Transcript Gallaugher2_0-PPT

Information Systems: A
Manager’s Guide to Harnessing
Technology, version 2.0
John Gallaugher
© 2013, published by Flat World Knowledge
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Published by:
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© 2013, published by Flat World Knowledge
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Chapter 12
The Data Asset: Databases,
Business Intelligence, Big Data,
and Competitive Advantage
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Learning Objectives
• Understand how increasingly standardized data,
access to third-party data sets, cheap, fast computing
and easier-to-use software are collectively enabling a
new age of decision making
• Be familiar with some of the enterprises that have
benefited from data-driven, fact-based decision
making
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Data and Decision Making
• Big data: Massive amount of data available to
today’s managers
– Unstructured, big, and costly to work through
conventional databases
– Made available by new tools for analysis and insight
• Decision making is data-driven, fact-based and
enabled by:
– Standardized corporate data
– Access to third-party datasets through cheap, fast
computing and easier-to-use software
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Data and Decision Making
• Business intelligence (BI): Combines aspects of
reporting, data exploration and ad hoc queries, and
sophisticated data modeling and analysis
• Analytics: Driving decisions and actions through
extensive use of:
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Data
Statistical and quantitative analysis
Explanatory and predictive models
Fact-based management
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Enterprises that Have Benefited from
Data Mastery
• Walmart - Entered the top of the Fortune 500 list
• Harrah’s Casino Hotels - Grew twice as profitable as
Caesars and rich enough to acquire it
• Capital One - Found valuable customers that
competitors were ignoring
– Its ten-year financial performance was ten times
greater than the S&P 500
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Learning Objectives
• Understand the difference between data and
information
• Know the key terms and technologies associated
with data organization and management
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Organizing Data - Key Terms and
Technology
• Database: Single table or a collection of related
tables
• Database management systems (DBMS): Software
for creating, maintaining, and manipulating data
– Known as database software
• Structured query language (SQL): Used to create and
manipulate databases
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Organizing Data - Key Terms and
Technology
• Database administrator (DBA): Job title focused on
directing, performing, or overseeing activities
associated with a database or set of databases
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Database design and creation
Implementation
Maintenance
Backup and recovery
Policy setting and enforcement
Security
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Key Terms Associated with
Database Systems
Table or file
• List of data, arranged in columns or fields and rows or
records
Column or field
• Column in a database table
• Represents each category of data contained in a record
Row or record
• Row in a database table
• Represents a single instance of whatever the table keeps
track of like student or faculty
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Key Terms Associated with
Database Systems
Key
• Code that unlocks encryption
• Field or combination of fields used to uniquely identify a
record, and to relate separate tables in a database like
social security number
Relational database
• Most common standard for expressing databases
• Tables or files are related based on common keys
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Learning Objectives
• Understand various internal and external sources for
enterprise data
• Recognize the function and role of data aggregators,
the potential for leveraging third-party data, the
strategic implications of relying on externally
purchased data, and key issues associated with
aggregators and firms that leverage externally
sourced data
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Transaction Processing Systems
• Record a transaction or some form of businessrelated exchange, such as a cash register sale, ATM
withdrawal, or product return
– Transaction: Some kind of business exchange
• Loyalty card: System that provides rewards in
exchange for consumers allowing tracking and
recording of their activities
– Enhances data collection and represents a significant
switching cost
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Enterprise Software
• Firms set up systems to gather additional data
beyond conventional purchase transactions or Web
site monitoring
• Customer relationship management systems (CRM) Empower employees to track and record data at
nearly every point of customer contact
• Includes other aspects that touch every aspect of the
value chain including SCM and ERP
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Surveys
• Firms supplement operational data with additional
input from surveys and focus groups
• Direct surveys can give better information than a
cash register
• Many CRM products have survey capabilities that
allow for additional data gathering at all points of
customer contact
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External Sources
• Organizations can have their products sold by
partners and can rely heavily on data collected by
others
• Data from external sources might not yield
competitive advantage on its own
