In this advanced business analysis training session, you will learn Data Analytics Business Intelligence. Topics covered in this session are: • What is Business Intelligence? • Data / information / knowledge • What is Data Analytics? • What is Business Analytics? • What is Big Data? • Types of Data • Types of Analytics • What is Business Intelligence? For more information, click here: https://www.mindsmapped.com/courses/business-analysis/advanced-business-analyst-training/

1. Advanced Business Analysis Training
Data Analytics and Business Intelligence

2. Page 2Classification: Restricted
Agenda
• What is Business Intelligence?
• Data / information / knowledge
• What is Data Analytics?
• What is Business Analytics?
• What is Big Data?
• Types of Data
• Types of Analytics
• What is Business Intelligence?

3. Organizations are drowning in data…
 … but lacking in information / knowledge
 Our ability to collect and store data seems to have surpassed
our ability to make sense of it!
 Important trends:
 Storage capacity continues to rise rapidly
 Cost of storage continues to drop

4. What is Business Intelligence?
A Simple Definition: The applications and technologies
transforming Business Data into Action
Fact-based decision making typically involves a subset of the
following skills/tools…
 Data querying / SQL
 Database design / data warehousing
 Data mining
 Decision support systems / simulations
 Data visualization / Dashboards

5. The BI goal
BI is about using data to
help enterprise users make
better business decisions

6. BI / analytics in all industries
A few examples
 Pro Sports
 Oakland A’s, New England Patriots – recruiting
players
 Dallas Cowboys – merchandising
 Gambling
 Harrah’s  Caesar’s
 Quote from Intel manager:
 “In God we trust, all others bring data”
- Demming

7. Managers value analytics (survey)

8. Media / Energy Aero / Industrial Insurance / Health Life Sciences Other
Communications Automotive Finance / Banking Consumer Goods High Tech
A sample of Oracle BI Customers

9. Low(1)
Severity
VeryHigh(5)
BI
1. Commodity
Price Increase (10%)
1. Collaboration
& BI (10%)
1. Power Shift (8%)
1. Global
Competition (10%)
1. SC
Fragmentation (31%)
1. Lack of Global
Project
Resources (12%)
1. Regulatory
Compliance (7%)
1. Developing
SCM Talent (7%)
Exec survey: What Keeps You Up at Night?
1. IT Integration (5%)
1. IP (2%)

10. Data / information / knowledge
 Data – a collection of raw value elements or facts
used for calculating, reasoning, or measuring.
 Information – the result of collecting and organizing
data in a way that establishes relationship between
data items, which thereby provides context and
meaning
 Knowledge – the concept of understanding
information based on recognized patterns in a way
that provides insight to information.

11. Famous “Beer and Diapers” BI example
 Consider the follow convenience-store transactional data (register
sales receipts)

12. Famous “Beer and Diapers” BI example
0
50
100
150
200
250
300
350
400
Mon Tue Wed Thu Fri Sat Sun
AverageSales
Beer and Diapers
D177: Diaper
B675: Beer
 What did managers learn? That beer & diapers are often bought by
men on Thursday and Saturdays. Why?
 Data  Information  Knowledge (Insight)

13. Goal: Convert Data to (Actionable) Knowledge
Data
Info
Knowledge
Increasing
Value
Hard to do in practice – requires commitment/effort.

14. Data Analytics – Business Analytics
Data Analytics and Business Intelligence
Day– 6 (6th July) 2018

15. Analytics is the use of:
data,
information technology,
statistical analysis,
quantitative methods, and
mathematical or computer-based models
to help managers gain improved insight about their
business operations and make better, fact-based
decisions.
Business Analytics (BI) is a subset of Data Analytics
What is Data Analytics?
1-15

16. Business Analytics Applications
 Management of customer relationships
 Financial and marketing activities
 Supply chain management
 Human resource planning
 Pricing decisions
 Sport team game strategies
What is Business Analytics?
1-16

17. Importance of Business Analytics
 There is a strong relationship of BA with:
- profitability of businesses
- revenue of businesses
- shareholder return
 BA enhances understanding of data
 BA is vital for businesses to remain competitive
 BA enables creation of informative reports
What is Business Analytics?
1-17

