TDWI Onsite Education

Transcription

1 TDWI Onsite Education World-Class Business Intelligence and Data Warehousing Education in Your Environment Cost-Effective. High-Impact. Convenient. Flexible.

2 Put TDWI s Expertise to Work for You TDWI Onsite delivers the highest quality business intelligence (BI) and data warehousing (DW) education to your location. For more than 10 years, TDWI has offered onsite training for all levels in your organization, so everyone involved in a project shares a common knowledge base and learns in support of the same corporate objectives. Every TDWI Onsite course is rich with business and technical concepts as well as techniques that are proven in practice. And every TDWI instructor is a business intelligence and data warehousing practitioner with real-world experience. Our Courses We are committed to delivering a high-quality training experience. By carefully selecting, managing, and maintaining our course materials, we ensure consistent messages, common learning objectives, and upto-date content. In addition to our core curriculum, we offer a range of special-skills, advanced topics, and hands-on training. Our course offerings are updated frequently to ensure that we offer the most current materials from the industry s best instructors. Visit our Web site for recent additions: Our Workshops Make your training even more valuable with a workshop. Many of TDWI s popular courses are available in this format. We set aside case study exercises and extend the course length to work hands-on with your projects and problems. Intermixing workshop activities with a traditional lecture format means you do more than hear about skills and techniques; you practice them. A team working together on your projects and problems gives the course material real meaning, improves teamwork, enhances communication, and produces results that are useful as the project moves forward. Hands-On Courses Benefits Cost-Effective Eliminate travel-related expenses and get more for your training dollar High-Impact Advance project goals by giving your team an equivalent understanding of core concepts Convenient Schedule training when it best accommodates your workload Flexible Tailor training to your specific needs and incorporate organizationspecific information We have expanded TDWI Onsite to offer hands-on courses. Formal lab exercises are conducted between lecture components in order to provide participants an opportunity to experience the fundamental features of leading technology tools. These labs are designed to allow participants to compare how each tool generally functions, its best features, and how well it integrates with your business intelligence and data warehousing solution. Certification The Certified Business Intelligence Professional (CBIP) credential is a recognized professional achievement in business intelligence and data warehousing. This test-based certification is offered in five key specialties: Leadership and Management, Business Analytics, Data Analysis and Design, Data Integration, and Administration and Technology. To learn more, please visit BI Executive Briefing TDWI Onsite Includes Learning needs assessment and tailored training plans Pre-class discussion with a TDWI-certified instructor to ensure content is aligned with your training objectives Instructor-led training at your workplace or location of your choosing All course materials Leading companies of the future will include those that are best able to make the transition from data warehousing to business intelligence. For the greatest success, this important next step must be undertaken as a business initiative, not as an IT project. TDWI s Business Intelligence Executive Briefing is designed to help both business leaders who want to get in the driver s seat of BI and IT managers who are challenged to get the sponsorship and business participation that they need. For more information, see page 4. 1

3 Onsite Course Offerings Our Instructors Our instructors undergo rigorous selection and certification processes. All have distinguished themselves as leading business intelligence and data warehousing practitioners who bring real-world experience and expertise to every class. TDWI Onsite Instructors Dean Abbott, Senior Consultant, The Modeling Agency Maureen Clarry, CEO/President, CONNECT: The Knowledge Network Jonathan Geiger, Executive Vice President, Intelligent Solutions, Inc. Michael Gonzales, CBIP, President, HandsOn-BI, LLC Karolyn Hepp, CBIP, Principal Consultant, Toguchi, LLC, and TDWI Fellow Steve Hoberman, CBIP, Data Explorer, Steve Hoberman & Associates Claudia Imhoff, President, Intelligent Solutions, Inc. Charles Kaplan, CBIP, Director, DecisionPath Consulting Deanne Larson, President, Larson & Associates Lisa Loftis, Senior Vice President, Intelligent Solutions, Inc. David Loshin, President, Knowledge Integrity, Inc. Dan Merriman, President, Chapin Consulting Group, Inc. Larissa Moss, President, Method Focus, Inc. John O Brien, CBIP, President and Executive Architect, Zukeran Technologies Mark Peco, CBIP, Managing Partner, Camar Solutions, Inc. Tony Rathburn, Senior Consultant, The Modeling Agency Lorna Rickard, Chief Workforce Architect, CONNECT: The Knowledge Network Paul Sheets, CBIP, Principal DW Consultant, DecisionPath Consulting James Thomann, Ph.D., CBIP, Principal Consultant, DecisionPath Consulting, and TDWI Fellow David Wells, CBIP, Director of Education, TDWI Nancy Williams, CBIP, Vice President and Principal Consultant, DecisionPath Consulting FOR MORE INFORMATION, PLEASE CONTACT: Yvonne M. Baho Director, Onsite Education, TDWI 64 Prospect Street, Lunenburg, MA Phone: Fax: ybaho@tdwi.org Core Skills TDWI Data Warehousing Concepts and Principles TDWI Business Intelligence Fundamentals TDWI Data Warehousing Architectures TDWI Testing Techniques for Business Intelligence and Data Warehousing Systems Leadership and Management TDWI Business Intelligence Executive Briefing TDWI Program Assessment Workshop for Business Intelligence and Data Warehousing TDWI Quality Management Workshop Project Management for Data Warehousing Bringing Business and IT Together Program Management for Business Intelligence Business Analytics TDWI Business Basics for IT Professionals TDWI Enterprise Business Metrics Enterprise Business Metrics in Practice Fundamentals of Business Analytics CRM: Fundamentals, Strategy, and Implementation Predictive Analytics Data Mining Techniques, Tools, and Tactics Data Mining Application Workshop Development Processes Business Intelligence Roadmap Six Steps to a Sustainable Corporate Information Factory The BI Pathway Approach Data Analysis and Design TDWI Data Modeling TDWI Dimensional Data Modeling Primer Enterprise Data Modeling for Business Intelligence Data Modeling in Practice Intermediate and Advanced Techniques for Effective Data Modeling Data Profiling for Reingineering, Knowledge Discovery, and Information Quality Data Integration TDWI Data Integration Techniques TDWI Data Cleansing Architecture and Design of the Operational Data Store One Thing at a Time An Evolutionary Approach to Metadata Management Administration and Technology HandsOn-ETL HandsOn-Data Integration HandsOn-OLAP HandsOn-Business Analytics HandsOn-Advanced Analytics HandsOn-Data Mining HandsOn-Technology Architecture Workshop Certified Business Intelligence Professional TDWI Exam Prep for the Certified Business Intelligence Professional (CBIP) For in-depth course descriptions, please visit: 2

