
(2 of 2)
• Bounded rationality is the idea that rationality is
limited by the tractability of the decision, cognitive
limitations of the mind and time available to make the
decision.
• Herbert Simon proposed that managers often make the
decisions that are satisficing rather than optimizing
that is “good enough” but not optimal.
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Three levels of data analytics
• Descriptive data analytics create a summary of
historical data to yield useful information and possibly
prepare the data for future more sophisticated
analysis.
• Predictive data analytics is the process of using data
analytics methods and techniques to model and make
predictions about unknown events from data.
• Prescriptive data analytics is dedicated to finding the
best course of action among various choices given the
known parameters.
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BI and Data Science
• Business intelligence (BI) is a set of best practices,
software, infrastructure and tools to acquire and
transform raw highly structured data into actionable
insights to help managers at all levels of the
organization make informed business decisions.
• Data science is a multi-disciplinary field that uses
domain expertise, scientific methods, programming
skills, algorithms and statistics to extract knowledge
and insights from structured, semi-structured and
unstructured big data sets to predict future behavior
and prescribe actions.
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Traditional and Modern BI
• Traditional BI provides managers with an easy to
understand “snapshot” of what is happening now and
what happened in the past to bring an organization to
its current state. It is a relatively unsophisticated data
analysis method that uses dashboards, data mashups,
and data visualization.
• Modern BI is a more flexible and accessible than
traditional BI. The focus of modern BI is to provide
visual interactive self-service analytics to improve the
speed and quality of decision-making. It includes
“embedded BI.”
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Finding and Hiring BI Professionals
• Right now, there is a huge shortage of BI professionals
who really understand big data.
• Future demand is expected to be even higher than it
currently is.
• BI professionals include BI analysts, BI developers, BI
managers, BI consultants, and business analysts.
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Software to Support BI Professionals
(1 of 2)
Seven key attributes of modern BI software:
• Speed
• Visualization
• Single source of truth
• Real-time collaboration
• Comprehensive governance
• Scalability
• Mobility
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Software to Support BI Professionals
(2 of 2)
Current leaders in BI and descriptive data analytics
platforms:
• Microsoft Power BI—offers easy-to-use data preparation, visualbased data discovery, interactive dashboards and augmented
analytics.
• Tableau—enables business users to access, prepare, analyze,
and present results of data queries
• Qlik—offers an integrating inference engine to replace the
query-based approach, which divorces data from its context.
• Thoughtspot—has a search-based interface that supports
complex questions with augmented analytics.
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Data
Science
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Adding Value with Data Science
• Insights gained through taking a data science approach
can then lead to game-changing business decisions
• Data science methods and techniques also develop
“data products” like those created by recommendation
engines that utilize user data to make personalized
suggestions
• Data product is a technical function that encapsulates an
algorithm and is designed to integrate directly into core
applications.
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Software to Support the Data Science Team
(1 of 2)
The most commonly used computer programming
languages that underly advanced data analytics solutions
include:
• Python—a high-level object-oriented programming language.
• R—an extensible, open source programming language that runs
on Windows, Macintosh, Unix, and Linux platforms.
• Apache Hadoop—Hadoop is an open source language that
places no conditions on the structure of the data it can process
and distributes computing problems across several servers.
• Apache Spark uses resilient distributed datasets (RDDs) and does
not provide a distributed file storage system.
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Software to Support the Data Science Team
(2 of 2)
The leaders in advanced analytics software are:
• Alteryx
• SAS Visual Data Mining and Machine Learning
• Azure Databricks
• Tibco
• Dataiku
• Mathworks
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Business Intelligence and Data Science:
Questions
1. What are the four phases of decision-making?
2. Why would an organization satisfice instead of optimize when
making a decision?
3. What is BI and why is it important in an organization?
4. Why are human expertise and judgment important to data
analytics? Give an example.
5. What is the relationship between data quality and the value of
analytics?
6. How can manufacturers and health care benefit from BI level
descriptive data analytics?
7. How does data science software for programmers differ from
data science software for business users?