18.  Descriptive analytics
- uses data to understand past and present
 Predictive analytics
- analyzes past performance
 Prescriptive analytics
- uses optimization techniques
Scope of Business Analytics
1-18

19. Retail Markdown Decisions
 Most department stores clear seasonal inventory
by reducing prices.
 The question is:
When to reduce the price and by how much?
 Descriptive analytics: examine historical data for
similar products (prices, units sold, advertising, …)
 Predictive analytics: predict sales based on price
 Prescriptive analytics: find the best sets of pricing
and advertising to maximize sales revenue
Scope of Business Analytics
1-19

20.  DATA
- collected facts and figures
 DATABASE
- collection of computer files containing data
 INFORMATION
- comes from analyzing data
Data for Business Analytics
1-20

21.  Metrics are used to quantify performance.
 Measures are numerical values of metrics.
 Discrete metrics involve counting
- on time or not on time
- number or proportion of on time deliveries
 Continuous metrics are measured on a continuum
- delivery time
- package weight
- purchase price
Data for Business Analytics
1-21

22. A Sales Transaction Database File
Data for Business Analytics
1-22
Figure 1.1
Entities
Records
Fields or Attributes

23. What is Big Data?
• Information from multiple internal and external sources:
• Transactions
• Social media
• Enterprise content
• Sensors
• Mobile devices
• Companies leverage data to adapt products and services to:
• Meet customer needs
• Optimize operations
• Optimize infrastructure
• Find new sources of revenue
• Can reveal more patterns and anomalies
• IBM estimates that by 2015 4.4 million jobs will be created globally to support
big data
• 1.9 million of these jobs will be in the United States

24. Types of Data

25. •When collecting or gathering data we collect
data from individuals cases on particular
variables.
• A variable is a unit of data collection whose
value can vary.
• Variables can be defined into types
according to the level of mathematical scaling
that can be carried out on the data.
• There are four types of data or levels of
measurement:
1. Categorical
(Nominal)
2. Ordinal
3. Interval 4. Ratio

26. Classifying Data Elements in a Purchasing Database
Data for Business Analytics
1-26
Figure 1.2

27. (continued)
Classifying Data Elements in a Purchasing
Database
Data for Business Analytics
1-27
Figure 1.2

28. • Nominal or categorical data is data that comprises of categories
that cannot be rank ordered – each category is just different.
•The categories available cannot be placed in any order and no
judgement can be made about the relative size or distance from one
category to another.
 Categories bear no quantitative relationship to one another
 Examples:
- customer’s location (America, Europe, Asia)
- employee classification (manager, supervisor,
associate)
•What does this mean? No mathematical operations can be
performed on the data relative to each other.
•Therefore, nominal data reflect qualitative differences rather than
quantitative ones.
Categorical (Nominal) data

29. Examples:
Nominal data
What is your
gender? (please
tick)
Male
Female
Did you enjoy
the film?
(please tick)
Yes
No

30. •Systems for measuring nominal data must
ensure that each category is mutually
exclusive and the system of measurement
needs to be exhaustive.
• Variables that have only two responses i.e.
Yes or No, are known as dichotomies.
Nominal data

31. • Ordinal data is data that comprises of categories that can
be rank ordered.
• Similarly with nominal data the distance between each
category cannot be calculated but the categories can be
ranked above or below each other.
 No fixed units of measurement
 Examples:
- college football rankings
- survey responses
(poor, average, good, very good, excellent)
•What does this mean? Can make statistical judgements and
perform limited maths.
Ordinal data

32. Example:
Ordinal data
How satisfied are you with
the level of service you have
received? (please tick)
Very satisfied
Somewhat satisfied
Neutral
Somewhat dissatisfied
Very dissatisfied

33. • Both interval and ratio data are examples of scale data.
• Scale data:
• data is in numeric format ($50, $100, $150)
•data that can be measured on a continuous scale
• the distance between each can be observed and as a
result measured
• the data can be placed in rank order.
Interval and ratio
data

34. • Ordinal data but with constant
differences between observations
• Ratios are not meaningful
• Examples:
•Time – moves along a continuous
measure or seconds, minutes and so on
and is without a zero point of time.
• Temperature – moves along a continuous
measure of degrees and is without a true
zero.
•SAT scores
Interval data

