Microsoft® SQL Server™ 2008 Delivering Business Intelligence About the Author Brian Larson is a Phi Beta Kappa graduate of Luther College in Decorah, Iowa, with degrees in physics and computer science Brian has 23 years of experience in the computer industry and 19 years experience as a consultant creating custom database applications He is currently the Chief of Technology for Superior Consulting Services in Minneapolis, Minnesota, a Microsoft Consulting Partner for Reporting Services Brian is a Microsoft Certified Solution Developer (MCSD) and a Microsoft Certified Database Administrator (MCDBA) Brian served as a member of the original Reporting Services development team as a consultant to Microsoft In that role, he contributed to the original code base of Reporting Services Brian has presented at national conferences and events, including the SQL Server Magazine Connections Conference, the PASS Community Summit, and the Microsoft Business Intelligence Conference, and has provided training and mentoring on Reporting Services across the country He has been a contributor and columnist for SQL Server Magazine In addition to this book, Brian is the author of Microsoft SQL Server 2008 Reporting Services, also from McGraw-Hill Brian and his wife Pam have been married for 23 years Pam will tell you that their first date took place at the campus computer center If that doesn’t qualify someone to write a computer book, then I don’t know what does Brian and Pam have two children, Jessica and Corey About the Technical Editor Robert M Bruckner is a senior developer with the SQL Server Reporting Services (SSRS) product group at Microsoft Prior to this role at Microsoft, he researched, designed, and implemented database and business intelligence systems as a scientific researcher at Vienna University of Technology, and as a system architect at T-Mobile Austria Robert joined the Reporting Services development team in early 2003 and has been specializing on the data and report processing engine that is running inside server and client components of Reporting Services Ever since the initial beta release of SSRS 2000, Robert has been sharing insights, tips, tricks, and expert advice about RDL, data and report processing, and SSRS in general, helping people learn about, understand, and succeed with SSRS (e.g., by posting on newsgroups and MSDN forums, publishing whitepapers, and speaking at conferences) Robert holds Master and PhD degrees with highest distinctions in Computer Science from Vienna University of Technology, Austria Microsoft® SQL Server™ 2008 Delivering Business Intelligence Brian Larson New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2009 by The McGraw-Hill Companies All rights reserved Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher ISBN: 978-0-07-154945-5 MHID: 0-07-154945-5 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-154944-8, MHID: 0-07-154944-7 All trademarks are trademarks of their respective owners Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark Where such designations appear in this book, they have been printed with initial caps McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs To contact a representative please visit the Contact Us page at www.mhprofessional.com Information has been obtained by McGraw-Hill from sources believed to be reliable However, because of the possibility of human or mechanical error by our sources, McGraw-Hill, or others, McGraw-Hill does not guarantee the accuracy, adequacy, or completeness of any information and is not responsible for any errors or omissions or the results obtained from the use of such information TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc (“McGraw-Hill”) and its licensors reserve all rights in and to the work Use of this work is subject to these terms Except as permitted under the Copyright Act of 1976 and the right to store and retrieve one copy of the work, you may not decompile, disassemble, reverse engineer, reproduce, modify, create derivative works based upon, transmit, distribute, disseminate, sell, publish or sublicense the work or any part of it without McGraw-Hill’s prior consent You may use the work for your own noncommercial and personal use; any other use of the work is strictly prohibited Your right to use the work may be terminated if you fail to comply with these terms THE WORK IS PROVIDED “AS IS.” McGRAW-HILL AND ITS LICENSORS MAKE NO GUARANTEES OR WARRANTIES AS TO THE ACCURACY, ADEQUACY OR COMPLETENESS OF OR RESULTS TO BE OBTAINED FROM USING THE WORK, INCLUDING ANY INFORMATION THAT CAN BE ACCESSED THROUGH THE WORK VIA HYPERLINK OR OTHERWISE, AND EXPRESSLY DISCLAIM ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE McGraw-Hill and its licensors not warrant or guarantee that the functions contained in the work will meet your requirements or that its operation will be uninterrupted or error free Neither McGraw-Hill nor its licensors shall be liable to you or anyone else for any inaccuracy, error or omission, regardless of cause, in the work or for any damages resulting therefrom McGraw-Hill has no responsibility for the content of any information accessed through the work Under no circumstances shall McGraw-Hill and/or its licensors be liable for any indirect, incidental, special, punitive, consequential or similar damages that result from the use of or inability to use the work, even if any of them has been advised of the possibility of such damages This limitation of liability shall apply to any claim or cause whatsoever whether such claim or cause arises in contract, tort or otherwise This book is dedicated to my parents To my father, Robert, who even after 40-plus years as a junior high mathematics teacher and computer instructor, has a love of teaching He has shown me a real commitment to sharing knowledge with others To my mother, Beverly, who was my