1 / 18

DATA WAREHOUSE CONCEPTS

DATA WAREHOUSE CONCEPTS. A Definition. A Data Warehouse: Is a repository for collecting, standardizing, and summarizing snapshots of transactional data contained in an organization’s operations or production systems provides a historical perspective of information

hamilton-kasen
Download Presentation

DATA WAREHOUSE CONCEPTS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. DATA WAREHOUSE CONCEPTS

  2. A Definition • A Data Warehouse: • Is a repository for collecting, standardizing, and summarizing snapshots of transactional data contained in an organization’s operations or production systems • provides a historical perspective of information • is most often, but not exclusively, used for decision support applications and business information queries • can be more than one database • Is not a new concept

  3. Another Definition • Decision Support: • is a set of tools to easily access data • is becoming a critical business tool • is usually graphically oriented • is empowering end users with tools to access vital business information • is moving lots of data down to the end user workstation • is a rapidly expanding area because of data warehousing efforts and projects

  4. Why a warehouse? • For analysis and decision support, end users require access to data captured and stored in an organization’s operational or production systems • This data is stored in multiple formats, on multiple platforms, in multiple data structures, with multiple names, and probably created using different business rules

  5. Why do we want a central data store

  6. Interesting Statistics • 85% of the Fortune 1000 companies have, are implementing, or are looking at, data warehouses (Meta Group) • 90% of all information processing organizations will be pursuing a data warehouse strategy in the next three years (Meta Group) • The Decision Support industry will be a $1 Billion industry by 1997 (IDC & Forrester)

  7. Data Warehouse Evolution - Stage 0 No end user access to production files “What we print” is “what you get”

  8. Data Warehouse Evolution - Stage 1 End users denied direct access to production files Snapshots or copies of production files are made available instead Solution: Provide end users access to production systems

  9. No Integration Between Systems

  10. Data Warehouse Evolution - Stage 2

  11. Data Characteristics Type Production Warehouse • Data Use Operational Mgt Reporting • Level of detail Detailed Summary • Currency Real time, Multiple Latest value generations • Longevity Relatively brief Forever • Stability Dynamic Static • Scope of definition Application wide Enterprise wide • Data Operations Capture/update Read only • Data values Coded Decoded

  12. Transforming the Logical Model

  13. Key Differences - Part 1 • Key differences between “data jails” (operational database) & warehouses • Subject orientation - operational systems are application-segmented (i.e. banks = auto loan, demand deposit accounting or mortgages). Subject areas for banks would be customer and each financial product • Level of integration - warehouses resolve years of application inconsistency in encoding/decoding, data name rationalization, etc • Update volatility - record at a time updates in operational database vs bulk loads in data warehouse • Time variance norms include: 30-90 days of transactions for operational system, 1-10 years for data warehouses

  14. Key Differences - Part 2 Characteristic Operational Warehouse Transaction volume High Low to huge Response time Very fast Reasonable Updating High volume Very Low Time Period Current Period Past to Future Scope Internal External Activities Focused, clerical Exploratory, operational analytical, managerial Queries Predictable, Can be Unpredictable, periodic Ad hoc

  15. Types of Warehouse Configurations • Enterprise • Division • Functional • Financial • Personnel • Engineering/Product • Departmental • Special Project

  16. What’s Really Involved?

  17. Typical Users of a Data Warehouse • Decision Support Analysts, Business Analysts • Marketing, Actuaries, Financial, Sales, Executive • Grocery Store attitudes • Going to the store, not knowing what they want • Close proximity says give me “everything” • Explorers • Don’t know what they want • Search on a random basis, non-repetitively • Frequently finds nothing, but when they do, there are huge rewards • Farmers • Know what they want • Non random searches, finds frequent “flakes of gold” • Finds small amounts of data

  18. Advanced Warehouse Topics • Metadata repositories • Information about the data in the warehouse • Like a library card catalog • Data about when the information was created, what files accessed, how much data • Data about changes in business rules, processes • Context versus Content • “What does it mean?” • Data Mining • Drilling down into databases with tools to find specific anomolies • Online Analysis Processing (OLAP) • Really means summary data

More Related