4 Onsite Course Offerings Core Skills Core Skills include the fundamental knowledge and abilities that are essential to every member of a business intelligence or data warehousing team. The body of knowledge for this area ranges from business drivers and value to strategies for technology implementation. Common applications, architectures, and approaches are within the core set of knowledge. For individuals, this knowledge is the foundation upon which more advanced skills are developed. For teams, shared concepts, common terminology, and mutual understanding are among the basics of effective teamwork. TDWI Data Warehousing Concepts and Principles: An Introduction to the Field of Data Warehousing Basic concepts of data warehousing Common language, terminology, and definitions in data warehousing Key factors that contribute to data warehousing success Risk factors for data warehousing projects Common approaches to data warehousing architecture Data warehousing roles and responsibilities Data warehousing development concepts and best practices Data warehousing operations and administration considerations Anyone new to data warehousing; data warehousing teams that need to develop a common base of concepts and terminology; and data warehousing team members who need to understand the roles and responsibilities of others on their team TDWI Business Intelligence Fundamentals: From Data Warehousing to Business Impact The factors that contribute to maximum business value Six common kinds of BI/DW business applications Key elements and common applications of business analytics The roles of dashboards, scorecards, and analytic applications The relationships between business intelligence and data warehousing Components of the business intelligence infrastructure people, processes, and technologies Best practices and common mistakes in business intelligence programs Anyone with a role in business intelligence programs; data warehousing managers and leaders who are seeking to increase the value delivered from the data warehouse; business and technical people who need to work together to implement business intelligence; and teams who need to develop a common base of concepts and terminology for business intelligence TDWI Data Warehousing Architectures: Choosing the Right Data Warehousing Approach This course assumes basic understanding of data warehousing concepts. or Two-Day Workshop Various data warehousing architectures To distinguish between top-down, bottom-up, and hybrid methodologies The dependencies between architectures and methodology To assess cost, value, and time-to-delivery implications of various approaches A systematic approach to determine the bestfit architecture and methodology for your data warehousing program BI/DW program and project managers; data architects; anyone who participates in making architecture and methodology decisions; anyone who needs to understand the differences between the various approaches EXTENDED WORKSHOP By taking this course as a two-day workshop, you will learn through practice: Architecture perceptions Defining the scope of architecture Assessing your current architecture Evaluating the factors that drive architecture decisions Describing and contrasting architectural alternatives Quantifying architecture strengths and weaknesses based on your needs Choosing the right data warehousing approach for your organization (For a description of TDWI s workshop approach, see page 1.) 3

