turban ch1 ch6

167
Chapter 1: Introduction to Business Intelligence Business Intelligence: A Managerial Approach (2 nd Edition)

Upload: pramod-khadka

Post on 27-Oct-2014

895 views

Category:

Documents


3 download

TRANSCRIPT

Page 1: Turban Ch1 Ch6

Chapter 1:Introduction to Business

Intelligence

Business Intelligence: A Managerial Approach

(2nd Edition)

Page 2: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-2

Business Pressures–Responses–Support Model

Page 3: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-3

Business Environment FactorsFACTOR DESCRIPTIONMarkets Strong competition

Expanding global marketsBlooming electronic markets on the InternetInnovative marketing methodsOpportunities for outsourcing with IT supportNeed for real-time, on-demand transactions

Consumer Desire for customizationdemand Desire for quality, diversity of products, and speed of delivery

Customers getting powerful and less loyalTechnology More innovations, new products, and new services

Increasing obsolescence rateIncreasing information overloadSocial networking, Web 2.0 and beyond

Societal Growing government regulations and deregulationWorkforce more diversified, older, and composed of more womenPrime concerns of homeland security and terrorist attacksNecessity of Sarbanes-Oxley Act and other reporting-related legislationIncreasing social responsibility of companiesGreater emphasis on sustainability

Page 4: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-4

Organizational Responses

Be Reactive, Anticipative, Adaptive, and Proactive

Managers may take actions, such as: Employing strategic planning. Using new and innovative business models. Restructuring business processes. Participating in business alliances. Improving corporate information systems. Improving partnership relationships. Encouraging innovation and creativity. …cont…>

Page 5: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-5

Organizational Responses, 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.

Page 6: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-6

Business Intelligence (BI)

BI is an evolution of decision support concepts over time. Meaning of EIS/DSS…

Then: Executive Information System Now: Everybody’s Information System (BI)

BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities.

The term BI emerged from industry apps.

Page 7: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-7

Definition of BI

BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies.

BI a content-free expression, so it means different things to different people.

BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis.

BI helps transform data, to information (and knowledge), to decisions and finally to action.

Page 8: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-8

The Architecture of BI

A BI system has four major components: a data warehouse, with its source data business analytics, a collection of tools for

manipulating, mining, and analyzing the data in the data warehouse;

business performance management (BPM) for monitoring and analyzing performance

a user interface (e.g., dashboard)

Page 9: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-9

A High-level Architecture of BI

Page 10: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-10

Components in a BI Architecture

The data warehouse is the cornerstone of any medium-to-large BI system. Originally, the data warehouse included only

historical data that was organized and summarized, so end users could easily view or manipulate it.

Today, some data warehouses include access to current data as well, so they can provide real-time decision support (for details see Chapter 2).

Business analytics are the tools that help users transform data into knowledge (e.g., queries, data/text mining tools, etc.).

Page 11: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-11

Styles of BI

MicroStrategy, Corp. distinguishes five styles of BI and offers tools for each:1. report delivery and alerting2. enterprise reporting (using dashboards

and scorecards)3. cube analysis (also known as slice-and-

dice analysis)4. ad-hoc queries5. statistics and data mining

Page 12: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-12

The Benefits of BI The ability to provide accurate information

when needed, including a real-time view of the corporate performance and its parts

A survey by Thompson (2004) Faster, more accurate reporting (81%) Improved decision making (78%) Improved customer service (56%) Increased revenue (49%)

See Table 1.2 for a list of BI analytic applications, the business questions they answer and the business value they bring.

Page 13: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-13

Intelligence Creation and Use

A Cyclical Process of Intelligence Creation And Use BI practitioners

often follow the national security model depicted in this figure.

Page 14: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-14

Intelligence Creation and Use

Steps Involved Data warehouse deployment Creation of intelligence

Identification and prioritization of BI projects By using ROI and TCO (cost-benefit analysis) This process is also called BI governance

BI Governance Who should do the prioritization?

Partnership between functional area heads Partnership between customers and providers

Page 15: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-15

BI Governance Issues/Tasks

1. Create categories of projects (investment, business opportunity, strategic, mandatory, etc.)

2. Define criteria for project selection3. Determine and set a framework for

managing project risk4. Manage and leverage project

interdependencies5. Continuously monitor and adjust the

composition of the portfolio

Page 16: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-16

Intelligence and Espionage

Stealing corporate secrets, CIA, … Intelligence vs. Espionage

IntelligenceThe way that modern companies ethically and legally organize themselves to glean as much as they can from their customers, their business environment, their stakeholders, their business processes, their competitors, and other such sources of potentially valuable information

Problem – too much data, very little value Use of data/text/Web mining (see Chapter 4, 5)

Page 17: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-17

Transaction Processing VersusAnalytic Processing

Transaction processing systems are constantly involved in handling updates (add/edit/delete) to what we might call operational databases. ATM withdrawal transaction, sales order entry via

an ecommerce site – updates DBs Online analytic processing (OLTP) handles routine

on-going business ERP, SCM, CRM systems generate and store data

in OLTP systems The main goal is to have high efficiency

Page 18: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-18

Transaction Processing VersusAnalytic Processing

Online analytic processing (OLAP) systems are involved in extracting information from data stored by OLTP systems Routine sales reports by product, by region, by

sales person, etc. Often built on top of a data warehouse where the

data is not transactional Main goal is effectiveness (and then, efficiency) –

provide correct information in a timely manner More on OLAP will be covered in Chapter 2

Page 19: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-19

BI and Business Strategy

To be successful, BI must be aligned with the company’s business strategy. BI cannot/should not be a technical exercise for

the information systems department.

BI changes the way a company conducts business by improving business processes, and transforming decision making to a more

data/fact/information driven activity.