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Learning Objectives (2 of 4)
Business
Intelligence
and Data
Science
Predictive and
Prescriptive
Data Analytics
Methods
and
Techniques
Big Data and
Advanced Data
Analytics
Descriptive
Data Analytics
Methods and
Techniques
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Big Data
• Big data is a data set that is too large or complex to be
analyzed using traditional data processing applications.
• Data analytics is the process of examining data sets to
draw conclusions about the information they contain,
usually with the help of computer software.
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The Four Vs of Big Data
(1 of 2)
• Volume: To handle the sheer volume of “big data” and
provide comprehensive analytics capabilities in the big
data platform.
• Variety: The analytic environment has expanded from
pulling most structured data from a single enterprise
data warehouse to include a variety of semi-structured
and unstructured sources such as social medial posts,
tweets, videos, images, sensor data, and customer
service calls.
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The Four Vs of Big Data
(2 of 2)
• Velocity: The speed with which data is stored, analyzed
and reports generated.
• Veracity: Data that are incomplete, missing or
duplicated need to be repaired.
• In addition to the four Vs, human expertise and
judgment must be added into the mix when analyzing
big data. Data are worthless if they cannot be easily
analyzed, interpreted, understood, and the results
applied effectively in context.
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Cut costs
Gain market share
Big Data
Goals
Establish a data-driven culture.
Create new ways to innovate and
disrupt with technology.
Accelerate speed of offering new
capabilities and services.
Launch new products and services.
Improve processes.
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Big Data Challenges
Cultural
• Encourage business units to share information across organizational silos.
• Determine what internal and external, structured and unstructured data to use
for different business decisions.
• Find and hire experienced data science professionals.
• Build high levels of trust between data science team and functional managers.
• Gain top management support for investments in big data and training.
• Create optimal way to organize big data programs.
• Understand where big data investments should be focused in the organization.
• Determine how to apply insights created from big data.
Technology-related
• Effectively handle the four Vs of big data.
• Determine best way of presenting data analysis results
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Predictive Data Analytics
• Predictive model is based on several factors likely to
influence future behavior and predicts at some
confidence level the outcome of an event.
• Predictive modeling is a process that uses data mining
and probabilities to forecast outcomes to create a
statistical model to predict outcomes.
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Prescriptive Data Analytics
• Prescriptive analytics is the third level of data analytics
and the most powerful.
• Just as predictive analytics anticipate what will happen
next, prescriptive analytics goes one step further to
advise organizations how to react in the best way
possible based on the prediction.
• Prescriptive analytics uses optimization technology and
machine learning to solve complex decisions by
suggesting multiple options for taking advantage of
future opportunities or mitigating risks and the
outcomes of each decision option.
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Big Data and Advanced Data Analytics:
Questions
1. What are the four Vs of big data?
2. What are the two biggest challenges associated with
using big data?
3. What is predictive analytics?
4. What is prescriptive analytics?
5. How are predictive analytics adding value in
organizations?
6. Name three industry sectors where prescriptive
analytics are being used to add value.
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Learning Objectives (3 of 4)
Business
Intelligence
and Data
Science
Predictive and
Prescriptive
Data Analytics
Methods
and
Techniques
Big Data and
Advanced Data
Analytics
Descriptive
Data Analytics
Methods and
Techniques
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Descriptive Data Analytics Tools
• Four of the most important tools used in descriptive
analytics are:
• Data mining: is the process of using software to analyze
unstructured, semi-structured and structured data from various
perspectives, categorize them, and derive correlations or
patterns among fields in the data.
• Data visualization: is the presentation of data in a graphical
format to make it easier for decision-makers to grasp difficult
concepts or identify new patterns in the data.
• Digital dashboards: is a static or interactive electronic interface
used to acquire and consolidate data across an organization.
• Mashups: Data mashups combine business data and applications
from two or more sources.
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Adding Value with Data Mining
Business value that organizations gain from data mining
falls into three categories:
• Making more informed decision at the time they need
to be made.
• Discovering unknown insights, patterns or
relationships.
• Automating and streamlining or digitizing business
processes.
• Ex: Amazon uses affinity analysis
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Adding Value through Learning, Exploration, and
Discovery with Data Visualization
• Data visualization enables learning and is also used as a
data explorer and data discovery tool.
