INFORMATION
TECHNOLOGY IN
BUSINESS AND
SOCIETY
SESSION 25 – REVIEW AND WRAP UP
SEAN J. TAYLOR
ADMINISTRATIVIA
• G1: Submit group feedback forms
• G2:
• You do not need to dress up.
• Bring slides on a USB drive or bring a
laptop.
• 15 minutes total. Optionally end 2-3
minutes early to take questions.
• Not everyone needs to present, but make
it clear to me who did what!
OUTLINE
•
•
•
•
•
•
Relational databases
SQL
Data mining
Visualizing data
Software engineering
Location-based
services
• Pricing
• Network effects
• Lock-in and Switching
costs
From the first half:
• Porter’s Five Forces
• Innovator’s Dilemma
• IT-enabled strategies
THE FIVE COMPETITIVE FORCES
firm = company = organization = business = competitor
Barriers to Entry, or
Threat of new Entrants
The extent of rivalry
between the existing firms
in the focal industry
The threat of entry
by potential entrants
(new firms) into
the focal industry
Industry that
you are analyzing
(focal industry)
Bargaining Power
of Suppliers
The bargaining power
of the firms that sell
inputs to the firms in
the focal industry
Rivalry Among
Existing Competitors
Threat of Substitute
Products or Services
The threat of products/services that could
substitute (be used
instead of) the finished products made by
the firms in the focal industry
Bargaining Power
of Buyers
The bargaining power
of the customers that buy
the finished products of
the firms in the focal industry
SUSTAINING VS. DISRUPTIVE
INNOVATIONS
FOUR KEY INTERNET-ENABLED
STRATEGIES
4 Key Internet-enabled Strategies for Competitive
Advantage:
1. Disintermediation
2. Mass Customization
3. Personalization
4. Global Reach
RELATIONAL DATABASES:
OBJECTIVES
• Know the names for all the key terms.
• Argue why using a RDBMS is important.
• Identify anomalies that could arise from
storing data the wrong way.
• Draw an E-R diagram for an application.
• Normalize a database I give you.
RELATIONAL DATABASES
Relational Database
Tables
Records
Fields
Field values
Bytes, bits
Field
Record
Field value
Student
Table
Last Name
SS#
DOB
Major
Smith
100201122
06/11/84
IS
Kim
200202222
1/1/85
FIN
Davis
300201232
12/31/81
MKT
Pat
999132212
3/3/88
ACC
ADVANTAGES
1. Consistency
•
We can restrict the values of certain fields (e.g.
dates, integers)
• We can impose other kinds of constraints (all costs
must be positive, last names must be included,
orders must have addresses)
• Data look the same to all users at the same time.
2. Centralization
•
Many different users can edit and view the data
simultaneously. Efficient sharing of information.
3. Efficient Querying
•
SQL and other query languages can be used to
create complex reports quickly
PROBLEMS WITH EXCEL?
When should you use a database instead of Excel?
–
Insertion anomalies
–
Deletion anomalies
–
Update anomalies
}
Data Quality Problems
Should we just create multiple workbooks in Excel?
–
The real power of a database: Querying
–
How would you answer the following question in Excel?
–
Find customers that spend on average $50 per book order,
that live on West Coast or on the East Coast (but not in
Midwest) and whose annual income is at least $150K
SIMPLE HOSPITAL SYSTEM ERD
WARD
has
assigned
NURSE
DOCTOR
accommodates
cares
for
treats
PATIENT
NORMALIZING AMAZON’S
DATA
• The process of assuring that a database can be implemented
effectively as a set of two-dimensional tables
• Unlike Excel though, the tables are connected
• Prevents insertion, deletion and update anomalies
SQL:
OBJECTIVES
• Given a query, describe in words what result it
would give you.
• Given a business question and a set of
tables/fields, write a query that answers that
question.
• Correct a query that has an error in the syntax or
otherwise will not run.
COMPLETE QUERY EXAMPLE
SELECT ISBN, BookName, Price, Publisher
FROM Book
WHERE
BookName like '*Information Systems*'
AND PubDate > #1/1/2002#
AND Price < 100
ORDER BY Price
ISBN is a
Foreign Key here
TABLE JOIN EXAMPLE
SELECT BookName, Date FROM Book, Order
Where Book.ISBN = Order.ISBN
Order By Book.ISBN,
Order.Date
Use
“table.column”
format to avoid
ambiguity
Joining/matching
criteria: very
important, don’t
forget!
