Summary: This chapter focuses on business
intelligence (BI) systems: information systems that can produce patterns,
relationships, and other information from organizational structured and
unstructured social data as well as from external, purchased data. Another source of knowledge
is employees themselves; a large amount of collective knowledge exists in the employees.
BI is the key technology supporting such marketing technology.
Q1: How
do organizations use business intelligence (BI) systems?
Five
standard IS components are present in BI systems: hardware, software,
data, procedures, and people. The boundaries of BI systems are blurry. BI is
used for collaborative tasks: Project Management, Problem Solving, Deciding,
and Informing. Falcon Security could use BI to determine whether it could save
costs by rerouting its drone flights. What Are
Typical Uses for BI?: (invoves classification/prediction) Identifying
changes in purchasing patterns. Ex. Important life events change what customers
buy. Entertainment Ex: Netflix has data on watching, listening, and rental
habits; Classify customers by viewing patterns. Predictive policing: Analyze
data on past crimes - location, date, time, day of week, type of crime, and
related data. Just-in-Time
Medical Reporting: EX- Injection notification services: Software
analyzes patient’s records, if injections needed, recommends as exam
progresses; Blurry edge of medical ethics.
Q2: What
are the three primary activities in the BI process?
The four
fundamental categories of BI analysis are reporting, data mining, BigData, and
knowledge management. Push publishing delivers business intelligence to
users without any request from the users; the BI results are delivered
according to a schedule or as a result of an event or particular data
condition. Pull publishing requires the user to request BI results. Using
Business Intelligence to Find Candidate Parts at Falcon Security: Identify
parts that might qualify: Provided by vendors who make part design files
available for sale, Purchased by larger customers, Frequently ordered parts,
and Ordered in small quantities; Used part weight and price surrogates for
simplicity.
Acquire
Data: Extracted Order Data: Query: Sales (Customer Name, Contact, Title,
Bill Yr., # orders, Units, Revenue, Source, Part #), Part (Part, Shipping
Weight, Vendor). IS department extracted the data.Actually
wouldn’t need all of the data columns in the Sales table. Data was divided into
different billing years, which wouldn’t affect analysis. Analyze: First
step was to combine the data in the two tables into a single table that
contained both the sales and part data. Creating a Customer Summary Query
sums the revenue, units, and average price for each customer. Publish Results: Qualifying
Parts Query Results- Publish results is the last activity in the BI
process. Publish Results: Sales History for Selected Parts: Importance
of the human component of an IS. Business intelligence is only as intelligent
as the people creating it.
Q3: How
do organizations use data warehouses and data marts to acquire data?
Functions
of a data warehouse: (a facility for managing an organization’s BI data)
Obtain data from operational, internal and external databases, Cleanse
data, Organize and relate data, Catalog data using metadata. Components:
Programs read operational and other data and extract, clean, and prepare that
data for BI processing. An organization might use Oracle for its operational
processing, but use SQL Server for its data warehouse. Other organizations use
SQL Server for operational processing, but use DBMSs from statistical package
vendors such as SAS or SPSS in the data warehouse. Purchase of data about other
organizations is not unusual or particularly concerning from a privacy
standpoint. Some
companies choose to buy personal, consumer data from data vendors Examples
of Purchasable Consumer Data: Name, Address, Phone, Age, Gender, Ethnicity,
Religion, Income, Education, Voter registration, Home ownership, Vehicles,
Magazine subscriptions, Hobbies, Catalog orders, Marital Status, life stage,
Height, Weight, hair and eye color, Spouse name, birth date, Children’s names
and birth dates. Possible
Problems with Source Data: Dirty data, missing values, inconsistent data,
Data not integrated, wrong granularity: too fine/not fine enough, Too much
data: too many attributes/data points. Data
Warehouses V.s. Data Marts: The data analysts who work with a data
warehouse are experts at data management, data cleaning, data
transformation, data relationships, and the like. However, they are not usually
experts in a given business function. A data mart is a subset of
a data warehouse. A date mart addresses a particular component or functional
area of the business
Q4: How
do organizations use reporting applications?
Reporting
application: a BI application that inputs data from one or more sources and
applies reporting operations to that data to produce business intelligence. Create
meaningful information from disparate data sources. Deliver information to user
on time. Basic operations: Sorting, Filtering, Grouping, Calculating, and Formatting.
RFM Analysis: considers how recently (R) a customer has ordered, how
frequently (F) a customer ordered, and how much money (M) the customer has
spent.
RFM
Analysis Classification Scheme: To produce an RFM score, a program sorts
customer purchase records by date of most recent (R) purchase, divides sorts
into quintiles, and gives customers a score of 1 to 5. Process is repeated for
Frequently and Money. (Top 20%, Mid 20%, Bottom 20%)
Q5: How
do organizations use data mining applications?
