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Why A Data Warehouse
- 1. Why aWhy a Data WarehouseData Warehouse Concepts in Design Fred A Kilby, MBA fred.kilby@pmbsa.com Copyright © 2009 Fred A. Kilby. All rights reserved
- 2. What is a Data WarehouseWhat is a Data Warehouse? The conglomeration of an organization’s data warehouse staging and presentation areas, where operational data is specifically structured for query and analysis performance and ease-of-use. Ralph Kimball,(2002) The Data Warehouse Toolkit.
- 3. Now in EnglishNow in English A data warehouse is a database organized in a way to allow for fast queries of information. It contains the data from the different database systems that is brought together for a single view.
- 4. So what’s the differenceSo what’s the difference? Transactional Sources • Centers around transactions • 2 dimension reports – Age by System • Individual data • Slow • “Cut-n-paste” into other applications Data Warehouse • Centers around business facts • Multi-dimensional reports – Age by Race by Program • Aggregated data • Fast • 3rd party reporting tools can be used.
- 5. Measures Facts not ActivitiesMeasures Facts not Activities Facts are business performance measurements – Meals provided – Dollars expended – Hours worked Facts are numerical and additive – Sum of dollars spent – Count of clients served Facts are stored to represent a measurement at a particular “grain”
- 6. What is a Grain?What is a Grain? A grain is the level of detail at which a business measurement is stored Different businesses have different fact needs – A Social Services grain • The number of food stamp dollars given to a case each month – In-Home Support Services grain • The number of hours of service a client received in a provider’s pay period • The number of dollars paid to a provider for a client during a pay period
- 7. What is a DimensionWhat is a Dimension? A dimension is a textual description that relates to a fact, for example: – Ethnicity (White, Black, Japanese) – Language (English, Spanish, Tagalog) – Gender (Male, Female) – Date (05/31/2003, 04/15/2003) – Location (California, Arizona, New Mexico)
- 8. Used in QueriesUsed in Queries Dimensions are used to restrict and frame queries on facts, for example: “Give me a count of all Spanish speaking white males in California” • The fact is the count (a number) • The dimensions are: – Spanish (language), – white (race), – male (gender), – and California (location)
- 9. Identifying Facts and DimensionsIdentifying Facts and Dimensions By Aid Type By Program By Month For (By) Year Count of Cases Sum of Aid Payments Average per Case
- 10. What makes a Data WarehouseWhat makes a Data Warehouse?
- 11. Cubes Answer Business QuestionsCubes Answer Business Questions How many Spanish speaking clients did H&HS serve in each department for each of the past 3 years? Which cities currently have the highest concentration of Asian clients? What has the trend been? How many people who receive Medi-Cal received a service in 2003 from health services, by service?
- 14. Drill Down CapableDrill Down Capable
- 17. Where do we startWhere do we start? • Choose the systems to include • Identify the exact grain of the business process • Identify the dimensions available for use with each fact table row • Choose the numeric facts of what is being measured
- 18. Key to SuccessKey to Success To ensure success end user involvement is required: Data warehouse success is tied directly to user acceptance. If the users haven’t accepted the data warehouse …then your efforts have been exercises in futility. (Kimball, 2002)
Editor's Notes
- What are some business facts that you need, or would like, to be able to report on?
- Here is an example of how to identify Facts and Dimensions on an existing report The Facts are “math” words - Count of Cases, Sum of Aid Payments, Average of Pay per Case The Dimensions are “grouping” words - (By) Program, (By) Aid Type, (By) Calendar Month, (For) Fiscal Year
- We start with data from operational sources We move this data into a staging area where business rules are applied Code values are translated to a common set; i.e., M vs. male Formats are changed to fit a standard; i.e., 5.1 vs. 5.1000 These rules make the data from different sources comparable (apples to apples) Once the data is made “standard” it is loaded into the warehouse’s fact and dimension tables We create the reporting cubes Users access the cubes to analyze and report on the data
- The data warehouse is to help you answer business questions. To help you answer these questions there are Reporting Cubes.
- Now I’m going to show some examples of how this comes together to help you in reporting and analyzing data. While going through these, think of what YOU would like see. In this report, the facts are Client counts, the dimensions are By Department, By Gender, and By Active Year.
- Here we see another report. Again the fact is a count of Clients, the dimensions are By Race group, By Department, For Active Year
- These cubes can provide for drilling down into greater level of detail. From the previous report we have “drilled” into the Social Services Division, “down” to the program level. Can you tell what the dimensions are here? By Race By Department By Program By Active Year
- We say that these cubes are multi-dimensional. This report shows that we can combine dimensions to find even more interest information. Notice that the Fact is Unique Client, the Dimensions are By Race Group, By Gender, By Marital Status, By Department, and the Filter, or Selection, is For Active Year
- Depending on the reporting tool, these reports can easily be converted in to visual graphs. Here we see the prior grid report in a graph format. This allows the user to quickly notice interesting information.
- Now that the value of the data warehouse can be seen, how do we begin?