35. • Ratio data measured on a continuous scale
and does have a natural zero point.
 Ratios are meaningful
 Examples:
• monthly sales
• delivery times
• Weight
• Height
• Age
Ratio data

36. Types of Analytics

37. Model:
 An abstraction or representation of a real system,
idea, or object
 Captures the most important features
 Can be a written or verbal description, a visual
display, a mathematical formula, or a
spreadsheet representation
Decision Models
1-37

38. Decision Models
1-38
Figure 1.3

39.  A decision model is a model used to understand,
analyze, or facilitate decision making.
 Types of model input
- data
- uncontrollable variables
- decision variables (controllable)
Decision Models
1-39

40. Descriptive Decision Models
 Simply tell “what is” and describe
relationships
 Do not tell managers what to do
Decision Models
An Influence Diagram for Total Cost
1-40

41. Descriptive Analytics
What has occurred?
Descriptive analytics, such as data
visualization, is important in helping
users interpret the output from
predictive and predictive analytics.
• Descriptive analytics, such as reporting/OLAP,
dashboards, and data visualization, have been
widely used for some time.
• They are the core of traditional BI.

42. A Break-even Decision Model
TC(manufacturing) = $50,000 + $125*Q
TC(outsourcing) = $175*Q
Breakeven Point:
Set TC(manufacturing)
= TC(outsourcing)
Solve for Q = 1000 units
Decision Models
1-42
Figure 1.7

43. • Predictive Decision Models often incorporate
uncertainty to help managers analyze risk.
• Aim to predict what will happen in the future.
• Uncertainty is imperfect knowledge of what will
happen in the future.
• Risk is associated with the consequences of what
actually happens.
Decision Models
1-43

44. Predictive Analytics
What will occur?
• Marketing is the target for many predictive analytics
applications.
• Descriptive analytics, such as data visualization, is
important in helping users interpret the output from
predictive and prescriptive analytics.
• Algorithms for predictive analytics, such as regression
analysis, machine learning, and neural networks, have
also been around for some time.
• Prescriptive analytics are often referred to as advanced
analytics.

45. A Linear Demand Prediction Model
As price increases, demand falls.
Decision Models
1-45
Figure 1.8

46. A Nonlinear Demand Prediction Model
Assumes price elasticity (constant ratio of %
change in demand to % change in price)
Decision Models
1-46
Figure 1.9

47. Prescriptive Decision Models help decision makers
identify the best solution.
 Optimization - finding values of decision variables
that minimize (or maximize) something such as
cost (or profit).
 Objective function - the equation that minimizes
(or maximizes) the quantity of interest.
 Constraints - limitations or restrictions.
 Optimal solution - values of the decision variables
at the minimum (or maximum) point.
Decision Models
1-47

48. Prescriptive Analytics
What should
occur?
• For example, the use of mathematical programming for revenue
management is common for organizations that have “perishable” goods
(e.g., rental cars, hotel rooms, airline seats).
• Harrah’s has been using revenue management for hotel room pricing for
some time.
• Prescriptive analytics are often referred to as
advanced analytics.
• Regression analysis, machine learning, and
neural networks
• Often for the allocation of scarce resources

49. Organizational Transformation
• Brought about by opportunity
or necessity
• The firm adopts a new business
model enabled by analytics
• Analytics are a competitive
requirement

50. 2013 Academic Research
• A 2011 IBM/MIT Sloan Management Review
research study found that top performing
companies in their industry are much more
likely to use analytics rather than intuition
across the widest range of possible
decisions.
• A 2011 TDWI report on Big Data Analytics
found that 85% of respondents indicated
that their firms would be using advanced
analytics within three years

51. Conditions that Lead to Analytics-based
Organizations
• The nature of the industry
• Seizing an opportunity
• Responding to a problem

52. Complex Systems
• Tackle complex problems and provide
individualized solutions
• Products and services are organized around the
needs of individual customers
• Dollar value of interactions with each customer is
high
• There is considerable interaction with each
customer
• Examples: IBM, World Bank, Halliburton

