BIG DATA CHAPTER 2 IN DSS.pptx

1
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Business Analytics

Data Analytic Capabilities
7
“The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.” ~Stephen Hawking

Data Analytics Pillars and Foundation
10

Data Analytics Architecture
Business
Information
Technology
Partnership and
Stewardship
Wisdom
Information &
Knowledge
Data
Data
Rules
Tools
Action
Action
“The greatest value of a picture is when
it forces to notice what we never
expected to see.” ~John Tukey
11
Analysis is on top –
discovery and decision
making

Current state – Reliability Data
12
(Looking back
What happened?)
(Looking forward
What will happen?)
Information
Manual
Data

Current state – Reliability Data
13
(Looking back) (Looking forward)
Uses variety of data, techniques to predict
future trends and behavior patterns

Design Differences
14
High volume of transactions
Data In
Historical reporting & analysis
Information Out
Key:
Organize the
data for
analytics

Know What
Data
Know Why
Wisdom
Know How
Information &
Knowledge
Analysi
s
Translation
Data
Turn Data Into Information
Turn Information into Knowledge
ODS
Repository
Automate the manual process
18
**New

BIG DATA CHAPTER 2 IN DSS.pptx

Businesses Need Support for
Decision Making
• Uncertain economics
• Rapidly changing environments
• Global competition
• Demanding customers
• Taking advantage of information acquired by companies is a
Critical Success Factor.

Changing Business Environment
• Companies are moving aggressively to computerized support of their operations
=>The environment in which organizations operate today is becoming more and
more complex, creating:
• opportunities, and
• problems.
• Example: globalization.

Business Pressures–Responses–Support Model
– Business pressures /factors are the results of today’s business climate that
facilitate change in an attempt to handle challenges and, thus, create
opportunities.
– Responses to counter the pressures
– Support to better facilitate the process
– Decisions and support facilitate the monitoring of the environment and enhances
the response actions taken by organizations; by computer support in the form of
data warehousing, software tools for data analysis and manipulation, monitoring
conditions through the use of dashboards, etc
–

Business Pressures–Responses–Support Model

Business Environment Factors
markets, consumer demands, technology, and societal.
FACTOR DESCRIPTION
Markets Strong competition
Expanding global markets
Blooming electronic markets on the Internet
Innovative marketing methods
Opportunities for outsourcing with IT support
Need for real-time, on-demand transactions
Consumer Desire for customization
demand Desire for quality, diversity of products, and speed of delivery
Customers getting powerful and less loyal
Technology More innovations, new products, and new services
Increasing obsolescence rate
Increasing information overload
Social networking, Web 2.0 and beyond
Societal Growing government regulations and deregulation
Workforce more diversified, older, and composed of more women
Prime concerns of homeland security and terrorist attacks
Necessity of Sarbanes-Oxley Act and other reporting-related legislation
Increasing social responsibility of companies
Greater emphasis on sustainability

Organizational Responses
• Business Responses are the actions taken by a company to respond to the pressures and survive in the business
environment; these responses must be reactive, anticipative, adaptive, and proactive.
• Managers may take actions, such as
– Employ strategic planning
– Use new and innovative business models
– Restructure business processes
– Participate in business alliances
– Improve corporate information systems
– Improve partnership relationships
– Encourage innovation and creativity …cont…>

Managers actions, continued
– Improving customer service and relationships.
– Moving to electronic commerce (e-commerce).
– Moving to make-to-order production and on-demand manufacturing and
services.
– Using new IT to improve communication, data access (discovery of information),
and collaboration.
– Responding quickly to competitors' actions (e.g., in pricing, promotions, new
products and services).
– Automating many tasks of white-collar employees.
– Automating certain decision processes.
– Improving decision making by employing analytics.

Characteristics of Data for Good Decision Making
Source: speakingdata blog

The Information Gap
• The shortfall between gathering information and using it for
decision making.
– Firms have inadequate data warehouses.
– Business Analysts spend 2 days a week gathering and formatting
data, instead of performing analysis. (Data Warehousing Institute).
– Business Intelligence (BI) seeks to bridge the information gap.