5 TDWI Testing Techniques for Business Intelligence and Data Warehousing Systems: A Foundation for Quality This course assumes basic understanding of business intelligence and data warehousing. or Two-Day Workshop The difference between quality assurance (QA) and quality control (QC) How QA and QC apply to quality management for business intelligence and data warehousing The role of testing in quality management what it does and doesn t do The differences between business intelligence testing and conventional software testing More than 80 distinct kinds of business intelligence deliverables that can be tested Seven classes of testing techniques Methods to determine what, when, and how to test How to apply testing throughout the business intelligence lifecycle Techniques for pragmatic testing, including test planning, test case management, riskbased testing, computer-assisted testing, and time-boxed testing Program managers and project managers; BI/DW architects, designers, and developers; and BI/DW technical and support staff EXTENDED WORKSHOP By taking this course as a two-day workshop, you will learn through practice: Determining the scope of testing Deciding what and when to test Choosing testing techniques Defining the test environment Building test cases (For a description of TDWI s workshop approach, see page 1.) Leadership and Management Leadership and Management is a key success factor for business intelligence programs and projects, with a strong focus on effectively integrating people, processes, and technology to deliver business value. The field requires in-depth process knowledge, including development methodology, program management, and project management, as well as organizational and team-building skills. An understanding of business topics such as business performance management (BPM), customer relationship management (CRM), and supply chain management (SCM) is also needed. High-level technical understanding of BI applications and data warehousing concepts is part of the Leadership and Management body of knowledge. TDWI Business Intelligence Executive Briefing Three-Hour Briefing This survey of the BI landscape provides a business-oriented, non-technical explanation of all facets of BI from data integration to business application. Starting from a business perspective, and based on the premise that value is realized only through positive business results, the briefing discusses many application areas for BI analytics including CRM, BPM, SCM, and more. Data warehousing is discussed briefly, primarily as an enabler of business intelligence. Critical processes and disciplines for effective business intelligence are described, including program management, governance, change management, and information quality. The many roles of business intelligence are described and discussed as they relate to optimal organization structures. Value realization and data warehousing to business intelligence transition are prominent themes throughout. Key topics include differences between data warehousing and BI, understanding the BI business case, and BI best practices. TDWI Program Assessment Workshop for Business Intelligence and Data Warehousing This workshop assumes you have one or more roles in a business intelligence or data warehousing program. Two-Day Workshop The similarities and differences between assessment for readiness, audit, and review Tools and techniques for business intelligence assessment Tools and techniques for data warehousing assessment Tools and techniques for organization and skills (OAS) assessment Tools and techniques for quality, service, and value (QSV) assessment How to analyze assessment results to identify strength, weakness, and risk Ways to leverage strength, remediate weakness, and mitigate risk The what, why, and how of periodic assessment as a part of program management For in-depth course descriptions, please visit: 4

6 Business intelligence and data warehousing program sponsors; business intelligence and data warehousing program and project managers; business managers who depend on business intelligence and data warehousing solutions; business analysts and knowledge workers; and business intelligence and data warehousing technical staff TDWI Quality Management Workshop: Total Quality Management for Data Warehousing This workshop assumes basic understanding of data warehousing concepts. One-Day Workshop PARTICIPANTS IN THIS WORKSHOP WILL DEVELOP: An understanding of dimensions of data warehousing quality, business quality, information quality, and technical quality Knowledge of the factors that contribute to quality for each dimension An appreciation for the role of measures and metrics in quality management A priority-sequenced list of the areas needing active quality management Defined measures and metrics for the highestpriority quality management areas A plan for continuous quality improvement IT managers; technical support staff; data/ database administrators; help desk personnel; and data analysts Project Management for Data Warehousing This course assumes basic understanding of data warehousing concepts. The differences between traditional projects and data warehousing projects How to create a scope for a data warehousing project The roles of the participants in a data warehousing project and their required skills The methodology for building the different components of the environment The business benefits, ROI, and organizational implications of managing data as a resource How to develop a detailed data warehousing project plan Data warehousing program and project managers; data warehousing developers Bringing Business and IT Together: Pitfalls and Paths to Organizational Partnership How to address issues systemically rather than personally The multiplicity of roles each of us plays in business intelligence, and more effective behavior for each role Why cross-functional involvement is critical in business intelligence Concrete strategies for working more constructively across organizational silos A unique framework for seeing roles and accountabilities in more effective ways How to overcome organizational barriers for effective governance and prioritization Business people who want to improve IT s ability to deliver IT people who want to increase the business intelligence initiative s success rate Business intelligence project or program managers Business intelligence participants who want to make a solid relationship even better Technical staff struggling to make sense of organizational dynamics Program Management for Business Intelligence This course assumes basic knowledge and understanding of business intelligence concepts. The importance of program management for effective BI development, deployment, operation, and administration Major functions of environment management including governance, infrastructure management, centers of excellence, quality management, application management, and metadata management How to develop and manage to servicelevel agreements How to manage growth and change throughout the BI lifecycle How to enhance the business value of BI systems BI and data warehousing program sponsors; program and project managers; and business managers involved in BI and data warehousing programs and projects 5