MULTIPLE JOINS
WITH WHERE AND GROUP BY
SELECT FavoriteMovie, count(*)
FROM Profiles, FavoriteBooks, FavoriteMovies
WHERE
FavoriteMovies.ProfileId = Profiles.ProfileId
and FavoriteBooks.ProfileID = Profiles.ProfileID
and FavoriteBook = "The Great Gatsby"
GROUP BY FavoriteMovie ORDER BY count(*) desc;
DATA MINING:
OBJECTIVES
• Explain how competitive advantages can result from
data-driven decision making.
• What obstacles are there to implementing a data-driven
strategy?
• Given an application, is it appropriate to use
classification? Regression? Clustering?
• Why do we need to reduce dimensionality of our
features?
• Why do we keep hold-out data and how does crossvalidation work?
THE DATA-DRIVEN FIRM
Why do we see these changes now?
• Collect: easier to collect, store information
about consumers, technologies, markets
• Respond: Fast internal communication means
that firms are agile enough to respond to
external information
• Process: Firms can process large volumes of
data to make intelligent decisions
DATA-DRIVEN
CHALLENGES
1. Measurement
What should be measured and how?
2. Incentives
How can we design incentives around these measures
without creating adverse consequences?
3. Infrastructure
Do we have the right infrastructure (servers, software,
etc) in place to measure and analyze the data we have?
4. Skills
Do we have the skills we need to accomplish these
tasks?
WHAT IS DATA MINING?
•
•
•
Data Mining – process of discovering new (nonobvious) patterns from large data sets (databases)
• artificial intelligence
• machine learning
• statistics
Data mining is an automatic or semi-automatic process
Types of data mining tasks:
•
classification – classify new data into existing structure
(categories or “classes”) e.g., email as “spam” or “not spam”
•
regression – model the shape of the data with least error
•
cluster analysis – discover groups or structures in the data
•
anomaly detection – find unusual or outlier data records
•
association rule mining – discover relationships between data
variables
DATA MINING AS A PROCESS
•
Cross Industry Standard Process for Data Mining (CRISP-DM)
•
Practically, the data mining process involves:
1)
2)
3)
4)
5)
6)
•
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
Pre-processing – compile a large enough data set and get it
“ready” for analysis e.g., “Data Warehousing”
•
•
Can involve integration of various databases within a business
sanitization/cleaning of data
•
Data mining task
•
Validation of results
•
Is the output “predictive” , i.e., how good is the process when
applied to new data that was not in the training set?
HOLDOUT & CROSS-VALIDATION
Data consists of sets of:
Data
{𝑥; 𝑦}
where x are covariates, y outcomes
(input)
data
(output)
models
Modeling
Algorithm
M1
holdout
𝑥ℎ1 ; 𝑦ℎ1
𝑥ℎ1
𝑦ℎ2,𝑚1
(the y of the 1st holdout data
as predicted by model 1)
How does 𝑦ℎ1 compare to 𝑦𝑚1 ?
How much does this depend on exactly which data we held out?
VISUALIZATION:
OBJECTIVES
• What is Anscombe’s quartet and why does it
matter?
• What are the benefits of exploratory data
analysis (EDA)?
• Interpret a histogram, scatter plot, density plot,
or box-plot. Tell me what you see and why it’s
important.
• Given a data set and a general question, which
visualization technique would you use and why?
ANSCOMBE’S QUARTET
Your brain can efficiently process properly visualized data.
EDA:
EXPLORATORY DATA ANALYSIS
• An approach to analyzing data sets to summarize
their main characteristics in easy-to-understand
form.
• Often with visual graphs, without using a
statistical model or having formulated a
hypothesis.
• Helps to formulate hypotheses that could be
tested on new data-sets.
SOFTWARE ENGINEERING:
OBJECTIVES
• Why is software engineering difficult?
• Discuss the essential difficulties of software
engineering.
• Explain each stage of the waterfall model.
• How does the agile methodology change the way
software is built compared to the waterfall
model?
ESSENTIAL DIFFICULTIES
1. Complexity
•
Hard to manage large teams
•
Hard to understand system, side-effects
2. Conformity
• Software is expected to meet all users’ needs
3. Changeability
• Pressure/ability to change
4. Invisibility
•
No way to see it all at once, visually
BUILD OR BUY?
WHY BUY?
WHY BUILD?
• Time to use
• Customized, all
requirements met
• External support
• No risk of project
failure
• Upgrades
• Network effects
WATERFALL MODEL
“AGILE” METHODOLOGY
LOCATION-BASED SERVICES:
OBJECTIVES
• Identify the components and participants in LBS
applications
• Describe the interaction of the components
• Types of context awareness
• Adaption in LBS apps
• Push vs. Pull
• Latency vs. Bandwidth
LBS: AN INTERSECTION OF
TECHNOLOGIES
GIS / Spatial
databases
Web
GIS
Internet
LBS
Mobile
GIS
Mobile
Internet
Mobile
Devices
CONTEXTUAL ADAPTION
1.
