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TRANSCRIPT
Chapter 1:Introduction to Business
Intelligence
Business Intelligence: A Managerial Approach
(2nd Edition)
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Business Pressures–Responses–Support Model
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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
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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…>
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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.
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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.
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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.
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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)
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A High-level Architecture of BI
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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.).
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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
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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.
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Intelligence Creation and Use
A Cyclical Process of Intelligence Creation And Use BI practitioners
often follow the national security model depicted in this figure.
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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
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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
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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)
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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
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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
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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!
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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
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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
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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.
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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
Chapter 2:Data Warehousing
Business Intelligence: A Managerial Approach
(2nd Edition)
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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”
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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)
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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
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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
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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
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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
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DW Architectures
Tier 1:Client workstation
Tier 2:Application & database server
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A Web-based DW Architecture
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Teradata Corp. DW Architecture
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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:
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Extraction, transformation, and load (ETL)
Data Integration and the Extraction, Transformation, and Load (ETL) Process
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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
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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
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Star vs Snowflake Schema
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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
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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
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OLAP vs. OLTP
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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
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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
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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
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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
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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
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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
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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
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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
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Real-time/Active DW at Teradata
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Enterprise Decision Evolution and DW
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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
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BI / OLAP Portal for Learning MicroStrategy, and much more… www.TeradataStudentNetwork.com Pw: <check with TDUN>
Chapter 3:Business Performance Management (BPM)
Business Intelligence: A Managerial Approach
(2nd Edition)
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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)
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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
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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)
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A Closed-loop Process to Optimize Business Performance
Process Steps1. Strategize2. Plan3. Monitor/analyze4. Act/adjust
Each with its own process steps…
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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
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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
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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)
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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
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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
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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
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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
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Monitor: How Are We Doing?
What if strategic assumptions (not the operations) are wrong?
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Act and Adjust: What Do We Need to Do Differently?
Harrah’s Closed-Loop Marketing Model
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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
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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
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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
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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
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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
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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)
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BPM MethodologiesBalanced Scorecard
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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
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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
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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
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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
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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
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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
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Performance Dashboards
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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)
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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
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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)
Chapter 4:Data Mining for Business
Intelligence
Business Intelligence: A Managerial Approach
(2nd Edition)
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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
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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
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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.).
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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?
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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
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A Taxonomy for Data Mining Tasks
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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
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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
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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
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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
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Data Mining Process: CRISP-DM
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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
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Data Preparation – A Critical DM Task
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Data Mining Process: SEMMA
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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?
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Classification Techniques
Decision tree analysis Statistical analysis Neural networks Support vector machines Case-based reasoning Bayesian classifiers Genetic algorithms Rough sets
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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
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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
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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)
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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).
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Cluster Analysis for Data Mining -k-Means Clustering Algorithm
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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!”
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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
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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)
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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.
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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
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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.
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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
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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
Chapter 5:Text and Web Mining
Business Intelligence: A Managerial Approach
(2nd Edition)
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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
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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
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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
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Text Mining Application Area
Information extraction Topic tracking Summarization Categorization Clustering Concept linking Question answering
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Text Mining Terminology
Unstructured or semistructured data Corpus (and corpora) Terms Concepts Stemming Stop words (and include words) Synonyms (and polysemes) Tokenizing
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Text Mining Terminology
Term dictionary Word frequency Part-of-speech tagging Morphology Term-by-document matrix
Occurrence matrix
Singular value decomposition Latent semantic indexing
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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
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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?
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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
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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
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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
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Text Mining Applications
Application Case 7.4: Mining for Lies
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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.
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Text Mining Process
The three-step text mining process
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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)
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Text Mining Process
Step 2: Create the Term–by–Document Matrix
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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
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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!
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Web Mining
Web mining (or Web data mining) is the process of discovering intrinsic relationships from Web data (textual, linkage, or usage)
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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.
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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
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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
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Web Usage Mining(clickstream analysis)
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Web Mining Success Stories
Amazon.com, Ask.com, Scholastic.com, etc. Website Optimization Ecosystem
Chapter 6:BI Implementation:
Integration and Emerging Trends
Business Intelligence: A Managerial Approach
(2nd Edition)
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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
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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
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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.
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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
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Connecting BI Systems to Databases and Other Enterprise Systems
Virtually every BI application requires database or data warehouse access
Multi-tiered Application Architecture
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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
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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
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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)
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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)
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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)
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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)
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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.
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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
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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
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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
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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)
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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.
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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 …>
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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%)
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Virtual Tradeshows
See iTradeFair.com
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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.
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How CDM Works
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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
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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
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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
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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, …
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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