7 Business Analytics Business Analytics focuses on the effective use of data and information to drive positive business actions. The body of knowledge for this area includes both business and technical topics, including concepts of performance management, definition and delivery of business metrics, data visualization, and deployment and use of technology solutions such as OLAP, dashboards, scorecards, analytic applications, and data mining. TDWI Business Basics for IT Professionals: Essentials for Business Literacy Concepts, terminology, and business considerations of: BPM (both as business process management and as business performance management) Customer relationship management (CRM) Supply chain management (SCM) Workforce management (also known as human capital management or HCM) Operations management Who in the business practices each of these management disciplines and why How business analytics and BI technology support these management disciplines IT people with an interest in business management and business analytics BI and DW team members who need to know more about business BI and DW consultants who bridge business needs with technology solutions Data architects and data modelers who need new skills to design for business analytics TDWI Business Basics for IT Professionals is available as a two-day course. Alternatively, for shorter duration or to focus on specific areas of business basics, you may choose any combination of the following half-day modules. TDWI CRM Basics for IT Professionals Half-Day Module The business role of CRM and the components of a comprehensive CRM strategy Terminology and metrics common to CRM Common CRM activities, both non-technical and technical How to manage factors that can throw your CRM program off track Roles and uses of technology in an integrated customer information environment Characteristics of CRM culture TDWI BPM Basics for IT Professionals Half-Day Module Terminology and definitions used to describe business process management (BPM) Two common forms of BPM and how they are related The business roles and importance of BPM How BPM is related to business intelligence Approaches and strategies for implementation Organizational considerations for achieving success TDWI SCM Basics for IT Professionals Half-Day Module The business purpose and components of supply chains Activities used to design, implement, and manage supply chains How supply chain management (SCM) is related to other business strategies such as BPM and CRM About the SCOR reference model TDWI Workforce and Operations Basics for IT Professionals Half-Day Module The strategic and tactical roles of workforce and operations analytics Terminology, activities, and metrics of workforce management Terminology, activities, and metrics common to operations management How workforce and operations analytics fit with BPM, CRM, and other performance management disciplines How business intelligence relates to and supports workforce management and operations management For in-depth course descriptions, please visit: 6

8 TDWI Enterprise Business Metrics: Designing Integrated Business Metrics for the Enterprise This course works well with Enterprise Business Metrics in Practice. How and why metrics bring new challenges to data integration How to identify and manage classes of metrics that span an enterprise How to define a comprehensive collection of business metrics that serve diverse information needs, communities of interest, and management approaches How to design for cohesion, assure consistency, and avoid conflict of metrics while serving a variety of business measurement needs How to use communities of interest, management disciplines, business domains, external drivers, internal perspectives, and measurement subjects to manage a catalog of business metrics BI program and project managers; business managers who depend on metrics; developers of dashboards and scorecards; data stewards and data administrators; and data modelers Enterprise Business Metrics in Practice: Using Metrics to Maximize Business Performance This course works well with TDWI Enterprise Business Metrics. One-Day Interactive Course or Two-Day Workshop How to define and use business metrics to improve business performance via major business and IT initiatives How to quantify and continually improve the business value enabled by DW/BI Best practices that can be used by joint business and IT teams to define business metrics, processes, and mechanisms (e.g., dashboards, scorecards, reports) Techniques for defining an integrated set of core value metrics that quantify bottom-line impact and analysis metrics that provide critical insight for identifying and diagnosing potential problems Methods for integrating new business metrics and processes with existing business performance management methodologies such as Balanced Scorecard and Six Sigma BI program and project managers; business managers who utilize business metrics; business analysts; developers of dashboards and scorecards; data stewards and data administrators; and finance professionals who support business performance programs Note: It is recommended, but not required, that representatives from the business organizations that will be using the metrics participate in the session. Fundamentals of Business Analytics This course assumes understanding of relational database terms. Best practices, in both data and technical architectures, for implementing a successful BI strategy The core components to effective query and reporting, OLAP, BI portals, data mining, metadata, spatial analysis, real-time DW, and more The right application of atomic-level data, star schemas, and the following OLAP structures and techniques: MOLAP HOLAP ROLAP How to effectively apply leading BI tools, including Hyperion Essbase, Cognos, MicroStrategy Intelligence Server, and Microsoft Analysis Services The application of data mining, including pure-play technologies like SAS as well as indatabase data mining from Oracle, Microsoft, and DB2 The application of advanced visualization tools and techniques The application and importance of spatial data and spatial analysis Understanding the implications of zero-latency (ZLE) and real-time data warehousing DW project planners; data architects; and anyone involved in the design and construction of a BI solution for an enterprise CRM: Fundamentals, Strategy, and Implementation The nature of the extended enterprise and the forces shaping it A comprehensive, product-independent definition of customer relationship management (CRM) How the consumer and the enterprise interact in the customer lifecycle How to align business functions for CRM Current issues surrounding consumer privacy and how permission marketing addresses these concerns How to apply technology in support of CRM in business operations, business intelligence, and business management How enterprises in a variety of industries are benefiting from CRM How to get started with CRM in your business CRM best practices, strategies, tactics, and techniques Common pitfalls to avoid CRM critical success factors Business leaders considering or deploying a CRM program; CIOs; business intelligence and data warehousing program and project managers; and data warehousing and information technology professionals involved in CRM initiatives 7

