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© 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Administrivia – HW #2 Homework #2 OLAP.

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Presentation on theme: "© 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Administrivia – HW #2 Homework #2 OLAP."— Presentation transcript:

1 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Administrivia – HW #2 Homework #2 OLAP option is posted to the wiki It seems to be challenging to get Analysis Services installed and working properly on student laptops I strongly encourage you to do the reporting option, unless you have significant system administration skills, patience, and a willingness to go to ‘plan B’ if the original plan doesn’t work out… That said, it’s an interesting assignment to work with the OLAP tools. Your call…

2 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Online Analytical Processing (OLAP) BI Tools and Techniques Robert Monroe April 8, 2008

3 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Key Take Aways OLAP tools support interactive analysis and exploration of large and complex dimensional data sets Much of the power of OLAP comes from the use of a standard data model (cubes) and offline processing, aggregation, and analysis of data To use OLAP tools effectively, you need to have a basic understanding of how and why data is structured in cubes and the kinds of analyses that this structure makes readily available to you

4 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Core OLAP Concepts

5 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques What Are OLAP Tools? OLAP tools provide a mechanism for interactive analysis and exploration of dimensional data –Interactive: users need to be able to easily specify queries –Analysis: it should be possible to perform (and reuse) complex analyses of the dimensional data –Exploration: answering one question with an OLAP tool frequently raises numerous subsequent questions A good OLAP tool allows the user to quickly pose follow-on queries –Dimensional: OLAP tools operate on dimensional data – data structured as facts and dimensions

6 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques OLAP’s Role In Decision Making Source: O’Brien, Management Information Systems, 6 th ed. OLAP excels at exploring complex, structured questions OLAP Sweet-Spot

7 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Quick OLAP Tools Demo Contour Components OLAP cube browser –Open http://olaplib.contourcomponents.com/ in IE 6.0 or higher –Ok the installation of any ActiveX controls that the site requests – Use the Samples > Government > Regional Employee Turnover menu in the upper left of the screen to open up sample OLAP cube. Demo requires IE 6.0 or later and ActiveX install –Installation for class is optional For first demo we will browse regional emloyee turnover data

8 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Why Not Just Write SQL Queries? Performance Complexity Exploration Presentation Difficulty in dealing with hierarchies Difficult or impossible to specify some desired queries

9 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Why Not Just Use Spreadsheets? Complexity (with > 2 dimensions) Presentation is tied to representation Does not scale to large data sets or many dimensions –Storage and representation is ill-suited to the task Inability to deal with hierarchies

10 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques OLAP’s Place In A Business Intelligence Solution Reconcile Data Derive Data OLAP Cube OLAP Tools Analyze Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

11 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Dimensional Modeling with HyperCubes: Basic Concepts

12 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Representing Dimensional Databases as Cubes OLAP tools represent dimensional data as cubes –Cubes are also sometimes referred to as hypercubes Dimension tables are represented as cube dimensions Facts are represented using measures –Measures can be thought of as the values stored in individual cells of the cube –Measures consist of two parts: A numerical value that represents the basic fact A formula for combining multiple measures into a single measure

13 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Quick Review: Dimensional Modeling Example Fact table provides statistics for sales broken down by product, period and store dimensions Dimension tables provides details on stores, products, and time periods Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

14 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Quick Review: Dimensional Example With Data Product (dimension) Period (dimension) Store (dimension) Sales (fact) Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

15 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Multiple Fact Tables It is frequently useful to store more than one type of fact in a single multidimensional database (star schema) This can be handled by using multiple fact tables that share dimensions Example: modeling products sold and products purchased Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

16 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Factless Fact Tables – Tracking Events “Factless” fact tables store only foreign keys, no facts Factless fact tables allow the tracking of what types of events happened, and under what circumstances they happened Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

17 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Conformed Dimensions When dimensions are shared across multiple fact tables they must be conformed dimensions Conformed dimensions –One or more dimension tables associated with two or more fact tables for which the dimension tables have the same business meaning and primary key with each fact table Conformed dimensions allow users to: –Query across multiple fact tables –Improve consistency of meaning and structure for derived and retrieved information

18 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Tabular Representation of Measures and Dimensions Simple example of viewing OLAP data in a grid: –Row headings (Store) represent dimension members –Columns represent different measures Store Sales Data for 2004 StoreGross SalesQuotaProfitsSales vs. Quota Chicago$3,250,000$2,750,000$624,352+ $500,000 New York$4,500,000$3,550,000$100,000+ $950,000 Pittsburgh$1,600,000$1,700,000$250,000- $100,000 Measures Dimension

19 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Tabular Representation of Measures and Dimensions Example 2: Store sales by year and store location –Column and row headings represent dimension values in this case –Cells represent measures, Name of table describes measure Store Sales Data 2004-2007 Store2004200520062007 Chicago$3,250,000$3,500,000$3,000,000$3,900,000 New York$4,500,000$4,350,000$5,100,000$5,450,000 Pittsburgh$1,600,000$1,700,000$1,800,000$1,650,000 Dimensions Measures

20 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Cube Representation of Measures and Dimensions Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

21 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Dimension Hierarchies Dimension tables are represented as cube dimensions –Cube dimensions use levels to represent hierarchies –Each sub-level subdivides the parent level with finer granularity Dimensions can be of fixed or variable height (jagged) Examples –Dimension: Time Period Levels – Year :: Quarter :: Month :: Week :: Day –Dimension: Organization Levels – Company :: Division :: Department :: Employee