53. Volume Operations
• Serves high-volume markets through standardized
products and services
• Each customer interaction has a low dollar value
• Customer interactions are generally conducted
through technology rather than person-to-person
• Are likely to be analytics-based
• Examples: Amazon.com, eBay, Hertz

54. The Nature of the Industry: Online
Retailers
BI Applications
• Analysis of clickstream data
• Customer profitability analysis
• Customer segmentation analysis
• Product recommendations
• Campaign management
• Pricing
• Forecasting
• Dashboards

55. The Nature of the
Industry
• Online retailers like Amazon.com and Overstock.com are high volume
operations who rely on analytics to compete.
• When you enter their sites a cookie is placed on your PC and all clicks
are recorded.
• Based on your clicks and any search terms, recommendation engines
decide what products to display.
• After you purchase an item, they have additional information that is used
in marketing campaigns.
• Customer segmentation analysis is used in deciding what promotions to
send you.
• How profitable you are influences how the customer care center treats
you.
• A pricing team helps set prices and decides what prices are needed to
clear out merchandise.
• Forecasting models are used to decide how many items to order for
inventory.
• Dashboards monitor all aspects of organizational performance

56. • What management, organization, and technology factors
were behind the Cincinnati Zoo losing opportunities to
increase revenue?
• Why was replacing legacy point-of-sale systems and
implementing a data warehouse essential to an
information system solution?
• How did the Cincinnati Zoo benefit from business
intelligence? How did it enhance operational
performance and decision making? What role was played
by predictive analytics?
• Visit the IBM Cognos Web site and describe the business
intelligence tools that would be the most useful for the
Cincinnati Zoo.
Analytics Help the Cincinnati Zoo Know
Its Customers

57. Consider the 4 posted analytics articles
 Company examples:
 Target
 Harrah’s
 Match.com
 Dallas Cowboys
 What do these companies have in common?
 What does this mean to your career?

58. Compare these examples
Company Where is their data
coming from?
How is it changing
what they do?
Dallas Cowboys
Match.com
Target
Harrah’s

59. Business Intelligence (BI)
Data Analytics and Business Intelligence
Day– 6 (6th July) 2018

60. What is Business Intelligence?
Business Intelligence enables the
business to make intelligent, fact-based
decisions
Aggregate
Data
Database, Data Mart, Data
Warehouse, ETL Tools,
Integration Tools
Present
Data
Enrich
Data
Inform a
Decision
Reporting Tools,
Dashboards, Static
Reports, Mobile Reporting,
OLAP Cubes
Add Context to Create
Information, Descriptive
Statistics, Benchmarks,
Variance to Plan or LY
Decisions are Fact-based
and Data-driven

61. • Content
– The business determines the “what”, BI enables the “how”
• Performance
– Minimize report creation and collection times (near zero)
• Usability
– Delivery Method Push vs Pull
– Medium  Excel, PDF, Dashboard, Cube, Mobile Device
– Enhance Digestion  “A-ha” is readily apparent, fewer clicks
– Tell a Story  Trend, Context, Related Metrics, Multiple Views
CPU – Content, Performance, Usability

62. How Important is BI?
Top 10 Business and Technology Priorities for 2011:
1. Cloud computing
2. Virtualization
3. Mobile technologies
4. IT Management
5. Business Intelligence
6. Networking, voice and data communications
7. Enterprise applications
8. Collaboration technologies
9. Infrastructure
10. Web 2.0
Source: Gartner’s 2011 CIO Agenda (aka “Reimagining IT: The 2011 CIO Agenda”).

63. The July 2010 Forrester report “Technology
Trends That Retail CIOs Must Tap to Drive
Growth” identified the following technologies
that retail CIOs should be considering as
part of an overall architecture strategy:
Mobile
Cloud
Social Computing
Supply Chain
Micropayments
Business Intelligence/Analytics

64. Why is Business Intelligence So Important?
Time
With Business Intelligence, we can get data to you in a timely manner.
Making Business
Decisions is a Balance
Data Opinion
(aka Best Professional
Judgment)
In the absence of data, business decisions are often made by the HiPPO.