– Can provide operational insight for increased
efficiency and cost savings
– May give firms a high-impact edge
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Data Aggregators
• Firms that collect and resell data
• One has to be aware of the digital tracking of
individuals
– Possible by the availability of personal information
online
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Learning Objectives
• Know and be able to list the reasons why many
organizations have data that can’t be converted to
actionable information
• Understand why transactional databases can’t always
be queried and what needs to be done to facilitate
effective data use for analytics and business
intelligence
• Recognize key issues surrounding data and privacy
legislation
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Reasons for Poor Information
• Incompatible systems
– Legacy systems: Older information systems that are
incompatible with other systems, technologies, and
ways of conducting business
• Operational data cannot always be queried
– Most transactional databases are not set up to be
simultaneously accessed for reporting and analysis
– Database analysis requires significant processing
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Learning Objectives
• Understand what data warehouses and data marts
are and the purpose they serve
• Know the issues that need to be addressed in order
to design, develop, deploy, and maintain data
warehouses and data marts
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Data Warehouses and Data Marts
• Set of databases designed to support decision
making in an organization
• Structured for fast online queries and exploration
• Collects data from many different operational
systems
• Data mart: Database or databases focused on
addressing the concerns of a specific problem or
business unit
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Data Warehouses and Data Marts
• Marts and warehouses may contain huge volumes of
data
• Building large data warehouses can be expensive and
time consuming
• Large-scale data analytics projects should build on
visions with business-focused objectives
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Figure 12.2 - Information Systems
Supporting Operations and Analysis
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Maintaining Data Warehouses and
Data Marts
• Firms can address the broader issues needed to
design, develop, deploy, and maintain its system
through data:
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Relevance
Sourcing
Quantity and quality
Hosting
Governance
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Insights from Unstructured Big Data
• Hadoop - Made up of half-dozen separate software
pieces and requires the integration of these pieces to
work
• Advantages
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Flexibility
Scalability
Cost effectiveness
Fault tolerance
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E-Discovery
• Identifying and retrieving relevant electronic
information to support litigation efforts
– Firm should account for it in its archiving and data
storage plans
– Data can be used later and therefore should be stored
in order
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Learning Objectives
• Know the tools that are available to turn data into
information
• Identify the key areas where businesses leverage
data mining
• Understand some of the conditions under which
analytical models can fail
• Recognize major categories of artificial intelligence
and understand how organizations are leveraging this
technology
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Business Intelligence Toolkit
Canned reports
• Provide regular summaries of information in a predetermined format
Ad hoc reporting tools
• Puts users in control so that they can create custom reports on an asneeded basis
• By selecting fields, ranges, summary conditions, and other parameters
Dashboards
• Heads-up display of critical indicators that allow managers to get a
graphical glance at key performance metrics
Online analytical processing (OLAP)
• Takes data from standard relational databases, calculates and summarizes
the data, and then stores the data in a special database called a data cube
• Data cube: Stores data in OLAP report
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Data Mining
• Using computers to identify hidden patterns in, and
to build models from, large data sets
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Customer segmentation and market basket analysis
Marketing and promotion targeting
Collaborative filtering and customer churn
Fraud detection, financial modeling, and hiring and
promotion
• Prerequisites
– Organization must have clean, consistent data
– Events in that data should reflect trends
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Problems in Data Mining
Using bad data can give wrong estimates
• Firm is overexposed to risk
Historical consistency
• When the market does not behave as it has in the past,
computer-driven investment models are not effective
Over-engineer
• Build a model with so many variables that the solution
arrived at might only work on the subset of data used to
create it
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Skills for Data Mining
Information
technology
Statistics
Business
knowledge
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Artificial Intelligence (AI)
• Computer software that seeks to reproduce or mimic
human thought, decision making, or brain functions
– Data mining has its roots in AI
• Neural network: Examines data and hunts down and
exposes patterns, in order to build models to exploit
findings
• Expert systems: Leverages rules or examples to
perform a task in a way that mimics applied human
expertise
