DAMA + Wilshire Meta-Data Conference - Data Strategy Track
This presentation is for experienced data warehouse architects and database designers. The presentation will describe the most challenging data warehouse data design problems the world of data warehousing has faced. Among these requirements are: handling aggregation, heterogeneous product and transaction types, handling time and history, handling changing dimensions, handling late arriving data, supporting data with different rates of change and stability, supporting large scale database environments such as MPP (massively parallel processing). Designing a data warehouse requires different roles and uses of data, a different use of normalization, and new modeling constructs. Key special requirements of the data warehouse focus on time, location, and dimensional aspects of data. These requirements are among the reasons that analytical data modeling demands different skills, perspectives and techniques.

Topics include:
  • Data warehouse architectures
  • New view of dimensional modeling
  • Required snowflakes
  • Conforming facts and dimensions
  • Heterogeneous dimensions and facts
  • Changing dimensions and facts
  • Mixed changes
  • Modeling for different types of time changes
  • Fact to fact joins
  • Do all facts have count, amount; are all dimensions without them.
  • Factless facts.
  • Fact or dimension
  • Design for parallel
  • Multiple roles
  • Use of surrogate keys
  • Handling multi-valued dimensions
  • Dimensions with varying characteristics
  • Handling complex dimensions, such as hierarchical, ragged, multiple dimensions
  • Handling time and history
  • Surrogate keys
  • Name value pairs
  • What changed?
  • Name value pairs
  • Detecting change data
  • Problems with flattening T1 and T2 dimensions
  • Designing aggregates
  • Aggregates vs. on-the-fly
  • Supporting restatement or aggregates
  • Predicate analysis for star joins
  • Designing for trickle load
  • Exercises
Business intelligence (BI) and data warehousing (DW) applications are playing an ever increasing and important role in driving and optimizing daily business operations. This trend is leading to major changes in both the functionality and the usability of BI-related technologies and products. Developing an operational BI strategy in this dynamic and constantly changing environment is not a simple task. This seminar shows how you can extend the traditional data warehousing and business intelligence environment with real-time data consolidation and federation, business process and performance management, business planning systems, and enterprise portal and collaboration capabilities. Also covered will be the steps to help you get started, things to watch out for, and other considerations for your implementation. The result is an operational BI environment that enables companies to build a smart and flexible business decision making environment for optimizing operational business processes.

Part 1: Why Operational BI?
  • The need for faster business decisions
  • Definition and characteristics of operational BI
  • The role of business intelligence and data warehousing in operational decision making and action taking
  • Extending the traditional BI/DW environment to support the agile business
  • The Extended Corporate Information Factory (CIFe)
  • Developing an operational BI strategy
Part 2: Building an Operational BI Environment
  • A detailed look at operational BI
  • Building an integrated operational BI environment
  • The importance of a process centric approach to operational BI
  • Using dashboards to manage operational business performance
  • Integrating operational BI with business processes
  • The role of workflow management
  • Integrating operational BI with business user collaborative workspaces
  • The important of standards and a services-oriented architecture
  • Case studies
Part 3: Operational BI Techniques, Technologies and Products
  • Finding the data: right-time data propagation, integration and federation
  • The three E’s of Data Integration: EAI, EII and ETL
  • The role of the operational data store - Operational BI and master data management
  • Operational BI reporting, analysis and performance management
  • Automating decisions, recommendations and actions
  • Selecting the right tools and products
Part 4: Learning from Experience
  • Getting started with operational BI
  • Understanding your operational BI business needs
  • Building the business case - Pitfalls to watch out for in integrating BI into operations
  • Best practices for building the agile business
  • The role of BI and DW competency centers - Data quality issues
  • Metadata considerations
  • Evolving to an operational BI environment
Data warehouse architectures are characterized by complex models, lots of latency and difficult maintenance. For example, a conventional data warehouse has a staging area, one or more Operational Data Stores, an "Enterprise" data warehouse, metadata schema and a slew of dimensional models, relational or otherwise. Loading new data is a cascading batch process and time-consuming, also frustrating the need for more current data. Modifying a schema requires examination of all of the dependencies in the Augean Stable, with all of the attendant latency in putting changes into effect.
  • EII proved that is possible to retrieve information in a federated way from online systems without destroying performance
  • Semantic technology makes it possible to design metamodels that can reason, allowing for dynamic transformation of data, much richer rules and the ability to build abstraction layers using open standards
  • Moore's Law allows us not to have to manage from scarcity anymore. A primary reason data warehouse's exist is because there was a lack of resources to perform more on-the-fly processing
  • Operational BI is forcing data warehouses to step up to not only real-time data, but real-time query performance
  • Open standards, SOA, web 2.0, open source
  • All of these factors are hastening the convergence of operational and analytical processing. Data warehouses won't disappear anytime soon, but it's time to start think about where they can add value and where they should step out of the way.
One of the major challenges DM professionals face is gaining the trust of business sponsors & end users. More often than not, the first time a user sees the data is during user acceptance testing or after deployment. Full scale prototyping is not typically done because it is painful and costly.