BI should help execute the business strategy and not be an impediment for it!

Page 20: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-20

Real-time, On-demand BI

The demand for “real-time” BI is growing! Is “real-time” BI attainable? Technology is getting there…

Automated, faster data collection (RFID, sensors,… )

Database and other software technologies (agent, SOA, …) are advancing

Telecommunication infrastructure is improving Computational power is increasing while the cost

for these technologies is decreasing

Trent -> Business Activity Management

Page 21: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-21

Issues for Successful BI

Developing vs. Acquiring BI systems Developing everything from scratch Buying/leasing a complete system Using a shell BI system and customizing it Use of outside consultants?

Justifying via cost-benefit analysis It is easier to quantify costs Harder to quantify benefits

Most of them are intangibles

Page 22: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-22

Issues for Successful BI

Security and Privacy Still an important research topic in BI How much security/privacy?

Integration of Systems and Applications BI must integrate into the existing IS

Often sits on top of ERP, SCM, CRM systems

Integration to outside (partners of the extended enterprise) via internet – customers, vendors, government agencies, etc.

Page 23: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-23

Major BI Tools and Techniques

Tool categories Data management Reporting, status tracking Visualization Strategy and performance management Business analytics Social networking & Web 2.0 New/advanced tools/techniques to handle

massive data sets for knowledge discovery

Page 24: Turban Ch1 Ch6

Chapter 2:Data Warehousing

Business Intelligence: A Managerial Approach

(2nd Edition)

Page 25: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-25

What is a Data Warehouse?

A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format

“The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”

Page 26: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-26

Characteristics of DW

Subject oriented Integrated Time-variant (time series) Nonvolatile Summarized Not normalized Metadata Web based, relational/multi-dimensional Client/server Real-time and/or right-time (active)

Page 27: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-27

Data Mart

A departmental data warehouse that stores only relevant data

Dependent data mart A subset that is created directly from a data warehouse

Independent data martA small data warehouse designed for a strategic business unit or a department

Page 28: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-28

Data Warehousing Definitions

Operational data stores (ODS)A type of database often used as an interim area for a data warehouse

Oper marts An operational data mart

Enterprise data warehouse (EDW)A data warehouse for the enterprise

Metadata Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use

Page 29: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-29

DW Framework

DataSources

ERP

Legacy

POS

OtherOLTP/wEB

External data

Select

Transform

Extract

Integrate

Load

ETL Process

EnterpriseData warehouse

Metadata

Replication

Data/text mining

Custom builtapplications

OLAP,Dashboard,Web

RoutineBusinessReporting

Applications(Visualization)

Data mart(Engineering)

Data mart(Marketing)

Data mart(Finance)

Data mart(...)

Access

No data marts option

Page 30: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-30

DW Architecture

Three-tier architecture1. Data acquisition software (back-end)2. The data warehouse that contains the data &

software3. Client (front-end) software that allows users to

access and analyze data from the warehouse

Two-tier architectureFirst 2 tiers in three-tier architecture is combined

into one

Sometimes there is only one tier

Page 31: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-31

DW Architectures

Tier 1:Client workstation

Tier 2:Application & database server

Page 32: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-32

A Web-based DW Architecture

Page 33: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-33

Teradata Corp. DW Architecture

Page 34: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-34

Data Warehousing Architectures

1. Information interdependence between organizational units

2. Upper management’s information needs

3. Urgency of need for a data warehouse

4. Nature of end-user tasks5. Constraints on resources

6. Strategic view of the data warehouse prior to implementation

7. Compatibility with existing systems

8. Perceived ability of the in-house IT staff

9. Technical issues10. Social/political factors

Ten factors that potentially affect the architecture selection decision:

Page 35: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-35

Extraction, transformation, and load (ETL)

Data Integration and the Extraction, Transformation, and Load (ETL) Process

Page 36: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-36

Representation of Data in DW

Dimensional Modeling – a retrieval-based system that supports high-volume query access

Star schema – the most commonly used and the simplest style of dimensional modeling Contain a fact table surrounded by and connected to several

dimension tables Fact table contains the descriptive attributes (numerical

values) needed to perform decision analysis and query reporting

Dimension tables contain classification and aggregation information about the values in the fact table

Snowflakes schema – an extension of star schema where the diagram resembles a snowflake in shape

Page 37: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-37

Multidimensionality Multidimensionality

The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)

Multidimensional presentation Dimensions: products, salespeople, market segments,

business units, geographical locations, distribution channels, country, or industry

Measures: money, sales volume, head count, inventory profit, actual versus forecast

Time: daily, weekly, monthly, quarterly, or yearly

Page 38: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-38

Star vs Snowflake Schema

Page 39: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-39

Analysis of Data in DW Online analytical processing (OLAP)

Data driven activities performed by end users to query the online system and to conduct analyses

Data cubes, drill-down / rollup, slice & dice, …

OLAP Activities Generating queries (query tools) Requesting ad hoc reports Conducting statistical and other analyses Developing multimedia-based applications

Page 40: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-40

Analysis of Data Stored in DWOLTP vs. OLAP OLTP (online transaction processing)

A system that is primarily responsible for capturing and storing data related to day-to-day business functions such as ERP, CRM, SCM, POS,

The main focus is on efficiency of routine tasks

OLAP (online analytic processing) A system is designed to address the need of

information extraction by providing effectively and efficiently ad hoc analysis of organizational data

The main focus is on effectiveness

Page 41: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-41

OLAP vs. OLTP

Page 42: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-42

OLAP Operations

Slice – a subset of a multidimensional array Dice – a slice on more than two dimensions Drill Down/Up – navigating among levels of

data ranging from the most summarized (up) to the most detailed (down)

