This document discusses business intelligence (BI) and analytics. It defines BI as using data and quantitative analysis to support fact-based decision making. Several BI techniques are identified, including data warehouses, data marts and data lakes. The document also discusses how organizations use BI and analytics in areas like finance, marketing, and healthcare. Self-service analytics and its pros and cons are also defined.

1. ITIA 111
LECTURER: OBARO, AZ

2.  Define the terms business intelligence (BI)
and analytics.
 Identify several BI techniques and discuss
how they are used.
 Define the term self-service analytics and
discuss its pros and cons.

3.  It helps organization to analyse large amounts of data to measure
past and current performance as well as make future predictions
about business opportunities.
 The predictions help to improve business strategies, strengthen
business operations, and enrich decision making and thus,
enable organization to become more competitive (outpace their
peers).
 Financial services use BI and analytics to understand their
customers in order to enhance service, create new and more
appealing products and better manage risk.
 It assist Marketing managers to analyze data related to the Web-
surfing habits, past purchases, and even social media activity of
existing and potential customers to create highly effective
marketing programs that generate consumer interest and
increased sales.
 It enables health care professionals to improve the patient
experience to maximize reimbursements, lower costs and
ultimately deliver higher quality care for patients.

4.  Business analytics is the extensive use of data
and quantitative analysis to support fact-
based decision making within organizations.
 It is used to understand current business
performance, reveal new business patterns
and relationships, explain why certain results
occurred, optimize current operations and
forecast future business results.

5. BI involves the use of wide range of applications,
practices and technologies for the extraction,
transformation, integration, visualization, analysis,
interpretation and presentation of data (usually
obtained from multiple sources) to support improved
decision making.
 The primary aim of BI is to obtain the vital
information and present the results of analysis in an
easy to understand manner that the layman can
understand.
 Applications of BI include: data warehouses, data
marts and data lakes.

6.  Detect fraud: Analytical software is used in special
investigations unit (SIU) to identify medical provider,
attorney and repair shop fraud.
 Improve forecasting: BI and A can assist
organizations to predict customer demand, visualize
inventory levels, adapt to user feedback and reduce
its overall inventory costs.
 Increase Sales: The BIA enables managers to evaluate
potential incentives, acquisition method and improve
sales.
 Optimize operations: Complex and constantly
changing industrial (such as petroleum industry and
financial services) operations can be simplified using
analytical system, in order to maximize profit.
 The BA can dramatically assist organizations to
minimize costs using analytics software.

7.  Data scientists are individuals who combine strong business
acumen, a deep understanding of analytics, and a healthy
appreciation of the limitations of their data, tools, and
techniques to deliver real improvements in decision making.
Functions include:
 Data scientists collect and report on data to present a
situation using appropriate data and tools.
 Data scientist provide valuable insights that can influence
organizational decisions and help organizations to achieve
competitive advantage.
 Data scientists continually ask questions, challenges
assumptions and existing processes.
 They have an ability to communicate their findings to
organizational leaders convincingly so that they are able to
strongly influence how an organization approaches a
business opportunity.
 Commonly used DS tools include SQL, Python, R and Java.

8.  Existence of a solid data management program and data
governance.
 Data management is an integrated set of functions that
defines the processes by which data is obtained, certified
fit for use, stored, secured, and processed in such a way
as to ensure that the accessibility, reliability and timeliness
of the data meet the needs of the data users within an
organization while Data governance is a core component
of data management that defines the roles, responsibilities
and processes for ensuring that data can be trusted and
used by the entire organization, with people who are
responsible for fixing and preventing issues with data.
 Creative data scientists—people who understand the
business, identify the limitations of their data, tools, and
techniques. These are needed to deliver real
improvements in decision making with an organization.
 Organization's management team must have a strong
commitment to data-driven decision making. This will
enable the organization to make quick superior decisions
in uncertain and changing situations so as to gain strong
competitive advantage.

9.  Spreadsheets: Data can be imported into an excel
spreadsheet program, which can then be used to perform
operations on the data based on formulas created by the end
user. Spreadsheets are also used to create reports and
graphs based on that data.
 Reporting and Querying Tools: Some reporting tools enable
organization's employees to get data access, present that
data in an easy-to-understand way (via formatted data,
graphs, and charts) so as to solve a problem or identify an
opportunity without additional help from IT organization.
 Data Visualization Tools: Data visualization is the
presentation of data in a pictorial or graphical format. This
will enhance human minds to understand better trends,
patterns and relationships of data than tabular data.
 Online Analytical Processing (OLAP): It is a method used to
analyze multidimensional data from many different
perspectives. It enables users to identify issues and
opportunities as well as perform trend analysis. Databases
built to support OLAP processing consist of data cubes that
contain numeric facts called measures, which are categorized
by dimensions, such as time and geography.

10.  Drill-down analysis: It involves the interactive
examination of high-level summary data in increasing
detail to gain insight into certain elements— an act
comparable to slowly peeling off the layers of an onion.
Drill-down analysis is a powerful tool used to gain
insight into details of business data to better
understand why something happened.
 Linear regression: A mathematical technique used to
predict the value of a dependent variable based on a
single independent variable and the linear relationship
between the two. It involves finding the best fitting
straight line through a set of observations of dependent
and independent variables.
The regression line is written as:
𝑌 = 𝑎 + 𝑏𝑋 + 𝜀, 𝑤ℎ𝑒𝑟𝑒
X = Observed value of the independent variable
Y = Predicted value of the dependent variable
a = Value of Y when X is zero, or the Y intercept
b = Slope of the regression line
𝜀 = Error in predicting the value of Y, given a value of X

11.  Criteria/Assumptions that must be satisfied
when using linear regression on a dataset:
 A linear relationship between the independent (X)
and dependent (Y) variables must exist.
 Errors in the prediction of the value of Y are
distributed in a manner that approaches the normal
distribution curve.
 Errors in the prediction of the value of Y are all
independent of one another.