9 Predictive Analytics: An Intensive Overview of Strategy, Application, and Best Practices for Data Mining Basic principles and terminology for predictive analytics Who is utilizing predictive analytics and why Common project pitfalls and how to avoid them Project deployment, performance, and maintenance issues How to define business objectives for a decision-support system How to get started IT/IS executives and managers: CIOs, CKOs, CTOs, functional officers, technical directors, and project managers Line-of-business executives and functional managers: risk managers, customer relationship managers, business forecasters, inventory flow analysts, financial forecasters, direct marketing analysts, medical diagnostic analysts, and e-commerce company executives Technology planners who survey emerging technologies in order to prioritize corporate investment Consultants whose competitive environment is intensifying and whose success requires competency with data mining and related emerging information technologies Data Mining Techniques, Tools, and Tactics Prerequisite: Predictive Analytics (recommended) The data mining process and general implementation How to prepare raw data and benefit from visualization Various data mining methods and how they compare Advanced model-building techniques Results analysis and validation Technology and product selection Solution integration, ongoing performance, and maintenance Where to begin and how to obtain resources and support IT professionals who wish to expand their skills in this increasingly visible area within the corporate IT agenda Project leaders who must report on developmental progress, resource requirements, and system performance Decision-support system architects who require a solid understanding of the infrastructures required for supporting a data mining solution Business analysts who must develop and interpret the models, communicate the results, and make actionable recommendations Functional analysts: customer relationship managers, risk analysts, business forecasters, statistical analysts, inventory flow analysts, direct marketing analysts, medical diagnostic analysts, market timers, e-commerce system architects, and Web data analysts Data Mining Application Workshop Prerequisite: Predictive Analytics (recommended); Data Mining Techniques, Tools, and Tactics (required) One-Day Workshop Hands-on experience through the data mining process via a staged progression of exercises using application data First-hand, vendor-neutral exposure to various data mining tools Real-world perspective of data preparation for data mining, model optimization, and results interpretation Cross-learning through team exercise comparisons to reveal what worked, what didn t, and why Development processes Data mining techniques, tools, and tactics participants with an interest in applying firsthand the methods and techniques presented and illustrated in the course Data mining practitioners who wish to expand their skills and analytical toolbox as well as hone proficiencies in maneuvering around data mining obstacles that stand in the way of superior model accuracy Business analysts who must develop and interpret models, communicate results, and make actionable recommendations Functional analysts: customer relationship managers, risk analysts, business forecasters, statistical analysts, inventory flow analysts, direct marketing analysts, medical diagnostic analysts, market timers, e-commerce systems architects, and Web data analysts For in-depth course descriptions, please visit: 8

10 Development Processes Development Processes are the learnable and repeatable approaches to building and deploying BI and data warehousing systems. Activities, techniques, and results from planning through implementation are important elements of development processes. Teamwork, quality, and speed of development all benefit from shared processes throughout a BI program. Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications This course assumes basic understanding of business intelligence and data warehousing. Why traditional waterfall methodologies do not work for business intelligence and data warehousing projects Project activities, deliverables, roles, and responsibilities for 16 development steps specifically designed for business intelligence and data warehousing projects How to deploy applications in incremental software releases How to create an appropriate work breakdown structure How to maximize the skills and talents of the project team members through self-organizing team structures Managers and developers Six Steps to a Sustainable Corporate Information Factory Methodology for developing a successful corporate information factory Roles and responsibilities for the implementation team Project planning activities and resource estimate considerations Project leaders and data warehousing practitioners The BI Pathway Approach: Delivering BI for Business Value This course assumes basic understanding of business intelligence and data warehousing fundamentals. Straightforward, business-oriented methods for identifying high-impact business intelligence and data warehousing opportunities and the associated business information requirements How to use BI-focused architectures to align and integrate business intelligence and data warehousing information delivery with strategic, tactical, and operational business processes that drive business value How to use the BI Pathway approach to guide business intelligence and data warehousing development, deployment, and integration with key, high-impact business processes How to manage rapid data mart delivery within overarching BI-focused architectures and the BI Pathway How to approach key topics and techniques such as the use of prototypes, achieving sponsor/business user commitment, and evolving the business intelligence and data warehousing environment over time to maintain business value Program managers; project managers; business analysts; data designers and architects; business managers and knowledge workers; ETL designers and developers; and business intelligence application designers and developers Data Analysis and Design Data Analysis and Design provides the foundation for delivery of BI applications. Analysis concentrates on understanding business needs for data and information. Design focuses on translating business information needs into data structures that are adaptable, extensible, and sustainable. Core skills include information needs analysis, specification of business metrics, and data modeling. Solid understanding of data warehousing concepts, architectures, and processes is also essential. TDWI Data Modeling: Data Analysis and Design for BI and Data Warehousing Systems This course assumes basic understanding of data warehousing fundamentals. or Four-Day Workshop Modeling techniques to gather business requirements Differences in modeling approaches for business transactions, business events, and business metrics Semantic and subject modeling techniques for the big-picture view Relational modeling skills and when to apply them Dimensional modeling skills and when to apply them State-transition modeling skills and when to apply them The role of normalization in data warehousing and business intelligence systems How time-variant data is represented in data models Optimization techniques for warehousing data stores Applied data modeling for data warehouses, data marts, and analytic applications 9