Roll Up – computing all of the data relationships for one or more dimensions

Pivot – used to change the dimensional orientation of a report or an ad hoc query-page display

Page 43: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-43

OLAP

Product

Time

Geo

grap

hy

Sales volumes of a specific Product on variable Time and Region

Sales volumes of a specific Region on variable Time and Products

Sales volumes of a specific Time on variable Region and Products

Cells are filled with numbers representing

sales volumes

A 3-dimensional OLAP cube with slicing operations

Slicing Operations on a Simple Tree-DimensionalData Cube

Page 44: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-44

DW Implementation Issues

11 tasks for successful DW implementation Establishment of service-level agreements and data-refresh

requirements Identification of data sources and their governance policies Data quality planning Data model design ETL tool selection Relational database software and platform selection Data transport Data conversion Reconciliation process Purge and archive planning End-user support

Page 45: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-45

DW Implementation Guidelines Project must fit with corporate strategy & business objectives There must be complete buy-in to the project by executives,

managers, and users It is important to manage user expectations about the

completed project The data warehouse must be built incrementally Build in adaptability, flexibility and scalability The project must be managed by both IT and business

professionals Only load data that have been cleansed and are of a quality

understood by the organization Do not overlook training requirements Be politically aware

Page 46: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-46

Successful DW ImplementationThings to Avoid

Starting with the wrong sponsorship chain Setting expectations that you cannot meet Engaging in politically naive behavior Loading the data warehouse with information

just because it is available Believing that data warehousing database

design is the same as transactional database design

Choosing a data warehouse manager who is technology oriented rather than user oriented

Page 47: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-47

Successful DW ImplementationThings to Avoid - Cont.

Focusing on traditional internal record-oriented data and ignoring the value of external data and of text, images, etc.

Delivering data with confusing definitions Believing promises of performance, capacity,

and scalability Believing that your problems are over when

the data warehouse is up and running Focusing on ad hoc data mining and periodic

reporting instead of alerts

Page 48: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-48

Failure Factors in DW Projects

Lack of executive sponsorship Unclear business objectives Cultural issues being ignored

Change management

Unrealistic expectations Inappropriate architecture Low data quality / missing information Loading data just because it is available

Page 49: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-49

Real-time/Active DW/BI

Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly Push vs. Pull (of data)

Concerns about real-time BI Not all data should be updated continuously Mismatch of reports generated minutes apart May be cost prohibitive May also be infeasible

Page 50: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-50

Real-time/Active DW at Teradata

Page 51: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-51

Enterprise Decision Evolution and DW

Page 52: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-52

The Future of DW Sourcing…

Open source software SaaS (software as a service) Cloud computing DW appliances

Infrastructure… Real-time DW Data management practices/technologies In-memory processing (“super-computing”) New DBMS Advanced analytics

Page 53: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-53

BI / OLAP Portal for Learning MicroStrategy, and much more… www.TeradataStudentNetwork.com Pw: <check with TDUN>

Page 54: Turban Ch1 Ch6

Chapter 3:Business Performance Management (BPM)

Business Intelligence: A Managerial Approach

(2nd Edition)

Page 55: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-55

Business Performance Management (BPM) Overview

Business Performance Management (BPM) is…A real-time system that alert managers to potential opportunities, impending problems, and threats, and then empowers them to react through models and collaboration.

Also called, corporate performance management (CPM by Gartner Group), enterprise performance management (EPM by Oracle), strategic enterprise management (SEM by SAP)

Page 56: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-56

Business Performance Management (BPM) Overview

BPM refers to the business processes, methodologies, metrics, and technologies used by enterprises to measure, monitor, and manage business performance.

BPM encompasses three key components A set of integrated, closed-loop management and

analytic processes, supported by technology Tools for businesses to define strategic goals and

then measure/manage performance against them Methods and tools for monitoring key performance

indicators (KPIs), linked to organizational strategy

Page 57: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-57

BPM versus BI

BPM is an outgrowth of BI and incorporates many of its technologies, applications, and techniques. The same companies market and sell them. BI has evolved so that many of the original

differences between the two no longer exist (e.g., BI used to be focused on departmental rather than enterprise-wide projects).

BI is a crucial element of BPM.

BPM = BI + Planning (a unified solution)

Page 58: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-58

A Closed-loop Process to Optimize Business Performance

Process Steps1. Strategize2. Plan3. Monitor/analyze4. Act/adjust

Each with its own process steps…

Page 59: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-59

Strategize: Where Do We Want to Go?

Strategic objective A broad statement or general course of action prescribing targeted directions for an organization

Strategic goal A quantified objective with a designated time period

Strategic visionA picture or mental image of what the organization should look like in the future

Critical success factors (CSF) Key factors that delineate the things that an organization must excel at to be successful

Page 60: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-60

Strategize: Where Do We Want to Go?

“90 percent of organizations fail to execute their strategies”

The strategy gap Four sources for the gap between

strategy and execution:1. Communication (enterprise-wide)2. Alignment of rewards and incentives3. Focus (concentrating on the core elements)4. Resources

Page 61: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-61

Plan: How Do We Get There?

Operational planning Operational plan: plan that translates an

organization’s strategic objectives and goals into a set of well-defined tactics and initiatives, resources requirements, and expected results for some future time period (usually a year).

Operational planning can be Tactic-centric (operationally focused) Budget-centric (financially focused)

Page 62: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-62

Plan: How Do We Get There?

Financial planning and budgeting An organization’s strategic objectives and

key metrics should serve as top-down drivers for the allocation of an organization’s tangible and intangible assets

Resource allocations should be carefully aligned with the organization’s strategic objectives and tactics in order to achieve strategic success

Page 63: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-63

Monitor: How Are We Doing?