A new concept being implemented within DM departments of leading organizations is the “Virtual Prototyping Warehouse.” This approach involves providing a temporary, virtual view into the combined data sources to allow end business users to rapidly define, test, edit, report from and validate the ultimate, physical data repository before it is implemented. By doing this, they gain immediate value from the data sharing initiative as well as a tool for managing changes to requirements.

In this session, Mr. Paat will outline:
  • the benefits of creating a "Virtual Prototyping Warehouse"
  • a detailed implementation process, including how to take advantage of existing investments and proven technologies
  • Real-life examples from a leading financial services company & a public sector agency
  • How this approach can be incorporated into an organization’s overall development lifecycle to reduce risk and uncertainty associated with major data integration efforts, while speeding business value and time-to-ROI.
 The U.S. Naval Academy uses a suite of tools and methods to accomplish its mission of creating officers and leaders. Business intelligence is performed by a Knowledge Navigator, which includes the data warehouse, the metadata repository, predictive analysis, and business rules engine. Careful integration and deployment of these products allows leveraging technical solutions for maximum information dissemination with a minimum of resources. This presentation will discuss our comprehensive vision and implementations of our roadmap.

We will share details of:
  • Data Warehouse, with the systems of record, which are the back bone of the data infrastructure
  • Metadata Repository which integrates the strategic, operational and application levels
  • Predictive Analysis which generates defensible predictions and business rule generation
  • Business Process Modeling (BPM) which has allowed us to re-engineer and gain organizational efficiencies.
It is clear that leveraging data for competitive advantage in the corporate world has the ultimate goal of increasing profits. However, the goal(s) of leveraging data in the non-profit world are not that monolithic and may not always be as obvious. This conference session will describe a methodology for analyzing the goals of a non-profit organization, seeking the best practices of related organizations in this regard, determining the data needed to support the goals, and factoring the data into an operational database or into a new or existing data warehouse, as appropriate. The methodology is based on a recently completed project at the University of Memphis on leveraging university data for competitive advantage, which will serve as a case study.
  • Analyzing the goals of non-profit organizations.
  • Seeking the best practices of related organizations.
  • Determining the data need to support the goals.
  • Factoring the data into an operational database or data warehouse.
  • A case study on leveraging university data.
Many businesses acknowledge the general importance of Customer Value, but shy away from measuring it precisely because they don’t know what to do with the results. This session answers that question by presenting specific applications employed by industry leaders in customer management. It shows how customer value measures can increase profits by guiding both strategic and tactical decisions. Attendees will learn the basic components of customer value calculations, how these are refined and extended for particular purposes, and best ways to present the results to business users. The session will also cover technical requirements for customer value analysis, including customer data integration, use of metadata to combine information gathered from different channel systems, alternative methods for building value models, and integration with touchpoint systems for real-time interactions. The final portion of the session will show how customer value measures can be integrated to ensure that decisions across the customer life cycle yield optimal results for the business as a whole, rather than the department executing a particular interaction.

Key points:
  • real-world examples of how customer value measures are used to improve business results
  • two categories of customer value measures, and why you need both
  • an incremental method for building a comprehensive customer data store
  • strengths and weaknesses of different techniques to calculate customer value
  • matching customer value applications to available company infrastructure
  • using customer value to ensure optimal business decisions throughout the organization
The debate about whether to use a star or a snowflake approach to dimensional modeling dates back to the early days of data warehousing. Increasingly sophisticated database systems and tools and faster hardware are changing the tradeoffs between performance and flexibility.

I will present the results of real-world tests comparing the performance of identical data marts implemented with both star and snowflake schemas.

This will feed into a framework for choosing which modeling approach is best for a given requirement. In particular, we will examine how the star/snowflake decision is driven by:
  • Load and query performance
  • Data model usability and maintainability
  • Aggregate design and flexibility
  • Ability to conform dimensions
  • Tool selection

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