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Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Business Intelligence: A Managerial Approach nd (2 Edition) Chapter 3: Business Performance Management (BPM)

Business Performance Management (BPM) Overview 

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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 (M by Gartner Group), enterprise performance management (EPM by Oracle), strategic enterprise management (SEM by SAP)


Business Performance Management (BPM) Overview 

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BPM refers to the business processes, methodologies, metrics, and technologies used by enterprises to measure, monitor, and manage business performance. BPM encomes three key components 

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A set of integrated, closed-loop management and analytic processes, ed 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


BPM versus BI 

BPM is an outgrowth of BI and incorporates many of its technologies, applications, and techniques.  

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 1-57

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)


A Closed-loop Process to Optimize Business Performance 

Process Steps 1. 2. 3. 4.

Strategize Plan Monitor/analyze Act/adjust

Each with its own process steps… 1-58


Strategize: Where Do We Want to Go? 

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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 vision A 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


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. 2. 3. 4.

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Communication (enterprise-wide) Alignment of rewards and incentives Focus (concentrating on the core elements) Resources


Plan: How Do We Get There? 

Operational planning 

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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  

1-61

Tactic-centric (operationally focused) Budget-centric (financially focused)


Plan: How Do We Get There? 

Financial planning and budgeting 

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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


Monitor: How Are We Doing? 

A comprehensive framework for monitoring performance should address two key issues: 

What to monitor  

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1-63

Critical success factors Strategic goals and targets

How to monitor



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 channel to allow information on variances between the outputs and the standard to be communicated and acted upon

1-64



Pitfalls of variance analysis 

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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


Monitor: How Are We Doing?

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

1-66


Act and Adjust: What Do We Need to Do Differently? Harrah’s Closed-Loop Marketing Model

1-67


Performance Measurement 

Performance measurement system A system that assists managers in tracking the implementations of business strategy by comparing actual results against strategic goals and objectives 

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Comprises systematic comparative methods that indicate progress (or lack thereof) against goals


Performance Measurement KPIs and Operational Metrics 

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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   

1-69

Strategy Targets Ranges

  

Encodings Time frames Benchmarks



Key performance indicator (KPI) Outcome KPIs vs. (lagging indicators e.g., revenues)



Operational areas covered by driver KPIs    

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Driver KPIs (leading indicators e.g., sales leads)

Customer performance Service performance Sales operations Sales plan/forecast



The drawbacks of using financial data as the core of a performance measurement: 

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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



Good performance measures should:   

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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.


BPM Methodologies 

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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 (HBR, 1992) That Drive Performance”


BPM Methodologies Balanced Scorecard

1-74



In BSC, the term “balance” arises because the combined set of measures are supposed to encom indicators that are:     

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Financial and nonfinancial Leading and lagging Internal and external Quantitative and qualitative Short term and long term


BPM Methodologies  

Aligning strategies and actions A six-step process 1. 2. 3. 4. 5. 6.

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Developing and formulating a strategy Planning the strategy Aligning the organization Planning the operations Monitoring and learning Testing and adapting the strategy



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Six Sigma A 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 How to Succeed in Six Sigma

        

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Six Sigma is integrated with business strategy Six Sigma s 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 Architecture and Applications 

BPM applications 1. 2. 3. 4. 5.

1-79

Strategy management Budgeting, planning, and forecasting Financial consolidation Profitability modeling and optimization Financial, statutory, and management reporting


Performance Dashboards 

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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


Performance Dashboards

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Dashboards versus scorecards 

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Performance dashboards Visual display used to monitor operational performance (free form) Performance scorecards Visual display used to chart progress against strategic and tactical goals and targets (predetermined measures)



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

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Three types of performance dashboards: 1. 2. 3.

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Operational dashboards Tactical dashboards Strategic dashboards



Dashboard design 

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“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)


Business Intelligence: A Managerial Approach nd (2 Edition) Chapter 4: Data Mining for Business Intelligence

Data Mining Concepts and Definitions Why Data Mining?   