A comprehensive framework for monitoring performance should address two key issues: What to monitor

Critical success factors Strategic goals and targets

How to monitor

Page 64: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-64

Monitor: How Are We Doing?

Diagnostic control system A cybernetic system that has inputs, a process for transforming the inputs into outputs, a standard or benchmark against which to compare the outputs, and a feedback channel to allow information on variances between the outputs and the standard to be communicated and acted upon

Page 65: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-65

Monitor: How Are We Doing?

Pitfalls of variance analysis The vast majority of the exception analysis

focuses on negative variances when functional groups or departments fail to meet their targets

Rarely are positive variances reviewed for potential opportunities, and rarely does the analysis focus on assumptions underlying the variance patterns

Page 66: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-66

Monitor: How Are We Doing?

What if strategic assumptions (not the operations) are wrong?

Page 67: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-67

Act and Adjust: What Do We Need to Do Differently?

Harrah’s Closed-Loop Marketing Model

Page 68: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-68

Performance measurement systemA system that assists managers in tracking the implementations of business strategy by comparing actual results against strategic goals and objectives Comprises systematic comparative

methods that indicate progress (or lack thereof) against goals

Performance Measurement

Page 69: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-69

Key performance indicator (KPI)A KPI represents a strategic objective and metric that measures performance against a goal

Distinguishing features of KPIs

Performance MeasurementKPIs and Operational Metrics

Strategy Targets Ranges

Encodings Time frames Benchmarks

Page 70: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-70

Key performance indicator (KPI)Outcome KPIs vs. Driver KPIs(lagging indicators (leading indicatorse.g., revenues) e.g., sales leads)

Operational areas covered by driver KPIs Customer performance Service performance Sales operations Sales plan/forecast

Performance Measurement

Page 71: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-71

The drawbacks of using financial data as the core of a performance measurement: Financial measures are usually reported by

organizational structures and not by the processes that produced them

Financial measures are lagging indicators, telling us what happened, not why it happened or what is likely to happen in the future

Financial measures are often the product of allocations that are not related to the underlying processes that generated them

Financial measures are focused on the short term returns

Performance Measurement

Page 72: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-72

Good performance measures should: Be focused on key factors. Be a mix of past, present, and future. Balance the needs of all stakeholders

(shareholders, employees, partners, suppliers, etc.).

Start at the top and trickle down to the bottom.

Have targets that are based on research and reality rather than be arbitrary.

Performance Measurement

Page 73: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-73

BPM Methodologies

Balanced scorecard (BSC)A performance measurement and management methodology that helps translate an organization’s financials, customer, internal process, and learning and growth objectives and targets into a set of actionable initiatives

"The Balanced Scorecard: Measures That Drive Performance” (HBR, 1992)

Page 74: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-74

BPM MethodologiesBalanced Scorecard

Page 75: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-75

In BSC, the term “balance” arises because the combined set of measures are supposed to encompass indicators that are: Financial and nonfinancial Leading and lagging Internal and external Quantitative and qualitative Short term and long term

BPM Methodologies

Page 76: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-76

Aligning strategies and actions A six-step process

1. Developing and formulating a strategy2. Planning the strategy3. Aligning the organization4. Planning the operations5. Monitoring and learning6. Testing and adapting the strategy

BPM Methodologies

Page 77: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-77

Six SigmaA performance management methodology aimed at reducing the number of defects in a business process to as close to zero defects per million opportunities (DPMO) as possible

BPM Methodologies

Page 78: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-78

How to Succeed in Six Sigma Six Sigma is integrated with business strategy Six Sigma supports business objectives Key executives are engaged in the process Project selection is based on value potential There is a critical mass of projects and resources Projects-in-process are actively managed Team leadership skills are emphasized Results are rigorously tracked

BSC + Six Sigma = Success (see Tech. Ins. 9.3)

BPM Methodologies

Page 79: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-79

BPM Architecture and Applications

BPM applications1. Strategy management2. Budgeting, planning,

and forecasting 3. Financial consolidation4. Profitability modeling

and optimization 5. Financial, statutory, and

management reporting

Page 80: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-80

Performance Dashboards

Dashboards and scorecards both provide visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily explored

Page 81: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-81

Performance Dashboards

Page 82: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-82

Performance Dashboards

Dashboards versus scorecards Performance dashboards

Visual display used to monitor operational performance (free form)

Performance scorecardsVisual display used to chart progress against strategic and tactical goals and targets (predetermined measures)

Page 83: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-83

Performance Dashboards

Dashboards versus scorecards Performance dashboard is a multilayered

application built on a business intelligence and data integration infrastructure that enables organizations to measure, monitor, and manage business performance more effectively

- Eckerson

Three types of performance dashboards:1. Operational dashboards 2. Tactical dashboards 3. Strategic dashboards

Page 84: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-84

Performance Dashboards

Dashboard design “The fundamental challenge of dashboard

design is to display all the required information on a single screen, clearly and without distraction, in a manner that can be assimilated quickly"

(Few, 2005)

Page 85: Turban Ch1 Ch6

Chapter 4:Data Mining for Business

Intelligence

Business Intelligence: A Managerial Approach

(2nd Edition)

Page 86: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-86

Data Mining Concepts and DefinitionsWhy Data Mining?

More intense competition at the global scale Recognition of the value in data sources Availability of quality data on customers,

vendors, transactions, Web, etc. Consolidation and integration of data

repositories into data warehouses The exponential increase in data processing

and storage capabilities; and decrease in cost Movement toward conversion of information

resources into nonphysical form

Page 87: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-87

Definition of Data Mining

The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases - Fayyad et al., (1996)

Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable

Data mining: a misnomer? Other names: knowledge extraction, pattern

analysis, knowledge discovery, information harvesting, pattern searching, data dredging

Page 88: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-88

Data Mining Characteristics/Objectives

Source of data for DM is often a consolidated data warehouse (not always!).

