Turning your Warehouse Data into Business Intelligence: Reporting Trends and Visibility Michael Armanious; Vice President Sales and Marketing Datex,

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1 Turning your Warehouse Data into Business Intelligence: Reporting Trends and Visibility Michael Armanious; Vice President Sales and Marketing Datex, Inc.

2 Overview Introduction What is Business Intelligence? Why use it? What are the benefits? How does it work? How to turn 3PL warehouse data into decision making information How do we get there?

3 Introduction You want to: Understand your business Identify and exploit areas of strength Address areas that are not performing well Identify areas of opportunity Understand the consequences of decisions Reward staff who perform well Educate staff performing less well Identify areas of business growth

4 Introduction Today users have: A suite of standard reports Excel, Crystal Reports, SQL Reporting Services But you may need to: Analyze, cross check and interrogate a number of reports to obtain the data you need.

5 Common Pain Points Data everywhere, information no where Different users have different needs Excel versus PDF Pull versus push On demand on schedule Your format my format Takes too long wasted resources/efforts Security Technical mumbo jumbo 5

6 Why Business Intelligence? What happened? What is happening? Why did it happen? What will happen? What do I want to happen? Past Present Future Data ERP CRM SCM TMS WMS 6

7 Business Intelligence Vision Improving business insight to all employees: Advanced analytics Self service reporting End-user analysis Business performance management Operational applications

8 OLAP (Online Analytical Processing) 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

9 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

10 OLAP s Role In Decision Making OLAP Sweet-Spot OLAP excels at exploring complex, structured questions

11 Why Not Just Write SQL Queries? Performance Complexity Exploration Presentation Difficulty in dealing with hierarchies Difficult or impossible to specify some desired queries

12 Traditional Analysis Structure

13 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

14 Traditional Analysis Structure

15 OLAP s Place In A BI Solution Reconcile Data Data Derive OLAP Tools OLAP Cube

16 What are cubes? An analytical tool not a reporting tool Adopt Business Intelligence (BI) To analyze data Understand the health of the company Collaboration Single point to share knowledge Reduce decision time Impact the bottom line by measuring operations Enhance competitive advantage Provide key performance indicators (KPI)

17 3PL Sample Data Cube Accesorials Storage Handling sum Date 1Qtr 2Qtr 3Qtr 4Qtr sum WH1 WH2 WH3 Warehouse sum

18 Cube of Measures and Dimensions Service Measure Month Storage Storage Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

19 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

20 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.

21 What are the Benefits Provide high performance queries Get information quickly Supports interactive analysis over large volumes of data Can interrogate multiple data sources multiple branches Enables drill down on your information No report writing skills required Does not impact the performance of your system

22 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

23 Standard vs Analytical Reporting What s the difference between standard versus analytical reporting? Standard reporting On line transactional processing Multiple reports needed to ensure you get a complete picture of the business Once the report has finished processing the criteria is set, you would need to re-run the report to view other criteria Data is structured for information creation and editing NOT reporting cause performance issues

24 Analytical Reporting Analytical Reporting (OLAP) On line analytical processing Data is snapshot at pre-determined times of the day. Therefore, no impact on database after initial load Data is structured for ad-hoc reporting

25 How does it work?

26 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

27 Dimensional Modeling Example Dimension tables provides details on stores, products, and time periods Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7 th ed.

28 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.

29 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.

30 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.

31 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

32 Tabular Representation of Measures and Dimensions Simple example of viewing OLAP data in a grid: Row headings (Warehouse) represent dimension members Columns represent different measures Dimension Warehouse Services Sales Data for 2006 Warehouse Gross Sales Quota Profits Sales 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

33 Tabular Representation of Measures and Dimensions Example 2: Warehouse sales by year and warehouse location Column and row headings represent dimension values in this case Cells represent measures, Name of table describes measure Dimensions Warehouse Services Sales Data Store 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 Measures

34 The End Result Cold Storage 3PL

35 Why Report Builder? Report customers Report Viewer Business users Report Builder Power users Developers Report Designer

36 What is Report Builder? A new ad-hoc report design tool for Microsoft SQL Server Reporting Services Targeted at business users who want to find and share answers to interesting questions Driven from a business model of the data, so users do not need to understand the underlying data structures Not a full analytical client or replacement for Microsoft Office Excel Pivot Tables Fully integrated with Reporting Services and delivered in SQL Server 2010

37 Report Builder Architecture Microsoft SQL Server Management Studio Report Designer Model Designer Report Manager Report Builder Client Data sources (SQL Server, Analysis Services) Web services interface Report Server Drillthrough report generation Query generation SQL Server catalog

38 Report Builder versus Report Designer Report Builder Targeted at business users Ad hoc reports Auto-generates queries using semantic layer on top of the source Reports built on templates ClickOnce application, easy to deploy and manage Cannot import Report Designer reports Report Designer Targeted at IT professionals and developers Production reports Native queries (SQL, OLE DB, XML/A, Open Database Connectivity (ODBC), Oracle) Free-form (nested, banded) reports Integrated into Microsoft Visual Studio development system Can work with reports built in Report Builder

39 Summary 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

40 Summary Business Intelligence It is the application of knowledge derived from analyzing an organizations data to effect a more positive outcome Ralph Kimball (Ph.D.) Collaborate on a shared view of the business Reduce the time to decision Provides flexibility in the ways you access data Low time to impact; low latency query results No reporting writing skills

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