11 Data architects; data modelers; project and program managers; DSS and analytics developers; and business people with data warehousing and business intelligence roles EXTENDED WORKSHOP: By taking this course as a four-day workshop, you will learn through practice: Business domain modeling Subject area modeling Fact/qualifier analysis Business metrics modeling Dimensional data modeling Developing data cleansing rules Star-schema design (For a description of TDWI s workshop approach, see page 1.) TDWI Dimensional Data Modeling Primer: From Requirements to Business Analytics Concepts of dimensional data modeling The relationship between business metrics and dimensional data Similarities and differences between relational and dimensional data models Requirements-gathering techniques for business metrics and dimensional data How to build a logical dimensional model How to translate a logical dimensional model to a star-schema design How dimensional data is used to deliver business analytics and OLAP capabilities Data architects; data mart developers; business analysts; and business intelligence and data warehousing program and project managers Enterprise Data Modeling for Business Intelligence Why so many EDM efforts failed in the past and why they are more successful today How data quality, master data management, and data warehouse initiatives are leveraging EDMs for big-picture understanding and planning How the level of detail, abstraction, time, and function influence the EDM Top 10 ingredients for success and top 10 pitfalls to avoid Three approaches to building an EDM, and the roles and skills required for each approach Program and project managers; business and functional analysts; architects; developers; and data modelers Data Modeling in Practice This workshop assumes a working knowledge of common data modeling techniques. Two-Day Case Study Based Workshop What assumptions to make when working with incomplete requirements or unrealistic timelines How to leverage the strengths of fellow team members How to validate requirements without a data model Which modeling shortcuts work and which shortcuts rapidly turn into nightmares How to balance ease of maintenance versus user friendliness in your data mart design Data architects; analysts; modelers; and project managers Intermediate and Advanced Techniques for Effective Data Modeling This course assumes understanding of entityrelationship and dimensional modeling basics. How to build subject area models that communicate how the business works, how information will be used to answer key business questions, and how proposed data marts fit within an existing data warehousing architecture Ten practical steps to normalize your logical data model to identify key business rules A proven technique for determining where in your physical data model you should denormalize, and which of several denormalization options would be most appropriate Where it is most beneficial to accommodate extra flexibility into your model Ways to leverage universal models How to build and where to apply several templates to capture and validate data requirements A Top 10 list of criteria to apply against your model to ensure its high quality A number of challenging design situations, including when to use surrogate keys and variations of slowly changing dimensions Data architects; analysts; modelers; and analysts, designers, and developers who validate and implement the logical and physical data models Data Profiling for Reengineering, Knowledge Discovery, and Information Quality Exposing embedded information secrets how data domains, business rules, implicit tables, and relational structure are buried inside your data Data profiling methods attribute analysis, dependency analysis, and redundancy analysis The underlying technology that makes it work; complexity and performance issues Embedded structure how data profiling can expose embedded tables, identify relationships across tables, and validate referential integrity Extracting and documenting discovered business rules Data validation using the results of data profiling to validate data Ongoing monitoring using profiling to define metrics for continuous information quality monitoring What to look for in data profiling tools Anyone with a role in a DW, BI, data reengineering, data integration, or information quality projects For in-depth course descriptions, please visit: 10