DM environment is usually a client-server or a Web-based information systems architecture.

Data is the most critical ingredient for DM which may include soft/unstructured data.

The miner is often an end user. Striking it rich requires creative thinking. Data mining tools’ capabilities and ease of use

are essential (Web, Parallel processing, etc.).

Page 89: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-89

Data in Data Mining

Data: a collection of facts usually obtained as the result of experiences, observations, or experiments

Data may consist of numbers, words, and images Data: lowest level of abstraction (from which

information and knowledge are derived)

- DM with different data types?

- Other data types?

Page 90: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-90

What Does DM Do? How Does it Work?

DM extracts patterns from data Pattern?

A mathematical (numeric and/or symbolic) relationship among data items

Types of patterns Association Prediction Cluster (segmentation) Sequential (or time series) relationships

Page 91: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-91

A Taxonomy for Data Mining Tasks

Page 92: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-92

Data Mining Applications

Customer Relationship Management Maximize return on marketing campaigns Improve customer retention (churn analysis) Maximize customer value (cross- or up-selling) Identify and treat most valued customers

Banking & Other Financial Automate the loan application process Detecting fraudulent transactions Maximize customer value (cross- and up-selling) Optimizing cash reserves with forecasting

Page 93: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-93

Data Mining Applications (cont.)

Retailing and Logistics Optimize inventory levels at different locations Improve the store layout and sales promotions Optimize logistics by predicting seasonal effects Minimize losses due to limited shelf life

Manufacturing and Maintenance Predict/prevent machinery failures Identify anomalies in production systems to

optimize manufacturing capacity Discover novel patterns to improve product quality

Page 94: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-94

Data Mining Applications (cont.)

Brokerage and Securities Trading Predict changes on certain bond prices Forecast the direction of stock fluctuations Assess the effect of events on market movements Identify and prevent fraudulent activities in trading

Insurance Forecast claim costs for better business planning Determine optimal rate plans Optimize marketing to specific customers Identify and prevent fraudulent claim activities

Page 95: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-95

Data Mining Applications (cont.)

Computer hardware and software Science and engineering Government and defense Homeland security and law enforcement Travel industry Healthcare Medicine Entertainment industry Sports Etc.

Highly popular application areas for data mining

Page 96: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-96

Data Mining Process: CRISP-DM

Page 97: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-97

Data Mining Process: CRISP-DM

Step 1: Business UnderstandingStep 2: Data UnderstandingStep 3: Data Preparation (!)Step 4: Model BuildingStep 5: Testing and EvaluationStep 6: Deployment

The process is highly repetitive and experimental (DM: art versus science?)

Accounts for ~85% of total project time

Page 98: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-98

Data Preparation – A Critical DM Task 

Page 99: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-99

Data Mining Process: SEMMA 

Page 100: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-100

Data Mining Methods: Classification

Most frequently used DM method Part of the machine-learning family Employ supervised learning Learn from past data, classify new data The output variable is categorical

(nominal or ordinal) in nature Classification versus regression? Classification versus clustering?

Page 101: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-101

Classification Techniques

Decision tree analysis Statistical analysis Neural networks Support vector machines Case-based reasoning Bayesian classifiers Genetic algorithms Rough sets

Page 102: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-102

Decision Trees

Employs the divide and conquer method Recursively divides a training set until each

division consists of examples from one class1. Create a root node and assign all of the training

data to it. 2. Select the best splitting attribute.3. Add a branch to the root node for each value of

the split. Split the data into mutually exclusive subsets along the lines of the specific split.

4. Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached.

A general algorithm for decision tree building

Page 103: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-103

Cluster Analysis for Data Mining

Used for automatic identification of natural groupings of things

Part of the machine-learning family Employ unsupervised learning Learns the clusters of things from past

data, then assigns new instances There is no output variable Also known as segmentation

Page 104: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-104

Cluster Analysis for Data Mining

Clustering results may be used to Identify natural groupings of customers Identify rules for assigning new cases to

classes for targeting/diagnostic purposes Provide characterization, definition,

labeling of populations Decrease the size and complexity of

problems for other data mining methods Identify outliers in a specific domain (e.g.,

rare-event detection)

Page 105: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-105

Cluster Analysis for Data Mining

k-Means Clustering Algorithm k : pre-determined number of clusters Algorithm (Step 0: determine value of k)Step 1: Randomly generate k random points as

initial cluster centers. Step 2: Assign each point to the nearest cluster

center. Step 3: Re-compute the new cluster centers. Repeat steps 3 and 4 until some convergence

criterion is met (usually that the assignment of points to clusters becomes stable).

Page 106: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-106

Cluster Analysis for Data Mining -k-Means Clustering Algorithm

 

Page 107: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-107

Association Rule Mining A very popular DM method in business Finds interesting relationships (affinities)

between variables (items or events) Part of machine learning family Employs unsupervised learning There is no output variable Also known as market basket analysis Often used as an example to describe DM to

ordinary people, such as the famous “relationship between diapers and beers!”