first editor, coaching me through elementary school papers on this state or that president She taught me the value of sticking with a job and seeing it through to the end I owe them both a debt of love, caring, and support that can never be adequately repaid This page intentionally left blank Contents at a Glance Part I Business Intelligence Chapter Equipping the Organization for Effective Decision Making   Chapter Making the Most of What You’ve Got—Using Business Intelligence   13 Chapter Seeking the Source—The Source of Business Intelligence   25 Chapter One-Stop Shopping—The Unified Dimensional Model   43 Chapter First Steps—Beginning the Development of Business Intelligence   61 Part II Defining Business Intelligence Structures Chapter Building Foundations—Creating Data Marts   91 Chapter Transformers—Integration Services Structure and Components   135 Chapter Fill ’er Up—Using Integration Services for Populating Data Marts   233 Part III Analyzing Cube Content Chapter Cubism—Measures and Dimensions   295 Chapter 10 Bells and Whistles—Special Features of OLAP Cubes   331 Chapter 11 Writing a New Script—MDX Scripting   389 Chapter 12 Pulling It Out and Building It Up—MDX Queries   433 Part IV Mining Chapter 13 Panning for Gold—Introduction to Data Mining   469 Chapter 14 Building the Mine—Working with the Data Mining Model   495 Chapter 15 Spelunking—Exploration Using Data Mining   529 vii viii Delivering Business Intelligence with Microsoft SQL Server 2008 Part V Delivering Chapter 16 On Report—Delivering Business Intelligence with Reporting Services   561 Chapter 17 Falling into Place—Managing Reporting Services Reports   643 Chapter 18 Let’s Get Together—Integrating OLAPwith Your Applications   683 Chapter 19 Another Point of View—Excel Pivot Tablesand Pivot Charts   723 Index   741 Contents Acknowledgments   xvii The Maximum Miniatures Databases and Other Supporting Materials   xviii Part I Business Intelligence Chapter Equipping the Organization for Effective Decision Making  Effective Decision Making  Who Is a Decision Maker?  What Is an Effective Decision?  Keys to Effective Decision Making  Are We Going Hither or Yon?  Is Your Map Upside-Down?  Panicked Gossip, the Crow’s Nest, or the Wireless  Business Intelligence  Business Intelligence and Microsoft SQL Server 2008  Chapter 4 6 11 12 Making the Most of What You’ve Got—Using Business Intelligence  13 What Business Intelligence Can Do for You  When We Know What We Are Looking For  Discovering New Questions and Their Answers  Business Intelligence at Many Levels  The Top of the Pyramid  Mid-Level  The Broad Base  Maximum Miniatures, Inc.  Business Needs  Current Systems  Building the Foundation  Chapter 14 14 15 16 16 19 19 20 20 21 23 Seeking the Source—The Source of Business Intelligence  25 Seeking the Source  Transactional Data  26 26 ix C h a p t e r :  E q u i p p i n g t h e O r g a n i z a t i o n f o r E f f e c t i v e D e c i s i o n M a k i n g draw up business plans, prepare massive presentations, and have endless meetings, all to succeed The first, largest, and ultimately fatal problem for many of these organizations is they not define exactly what that success looks like They don’t know what the destination is An organization may have some nebulous goals in its mission statement Phrases such as “superior customer satisfaction,” “increased profit margin,” or “better meeting our community’s needs” grace the reception area, the annual report, and the entryway to the shareholders’ meeting These are great slogans for building a marketing campaign or generating esprit de corps among the employees They not, however, make good milestones for measuring business performance “Superior customer satisfaction” is a wonderful goal (The world would be a much happier place if even half the companies that profess to strive toward superior customer satisfaction would actually make some progress in that direction.) The issue is how to measure “customer satisfaction.” How we know when we have reached this goal or if we are even making progress in that direction? What is required is something a bit more concrete and a lot more measurable Rather than the ill-defined “superior customer satisfaction,” a better goal might be “to maintain superior customer satisfaction as measured by repeat customer orders with a goal of 80% repeat orders.” This goal may need a few more details filled in, but it is the beginning of a goal that is specific and measurable We can measure whether our decisions are taking us in the right direction based on the increase or decrease in repeat orders “Increased profit margin” makes the shareholders happy Still, the organization must decide what operating costs impact profit margin and how they are divvied up among a number of concurrent projects We may also want to state how large the increase to profit margin must be in order to satisfy the investors Does a 1% increase put a smile on the shareholders’ faces, or does our target need to be more in the 5% range? Once these details are added, we have a specific target to work toward “Better meeting our community’s needs” is a noble goal, but what are those needs and how can we tell when they are met? Instead, we need to select a specific community need, such as increasing the number of quality, affordable housing units We can then define what is meant by quality, affordable housing and just what size increase we are looking to achieve To function as part of effective decision making, a goal must: c c Contain a specific target Provide a means to measure whether we are progressing toward that target As with the dartboard in Figure 1-2, we need both a bullseye to aim for and a method for scoring how close we came to that target Delivering Business Intelligence with Microsoft SQL Server 2008 15 25 10 21 24 13 12 23 Figure 1-2 19 22 Required elements of a goal