Presentation on theme: "Learning Objectives Describe the major business intelligence (BI) implementation issues List some critical success factors of BI implementation Describe."— Presentation transcript:
Learning Objectives Describe the major business intelligence (BI) implementation issues List some critical success factors of BI implementation Describe the importance and issues in integrating BI technologies and applications Understand the needs for connecting BI systems with other information systems Define on-demand BI and its advantages/limitations List and describe representative privacy, major legal and ethical issues of BI implementation
Learning Objectives Understand Web 2.0 and its characteristics as related to BI and decision support Understand social networking concepts, selected applications, and their relationship to BI Describe how virtual world technologies can change the use of BI applications Describe the integration of social software in BI Know how Radio Frequency Identification (RFID) data analysis can help improve supply chain management (SCM) and other operations Describe how massive data acquisition techniques can enable reality mining
Opening Vignette… “BI Eastern Mountain Sports Increases Collaboration and Productivity” Company background Problem description Proposed solution Results Answer & discuss the case questions
Opening Vignette Collaborative Decision Making at Eastern Mountain Sports
Implementing BI – An Overview Decisional Factors in BI Implementation Reporting and analysis tools Features, functionality, flexibility, scalability Database Scalability, performance, security ETL Tools Accessibility, efficiency, usability Costs Hardware/software, development/training Benefits Tangibles/intangibles - time saving, improved decisions/operations/customer satisfaction/
Implementing BI – An Overview Critical Success Factors for BI Implementation a. Business driven methodology and project management b. Clear vision and planning c. Committed management support and sponsorship d. Data management and quality issues e. Mapping the solutions to the user requirements f. Performance considerations of the BI system g. Robust and extensible framework
Managerial Issues Related to BI Implementation 1. System development and the need for integration 2. Cost–benefit issues and justification 3. Legal issues and privacy 4. BI and BPM today and tomorrow 5. Cost justification; intangible benefits 6. Documenting and securing support systems 7. Ethical issues 8. BI Project failures
BI and Integration Implementation Types of Integration Functional integration different [physically separate] applications are provided/used as if it is a single system Physical integration packaging the hardware, software, and communication features required to accomplish functional integration Primary focus in BI (and in this book) is functional-application integration
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.
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
Connecting BI Systems to Databases and Other Enterprise Systems Virtually every BI application requires database or data warehouse access Multi-tiered Application Architecture
Connecting BI Systems to Databases and Other Enterprise Systems Integrating BI applications and back-end systems Web scripting languages (e.g., PHP, JSP, ASP) Application integration servers (e.g., WebLogic) Enterprise application integration – integration of large systems (BI to ERP, SCM, CRM, KM, etc.) Integrating BI and ERP for DSS ERP captures and stores data BI converts data into information/knowledge Middleware?
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
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
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)
Issues of Legality, Privacy and Ethics Legal issues Liability for the actions of advice provided by BI Who is liable, if the software advice fails? Privacy Right to be left alone and the right to be free from unreasonable personal intrusions Collecting information about individuals The Web and information collection Mobile user privacy Homeland security and individual privacy
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)
Issues of Legality, Privacy and Ethics A Model of Ethical Problem Formulation
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)
Emerging Topics in BI – An Overview The 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)
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.
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.x Use of Web for collaboration among Internet users and other users, content providers, and enterprises
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
The Web 2.0 Revolution Characteristics of the Web 2.0 The ability to tap into the collective intelligence of users. The more users contribute, the better. Data is made available in new or never-intended ways. Web 2.0 data can be remixed or “mashed up”. Web 2.0 relies on user-generated and user-controlled content and data (enhanced collaboration). Lightweight programming techniques and tools let nearly anyone act as a Web site developer. The virtual elimination of software-upgrade cycles makes everything a perpetual beta or work-in- progress and allows rapid prototyping, using the Web as an application development platform.
The Web 2.0 Revolution Characteristics of the Web 2.0 Users can access and manage applications entirely through a browser. An architecture of participation and digital democracy encourages users to add value to the application as they use it. There is a major emphasis on social networks and computing. Information sharing and collaboration is greatly supported. This allows for rapid and continuous creation of new business models. “dynamic content, rich user experience, metadata, scalability, open source, and freedom (net neutrality)”
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
Online Social Networking – Social Network Analysis Software It is used to identify, represent, analyze, visualize, or simulate networks with Nodes – agents, organizations, or knowledge Edges – relationships identified from various types of input data (relational and non-relational) Various input and output file formats exist SNA software tools include Business-oriented social network tools such as InFlow and NetMiner Social Networks Visualizer, or SocNetV, which is a Linux-based open source package
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)
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.
Major Social Network Services Orkut: Exploring the Very Nature of Social Networking Sites The brainchild of a Turkish Google programmer It was to be Google's homegrown answer to MySpace and Facebook Format is similar to others: a homepage where users can display every facet of their personal life they desire using various multimedia applications A major highlight of Orkut – ability to create and control communities Also supports many languages
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 …>
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%)
Virtual Worlds Virtual worlds have existed for a long time in various forms — stereoscopes, Cinerama, simulators, computer games, … They are artificial worlds created by computer systems in which the user has the impression of being immersed Examples: Second Life (secondlife.com) Google Lively (lively.com) EverQuest (everquest.com) Avatars ?
Second Life as a DSS Advantages: Easy access and low cost Experienced and dedicated designer/builders Tools and venues for communications-driven decision support (DecisionSupportWorld.com) A large, dedicated user base Impression management / creativity enhancement Time compression Easy data integration from real life using RSS feeds Encourages active participation and experiential learning
Second Life as a DSS Disadvantages: Learning time and training costs Distractions are numerous Pranksters and spam are common Technology problems persist Chat is a very slow communication tool Resistance to use Addiction Participation in most of these virtual environments requires downloading of a "plug-in"
Virtual Tradeshows See iTradeFair.com
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.
How CDM Works
RFID and BI Wal-Mart's RFID mandate in June 2003 DoD, Target, Albertson's, Best Buy,… RFID is a generic technology that refers to the use of radio frequency waves to identify objects. RFID is a new member of the automatic identification technologies family, which also includes the ubiquitous barcodes and magnetic strips.
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
Data Representation for RFID RFID tags contain 96 bits of data in the form of serialized global trade identification numbers (SGTIN) [see epcglobalinc.org]
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 dock 1. 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
RFID for Supply Chain BI RFID in Retail Systems
RFID Data Sample RFID in Retail Systems
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
RFID + Sensors for Better BI Knowing the location and health of goods (i.e., exception) during transportation
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, …
Reality Mining Citisense: finding people with similar interests See sense.php for real-time animation of the content. sense.php A map of an area of San Francisco with density designation at place of interests