Information level: the content of the
information presented is adapted.
(e.g. filtering based on proximity)
2.
User interface level: the interface is
adapted to suit small screens, onthe-go users.
3.
Presentation level: the visualization
of the info is adapted.
PRICING:
OBJECTIVES
• Unique properties of digital goods.
• Cost leadership vs. product differentiation.
• Consumer surplus, producer surplus, dead-weight loss.
• Personalized pricing (1st degree PD).
• Group pricing (3rd degree PD).
• Versioning (2nd degree PD), versioning dimensions.
• Bundling.
• For a given set of consumers, I expect you to be able to
tell me what revenue a firm will earn given different
pricing regimes.
PERSONALIZED PRICING
p
p
vs.
PRICE
REVENUE
REVENUE
DEMAND
q
• The idealized economic scenario (ideal for the seller)
• Find out what each customer is willing to pay, and charge
them as close as possible to this
• Can work in conjunction with increasing product fit
• Made more easily feasible by a web-based sales channel
q
GROUP PRICING
Same product, different prices for different groups
•
Identify groups willing to pay less, offer them lower prices
•
Need to be able to identify group membership easily
•
What types of groups are systematically willing to pay
less?
p
p
vs.
PRICE
SEGMENT 1 SEGMENT 2
PRICE 1
PRICE 2
REVENUE
REVENUE
DEMAND
q
DEMAND
q
VERSIONING
p
p
High-end version
Single version
PRICE 1
Low-end version
vs.
PRICE
REVENUE
PRICE 2
DEMAND
q
REVENUE
DEMAND
• Different versions, different prices
• Segmentation based on self-selection based on
willingness to pay for different versions
q
BUNDLING: A SIMPLE
EXAMPLE
Product 1
Word Processor
Product 2
Spreadsheet
Alice
$60
$40
Bob
$40
$60
Optimal prices
•
$40 for product 1
•
$40 for product 2
•
$100 for the bundle of product 1 and product 2
NETWORK EFFECTS:
LEARNING OBJECTIVES
•
Understand the idea of positive feedback and describe the role it has
played in some prior technology industries (railroad, electricity,
telephony)
•
Define network effects (demand-side economies of scale) and
understand how they lead to positive feedback
•
Describe the difference between supply-side and demand-side
economies of scale
•
Understand the typical sources of network effects in information
technology industries
•
Be able to recognize these sources for specific technology products or
in specific business contexts
•
Understand the trade-offs between performance and compatibility,
and between openness and proprietary control of a technology
POSITIVE FEEDBACK: OVERVIEW
What is positive feedback?
•
when a firm becomes successful, its past and current success make it
more likely to succeed in the future
•
‘…success feeds on itself, the strong get stronger…’
When does this happen?
•
More customers  lower unit cost (supply-side economies of scale)
•
More customers  larger ‘network’  more valuable product (demandside economies of scale caused by network effects)
Possible consequences of positive feedback
•
Dominance of a single firm or technology
•
Dominance of an inferior technology that got an early lead
•
Critical Mass: below the critical mass, few are willing to buy (inertia);
beyond the critical mass, the market takes off.
•
Introducing a new product is difficult because of collective switching costs
Historical examples
•
Railroad gauges, AC versus DC power, telephone networks
SOURCES OF POSITIVE FEEDBACK
Supply-side economies of scale (Traditional markets)
•
•
More customers  more units produced  lower average
cost per unit
Marginal cost less than average cost
Spreading fixed costs across more units
•
Manufacturing efficiencies, learning by doing
•
Demand-side economies of scale (Digital markets)
•
More units consumed  higher value per unit
•
The value of the good comes from the network of consumers
who use it (at least in part)
•
Most commonly caused by network effects (Microsoft,
Playstation, Facebook)
•
Positive relationship between popularity and value
Consumer expectations are key!
SWITCHING COSTS:
LEARNING OBJECTIVES
1.
Explain switching costs, technological lock-in and how
switching costs lead to technological lock-in.
2.
Understand how switching costs affect industry
competition.
3.
Understand the sources of lock-in and switching costs:
•
durable purchases
•
specialized suppliers
•
brand-specific training
•
search costs
•
information and databases
•
loyalty programs
•
contracts
4.
Be able to identify and analyze sources of lock-in for specific
products or in specific business contexts.
5.
Explain strategies for firms to create lock-in and for consumers
to mitigate the consequences of lock-in.
NEXT CLASS:
GROUP PRESENTATIONS
• Group 7
• Group 1
• Group 5
• Group 2