65. Major BI Trends
• Mobile
• Cloud
• Social Media
• Advanced Analytics

66. What BI technologies will be the most
important to your organization in the next 3
years?
1. Predictive Analytics
2. Visualization/Dashboards
3. Master Data Management
4. The Cloud
5. Analytic Databases
6. Mobile BI
7. Open Source
8. Text Analytics
TDWI Executive Summit – August 2010

67. Advanced Analytics / Predictive Analytics
• Data Mining
• Regression
• Monte Carlo Simulation
• “Statistically Significant”
• Predicting Customer Behavior
– Churn/Attrition
– Purchases
– Profiling

68. BI Today vs Tomorrow
• “BI today is like reading the newspaper”
– BI reporting tool on top of a data
warehouse that loads nightly and produces
historical reporting
• BI tomorrow will focus more on real-time
events and predicting tomorrow’s
headlines

69. Collegiate Admissions Criteria
• Test Scores: SAT, ACT, AP Exams
• Grade Point Average
• Class Rank
• High School “Strength”
• Extracurricular Activities: Band/Choir, Clubs, Sports
• Non-School Activities: Work, Volunteer, Community Groups
• Area of Focus – Intended Major
• Family legacy
• Home State or Country
Regression Outcome = Graduation (binary) + GPA (linear)
69

70. Retail Analytics
• Market Basket Analytics
• Text Analytics
• Customer Segmentation/Clustering
• Tailored Product Assortments
• Inventory Forecasting

71. 71
Amazon.com and NetFlix
Collaborative Filtering tries to predict other items a
customer may want to purchase based on what’s in their
shopping cart and the purchasing behaviors of other
customers

72. 72
What Is Text Analytics?
…turning unstructured customer comments into
actionable insights
…finding nuggets of insight in text data that will
improve our business
From Wikipedia:
… a set of linguistic, statistical, and machine learning
techniques that model and structure the information
content of textual sources for business intelligence,
exploratory data analysis, research, or investigation

73. 73
Customer Sat
Survey
Comments
Unstructured Text Processing
Facebook
Page
Blogs
Competitors’
Facebook
Pages
Public Web Sites,
Discussion Boards,
Product Reviews
Alerts,
Real-time
Action
Twitter
Page
Services
Quality Cost Friendliness
Email
Adhoc
Feedback
Call
Center
Notes,
Voice

77. What is Information Governance?
Information Governance
•Data Stewardship
•Data Quality
•Data Governance
•Master Data Management
•Data Stewards for Master Data “Hubs”
•Customer, Vendor, Product, Location, Employee, G/L Accounts
PREVENTS
Garbage
In
Garbage
Out
BY
ENCOMPASSING
•Report Governance
•Metric Governance
77
CREATING SIGNIFICANT
BUSINESS VALUE

78. BI Technologies
•Analytic Databases
•BI is a consolidating industry
– Oracle: Siebel, Hyperion, Brio, Sun
– SAP: Business Objects, Sybase
– IBM: Cognos, SPSS, Coremetrics, Unica, Netezza
– EMC: Greenplum
– HP: Vertica
– Teradata: Aster Data
•Independent vendors: MicroStrategy, Informatica, SAS
•Reporting standards determined mainly by Microsoft,
Apple and Adobe
Teradata
Netezza
DB2
Oracle
SQL Server
Vertica
Aster Data
Par Accel
Greenplum
Semantic Databases
(TIDE)

79. BI Technologies (cont’d)
•If you want to learn more about Analytic Databases:
http://hosted.mediasite.com/mediasite/Viewer/?peid=120d6
b7ba227498b96a8c0cd01349a791d
•If you want to learn more about BI in the Cloud:
http://hosted.mediasite.com/mediasite/Viewer/?peid=e6d91
148a71a47969824c22b3b20d6221d

80. Recommended Reading List
1. Outliers -- Malcolm Caldwell
2. Moneyball -- Michael Lewis
3. The Black Swan -- Nassim Nicholas Taleb
4. Competing on Analytics -- Tom Davenport
5. How to Lie with Statistics -- Darrell Huff
6. Bringing Down the House -- Ben Mezrich
7. Super Crunchers -- Ian Ayres
8. Priceless -- William Poundstone
9. Drilling Down -- Jim Novo
10.The New Rules of Marketing -- Fred Newell

81. Page 81Classification: Restricted
Thank You!