Closing the Strategy Gap
• One of the major objectives of computerized decision support is to facilitate closing
the gap between the current performance of an organization and its desired
performance, as expressed in its mission, objectives, and goals, and the strategy to
achieve them

Data Mining
• “Data mining is an interdisciplinary subfield of computer
science. It is the computational process of discovering patterns
in large data sets involving methods at the intersection of
artificial intelligence, machine learning, statistics, and database
systems.” - Wikipedia
• Examining large databases to produce new information.
– Uses statistical methods and artificial intelligence to analyze data.
– Finds hidden features of the data that were not yet known.

BI
• Tools and techniques to turn data into meaningful information.
– Process: Methods used by the organization to turn data into
knowledge.
– Product: Information that allows businesses to make decisions.

What is Business Intelligence?
• Collecting and refining information from many sources
(internal and external)
• Analyzing and presenting the information in useful ways
(dashboards, visualizations)
• So that people can make better decisions
• That help build and retain competitive advantage.

Klipfolio - sample of a marketing dashboard

BI Applications
• Customer Analytics
• Human Capital Productivity Analysis
• Business Productivity Analytics
• Sales Channel Analytics
• Supply Chain Analytics
• Behavior Analytics

BI Initiatives
• 70% of senior executives report that analytics will be important
for competitive advantage. Only 2% feel that they’ve achieved
competitive advantage. (zassociates report)
• 70-80% of BI projects fail because of poor communication and
not understanding what to ask. (Goodwin, 2010)
• 60-70% of BI projects fail because of technology, culture and
lack of infrastructure (Lapu, 2007)

Evolution of BI
Source: Delaware Consulting

Evolution of BI (contd.)
Source: b-eye-network.com

Data Warehouse
• Collection of data
from multiple
sources (internal
and external)
• Summary, historical and raw data from operations.
• Data “cleaning” before use.
• Stored independently from
operational data.
• Broken down into DataMarts for
use.
Chapter 4 of ISBB Text

Data Warehouses
• Data warehouse
– Collection of data used to support decision-making applications and
generate business intelligence
• Multidimensional data
• Characteristics
– Subject oriented
– Integrated
– Time variant
– Type of data
– Purpose

Input
• Variety of sources
– External
– Databases
– Transaction files
– ERP systems
– CRM systems

ETL
• Extraction, transformation, and loading (ETL)
• Extraction
– Collecting data from a variety of sources
– Converting data into a format that can be used in transformation
processing
• Transformation processing
– Make sure data meets the data warehouse’s needs
• Loading
– Process of transferring data to the data warehouse

Exhibit 3.9 A Data Warehouse Configuration

Storage
• Raw data
• Summary data
• Metadata

Output
• Data warehouse supports different types of analysis
– Generates reports for decision making
• Online analytical processing (OLAP)
– Generates business intelligence
– Uses multiple sources of information and provides multidimensional
analysis
– Hypercube
– Drill down and drill up

Exhibit 3.10 Slicing and Dicing Data

Output (cont’d.)
• Data-mining analysis
– Discover patterns and relationships
• Reports
– Cross-reference segments of an organization’s operations for
comparison purposes
– Find patterns and trends that can’t be found with databases
– Analyze large amounts of historical data quickly

Data Warehouse Applications at InterContinental
Hotels Group (IHG)
• The new system has increased the company’s query response
time from hours to minutes
• It has generated valuable BI on both its customers and the
competition
• Future plans include the migration of financial data, which will
enable IHG to perform side-by-side analyses of operations,
marketing, sales, and financial data

Data Marts
• Data mart
– Smaller version of data warehouse
– Used by single department or function
• Advantages over data warehouses
• More limited scope than data warehouses

Summary
• Databases
– Accessing files
– Design principles
– Components
– Recent trends
• Data warehouses and data marts

5 Tasks of Data Mining in Business
• Classification – Categorizing data into actionable groups. (ex.
loan applicants)
• Estimation – Response rates, probabilities of responses.
• Prediction – Predicting customer behavior.
• Affinity Grouping – What items or services are customers likely
to purchase together?
• Description – Finding interesting patterns.

Data Mining Techniques
• Market Basket Analysis
• Cluster Analysis
• Decision Trees and Rule Induction
• Neural Networks

Market Basket Analysis
• Finding patterns or sequences in the way that people purchase
products and services.
• Walmart Analytics
– Obvious: People who buy Gin also buy tonic.
– Non-obvious: Men who bought diapers would also purchase beer.