• Data discovery is the process of using BI to collect data
from various databases and consolidate it into a single
source that can be easily and instantly evaluated.
• In addition to charts, graphs, and timelines data
visualizations also include heat maps.
• Heat maps are the most-used tool for representing
complex statistical data and use a warm-to-cool color
spectrum to show differences in classes of data.
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Augmented Reality (AR)
• The highest level of data visualization currently available
• Augmented reality (AR) is the use of more contemporary 3D visualization methods and techniques to illustrate the
relationships within data including smart mapping, smart
routines, machines learning, and natural language
processing. Some uses of AR:
• Coca-Cola has developed an AR application that assists
retailers in visualizing how a beverage cooler would fit
into their stores.
• IKEA uses augmented reality to assist customers with
AR visualizations of how furniture will look in different
living spaces.
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Dashboards
• Digital dashboard systems
combine multiple data
visualizations into a single screen
to enhance data reporting and
facilitate smooth business
operations and decisions.
• Dashboards improve information
integration by collecting multiple,
disparate data feeds and sources,
extracting features of interest, and
manipulating the data, so the
information is in a more accessible
format.
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Major Components of a Dashboard
Design The visualization method and descriptive captions to convey
information so that they are correctly understood.
Performance metrics KPIs and other real-time content displayed
on the dashboard.
API APIs connect disparate data sources and feeds to display on the
dashboard.
Access Preferred access is via a secure Web browser from a mobile
device.
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Dashboards Are Real Time
• The purpose of dashboards is to give users a clear view
of the current state of KPIs, real-time alerts, and other
metrics about operations.
• Dashboard design is a critical factor because business
users need to be able to understand the significance of
the dashboard information at a glance and have the
capability to drill down to one or more levels of detail.
• Having real time, or near real time, data is essential
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• Visibility Blind spots are minimized or
eliminated. Threats and opportunities are
detected as soon as possible.
• Continuous improvement Executive
dashboards are custom designed to display
the user’s critical metrics and measures.
Adding
Value with
Digital
Dashboards
• Single sign-on Single-sign-on dashboards save
time and effort.
• Deviations from what was budgeted or
planned Any metrics can be programmed to
display deviations from targets, such as
comparisons of actual and planned or
budgeted.
• Accountability When employees know that
their performance is tracked in near real time
and can see their results, they tend to be
motivated to improve their performance.
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Data Mashups for Actionable Dashboards
• Data mashups combine business data and applications from
two or more sources
• They enhance the interactive capabilities of dashboards,
allow users to gain new insights, and spot trends within
data in businesses of all sizes
• Mashups are a quick, cost-effective solution to a range of
data analysis issues
• With mashups users can filter down the data based on their
needs so that only the information needed is provided by
the available data services
• Mashups remain behind the scene and are invisible because
the data are presented as if coming from a single source
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46
Adding Value with Mashups
Mashup benefits can be summarized as:
• Dramatically reduces time and effort needed to
combine disparate data sources.
• Users can define their own data mashups by combining
fields from different data sources that were not
previously modeled.
• Users can import external data sources, for example,
spreadsheets and competitor data, to create new
dashboards.
• Enables the building of complex queries by nonexperts
with a drag-and-drop query building tool.
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Descriptive Data Analytics Methods and
Techniques: Questions
1. Why is data mining valuable to organizations?
2. How does data visualization contribute to organizational learning?
3. How do heat maps and tag clouds convey information?
4. Give two examples of data visualization for performance
management
5. Why do you think dashboards must be in real time and customized
for the executive or manager?
6. What are benefits of dashboards?
7. Explain why business managers need data mashup technology.
8. What are the three benefits of mashup technology to the
organization?
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Learning Objectives (4 of 4)
Business
Intelligence
and Data
Science
Predictive and
Prescriptive
Data Analytics
Methods
and
Techniques
Big Data and
Advanced Data
Analytics
Descriptive
Data Analytics
Methods and
Techniques
Copyright ©2021 John Wiley & Sons, Inc.
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Predictive and Prescriptive Data
Analytics Methods and Techniques
The most common predictive and prescriptive data
analytics tools are:
• Text mining
• Spatial data mining
• Regression
• Optimization and rules-based decision-making
• Machine learning
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Text mining
• Text mining is a specialized form of data mining.