12 Data Integration Data Integration is fundamental to data warehousing and is a vital process for a rich and robust data resource to deliver BI solutions. Integration includes all of the activities necessary to acquire data from sources and to transform and cleanse the data. The body of knowledge includes concepts and skills for source data analysis and source qualification, data profiling, source/target mapping, data cleansing and transformation, and ETL development. TDWI Data Integration Techniques: ETL and Alternatives for Data Consolidation Analysis techniques to capture data integration requirements, including those for source data, data consolidation, data quality, data granularity, data currency, and historical data How the alphabet soup of integration technologies ETL, EII, EAI, MDM, and CDI fits into overall data integration architecture Design techniques for the mainstream of data integration, including source-to-target mapping, source data capture, data transformation and cleansing, and database loading Techniques to enrich the data integration design with processes for automated scheduling, execution monitoring, metadata capture, restart and recovery, and more Tips to design for the complex issues of data integration, including detecting data changes, identifying data quality defects, managing complex schedule dependencies, meeting real-time data demands, and more BI and data warehousing architects; data integration process designers and developers; and BI and data warehousing program and project managers TDWI Data Cleansing: Delivering High-Quality Warehouse Data This course assumes basic understanding of data warehousing fundamentals. or Two-Day Workshop The components of a data quality plan Rules for data integrity and data correctness The roles of defect detection, correction, and prevention To make informed choices between source data cleansing and target data cleansing To customize a data quality plan to your needs and environment Data warehousing designers and developers; data warehousing program and project managers; and data warehousing administrators EXTENDED WORKSHOP: By taking this course as a two-day workshop, you will learn through practice: Data quality perceptions Setting the scope of effort for data cleansing Understanding data quality rules Identifying data quality rules Expressing data quality rules Developing data cleansing rules Developing a data quality plan (For a description of TDWI s workshop approach, see page 1.) Architecture and Design of the Operational Data Store (Architecture for Near Real Time) This course assumes basic understanding of data warehousing concepts. Architectural differences between legacy systems, operational data stores (ODS), data warehouses, and data marts Classes of the operational data store How organizations are implementing the ODS for business management solutions Methodology, including data and process modeling approaches, for building the ODS Architectural approaches for the ODS Quality issues in the ODS Industry examples To avoid architectural chaos surrounding the ODS Data warehousing program and project managers; data warehousing architects; and data warehousing developers One Thing at a Time An Evolutionary Approach to Metadata Management This course assumes basic knowledge of data warehousing fundamentals. The role of metadata in supporting data warehousing A phased approach for a metadata management strategy, using existing tools in its initial iteration How to develop a pragmatic metadata program around specific business/technical uses for metadata Managers and analysts responsible for data warehousing efforts or metadata management programs 11

13 Administration and Technology Administration and Technology covers those areas related to managing the infrastructure and ensuring continuous operation of business intelligence and data warehousing solutions. Technology architecture, technology planning and configuration, system and network administration, capacity planning, growth management, database administration, system and network administration, and access and security administration are essential skills in this area. Hands-On Courses These labs are designed to allow participants to compare how each tool generally functions, its best features, and how well it integrates with your business intelligence and data warehousing solution. Formal lab exercises are conducted between lecture components in order to provide participants an opportunity to experience the fundamental features of leading technology tools. HandsOn-ETL This course assumes understanding of relational database and DW terms and concepts. Best practices, in both data and technical architectures, for implementing a successful extraction, transformation, and loading process The core components to effective ETL processes Through extensive lab exercises, you will gain hands-on experience with leading ETL tools, including: Ascential Software DataStage Microsoft SQL Server Integration Services Syncsort s DMExpress High Performance Program Oracle Warehouse Builder DFD-Pro Data Flow Diagramming Utility Other (the combination of products is based on availability) How and when to effectively apply leading ETL tools How to compare and contrast ETL features in order to make the best decision for your organization Anyone involved in the selection of ETL technology; those involved in the design and construction of extraction, transformation, and loading of a DW HandsOn-Data Integration The best practices for designing data integration solutions to address modern BI solutions Core components to modern data integration technologies and techniques, including in-flight enrichment, Web services, data quality, and master data management Through extensive lab exercises, you will gain hands-on experience with leading BI tools, including: Microsoft SQL Server Integration Services DMExpress from SyncSort - Oracle Warehouse Builder - IBM s Ascential Data Stage - Hyperion Master Data Management - Trillium Data Quality How and when to effectively apply these tools Anyone involved in the product selection, design, and implementation of data integration technology HandsOn-OLAP This course assumes basic understanding of relational database and data warehousing terms and concepts. The best practices, in both data and technical architectures, for implementing an OLAP strategy The core components to effective OLAP and more Through extensive lab exercises, you will gain hands-on experience with leading OLAP tools such as: MOLAP: using Hyperion Essbase and Cognos PowerPlay HOLAP: using MS Analysis Services ROLAP: using MicroStrategy Intelligence Server The right application of atomic-level data, star schemas, and MOLAP cubes How to effectively apply leading OLAP tools, including MS Analysis Services, Hyperion Essbase, MicroStrategy, and Cognos OLAP Exploratory Data Mining with PolyVista To compare and contrast OLAP features in order to make the best decision for your organization Anyone involved in the product selection, design, and/or construction of multidimensional data access methods for the organization For in-depth course descriptions, please visit: 12