Cluster Analysis
• Grouping data into like clusters based on specific attributes.
• Examples
– Crime map clusters to better deploy police.
– Where to build a cellular tower.
– Outbreaks of Zika virus.

Summary
• Explained BI, Analytics, Data Marts and Big
Data.
• Defined the characteristics of data for good
decision making.
• Described data mining in detail.
• Explained and gave examples of
market basket and cluster analysis.

Big Data
Volume
Velocity
Variety
Veracity

Big Data (Slide 1 of 7)
• Big data: Any set of data that is too large or too complex to be
handled by standard data-processing techniques and typical desktop
software.
• IBM describes the phenomenon of big data through the four Vs (as
shown in Figure 1.1):
• Volume.
• Velocity.
• Variety.
• Veracity.
• Walmart handles over one million purchase transactions per hour.
• Facebook processes more than 250 million picture uploads per day.

Figure 1.1: The 4 Vs of Big Data
Source: IBM

Volume:
• Because data are collected electronically, we are able to collect more of it.
• To be useful, these data must be stored, and this storage has led to vast
quantities of data.
Velocity:
• Real-time capture and analysis of data present unique challenges both in
how data are stored and the speed with which those data can be analyzed
for decision making.

Variety:
• More complicated types of data are now available and are proving to be of
great value to businesses.
• Text data are collected by monitoring what is being said about a company’s
products or services on social media platforms.
• Audio data are collected from service calls.
• Video data are collected by in-store video cameras and used to analyze
shopping behavior.
• Analyzing information generated by these nontraditional sources is more
complicated in part because of the processing required to transform the
data into a numerical form that can be analyzed.

Veracity:
• Veracity has to do with how much uncertainty is in the data.
• Inconsistencies in units of measure and the lack of reliability of responses
in terms of bias also increase the complexity of the data.

• Represents opportunities.
• Presents challenges in terms of data storage and processing, security,
and available analytical talent.
• The four Vs have led to new technologies:
• Hadoop: An open-source programming environment that supports big
data processing through distributed storage and processing on clusters
of computers.
• MapReduce: A programming model used within Hadoop that performs
two major steps: the map step and the reduce step.

• Data security, the protection of stored data from destructive forces or
unauthorized users, is of critical importance to companies.
• The complexities of the 4 Vs have increased the demand for analysts,
but a shortage of qualified analysts has made hiring more challenging.
• More companies are searching for data scientists, who know how to
process and analyze massive amounts of data.
• The Internet of Things (IoT) is the technology that allows data,
collected from sensors in all types of machines, to be sent over the
Internet to repositories where it can be stored and analyzed.

Business Analytics in Practice
Financial Analytics
Human Resource (HR) Analytics
Marketing Analytics
Health Care Analytics
Supply-Chain Analytics
Analytics for Government and Nonprofits
Sports Analytics
Web Analytics

Business Analytics in Practice (Slide 1 of 11)
Figure 1.2: The Spectrum of Business Analytics
Source: Adapted from SAS

• Predictive and prescriptive analytics are sometimes referred to as
advanced analytics.
Financial Analytics:
• Use of predictive models to:
• Forecast financial performance.
• Assess the risk of investment portfolios and projects.
• Construct financial instruments such as derivatives.
• Construct optimal portfolios of investments.
• Allocate assets.
• Create optimal capital budgeting plans.
• Simulation is also often used to assess risk in the financial sector.

Human Resource (HR) Analytics:
• New area of application for analytics.
• The HR function is charged with ensuring that the organization:
• Has the mix of skill sets necessary to meet its needs.
• Is hiring the highest-quality talent and providing an environment that
retains it.
• Achieves its organizational diversity goals.

Marketing Analytics:
• Marketing is one of the fastest-growing areas for the application of
analytics.
• A better understanding of consumer behavior through the use of scanner
data and data generated from social media has led to an increased
interest in marketing analytics.

Marketing Analytics (cont.):
• A better understanding of consumer behavior through marketing
analytics leads to:
• Better use of advertising budgets.
• More effective pricing strategies.
• Improved forecasting of demand.
• Improved product-line management.
• Increased customer satisfaction and loyalty.