Source
disciplines: Statistics/mathematics, Huge databases, Cheap computer
Processing and storage Artificial Intelligence machine learning, Data
management tech., Sophisticated marketing, finance, and other business
professionals. Sometimes people use the term knowledge discovery in databases
(KDD) as a synonym for data mining.There are many interesting and rewarding
careers for business professionals who are knowledgeable about data mining
techniques. Unsupervised
Data Mining: Not a priori hypothesis or model. Findings obtained solely by
data analysis. Hypothesized model created to explain patterns found. Example:
Cluster analysis: Statistical technique to identify groups of entities with
similar characteristics; used to find groups of similar customers from customer
order and demographic data. Supervised
Data Mining: Uses a priori model. Prediction, such as regression analysis. Ex: Cell Phone
Weekend Minutes = (12 + (17.5*Customer Age) + (23.7*Number Months of Account) = 12 + 17.5*21 + 23.7*6 = 521.7 minutes. Market-Basket
Analysis: Identify sales patterns in large volumes of data, Identify what products
customers tend to buy together, Computes probabilities of purchases, and Identify
cross-selling opportunities. Customers who bought fins also bought a mask.Decision
Trees: used to select attributes most useful for classifying entities. Unsupervised
data mining technique, Hierarchical arrangement of criteria to predict a value
or classification, Basic idea- Select attributes most useful for classifying
“pure groups.” Creates decision rules. Decision
Rules for Accepting or Rejecting Offer to Purchase Loans: If percent past
due is less than 50 percent, then accept loan. If percent past due is greater
than 50 percent and, If Credit Score is greater than 572.6 and, If Current LTV
is less than .94, then accept loan. Otherwise, reject loan.
Q6: How
do organizations use Big Data applications?
Huge volume
– petabyte and larger. Rapid velocity – generated rapidly. Great variety
- Structured
data, free-form text, log files, graphics, audio, and video. MapReduce
Processing Summary: Technique for harnessing power of thousands of
computers working in parallel. Big Data collection is broken into pieces, and
hundreds or thousands of independent processors search these pieces for
something of interest.Hadoop:
Open-source program supported by Apache Foundation2. Manages thousands of
computers. Implements MapReduce- Written in Java.Amazon.com supports Hadoop as
part of EC3 cloud. Query language entitled Pig (platform for large dataset analysis):
Easy to master, Extensible, Automatically optimizes queries on map-reduce level.
Experts are required to use it; you may be involved, however, in planning a Big
Data study or in interpreting results.
Q7: What
is the role of knowledge management systems?
Knowledge
Management (KM): Creating value from intellectual capital and sharing knowledge
with those who need that capital. Preserving organizational memory: Capturing
and storing lessons learned and best practices of key employees. Scope
of KM same as SM in hyper-social organizations. Benefits: Improve
process quality, Increase team strength. Goal: Enable employees to use
organization’s collective knowledge. What Are
Expert Systems? : Expert systems are rule-based systems that encode human
knowledge as If/Then rules. Expert systems shells – programs that process a set
of rules. Drawbacks: 1) Difficult and expensive to develop: Labor
intensive, Ties up domain experts. 2) Difficult to maintain: Changes cause
unpredictable outcomes, Constantly need expensive changes. 3) Don’t live up to
expectations: Can’t duplicate diagnostic abilities of humans What are
Content Management System (CMS)? : (huge, complex) Support management and
delivery of documents, other expressions of employee knowledge. Challenges
of Content Management: Huge databases, Dynamic content, Documents refer to
one another, Perishable contents, In many languages. What are
CMS Application Alternatives? : In-house custom development: Customer
support develops in-house database applications to track customer problems. Off-the-shelf:
Horizontal market products (SharePoint). Vertical market applications. Ex: Horizontal
market: An accounting firm, for example, may license a vertical market
application to manage document flow for the processing of tax returns or the
management of audit documents. Public search engine: Google, Bing. How Do
Hyper-Social Organizations Manage Knowledge? : Hyper-social knowledge
management:Social
media, and related applications, for management and delivery of organizational
knowledge resources. Hyper-organization theory: Framework for
understanding KM. Focus shifts from knowledge and content to fostering
authentic relationships among knowledge creators and users. Rich
directory: an employee directory that includes not only the standard name,
email, phone, and address but also organizational structure and expertise. Particularly
useful in large organizations where people with particular expertise are
unknown. Resistance
to knowledge Sharing: Employees reluctant to exhibit their ignorance. Employee
competition. Remedy: Strong management endorsement, Strong positive feedback, “Nothing
wrong with praise or cash . . . especially cash.”
Q8: What
are the alternatives for publishing BI?
Server Report Type
Push Option Skill
Level Needed
Email/collaboration
tool Static Manual Low
Web server Static/Dynamic Alert/RSS Low = static/High = dynamic
SharePo Static/Dynamic Alert/RSS, Workflow Low = static/High = dynamic
By Server Dynam Alert/RSS, Subscription High
Q9: 2026?
Exponentially
more information about customers, better data mining techniques. Companies
buy and sell your purchasing habits and psyche. Singularity:
Computer systems adapt and create their own software without human
assistance. Machines
will possess and create information for themselves.
Three
things I learn:
1. An
organization can encourage knowledge sharing through a strong management who
endorses it and provides positive feedback to its employees.
2. Progressive
organizations actually encourages their employees to Tweet, post on Facebook or
other social media sites, write blogs, and post videos on YouTube, etc.
3. Big
Data is characterized by volumes, velocities, and variations, which exceeds farther
than those of traditional reporting and data mining.
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