Data Warehousing 101 Howard Sherman Director – Business Intelligence xwave

Agenda  Introduction  Definitions  Why Create a Data Warehouse  Complexities You Will Encounter  Best Practices  Questions

xwave Overview  Full services IT solutions provider - we fulfill the complete range in enterprise system requirements.  Our legacy is as a high quality systems integration company with deep infrastructure and product fulfillment capabilities.  Possess extensive COTS and custom development experience; leveraging the best of breed in applications and business processes.  Focused on key industries in which we have relevant experience.  xwave is a $346M division of Bell Aliant Regional Communications—an ICT provider with more than 10,000 employees, 100-plus years of customer service and an international client list.

The BI Practice at xwave  Over 65 BI Professionals with Access to Many More  Specialized and Certified BI Consultants  End to End Capabilities  Experienced in a Full Range of Tools/Products Including: Cognos, Business Objects, CA, Oracle, Microsoft and Trillium  Over 10 Years of Experience Delivering Industry Leading BI Solutions

Definitions Business Intelligence n. Process of assembling disparate data, transforming it to a consistent state for business decision making, and empowering users by providing them with access to this information in multiple views. Data Warehouse n. A collection of corporate information, derived directly from operational systems and some external data sources. Its specific purpose is to support business decisions, not business operations.

Why Create a Data Warehouse?  To perform server/disk bound tasks associated with querying and reporting on servers/disks not used by transaction processing systems.  To use data models and/or server technologies that speed up querying and reporting and that are not appropriate for transaction processing.  To provide an environment where a relatively small amount of knowledge of the technical aspects of database technology is required to write and maintain queries and reports and/or to provide a means to speed up the writing and maintaining of queries and reports by technical personnel.  To provide a repository of "cleaned up" transaction processing systems data that can be reported against and that does not necessarily require fixing the transaction processing systems.

Why Create a Data Warehouse?  To make it easier, on a regular basis, to query and report data from multiple transaction processing systems and/or from external data sources and/or from data that must be stored for query/report purposes only.  To provide a repository of transaction processing system data that contains data from a longer span of time than can efficiently be held in a transaction processing system and/or to be able to generate reports "as was" as of a previous point in time.  To prevent persons who only need to query and report transaction processing system data from having any access whatsoever to transaction processing system databases and logic used to maintain those databases.  To perform complex joins, transformations and business logic once and not every time a new report is created.

Why Create a Data Warehouse? Performance - Operational and Data Warehouse Systems Simplify - Make Complex Data from Many Systems Available in One Accuracy - Standardize and Cleanse Business Value - Provide the Foundation for the Business to Have Access to Information to Make Timely, Informed Decisions

Complexities of Creating a Data Warehouse  Incomplete errors  Missing Fields  Records or Fields That, by Design, are not Being Recorded  Incorrect errors  Wrong Calculations, Aggregations  Duplicate Records  Wrong Information Entered into Source System

Complexities of creating a Data Warehouse  Incomprehensibility errors  Multiple Fields Within One Field  Inconsistency errors  Inconsistent Use of Different Codes  Overlapping Codes  Inconsistent Grain of the Most Atomic Information

Best Practices  Data Warehousing is a process and not a project  Complete requirements and design  Prototyping is key to business understanding  Utilizing proper aggregations and detailed data  A full iterative approach is essential  Training is an on-going process  Build data integrity checks into your system

Questions or Comments? Thank You