12.  Data Mining: A BI analytics tool used to explore large
amounts of data for hidden patterns to predict future trends
and behaviors for use in It enable organizations to make
predictions about what will happen so that managers can be
proactive in capitalizing on opportunities and avoiding
potential problems decision making.
 Most commonly used data mining techniques are:
1. Association analysis: A specialized set of algorithms sorts
through data and forms statistical rules about relationships
among the items
2. Neural computing: Historical data is examined for patterns
that are then used to make predictions
3. Case-based reasoning: Historical if-then-else cases are
used to recognize patterns.
 Cross-Industry Process for Data Mining (CRISP-DM): A six-
phase structured approach used for the planning and
execution of a data mining project.

13. Phase Goal
Business
understanding
Clarify the business goals for the data mining project, convert
the goals into a predictive analysis problem and design a
project plan to accomplish these objectives.
Data
understanding
Gather data to be used (may involve multiple sources),
familiarize with the data and identify missing/null data
Data
preparation
Select a subset of data to be used, clean data to address quality
issues, and transform data into form suitable for analysis.
Modeling Apply selected modeling techniques
Evaluation Assess if the model achieves business goals.
Deployment Deploy the model into organization’s decision-making process

14.  Based on past responses to promotional mailings,
identify those consumers most likely to take
advantage of future mailings.
 Examine retail sales data to identify unrelated
products that are frequently purchased.
 Monitor credit card transactions to identify likely
fraudulent requests for authorization.
 Use hotel booking data to adjust room rates so as to
maximize revenue.
 Analyze demographic and behavior data to identify
potential customers.
 Study demographic data about valuable employees to
help focus future recruiting efforts.
 Recognize how changes in individual’s DNA sequence
affect the risk of developing common diseases such
as Alzheimer’s or cancer.

15.  Dashboards: A dashboard presents a set of KPIs about
the state of a process at a specific point in time in order
to helps organizations run effectively.
 Key performance indicators (KPIs) are metrics that track
progress in executing chosen strategies to attain
organizational objectives and goals. It consist of a
direction, measure, target and time frame.
 Examples of well-defined KPIs:
 For a university: Increase (direction) the five-year
graduation rate for incoming freshmen (measure) to at
least 80 percent (target) starting with the graduating
class of 2022 (time frame).
 For a customer service department: Increase (direction)
the number of customer phone calls answered within the
first four rings (measure) to at least 90 percent (target)
within the next three months (time frame).
 Options for displaying results in a dashboard include:
maps, gauges, bar charts, trend lines, scatter diagrams.

17.  Self-service analytics involves training, techniques and
processes that empower end users to work independently to
access data from approved sources to perform their own
analyses using an endorsed set of tools. It encourages
nontechnical end users to make decisions based on facts and
analyses rather than intuition and thus, eliminates decision-
making delays.
 Steps to ensure an effective self-service analytics program:
I. Data managers should collaborate with business units to
determine key metrics, agreed vocabulary, processes for
creating and publishing reports, define and implement
security and privacy policies.
II. Technology professionals should be allowed to retain data
control and governance while limiting information systems
staff involvement in routine tasks.
III. A self-service analytics tools must be intuitive, customizable
and easy to use.
 Commonly used BI software include: Autonomy IDOL (HP),
Cognos Business Intelligence (IBM), WebFOCUS1 (Information
builders), Business Intelligence & Hyperion (Oracle), Business
Objects (SAP). Refer to page 398 for details ….

18. Pros Cons
End users can easily get valuable
data.
It can create the risk of erroneous
analysis and reporting if not well
managed, and thus, can lead to
disastrous decisions within an
organization.
Encourages non-technical end
users to make decisions based on
facts and analyses rather than
intuition.
Different analyses can yield inconsistent
conclusions, resulting in wasted time
trying to explain the differences.
Accelerates and improves decision
making.
It can lead to over spending on
unapproved data sources and business
analytics tools.
Business user can access and use
the data they need for decision
making, without consulting
technology experts.
It can worsen problems by removing the
checks and balances on data preparation
and use (bad analysis).

19.  Word cloud: A visual depiction of a set of words that have
been grouped together because of the frequency of their
occurrence.
 Conversion funnel: It is a graphical representation that
summarizes the steps a consumer takes in making the
decision to buy a product and become a customer. It provides
a visual representation of the conversion data between each
step and enables decision makers to see what steps are
causing customers confusion or trouble.
Figure 9.3: The conversion funnel

20.  Data cube: A collection of data that contains numeric
facts called measures, which are categorized by
dimensions, such as time and geography.
 It is important to note that data within a data
cube has been summarized at a given level and
thus, data cube cannot present a more detailed
information. Hence, Data cubes is restricted to
only three dimensions.
 Cross-Industry Process for Data Mining (CRISP-
DM): A six-phase structured approach used for
the planning and execution of a data mining
project.
FIGURE 9.5: A data cube

21.  What kinds of BI analytics tools and techniques is
recommended for a Fire department likely to use in
sifting through all data and determining a
building’s risk score?
 What are the key components that an organization
must put into place to create an environment for a
successful BI and analytics program.