Page 108: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-108

Association Rule Mining Input: the simple point-of-sale transaction data Output: Most frequent affinities among items Example: according to the transaction data…

“Customer who bought a laptop computer and a virus protection software, also bought extended service plan 70 percent of the time"

How do you use such a pattern/knowledge? Put the items next to each other for ease of finding Promote the items as a package (do not put one on sale if the

other(s) are on sale) Place items far apart from each other so that the customer

has to walk the aisles to search for it, and by doing so potentially see and buy other items

Page 109: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-109

Association Rule Mining

Representative applications of association rule mining include In business: cross-marketing, cross-selling, store

design, catalog design, e-commerce site design, optimization of online advertising, product pricing, and sales/promotion configuration

In medicine: relationships between symptoms and illnesses; diagnosis and patient characteristics and treatments (to be used in medical DSS); and genes and their functions (to be used in genomics projects)

Page 110: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-110

Artificial Neural Networks for Data Mining

Artificial neural networks (ANN or NN) is a brain metaphor for information processing

a.k.a. Neural Computing Very good at capturing highly complex

non-linear functions! Many uses – prediction (regression, classification),

clustering/segmentation

Many application areas – finance, medicine, marketing, manufacturing, service operations, information systems, etc.

Page 111: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-111

Biological versus Artificial Neural Networks

Neuron

Axon

Axon

SynapseSynapse Dendrites

Dendrites Neuron

w1

w2

wn

x1

x2

xn

Y

Y1

Yn

Y2

Inputs

Weights

Outputs

...

Processing Element (PE)

n

iiiWXS

1

)( Sf

Summation

TransferFunction

Biological Artificial

NeuronDendritesAxonSynapseSlowMany (109)

Node (or PE)InputOutputWeightFastFew (102)

Biological NN

Artificial NN

Page 112: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-112

Data Mining Myths

Data mining … provides instant solutions/predictions. is not yet viable for business applications. requires a separate, dedicated database. can only be done by those with advanced

degrees. is only for large firms that have lots of

customer data. is another name for good-old statistics.

Page 113: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-113

Common Data Mining Blunders

1. Selecting the wrong problem for data mining2. Ignoring what your sponsor thinks data

mining is and what it really can/cannot do3. Not leaving sufficient time for data

acquisition, selection and preparation4. Looking only at aggregated results and not

at individual records/predictions5. Being sloppy about keeping track of the data

mining procedure and results

Page 114: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-114

Common Data Mining Mistakes

6. Ignoring suspicious (good or bad) findings and quickly moving on

7. Running mining algorithms repeatedly and blindly, without thinking about the next stage

8. Naively believing everything you are told about the data

9. Naively believing everything you are told about your own data mining analysis

10. Measuring your results differently from the way your sponsor measures them

Page 115: Turban Ch1 Ch6

Chapter 5:Text and Web Mining

Business Intelligence: A Managerial Approach

(2nd Edition)

Page 116: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-116

Text Mining Concepts 85-90 percent of all corporate data is in some

kind of unstructured form (e.g., text). Unstructured corporate data is doubling in

size every 18 months. Tapping into these information sources is not

an option, but a need to stay competitive. Answer: text mining

A semi-automated process of extracting knowledge from unstructured data sources

a.k.a. text data mining or knowledge discovery in textual databases

Page 117: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-117

Data Mining versus Text Mining

Both seek novel and useful patterns Both are semi-automated processes Difference is the nature of the data:

Structured versus unstructured data Structured data: databases Unstructured data: Word documents, PDF

files, text excerpts, XML files, and so on

Text mining – first, impose structure to the data, then mine the structured data

Page 118: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-118

Text Mining Concepts

Benefits of text mining are obvious especially in text-rich data environments e.g., law (court orders), academic research

(research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), marketing (customer comments), etc.

Electronic communication records (e.g., Email) Spam filtering Email prioritization and categorization Automatic response generation

Page 119: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-119

Text Mining Application Area

Information extraction Topic tracking Summarization Categorization Clustering Concept linking Question answering

Page 120: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-120

Text Mining Terminology

Unstructured or semistructured data Corpus (and corpora) Terms Concepts Stemming Stop words (and include words) Synonyms (and polysemes) Tokenizing

Page 121: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-121

Text Mining Terminology

Term dictionary Word frequency Part-of-speech tagging Morphology Term-by-document matrix

Occurrence matrix

Singular value decomposition Latent semantic indexing

Page 122: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-122

Natural Language Processing (NLP)

Structuring a collection of text Old approach: bag-of-words New approach: natural language processing

NLP is a very important concept in text mining. a subfield of artificial intelligence and computational

linguistics. the study of "understanding" the natural human

language.

Syntax versus semantics based text mining

Page 123: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-123

Natural Language Processing (NLP)

What is “Understanding” ? Human understands, what about computers? Natural language is vague, context driven True understanding requires extensive knowledge

of a topic

Can/will computers ever understand natural language the same/accurate way we do?

Page 124: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-124

Natural Language Processing (NLP)

Challenges in NLP Part-of-speech tagging Text segmentation Word sense disambiguation Syntax ambiguity Imperfect or irregular input Speech acts

Dream of AI community to have algorithms that are capable of automatically

reading and obtaining knowledge from text

Page 125: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-125

NLP Task Categories

Information retrieval Information extraction Named-entity recognition Question answering Automatic summarization Natural language generation & understanding Machine translation Foreign language reading & writing Speech recognition Text proofing Optical character recognition

Page 126: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-126

Text Mining Applications Marketing applications

Enables better CRM

Security applications ECHELON, OASIS Deception detection

example coming up

Medicine and biology Literature-based gene identification

example coming up

Academic applications Research stream analysis - example coming up

Page 127: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-127

Text Mining Applications

Application Case 7.4: Mining for Lies

Page 128: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-128

Text Mining Applications

Application Case 7.4: Mining for LiesCategory Example Cues

Quantity Verb count, noun-phrase count, ...

Complexity Avg. no of clauses, sentence length, …

Uncertainty Modifiers, modal verbs, ...

Nonimmediacy Passive voice, objectification, ...