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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


Definition of Data Mining 

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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


Data Mining Characteristics/Objectives 

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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 . Striking it rich requires creative thinking. Data mining tools’ capabilities and ease of use are essential (Web, Parallel processing, etc.).


Data in Data Mining 

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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?

1-89


What Does DM Do? How Does it Work? 

DM extracts patterns from data 



Types of patterns    

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Pattern? A mathematical (numeric and/or symbolic) relationship among data items Association Prediction Cluster (segmentation) Sequential (or time series) relationships


A Taxonomy for Data Mining Tasks

1-91


Data Mining Applications 

Customer Relationship Management    

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Banking & Other Financial    

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

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


Data Mining Applications (cont.) 

Retailing and Logistics    

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Manufacturing and Maintenance  

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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

Predict/prevent machinery failures Identify anomalies in production systems to optimize manufacturing capacity Discover novel patterns to improve product quality


Data Mining Applications (cont.) 

Brokerage and Securities Trading    

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Insurance    

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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

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


Data Mining Applications (cont.)           1-95

Computer hardware and software Science and engineering Government and defense Homeland security and law enforcement Travel industry Healthcare Highly popular application areas for data mining Medicine Entertainment industry Sports Etc.


Data Mining Process: CRISP-DM

1-96


Data Mining Process: CRISP-DM Step Step Step Step Step Step 

1-97

1: 2: 3: 4: 5: 6:

Business Understanding Data Understanding Data Preparation (!) Model Building Testing and Evaluation Deployment

s for ~85% of total project time

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


Data Preparation – A Critical DM Task

1-98


Data Mining Process: SEMMA

1-99


Data Mining Methods: Classification     

  1-100

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?


Classification Techniques         1-101

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


Decision Trees  

A general algorithm for decision tree building

Employs the divide and conquer method Recursively divides a training set until each division consists of examples from one class 1. 2. 3.

4.

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Create a root node and assign all of the training data to it. Select the best splitting attribute. 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. Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached.


Cluster Analysis for Data Mining 

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  1-103

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



Clustering results may be used to  

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1-104

Identify natural groupings of customers Identify rules for asg 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)



k-Means Clustering Algorithm 

k : pre-determined number of clusters

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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). 1-105


Cluster Analysis for Data Mining k-Means Clustering Algorithm

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Association Rule Mining  

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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!”


Association Rule Mining   

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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?  

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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


Association Rule Mining 

Representative applications of association rule mining include 

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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)


Artificial Neural Networks for Data Mining 

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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. 1-110


Biological NN

Dendrites

Synapse

Synapse

Axon Axon

Biological versus Artificial Neural Networks

Artificial NN

x1

Y1

w1 Inputs Outputs

x2

w2

Processing Element (PE)

S  Weights

f (S )

n



i 1

X iW

i

Y

Transfer Function

Summation

xn

1-111

Neuron

Dendrites

Neuron

wn

Biological

Neuron Dendrites Axon Synapse Slow Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Many (109)

Artificial Node (or PE) Input Output Weight Fast Few (102)

Y2 . . . Yn

Data Mining Myths 

Data mining …    

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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.


Common Data Mining Blunders 1. 2. 3. 4. 5.

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Selecting the wrong problem for data mining Ignoring what your sponsor thinks data mining is and what it really can/cannot do Not leaving sufficient time for data acquisition, selection and preparation Looking only at aggregated results and not at individual records/predictions Being sloppy about keeping track of the data mining procedure and results


Common Data Mining Mistakes 6. 7. 8. 9. 10. 1-114

Ignoring suspicious (good or bad) findings and quickly moving on Running mining algorithms repeatedly and blindly, without thinking about the next stage Naively believing everything you are told about the data Naively believing everything you are told about your own data mining analysis Measuring your results differently from the way your sponsor measures them