to be used in effective decision making Is Your Map Upside-Down? Goals are important In fact, they are essential to effective decision making, as discussed in the previous section However, goals are useless without some sort of movement toward reaching them The finish line can only be reached if the ladies and gentlemen start their engines and begin the race This is where decision making comes into play Each decision moves the company in a particular direction Some decisions produce a giant leap These are the policy and priority decisions usually made in the upper levels of management These decisions determine the general course the organization is going to take over a lengthy period of time, a fiscal year, a school calendar period, or perhaps even the entire lifetime of the organization It is essential that these decisions point the organization toward its goals if those goals are ever going to be reached Some decisions cause the organization to make smaller movements one way or another These decisions range from workgroup policies to daily operating decisions It could even come down to the way a particular employee decides to handle a specific customer complaint or which phone number a sales representative decides to dial next These small variations in the organization’s direction, these small steps forward or backward, when added together become a large determinant of whether the organization ultimately reaches its goals For this reason, effective decision making is needed at all levels of the organization But how we know when a decision moves the organization, either by a leap or a baby step, toward the goal? We need a method of navigation As shown in Figure 1-3, we need a map or, these days, perhaps a global positioning system (GPS), to tell us where we are relative to our goal and to show us if we are moving in the right direction C h a p t e r :  E q u i p p i n g t h e O r g a n i z a t i o n f o r E f f e c t i v e D e c i s i o n M a k i n g N Starting Position Hw I-37 y 57 Shelton Bypass I-42 Rd 15 nty y 12 Hwy u Co Hw ve n A Goal nso a Sw C Figure 1-3 Measuring progress toward the goal This is the reason why goals must include a means of measuring progress By repeatedly checking these measures, we can determine whether the organization is making effective decisions When the measures show we are heading away from the goals, the decision making can be adjusted accordingly As long as our measures are correctly defined to match our goals, we have a good chance of moving ever closer to those goals Panicked Gossip, the Crow’s Nest, or the Wireless The sinking of the Titanic provides a catastrophic illustration of poor decision making The ship was traveling at high speed in ice-laden waters—an unfortunate decision This tragedy also provides us with an illustration of how important it is to receive feedback information in a timely manner News that there were “icebergs about” reached different people aboard the ship at different times Most passengers found out about the fatal iceberg through panicked gossip as they were boarding the lifeboats Of course, the passengers were not in a position to take direct action to correct the situation and, by the time they found out, it was far too late The ship’s captain got news of the iceberg from the lookout in the crow’s nest of the Titanic This warning was received before the collision, and the captain attempted to correct the situation However, the Titanic could neither stop nor turn on a dime so, ultimately, this warning turned out to be too late 10 Delivering Business Intelligence with Microsoft SQL Server 2008 Another warning had been received earlier on board the Titanic The wireless operator received an ice warning from another ship, the America, and even passed that warning on to a land-based wireless station This message was received hours ahead of the collision—plenty of time to take precautions and avoid the tragedy Because of the large workload on the wireless operator, however, this warning was never relayed to anyone on the Titanic with the authority to take those precautions The feedback information aboard the Titanic is shown in Figure 1-4 In the previous section, we learned about the need to use defined measures to get information to our decision makers As the story of the Titanic illustrates, the timing of this feedback information is as important as its content Feedback information that does not reach the proper decision makers in a timely manner is useful only to those investigating the tragedy after it has occurred The goal of effective decision making is to avoid the tragedy in the first place! As with the passengers on the Titanic, information in our organizations may come in the form of panicked gossip among lower-level personnel Unlike those passengers, these people might even pick up some important information in advance of a calamity Even if this is the case, these people are not in a position to correct the problem Furthermore, we need to base our decision making on solid information from well-designed measures, not gossip and rumors Like the captain of the Titanic, the decision makers in our organizations often get feedback information when it is too late to act The information may be extremely Iceberg right ahead! Ice warning! We struck an iceberg! Figure 1-4 Aboard the Titanic, feedback information was not given to the decision makers in a timely manner C h a p t e r :  E q u i p p i n g t h e O r g a n i z a t i o n f o r E f f e c t i v e D e c i s i o n M a k i n g accurate, but if it does not get to the decision makers in time to make corrections, it is not helpful The numbers in the year-end report are not helpful for making decisions during the current year Similar to the wireless operator, our organizations often have a person who has the appropriate information at the appropriate time The situation