Health Care Analytics:
• Descriptive, predictive, and prescriptive analytics are used to improve:
• Patient, staff, and facility scheduling.
• Patient flow.
• Purchasing.
• Inventory control.
• Use of prescriptive analytics for diagnosis and treatment may prove to be
the most important application of analytics in health care.

Supply-Chain Analytics:
• The core service of companies such as UPS and FedEx is the efficient
delivery of goods, and analytics has long been used to achieve efficiency.
• The optimal sorting of goods, vehicle and staff scheduling, and vehicle
routing are all key to profitability for logistics companies such as UPS and
FedEx.
• Companies can benefit from better inventory and processing control and
more efficient supply chains.

Analytics for Government and Nonprofits:
• Analytics for government to:
• Drive out inefficiencies.
• Increase the effectiveness and accountability of programs.
• Analytics for nonprofit agencies to ensure their effectiveness and
accountability to their donors and clients.

Sports Analytics
• Professional sports teams use to:
• Assess players for the amateur drafts.
• Decide how much to offer players in contract negotiations.
• Professional motorcycle racing teams use sophisticated optimization for
gearbox design to gain competitive advantage.
• Teams use to assist with on-field decisions such as which pitchers to use
in various games of a MLB playoff series.

Sports Analytics (cont.):
• The use of analytics for off-the-field business decisions is increasing
rapidly.
• Using prescriptive analytics, franchises across several major sports
dynamically adjust ticket prices throughout the season to reflect the
relative attractiveness and potential demand for each game.

Web Analytics:
• The analysis of online activity, which includes, but is not limited to, visits
to web sites and social media sites such as Facebook and LinkedIn.
• Leading companies apply descriptive and advanced analytics to data
collected in online experiments to determine the best way to:
• Configure web sites.
• Position ads.
• Utilize social networks for the promotion of products and services.

Legal and Ethical Issues in the
Use of Data and Analytics

Legal and Ethical Issues in the Use of Data and
Analytics(Slide 1 of 4)
• Increased attention has been paid to ethical concerns around data
privacy and the ethical use of models based on data.
• Companies have an obligation to protect the data and to not misuse
that data.
• Clients and customers have an obligation to understand trade-offs
between allowing their data to be collected, and the benefits they
accrue from allowing a company to collect and use that data.
• An agreement must be signed between the customer and the
company.

• One of the strictest privacy laws is the General Data Protection
Regulation.
• Went into effect in the European Union in May 2018.
• Stipulations:
• The request for consent to use an individual’s data must be easily
understood and accessible.
• The intended use of data must be specified.
• Must be easy to withdraw consent.
• The individual has a right to a copy of their data and the right to
demand their data be erased.

• Analytics professionals have a responsibility to behave ethically.
• This includes protecting data, being transparent about the data and
how it was collected, and what it does and does not contain.
• Analysts must be transparent about the methods used to analyze the
data and any assumptions that have to be made for the methods
used.
• Analysts must provide valid conclusions and understandable
recommendations to their clients.

• The American Statistical Association (ASA) and the Institute for
Operations Research and the Management Sciences (INFORMS)
provide ethical guidelines for analysts.
• The guidelines state that “Good statistical practice is fundamentally
based on transparent assumptions, reproducible results, and valid
interpretations.”

Characteristics of Data for Good Decision Making
Source: speakingdata blog

The Information Gap
• The shortfall between gathering information and using it for decision
making.
• Firms have inadequate data warehouses.
• Business Analysts spend 2 days a week gathering and formatting data, instead
of performing analysis. (Data Warehousing Institute).
• Business Intelligence (BI) seeks to bridge the information gap.

Closing the Strategy Gap
• One of the major objectives of computerized decision support is to facilitate
closing the gap between the current performance of an organization and its
desired performance, as expressed in its mission, objectives, and goals, and the
strategy to achieve them

• Business intelligence
• Infrastructure for collecting, storing, analyzing data produced by
business
• Databases, data warehouses, data marts, Hadoop, analytic platforms
• Business analytics
• Tools and techniques for analyzing data
• OLAP, statistics, models, data mining
Copyright © 2022, 2020, 2018 Pearson Education, Ltd. All Rights Reserved

BI
• Tools and techniques to turn data into meaningful information.
• Process: Methods used by the organization to turn data into knowledge.
• Product: Information that allows businesses to make decisions.