14 HandsOn-Business Analytics This course assumes understanding of relational database and data warehouse terms and concepts. The best practices for blending data mining, dashboards, scorecards, advanced visualization, and spatial data technology into your BI environments The core components to effective spatial analysis, data mining, dashboards/scorecards, and visualization applications Through extensive lab exercises, you will gain hands-on experience with leading BI tools, including: Microsoft Data Mining Microsoft Scorecard ESRI Business Analyst PolyVista Tableau How and when to effectively apply advanced BI technology in order to enhance your information content and analytical landscape Anyone involved in the sponsorship, management, design, and construction of BI solutions for an enterprise HandsOn-Advanced Analytics UPDATED! This course assumes basic understanding of the roles and uses of business intelligence and data warehousing technologies. Real-time analytics Human-machine intelligence Establishing business rules engines XML as an enabler Data mining Enhancing the warehouse with in-database data mining Exploiting SQL data mining extensions Exploratory OLAP mining Advanced ETL transformation with mining algorithms Spatial analysis Enhancing the warehouse with in-database spatial data Exploiting SQL spatial extensions Blending spatial analysis into typical BI technology Incorporating Web services Application development environment Understanding the workbench technology trends Solution strategists; data architects; consultants; BI/DW managers; and anyone who influences the decisions regarding the BI platform and/or those involved in its implementation HandsOn-Data Mining This course assumes knowledge of data warehouse and BI terminology and concepts. How to establish data mining as an integral component of the DW effort and BI solutions Why and when to implement data mining applications How to recognize data mining opportunities Technology/techniques that must be considered for effective data mining Through extensive lab exercises, you will gain hands-on experience with leading data mining tools, including: PolyVista (Text Mining) Microsoft SQL Server 2005 Data Mining Teradata Warehouse Miner SAS Enterprise Miner Project managers; project sponsors; data architects; and anyone who wants to understand (1) how data mining advances BI, (2) how to make mining a natural part of the warehouse effort, and (3) how to recognize a mining opportunity in your organization HandsOn-Technology Architecture Workshop This course assumes working knowledge of business intelligence and data warehousing; and a role in business intelligence or data warehousing program or project. The many dimensions of BI architecture and techniques to approach them systemically How to choose between integratedsuite and best-of-breed approaches to technology selection How to separate must-have from nice-to-have technology How to distinguish between fact and fantasy in vendor product marketing Techniques to define roles, relationships, dependency, and compatibility among tools and technologies How to ensure that your technology architecture supports the business and data architectures for your program or project The workshop is specifically oriented to BI and DW teams who need to confidently define technology architecture that meets short-term needs and will scale to satisfy long-term requirements 13

15 CBIP Now Offered Onsite What Your Peers are Saying about TDWI Onsite The Certified Business Intelligence Professional (CBIP) program is designed for those who have knowledge and experience within a particular specialty and need a respected credential that communicates that expertise to others. This exam-based certification program tests industry knowledge, skills, and experience within five areas of specialization, providing the most meaningful and credible certification available in the industry. TDWI offers a course that can be brought onsite, titled TDWI Exam Prep for the Certified Business Intelligence Professional, for those who are preparing to take the examinations. Onsite testing is also available by request. For more information on CBIP, please visit TDWI Exam Prep for the Certified Business Intelligence Professional (CBIP) What makes CBIP a meaningful certification How to prepare for the exams The body of knowledge for each exam How to evaluate your readiness to take the exams How to use the exam software CBIP roles: yours, TDWI s, and ICCP s Those who plan to take the exams Those who want to learn more before taking the exams Thank you again for all of your help in arranging this session especially on such a quick turnaround. I have been very impressed with the experience of arranging training through TDWI. I have worked with several other vendors to arrange in-house training sessions, and based on the customer service I received, will definitely consider TDWI again. L. Hilbert-Trice, Director, CRM Training American Cancer Society By bringing a customized version of a TDWI course in-house, we were able to take full advantage of the deep expertise of the TDWI instructor while at the same time complementing that with an internal perspective by some of our senior practitioners. Our participants not only learned the content; they learned how the content will be applied in our company s context. Highly valuable! R. Stern, Director of Training Parson Consulting The TDWI Onsite training was outstanding. It allowed us the opportunity to provide our data warehousing staff with a solid conceptual understanding of data warehousing concepts, while at the same time allowing us to involve a broad range of campus constituents, who gained from the class not only an appreciation of the benefits provided by a data warehouse, but also the solid level of organizational commitment required to make it successful. D. Ross, Director, Application & Information Mgmt ITS California Polytechnic State University Training was excellent, and the instructor was outstanding! He was not only knowledgeable about the subject matter but his industry insights were invaluable. He was also quite flexible about tailoring the TDWI material to our needs, which made it all the more relevant to us. Overall, a successful two days from our point of view. U. Thakrar, Software Architect Openwave Systems Inc. For in-depth course descriptions, please visit: 14