• While data mining primarily focuses on analyzing
structured numerical data, text mining interprets
words and concepts in context.
• Social commentary and social media are also being
mined for sentiment analysis to understand consumer
intent.
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Spatial Data Mining
• Geographic information systems (GIS) and data mining
software are naturally synergistic technologies.
• GIS connects data with geography to understand what
belongs where.
• GIS is not just about mapping data, government,
businesses, and individuals find GIS useful in solving
everyday problems using geospatial data.
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Geocoding
Geocoding can convert postal
addresses to geospatial data that
can then be measured and
analyzed.
By tapping into this resource,
decision-makers can use the
geographic or spatial context to
detect and respond to
opportunities.
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Adding Value with Spatial Data Mining
Business applications include the following:
• Analysts can pinpoint the geographic areas where the highest
performing stores are established.
• Retailers can learn how store sales are impacted by population or the
proximity to competitors’ stores.
• A retail chain with plans to open a hundred new stores can use GIS to
identify relevant demographics, proximity to highways, public
transportation, and competitors’ stores to select the best location
options.
• Food and consumer products companies can chart locations of
complaint calls, enabling product traceability in the event of a crisis or
recall.
• Sales reps might better target their customer visits by analyzing the
geography of sales targets.
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Regression Modeling: Linear regression
• Linear regression modeling is used to predict the value of a
variable that is dependent on the value of one or more
other variables.
• The variable you want to predict is called the dependent
variable. The variable(s) you are using to predict the other
variable’s value is called the independent (or explanatory)
variable.
• Linear regression fits a straight line or surface that
minimizes the discrepancies between predicted and actual
output values.
• Linear regression is used to make data-driven decisions
rather than relying on experience and intuition.
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Regression Modeling: Time-Series Regression
• A time series is a collection of data values over time.
• Time-series regression is performed by plotting a series
of well-defined data points and attempting to predict
what will happen to it in the future based on measuring
the data at consistent time intervals over a specific
period of time, such as monthly, quarterly or annually.
• The trend line shows the direction in which a variable is
moving as time passes.
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Time-Series Regression
Three ways data can be analyzed using a time-series
regression are:
1. Trend—series of data points go up, down or stay flat
over time
2. Rate of Change—the extent of relative change
between data points over time.
3. Cycles—regularly repeating patterns in the data, such
as at the end of a quarter when sales reps typically
close sales out and see if they have made their target.
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Decision Optimization and Rules-Based
Decision-Making
• Optimization is the process of calculating values of
variables that lead to an optimal value of the event
under investigation.
• Rules-based decision-making is decision-making that
helps novices make decisions like an expert.
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Machine learning is scientific algorithms that identify
patterns in big data to learn from the data and create
insights based on the data.
Machine
Learning
Cognitive computing is the technology that uses machine
learning algorithms.
The four main tasks that machine learning applies known
rules to include:
Categorizing people
or things.
Predicting likely
outcomes or actions
based on identified
patterns.
Identifying previously
unknown patterns
and relationships.
Copyright ©2021 John Wiley & Sons, Inc.
Detecting
unexpected
behaviors.
63
Predictive and Prescriptive Data Analytics
Methods and Techniques: Questions
1. How are the methods and techniques used in predictive
and prescriptive data analytics different from those used
in descriptive data analytics?
2. How does text mining provide value? Give an example?
3. Give an example of how geospatial data would be useful
to an organization?
4. Why does organization use regression analysis?
5. How do organizations benefit from using optimization and
rules-based decision-making?
6. What is machine learning and why do organizations use
it?
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Copyright
Copyright © 2021 John Wiley & Sons, Inc.
All rights reserved. Reproduction or translation of this work beyond that permitted in
Section 117 of the 1976 United States Act without the express written permission of the
copyright owner is unlawful. Request for further information should be addressed to the
Permissions Department, John Wiley & Sons, Inc. The purchaser may make back-up
copies for his/her own use only and not for distribution or resale. The Publisher assumes
no responsibility for errors, omissions, or damages, caused by the use of these programs
or from the use of the information contained herein.
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