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Artificial Intelligence
• Genetic algorithms: Model building techniques
where computers examine many potential solutions
to a problem
– Modifies various mathematical models that have to be
searched for a best alternative
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Learning Objectives
• Understand how Walmart has leveraged information
technology to become the world’s largest retailer
• Be aware of the challenges that face Walmart in the
years ahead
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Walmart - Data-Driven Value Chain
• Largest retailer in the world
– Source of competitive advantage is scale
• Efficiency starts with a proprietary system called
retail link
– Retail link - Records a sale and automatically triggers
inventory reordering, scheduling, and delivery
– Inventory turnover ratio: Ratio of a company’s annual
sales to its inventory
• Back-office scanners keep track of inventory as
supplier shipments come in
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Data Mining Prowess
• Gets data from varying environmental conditions
• Protects the firm from a retailer’s twin nightmares
– Too much inventory
– Too little inventory
• Helps the firm tighten operational forecasts
– Enables prediction
• Data drives the organization
– Reports form the basis of sales meetings and
executive strategy sessions
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Sharing Data and Keeping Secrets
• Walmart shares sales data with relevant suppliers
– Stopped sharing data with information brokers
– Custom builds large portions of its information
systems to keep competitors off its trail
– Other aspects of the firm’s technology remain
confidential
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Challenges
• Finding huge markets or dramatic cost savings
– To boost profits and continue to move its stock price
higher
• Criticisms
– Accusations of sub par wages and a magnet for union
activists
– Poor labor conditions at some of the firm’s contract
manufacturers
– Demand prices so aggressively low that suppliers end
up cannibalizing their own sales at other retailers
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Learning Objectives
• Understand how Caesars has used IT to move from
an also-ran chain of casinos to become the largest
gaming company based on revenue
• Name some of the technology innovations that
Caesars is using to help it gather more data, and help
push service quality and marketing program
successaa
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Caesars’ Solid Gold CRM for the
Service Sector
• Caesars Entertainment provides an example of
exceptional data asset leverage in the service sector
– Focus on how this technology enables world-class
service through customer relationship management
• Leveraged its data-powered prowess to move:
– From a chain of casinos
– To largest gaming company by revenue
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Collecting Data
• Caesars’ collects customer data on everything one
might do at their properties
– Used to track preferences and see if a customer is
worth pursuing
• Total rewards loyalty card system
– Opt-in: Marketing effort that requires customer
consent
– Opt-out programs - Enroll all customers by default
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Most Valuable Customers
• Customer lifetime value (CLV): Present value of the
likely future income stream generated by an
individual purchaser
• Tracks over ninety demographic segments
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Each responds differently to different approaches
Iterative model of mining the data to identify patterns
Creates and tests a hypothesis against a control group
Analyzes to statistically verify the outcome
Profits come from locals and people 45 years and
older
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Data Driven Service
• Identifies the high value customers and gives them
special attention
• Customers could obtain reserved tables and special
offers
• Tracks gamblers suffering unusual losses and
provides feel-good offers to them
• CRM effort monitors any customer behavior changes
• Customers come back as they feel they are treated
better than competitors
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Data Driven Service
• Focuses on service quality and customer satisfaction
– Embedded in its information systems and operational
procedures
• Employees are measured on metrics that include
speed and friendliness
– Compensated based on guest satisfaction ratings
• Changed the corporate culture at Caesars
– From very-property-for-itself mentality
– To a collaborative, customer-focused enterprise
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Innovation and Strategy
Innovation
Strategy
• Has new innovations that help
it gather more data
• Push service quality and
marketing program success
• Firm launched:
• Interactive bill boards
• RFID-enabled poker chips
and under-table RFID
readers
• Incorporation of drink
ordering to gaming
machines
• Data advantage creates
intelligence for a high-quality
and highly personal customer
experience
• Data gives the firm a service
differentiation edge
• Loyalty program represents a
switching cost
• Firm’s technology is unique
and holds many patents
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Challenges
• Gaming is a discretionary spending item, and when
the economy tanks, gambling is one of the first
things consumers will cut
– Taken private: Publicly held company has its
outstanding shares purchased by an individual or by a
small group of individuals who wish to obtain
complete ownership and control
• Has been through a risky overly optimistic buyout
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