breaks down when this person does not pass the information along to the appropriate decision maker This may occur, as in the case of the Titanic’s wireless operator, because that person is overworked and has too much information to get out to too many people It may also occur because organizational policies or structures prevent the flow of information Finally, this may occur because the infrastructure is not in place to facilitate this communication Business Intelligence The first step in effective decision making is to set specific, measurable goals As these goals are being set, the objective is to get accurate, useful information to the appropriate decision makers to serve as a foundation for the decision and as feedback on the effectiveness of that decision Having the foundation and feedback information available at the appropriate time is extremely important The question becomes: How does an organization go about obtaining and distributing this information? As the title of this book suggests, the answer is through the use of business intelligence In fact, this objective serves as our definition of business intelligence Definition Business intelligence is the delivery of accurate, useful information to the appropriate decision makers within the necessary timeframe to support effective decision making Business intelligence is not simply facts and figures on a printed report or a computer screen Rows upon rows of numbers showing detailed sales figures or production numbers may be extremely accurate, but they are not business intelligence until they are put in a format that can be easily understood by a decision maker who needs to use them Concise summaries of customer satisfaction or assembly-line efficiency may be easily understood, but they are not business intelligence until they can be delivered in time to meaningfully affect daily decision making We also discovered earlier in this chapter that effective decision making is important at all organizational levels Timely foundation and feedback information is needed as part of that effective decision making Therefore, we need to make business intelligence available throughout our organizations 11 12 Delivering Business Intelligence with Microsoft SQL Server 2008 Business Intelligence and Microsoft SQL Server 2008 Fortunately, Microsoft SQL Server 2008 provides tools to support all aspects of business intelligence Integration Services enables us to create automated processes to cleanse data and move it into a business intelligence warehouse, when necessary, to ensure we have accurate information available in a timely manner Numerous online analytical processing (OLAP) features, such as Key Performance Indicators (KPIs), multidimensional expression (MDX) queries and scripts, and the Unified Dimensional Model (UDM) enable us to slice, dice, and summarize information so it can be presented in a meaningful manner Data mining permits us to find and present patterns and behavior predictors that might not otherwise be found in the data Finally, Reporting Services and Microsoft Office Business Intelligence Accelerators facilitate the delivery of this information to decision makers throughout the entire organization In Chapter of this book, we learn more about the concepts used when creating and delivering business intelligence We see the types of questions business intelligence can help us answer We also examine the kinds of information and the timeliness of that information required at various levels within an organization Finally, we become acquainted with Maximum Miniatures, Incorporated, the sample company we use throughout the remainder of the book To make the business intelligence features of SQL Server 2008 easier to understand, we perform several hands-on exercises to create business intelligence solutions Rather than looking at code snippets without any business context, we use the business needs of Maximum Miniatures The goal of this book is not to enable you to use this or that feature of SQL Server 2008, but to help you understand how to use those features to meet business needs Chapter Making the Most of What You’ve Got—Using Business Intelligence In This Chapter c  What Business Intelligence Can Do for You c  Business Intelligence at Many Levels c  Maximum Miniatures, Inc c  Building the Foundation 14 Delivering Business Intelligence with Microsoft SQL Server 2008 Out of clutter find simplicity From discord find harmony I —Albert Einstein’s First Two Rules of Work n the previous chapter, we discussed the importance of effective decision making to the success of any organization We also learned that effective decision making depends on specific goals, concrete measures to evaluate our progress toward those goals, and foundation and feedback information based on those measures The latter two items, concrete measures and foundation/feedback information, we referred to as business intelligence In this chapter, we take a look at the types of questions this business intelligence can help us answer We also discuss the types of business intelligence that are needed at various levels of an organization The chapter ends by talking about Maximum Miniatures, Incorporated, the company we are going to use for our examples throughout the book What Business Intelligence Can Do for You In Chapter 1, we saw how business intelligence is used to support effective decision making It provides foundational information on which to base a decision Business intelligence also provides us with feedback information that can be used to evaluate a decision It can provide that foundational and feedback information in a number of different ways When We Know What We Are Looking For In some cases, we know what information we are looking for We have a set of particular questions we want answered What is the dollar amount of the sales or services our