22 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Measures Measures represent the interesting data at the intersection of different dimensions There is a space for a measure at every intersection of every level of every dimension –Base facts are stored in the intersections of lowest-level dimensions (either simple or calculated measures) –Aggregate or computed values are stored at the intersections of where all of the dimensions are not at the lowest level (aggregate values must be calculated measures)

23 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Three Categories Of Measures Additive measures can be meaningfully combined along any dimensions –Example: total sales by product, location, or time Semi-additive measures cannot be combined along one or more dimensions –Example: summing inventory levels across time Non-additive measures cannot be combined along any dimensions –Example: weighted averages without weight information Exercise: –Identify three measures of interest for a cube that tracks sales data –Be sure to identify numeric value tracked and aggregation function Definition source: Pedersen and Jensen, Multidimensional Database Technology, IEEE Computer 12/01

24 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Why OLAP Performs So Well Pre-computation of aggregates, and other values at cube-building time enable very rapid responses to many common queries Ability to specify other formulas/values to precompute on cube build Use of standardized structure and dimensional model allows query engine to make many assumptions about how to best answer queries and take advantage of pre- computed values

25 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Dimensions Examples What dimensions are available in the regional employee turnover example? –Are there any important dimensions missing that you might want to use for an analysis if you were a governmental official trying to improve the employment outlook in your region? The worldwide population cube has an example of a hierarchical dimension –Which one is hierarchical? –Is it a fixed or jagged dimension? –What are the measures in this cube?

26 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Analytics Analytics are specific analyses that can be performed on an OLAP cube –Simple pre-defined analytics (sums, counts, percentages) –Complex pre-canned analytics defined as part of the cube model/build –Ad-hoc exploration Examples: –Actual sales vs. quota by sales region –Supplier count by commodity category by division –Deviation from contracted pricing by supplier, commodity category, and division over the previous 3 years –Examples of analytics related to sourcing or procurement?

27 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Analytics Examples Revenue cube analytics Automobile traffic analytics Marketing dynamics cube (multiple slices preset)

28 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Drilling Down The drilling down operation analyzes the data presently displayed in greater detail. Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

29 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Slicing The slicing operation selects specific values for one or more dimensions of a cube and renders measures for those dimensions in a two-dimensional table Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

30 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Filtering Filtering reduces the elements included in a calculation Filtering can cross multiple slices Example: filter previous results to only show February, April, May Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

31 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques In-Class Exercise Open the Contour Cubes Automobile Traffic sample Which intersection and day in London has the most overutilization of the roads? Which intersection has the worst overutilization of roads across all of the days? Which intersection has the highest overall hourly traffic flow?

32 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Pivoting Data OLAP tools generally let you pivot dimensions –This involves switching which dimensions are displayed horizontally and which are displayed vertically This can be useful when exploring and trying to visualize data Store Sales Data ‘97 – ‘00 ($ Millions) Store1997199819992000 Chicago$3.25$3.5$3.0$3.9 NY$4.5$4.35$5.1$5.45 Pgh$1.6$1.7$1.8$1.65 Annual Sales, By Store ‘97 – ‘00 ($ Millions) YearChicagoNYPGH 1997$3.25$4.5$1.6 1998$3.5$4.35$1.7 1999$3.0$5.1$1.8 2000$3.9$5.45$1.65 Pivot

33 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Modeling Hierarchies Dimension tables frequently model hierarchies Example: –Customers dimension stores data about your customers –You may sell to several divisions of a single company –You want to be able to analyze sales to the individual divisions and also capture “rolled-up” values for the parent company Divisions of ABC Automotive Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

34 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Modeling Hierarchies With Denormalized Tables (I) Hierarchical dimensions are frequently represented with denormalized tables Simplifies and speeds queries at the cost of introducing anomalies This example represents a ‘jagged’ or ‘arbitrary’ hierarchy Customer_Dimension Parent_CompanyCustomer_KeyNameAddressType C000001ABC Automotive100 1 st St.Dealer C000001C000002ABC Auto Sales110 1 st St.Sales C000001C000003ABC Repair130 1 st St.Service C000002C000004ABC Auto New Sales110 1 st St.Sales C000002C000005ABC Auto Used Sales110 1 st St.Sales C000006Bubba’s House O’ Cars5432 Maple LnDealer

35 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Modeling Hierarchies With Denormalized Tables (II) Similar example but with a well-defined hierarchy depth –Same number of levels for all entries in the dimension table –Simpler structureThis approach requires a fixed height to hierarchy –, CityID serves as primary key for the whole table City_Geography_Dimension CityIDCityNameStateIDStateNameTimeZone 45Little Rock2ArkansasCentral 263Denver15ColoradoMountain 423Aspen15ColoradoMountain 522Pittsburgh36PennsylvaniaEastern 771Philadelphia36PennsylvaniaEastern

36 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Wrap Up

37 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Key Take Aways OLAP tools support interactive analysis and exploration of large and complex dimensional data sets Much of the power of OLAP comes from the use of a standard data model (cubes) and offline processing, aggregation, and analysis of data To use OLAP tools effectively, you need to have a basic understanding of how and why data is structured in cubes and the kinds of analyses that this structure makes readily available to you

38 © 2007 Robert T. Monroe Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques 7 th Inning Stretch

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