Expressivity Emotiveness

Diversity Lexical diversity, redundancy, ...

Informality Typographical error ratio

Specificity Spatiotemporal, perceptual information …

Affect Positive affect, negative affect, etc.  

Page 129: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-129

Text Mining Process

 

The three-step text mining process

Page 130: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-130

Text Mining Process

Step 1: Establish the corpus Collect all relevant unstructured data

(e.g., textual documents, XML files, emails, Web pages, short notes, voice recordings…)

Digitize, standardize the collection (e.g., all in ASCII text files)

Place the collection in a common place (e.g., in a flat file, or in a directory as separate files)

Page 131: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-131

Text Mining Process

Step 2: Create the Term–by–Document Matrix

Page 132: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-132

Text Mining Process

Step 2: Create the Term–by–Document Matrix (TDM) Should all terms be included?

Stop words, include words Synonyms, homonyms Stemming

What is the best representation of the indices (values in cells)? Row counts; binary frequencies; log frequencies; Inverse document frequency

Page 133: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-133

Web Mining Overview

Web is the largest repository of data Data is in HTML, XML, text format Challenges (of processing Web data)

The Web is too big for effective data mining The Web is too complex The Web is too dynamic The Web is not specific to a domain The Web has everything

Opportunities and challenges are great!

Page 134: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-134

Web Mining

Web mining (or Web data mining) is the process of discovering intrinsic relationships from Web data (textual, linkage, or usage)

Page 135: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-135

Web Content/Structure Mining

Mining of the textual content on the Web Data collection via Web crawlers

Web pages include hyperlinks Authoritative pages Hubs hyperlink-induced topic search (HITS) alg.

Page 136: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-136

Web Usage Mining

Extraction of information from data generated through Web page visits and transactions data stored in server access logs, referrer logs,

agent logs, and client-side cookies user characteristics and usage profiles metadata, such as page attributes, content

attributes, and usage data

Clickstream data Clickstream analysis

Page 137: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-137

Web Usage Mining

Web usage mining applications Determine the lifetime value of clients Design cross-marketing strategies across products. Evaluate promotional campaigns Target electronic ads and coupons at user groups

based on user access patterns Predict user behavior based on previously learned

rules and users' profiles Present dynamic information to users based on

their interests and profiles

Page 138: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-138

Web Usage Mining(clickstream analysis)

Page 139: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-139

Web Mining Success Stories

Amazon.com, Ask.com, Scholastic.com, etc. Website Optimization Ecosystem

Page 140: Turban Ch1 Ch6

Chapter 6:BI Implementation:

Integration and Emerging Trends

Business Intelligence: A Managerial Approach

(2nd Edition)

Page 141: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-141

Implementing BI – An Overview

Critical Success Factors for BI Implementationa. Business driven methodology and project

managementb. Clear vision and planningc. Committed management support and sponsorshipd. Data management and quality issuese. Mapping the solutions to the user requirementsf. Performance considerations of the BI systemg. Robust and extensible framework

Page 142: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-142

Managerial Issues Related to BI Implementation

1. System development and the need for integration

2. Cost–benefit issues and justification3. Legal issues and privacy4. BI and BPM today and tomorrow5. Cost justification; intangible benefits6. Documenting and securing support systems7. Ethical issues8. BI Project failures

Page 143: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-143

BI and Integration Implementation

Why integrate? To better implement a complete BI system To increase the capabilities of the BI

applications To enable real-time decision support To enable more powerful applications To facilitate faster system development To enhance support activities such as

blogs, wikis, RSS feeds, etc.

Page 144: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-144

BI and Integration Implementation

Levels of BI Integration Functional integration can be within the

same BI or across different BI systems Integration across different BI systems can be

accomplished in a loosely coupled fashion –input output passing, messaging (SOA)

Integration within a BI system is more cohesive with several sub-systems constituting the whole

Embedded Intelligent Systems Serving as the intelligent agents within BI

Page 145: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-145

Connecting BI Systems to Databases and Other Enterprise Systems

Virtually every BI application requires database or data warehouse access

Multi-tiered Application Architecture

Page 146: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-146

On-Demand BI

The limitations of Traditional BI Complex, time-consuming, expensive

The On-Demand Alternative On-demand computing = Utility computing SaaS (Software as a service) Allows SMEs to utilize affordable BI On-demand function alternatives

Internally sharing licenses within a firm Sharing licenses with many firms via an ASP

Page 147: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-147

Benefits of On-Demand BI

Ability to handle fluctuating demand Flexible use of the BI technology pool

Reduced investment/cost Hardware (servers and peripherals) Software (more features for less) Maintenance (centralized timely updates)

Embodiment of recognized best practices Better flexibility and connectivity with other

systems via SaaS infrastructure Better RIO

Page 148: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-148

The Limitations of On-Demand BI

Integration of vendors’ software with company’s software may be difficult

The vendor can go out of business, leaving the company without a service

It is difficult or even impossible to modify hosted software for better fit with the users’ needs

Upgrading may become a problem You may relinquish strategic data to strangers

(lack of privacy/security of corporate data)

Page 149: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-149

Issues of Legality, Privacy and Ethics Ethics in Decision Making and Support

Electronic surveillance Software piracy Use of proprietary databases Use of intellectual property such as knowledge Computer accessibility for workers with disabilities Accuracy of data, information, and knowledge Protection of the rights of users

Use of corporate computers for non-work-related purposes (personal use of Internet while working)

Page 150: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-150

Emerging Topics in BI – An Overview

Web 2.0 revolution as it relates to BI in (Section 6.7)

Online social networks (Section 6.8) Virtual worlds as related to BI (Section 6.9) Integration social networking and BI

(Section 6.10) RFID and BI (Section 6.11) Reality Mining (Section 6.12)

Page 151: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-151

Emerging Topics in BI – An OverviewThe Future of BI

Web 2.0 revolution as it related to BI (Section 6.7)

Online social networks (Section 6.8) Virtual worlds as related to BI (Section 6.9) Integration social networking and BI

(Section 6.10) RFID and BI (Section 6.11) Reality Mining (Section 6.12)

Page 152: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-152

Emerging Topics in BI – An Overview In 2009, collaborative decision making emerged as a new

product category that combines social software with business intelligence platform capabilities.