Business Intelligence: A Managerial Approach nd (2 Edition) Chapter 5: Text and Web Mining

Text Mining Concepts 

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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 

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A semi-automated process of extracting knowledge from unstructured data sources a.k.a. text data mining or knowledge discovery in textual databases


Data Mining versus Text Mining   

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

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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


Text Mining Concepts 

Benefits of text mining are obvious especially in text-rich data environments 



Electronic communication records (e.g., Email)   

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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. Spam filtering Email prioritization and categorization Automatic response generation


Text Mining Application Area       

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Information extraction Topic tracking Summarization Categorization Clustering Concept linking Question answering


Text Mining Terminology         1-120

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


Text Mining Terminology     

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



Singular value decomposition 

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Occurrence matrix Latent semantic indexing


Natural Language Processing (NLP) 

Structuring a collection of text  

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NLP is  



 1-122

Old approach: bag-of-words New approach: natural language processing 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



What is “Understanding” ?   

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1-123

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?



Challenges in NLP      

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Dream of AI community 

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

to have algorithms that are capable of automatically reading and obtaining knowledge from text


NLP Task Categories            1-125

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


Text Mining Applications 

Marketing applications 

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Enables better CRM

Security applications  

ECHELON, OASIS Deception detection 



Medicine and biology 

Literature-based gene identification 



example coming up

Academic applications 

1-126

example coming up

Research stream analysis

- example coming up



1-127

Application Case 7.4: Mining for Lies



1-128

Application Case 7.4: Mining for Lies Category

Example Cues

Quantity

Verb count, noun-phrase count, ...

Complexity

Avg. no of clauses, sentence length, …

Uncertainty

Modifiers, modal verbs, ...

Nonimmediacy

ive voice, objectification, ...

Expressivity

Emotiveness

Diversity

Lexical diversity, redundancy, ...

Informality

Typographical error ratio

Specificity

Spatiotemporal, perceptual information …

Affect

Positive affect, negative affect, etc.


Text Mining Process

The three-step text mining process 1-129


Text Mining Process 

Step 1: Establish the corpus 

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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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Step 2: Create the Term–by–Document Matrix



Step 2: Create the Term–by–Document Matrix (TDM) 

Should all be included?   



What is the best representation of the indices (values in cells)?  

1-132

Stop words, include words Synonyms, homonyms Stemming

Row counts; binary frequencies; log frequencies; Inverse document frequency


Web Mining Overview   

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

 1-133

The The The The The

Web Web Web Web Web

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

Opportunities and challenges are great!


Web Mining 

1-134

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


Web Content/Structure Mining 

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

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Web pages include hyperlinks

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


Web Usage Mining 

Extraction of information from data generated through Web page visits and transactions 

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data stored in server access logs, referrer logs, agent logs, and client-side cookies characteristics and usage profiles metadata, such as page attributes, content attributes, and usage data

Clickstream data Clickstream analysis


Web Usage Mining 

Web usage mining applications    

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Determine the lifetime value of clients Design cross-marketing strategies across products. Evaluate promotional campaigns Target electronic ads and coupons at groups based on access patterns Predict behavior based on previously learned rules and s' profiles Present dynamic information to s based on their interests and profiles


Web Usage Mining

(clickstream analysis)

1-138


Web Mining Success Stories  

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


Business Intelligence: A Managerial Approach nd (2 Edition) Chapter 6: BI Implementation: Integration and Emerging Trends

Implementing BI – An Overview 

Critical Success Factors for BI Implementation a. b. c. d. e. f. g.

1-141

Business driven methodology and project management Clear vision and planning Committed management and sponsorship Data management and quality issues Mapping the solutions to the requirements Performance considerations of the BI system Robust and extensible framework


Managerial Issues Related to BI Implementation 1. 2. 3. 4. 5. 6. 7. 8. 1-142

System development and the need for integration Cost–benefit issues and justification Legal issues and privacy BI and BPM today and tomorrow Cost justification; intangible benefits Documenting and securing systems Ethical issues BI Project failures


BI and Integration Implementation 

Why integrate?  

