Teaching your ‘Old’ Data New Tricks: Revealing the Things You Didn’t Know Your Data Warehouse Could Do
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Presented at InnoTech Oklahoma on October 4, 2012. All rights reserved.
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Teaching your ‘Old’ Data New Tricks: Revealing the Things You Didn’t Know Your Data Warehouse Could Do
- 3. Who We Are • We have assembled a great team of people with a variety and depth of skill sets. • Our reputation has been built by outstanding customer service and a dedication to exceeding expectations.
- 4. What We Do • founded in 1996 • representatives across U.S. & Europe • capable of serving businesses of all sizes from nearly any industry
- 5. Business Intelligence • data visualization • database design/development • ad hoc analysis/reporting • custom analytics training
- 6. Teaching your ‘Old’ Data New Tricks • Why a Data Warehouse • Going Beyond the Basics • Unleashing the Power
- 7. Why a Data Warehouse? • One version of the Truth • Preserving History • Integrate Data Silos
- 8. Why a Data Warehouse? • Mission Level • Faster Reports
- 9. Why a Data Warehouse? It’s the glue that connects your data together.
- 10. One Truth: Star Schema • Simplest to use • Simplest to understand • Simplest for the database to process
- 11. One Truth: Star Schema Fact Tables
- 12. One Truth: Star Schema Dimension Tables
- 13. One Truth: Star Schema Sales
- 14. Preserving History Slowing Changing Dimensions Type 1: Overwrite data Type 2: Add a dimension row Type 3: Add a dimension column
- 15. Preserving History Slowing Changing Dimensions Type 1: Overwrite data Type 2: Add a dimension row Type 3: Add a dimension column Customer Sales Rep Customer Rep ABC Corp John
- 16. Preserving History Slowing Changing Dimensions Type 1: Overwrite data Type 2: Add a dimension row Type 3: Add a dimension column Customer Sales Rep Customer Rep StartDate EndDate ABC Corp John 2011-01-01 2012-03-01 ABC Corp Rachel 2012-03-01 2099-12-12
- 17. Preserving History Slowing Changing Dimensions Type 1: Overwrite data Type 2: Add a dimension row Type 3: Add a dimension column Customer Sales Rep Customer Rep Prior Rep ABC Corp John Rachel
- 18. Preserving History From Transaction to Snapshots Other Types of Facts
- 19. Preserving History From Transaction to Snapshots Product Store Inventory Attributes Date Product Key Store Key Store Quantity on Hand Attributes Periodic Snapshot
- 20. Preserving History From Transaction to Snapshots Product Store Inventory Attributes Date Product Key Store Key Store Quantity on Hand Attributes A word on Semi-additive facts
- 21. Preserving History From Transaction to Snapshots Order Fullfilment Product Order Key Attributes Product Key Store Key Warehouse Qty Ordered Attributes Qty Shipped Date Received Product Date Shipped Attributes Accumulating Snapshot
- 22. Integrating Silos: Leveraging Multiple Stars Navigating your data Universe
- 23. Integrating Silos: Leveraging Multiple Stars CONFORMED SALES Marketing Spend DIMENSIONS
- 24. Unleashing the Power: The Presentation Layer
- 25. Unleashing the Power: The Presentation Layer Find the right tool. It should: • Allow you to explore your data • Answer questions as you ask them • Help you not just create insight but impact • Be intuitive • Be visual
- 26. Unleashing the Power: Visualization versus Credit: Andy Kriebel
- 27. Unleashing the Power: Visualization 30% 70% Credit: Andy Kriebel
- 28. Unleashing the Power: Visualization Count how many times the number “5” appears Credit: Andy Kriebel
- 29. Unleashing the Power: Visualization Now count the number of time “5” appears in the same set of numbers Credit: Andy Kriebel
- 31. Unleashing the Power: Visualization Anscombe's Quartet Credit: Andy Kriebel
- 32. Unleashing the Power: The Presentation Layer My normal approach is useless Credit: Andy Kriebel
- 33. Unleashing the Power: The Presentation Layer Credit: Andy Kriebel
- 34. Questions?
Editor's Notes
- Keeping it high level
- Data Generated by individual usersIn systems built for a single purpose (Sales, AR, CRM)Custom Reports
- Mission Level “Who are my most Valuable Customers”It is a database housing data from various operational systems for the purpose of reporting and data analysis.
- Remember: There are different design philosophies in data warehousingThere’s an art to the application of any of them. Design to meet business needs and objectives of your organization.
- “How much did we sell?”, “What is the account balance?”, “What is our inventory?”Facts should be additiveMost basic: One row transaction
- “Who did this?”, “Where did it happen?”, ‘What did they buy?”Snowflakes
- Where many stopOne TruthAsk lots of questionsIs it enough?-Not capturing all History-Not quite mission level-Not quite integrated
- Dimension that changes over timeDo we need to track changes?What kind of data do we need?What kind of performance?
- Not tracking historyCustomer Address (probably don’t need where they used to live)
- Maybe not dates, maybe just new keys (Customer Sales Rep Id)Keeps all data
- Only keeps a partial history
- Inventory over timeWithout this type of table you would have to build the logic between sales and purchasesEnhance by adding other factgs (Dollar value, latest selling price)Gross Margin Return on Inventory
- Freezing data to spot trendsA picture of a business at specific intervalsWithout this type of table you would have to build the logic between sales and purchasesEnhance by adding other facts (Dollar value, latest selling price)Gross Margin Return on Inventory
- Inventory levels can be added across stores or productsNOT DATECan do things like AveragesTrack trends over time
- Tracks a single things that is important Update at each state of OrderCould add many stages (Returns, Boxed, Damaged, Lost)Start questions like Order turnaround time, Days in InventoryCould set this up as a flow chart (Time between stages)Pipeline Analysis (How many at each state)?Another example: Accounts Recievable
- Multiple StarsROI on MarketingGrain of tablesDate Dimension
- Multiple StarsROI on MarketingGrain of tablesDate Dimension
- Preattentive Processing
- Preattentive Processing
- Preattentive Processing
- Preattentive Processing