In 2010, 20 percent of organizations will have an industry-specific analytic application delivered via software as a service as a standard component of their business intelligence portfolio.

By 2012, business units will control at least 40 percent of the total budget for BI.

By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.

Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.

Page 153: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-153

The Web 2.0 Revolution

Web 2.0: a popular term for describing advanced Web technologies and applications, including blogs, wikis, RSS, mashups, user-generated content, and social networks

Objective: enhance creativity, information sharing, and collaboration

Difference between Web 2.0 and Web 1.xUse of Web for collaboration among Internet users and other users, content providers, and enterprises

Page 154: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-154

The Web 2.0 Revolution

Web 2.0: an umbrella term for new technologies for both content as well as how the Web works

Web 2.0 has led to the evolution of Web-based virtual communities and their hosting services, such as social networking sites, video-sharing sites

Companies that understand these new applications and technologies—and apply the capabilities early on—stand to greatly improve internal business processes and marketing

Page 155: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-155

Online Social Networking –Basics and Examples

A social network is a place where people create their own space, or homepage, on which they write blogs; post pictures, videos, or music; share ideas; and link to other Web locations they find interesting. The mass adoption of social networking Web sites

points to an evolution in human social interaction

The size of social network sites are growing rapidly, with some having over 100 million members – growth for successful ones 40 to 50 % in the first few years and 15 to 25 % thereafter

Page 156: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-156

Mobile Social Networking

Social networking where members converse and connect with one another using cell phones or other mobile devices

MySpace and Facebook offer mobile services Mobile only services: Brightkite, and Fon11 Basic types of mobile social networks

1. Partnership with mobile carriers (use of MySpace over AT&T network)

2. Without a partnership (“off deck”) (e.g., MocoSpace and Mobikade)

Mobile Enterprise Networks Mobile Community Activities (e.g., Sonopia)

Page 157: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-157

Major Social Network Services

Facebook: The Network Effect Launched in 2004 by Mark Zuckerberg (former

Harvard student) It is the largest social network service in the world

with over 500 million active users worldwide Initially intended for college and high school

students to connected to other students at the same school

In 2006 opened its doors to anyone over 13; enabling Facebook to compete directly with MySpace.

Page 158: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-158

Implications of Business and Enterprise Social Networks

Business oriented social networks can go beyond “advertising and sales”

Emerging enterprise social networking apps: Finding and Recruiting Workers

See Application Case 14.2 for a representative example

Management Activities and Support Training Knowledge Management and Expert Location

e.g., innocentive.com; awareness.com; Caterpillar

Enhancing Collaboration Using Blogs and Wikis Within the Enterprise …>

Page 159: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-159

Implications of Business and Enterprise Social Networks

Survey shows that best-in-class companies use blogs and wikis for the following applications: Project collaboration and communication (63%) Process and procedure document (63%) FAQs (61%) E-learning and training (46%) Forums for new ideas (41%) Corporate-specific dynamic glossary and

terminology (38%) Collaboration with customers (24%)

Page 160: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-160

Virtual Tradeshows

See iTradeFair.com

Page 161: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-161

Social Networks and BI:Collaborative Decision Making

Collaborative decision making (CDM) –combines social software and BI CDM is a category of decision-support system for

non-routine, complex decisions that require iterative human interactions.

Ad hoc tagging regarding value, relevance, credibility, and decision context can substantially enrich both the decision process and the content that contributes to the decisions.

Tying BI to decisions and outcomes that can be measured will enable organizations to better demonstrate the business value of BI.

Page 162: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-162

How CDM Works

Page 163: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-163

How does RFID work?

RFID system a tag (an electronic chip attached to the

product to be identified) an interrogator (i.e., reader) with one or

more antennae attached a computer (to manage the reader and

store the data captured by the reader)

Tags Active tag versus Passive tags

Page 164: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-164

RFID for Supply Chain BI

RFID in Retail Systems Functions in a distribution center

receiving, put-away, picking, and shipping

Sequence of operations at a receiving dock1. unloading the contents of the trailer2. verification of the receipt of goods against

expected delivery (purchase order)3. documentation of the discrepancy 4. application of labels to the pallets, cases, items 5. sorting of goods for put-away or cross-dock

Page 165: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-165

RFID for BI in Supply Chain

Better SC visibility with RFID systems Timing/duration of movements between

different locations – especially important for products with limited shelf life

Better management of out-of-stock items (optimal restocking of store shelves)

Help streamline the backroom operations: eliminate unnecessary case cycles, reorders

Better analysis of movement timings for more effective and efficient logistics

Page 166: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-166

Reality Mining

Identifying aggregate patterns of human activity trends (see sensenetworks.com by MIT & Columbia University)

Many devices send location information Cars, buses, taxis, mobile phones, cameras, and

personal navigation devices Using technologies such as GPS, WiFi, and cell

tower triangulation

Enables tracking of assets, finding nearby services, locating friends/family members, …

Page 167: Turban Ch1 Ch6

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1-167

Reality Mining Citisense: finding people with similar interests

See www.sensenetworks.com/citysense.php for real-time animation of the content.

A map of an area of San Francisco with density designation at place of interests