• Collecting and refining information from many sources (internal and
external)
• Analyzing and presenting the information in useful ways (dashboards,
visualizations)
• So that people can make better decisions
• That help build and retain competitive advantage.

Klipfolio - sample of a marketing dashboard

FitBit – Health Dashboard

BI Applications
• Customer Analytics
• Human Capital Productivity Analysis
• Business Productivity Analytics
• Sales Channel Analytics
• Supply Chain Analytics
• Behavior Analytics

BI Initiatives
• 70% of senior executives report that analytics will be important for
competitive advantage. Only 2% feel that they’ve achieved
competitive advantage. (zassociates report)
• 70-80% of BI projects fail because of poor communication and not
understanding what to ask. (Goodwin, 2010)
• 60-70% of BI projects fail because of technology, culture and lack of
infrastructure (Lapu, 2007)

Evolution of BI
Source: Delaware Consulting

Evolution of BI (contd.)
Source: b-eye-network.com

Data Warehouse
• Collection of data
from multiple
sources (internal
and external)
• Summary, historical and raw data from operations.
• Data “cleaning” before use.
• Stored independently from
operational data.
• Broken down into DataMarts for
use.
Chapter 4 of ISBB Text

Data Warehouses
• Data warehouse
• Collection of data used to support decision-making applications and generate
business intelligence
• Multidimensional data
• Characteristics
• Subject oriented
• Integrated
• Time variant
• Type of data
• Purpose

Input
• Variety of sources
• External
• Databases
• Transaction files
• ERP systems
• CRM systems

ETL
• Extraction, transformation, and loading (ETL)
• Extraction
• Collecting data from a variety of sources
• Converting data into a format that can be used in transformation processing
• Transformation processing
• Make sure data meets the data warehouse’s needs
• Loading
• Process of transferring data to the data warehouse

Exhibit 3.9 A Data Warehouse Configuration

Storage
• Raw data
• Summary data
• Metadata

Output
• Data warehouse supports different types of analysis
• Generates reports for decision making
• Online analytical processing (OLAP)
• Generates business intelligence
• Uses multiple sources of information and provides multidimensional analysis
• Hypercube
• Drill down and drill up

Exhibit 3.10 Slicing and Dicing Data

Output (cont’d.)
• Data-mining analysis
• Discover patterns and relationships
• Reports
• Cross-reference segments of an organization’s operations for comparison
purposes
• Find patterns and trends that can’t be found with databases
• Analyze large amounts of historical data quickly

Data Warehouse Applications at InterContinental
Hotels Group (IHG)
• The new system has increased the company’s query response time
from hours to minutes
• It has generated valuable BI on both its customers and the
competition
• Future plans include the migration of financial data, which will enable
IHG to perform side-by-side analyses of operations, marketing, sales,
and financial data

Data Marts
• Data mart
• Smaller version of data warehouse
• Used by single department or function
• Advantages over data warehouses
• More limited scope than data warehouses

Summary
• Databases
• Accessing files
• Design principles
• Components
• Recent trends
• Data warehouses and data marts

5 Tasks of Data Mining in Business
• Classification – Categorizing data into actionable groups. (ex. loan
applicants)
• Estimation – Response rates, probabilities of responses.
• Prediction – Predicting customer behavior.
• Affinity Grouping – What items or services are customers likely to
purchase together?
• Description – Finding interesting patterns.

Data Mining Techniques
• Market Basket Analysis
• Cluster Analysis
• Decision Trees and Rule Induction
• Neural Networks

Market Basket Analysis
• Finding patterns or sequences in the way that people purchase
products and services.
• Walmart Analytics
• Obvious: People who buy Gin also buy tonic.
• Non-obvious: Men who bought diapers would also purchase beer.

Cluster Analysis
• Grouping data into like clusters based on specific attributes.
• Examples
• Crime map clusters to better deploy police.
• Where to build a cellular tower.
• Outbreaks of Zika virus.

Summary
• Explained BI, Analytics, Data Marts and Big Data.
• Defined the characteristics of data for good decision
making.
• Described data mining in detail.
• Explained and gave examples of
market basket and cluster analysis.

BIG DATA CHAPTER 2 IN DSS.pptx

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