   

1-143

To better implement a complete BI system To increase the capabilities of the BI applications To enable real-time decision To enable more powerful applications To facilitate faster system development To enhance activities such as blogs, wikis, RSS feeds, etc.


BI and Integration Implementation 

Levels of BI Integration 

Functional integration can be within the same BI or across different BI systems 





Embedded Intelligent Systems 

1-144

Integration across different BI systems can be accomplished in a loosely coupled fashion – input output ing, messaging (SOA) Integration within a BI system is more cohesive with several sub-systems constituting the whole

Serving as the intelligent agents within BI


Connecting BI Systems to Databases and Other Enterprise Systems 

Virtually every BI application requires database or data warehouse access

Multi-tiered Application Architecture 1-145


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  

1-146

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


Benefits of On-Demand BI 

Ability to handle fluctuating demand 



Reduced investment/cost   

 

 1-147

Flexible use of the BI technology pool 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


The Limitations of On-Demand BI 





 

1-148

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 s’ needs Upgrading may become a problem You may relinquish strategic data to strangers (lack of privacy/security of corporate data)


Issues of Legality, Privacy and Ethics 

Ethics in Decision Making and       



1-149

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 s

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


Emerging Topics in BI – An Overview 

  

 

1-150

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)


Emerging Topics in BI – An Overview The Future of BI 

  

 

1-151

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)


Emerging Topics in BI – An Overview 

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

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1-152

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 industryspecific 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.


The Web 2.0 Revolution 





1-153

Web 2.0: a popular term for describing advanced Web technologies and applications, including blogs, wikis, RSS, mashups, generated content, and social networks Objective: enhance creativity, information sharing, and collaboration Difference between Web 2.0 and Web 1.x Use of Web for collaboration among Internet s and other s, content providers, and enterprises


The Web 2.0 Revolution 



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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


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. 

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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 – 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 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. 2.

  1-156

Partnership with mobile carriers (use of MySpace over AT&T network) Without a partnership (“off deck”) (e.g., MocoSpace and Mobikade)

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


Major Social Network Services 

Facebook: The Network Effect 

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Launched in 2004 by Mark Zuckerberg (former Harvard student) It is the largest social network service in the world with over 500 million active s 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.


Implications of Business and Enterprise Social Networks 

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Business oriented social networks can go beyond “advertising and sales” Emerging enterprise social networking apps: 

Finding and Recruiting Workers 

  

Management Activities and Training Knowledge Management and Expert Location 

  1-158

See Application Case 14.2 for a representative example

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

Enhancing Collaboration Using Blogs and Wikis Within the Enterprise …>


Implications of Business and Enterprise Social Networks 

Survey shows that best-in-class companies use blogs and wikis for the following applications:      

 1-159

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%)


Virtual Tradeshows

See iTradeFair.com 1-160


Social Networks and BI: Collaborative Decision Making 

Collaborative decision making (CDM) – combines social software and BI 

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CDM is a category of decision- 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.


How CDM Works

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How does RFID work? 

RFID system 

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Tags 

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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) Active tag versus ive tags


RFID for Supply Chain BI 

RFID in Retail Systems 

Functions in a distribution center 

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receiving, put-away, picking, and shipping

Sequence of operations at a receiving dock unloading the contents of the trailer 2. 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 1.

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RFID for BI in Supply Chain 

Better SC visibility with RFID systems 

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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


Reality Mining 

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Identifying aggregate patterns of human activity trends (see sensenetworks.com by MIT & Columbia University) Many devices send location information 

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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 , …


Reality Mining 

Citisense: finding people with similar interests

A map of an area of San Francisco with density designation at place of interests See www.sensenetworks.com/city sense.php for real-time animation of the content. 1-167


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