organization is providing in each region? Who are our top salespeople? In some of these situations, we not only know what we are looking for, but we also have a good idea where to find the information when we design the business intelligence solution Layout-led Discovery When we know the question we want answered and have a good idea where that answer is going to be found, we can use printed reports to deliver our business intelligence This is the most common form of business intelligence and one we are all familiar with For many situations, this format works well For example, if we want to know the dollar amount of the sales or services provided in each region, we know where to find this information We can design a report to C h a p t e r :  M a k i n g t h e M o s t o f W h a t Yo u ’ v e G o t retrieve the information, and the report will consistently deliver what we need The report serves as an effective business intelligence tool This is an example of layout-led discovery With layout-led discovery, we can only learn information that the report designer thought to put in the report layout when it was first designed If the information wasn’t included at design time, we have no way to access it at the time the report is read Suppose our report shows the dollar amount for a given region to be unusually low If the report designer did not include the supporting detail for that region, we have no way to drill into the region and determine the cause of the anomaly Perhaps a top salesperson moved to another region Maybe we have lost a key client The report won’t give us that information We quickly come to a dead end Data-led Discovery In some cases, we know the question, but we don’t know exactly where to look for our answer This often occurs when the information we initially receive changes the question slightly As in the example from the previous section, an anomaly in the information may cause us to want to look at the data in a slightly different way The unusually low dollar amount for sales or services provided in a specific region led us to want detailed numbers within that region In other cases, we know where to look, but it is not practical to search through all of the detailed information Instead, we want to start at an upper level, find a number that looks interesting, and then drill to more detail We want to follow the data that catches our attention to see where it leads This is data-led discovery: The information we find determines where we want to go next The developer of this type of solution cannot know everywhere the report user may want to go Instead, the developer must provide an interactive environment that enables the user to navigate at will To implement data-led discovery, we need some type of drilldown mechanism When we see something that looks interesting, we need to be able to click that item and access the next level of detail This is, of course, not going to happen on a sheet of paper Data-led discovery must be done online Discovering New Questions and Their Answers In some cases, our data may hold answers to questions we have not even thought to ask The data may contain trends, correlations, and dependencies at a level of detail that would be impossible for a human being to notice using either layout-led or data-led discovery These relationships can be discovered by the computer using data mining techniques 15 16 Delivering Business Intelligence with Microsoft SQL Server 2008 Definition Data mining uses a complex mathematical algorithm to sift through detail data to identify patterns, correlations, and clustering within the data Where layout-led discovery and data-led discovery usually start with summarized data, data mining works at the lowest level of detail Highly sophisticated mathematical algorithms are applied to the data to find correlations between characteristics and events Data mining can uncover such nuggets as the fact that a customer who purchased a certain product is more likely to buy a different product from your organization (we hope a product with a high profit margin) Or, a client receiving a particular service is also likely to need another service from your organization in the next three months This type of information can be extremely helpful when planning marketing campaigns, setting up cross-product promotions, or doing capacity planning for the future It can also aid in determining where additional resources and effort would produce the most effective result Business Intelligence at Many Levels In Chapter 1, we discussed the fact that business intelligence should be utilized at all levels of an organization to promote effective decision making While it is true that business intelligence is useful throughout the organization, the same type of information is not needed at each level Different levels within the organization require different types of business intelligence for effective decision making As we look at what is required at each level, keep in mind the Effective Decisions Triangle from Figure 1-1 We will transform that triangle into a pyramid as we examine the specific goals, concrete measures, and the timing of the foundation and feedback information required at each level (See Figure 2-1, Figure 2-2, and Figure 2-3.) The Top of the Pyramid Decision makers at the upper levels of our organizations must look at the big picture They are charged with setting long-term goals for the organization Decision makers need to have a broad overview of their area of responsibility and not get caught up in the minutiae C h a p t e r :  M a k i n g t h e M o s t o f W h a t Yo u ’ v e G o t r nt pe e Up gem a an M l ve nt -le e id em M ag s, er an ag s M an er , M ad ns Le rso up pe ro re G Fo and Longterm Goals Short-term Goals Day-to-Day Operational Goals Specific Goals Figure 2-1 Specific goals at each level of the organization Highly Summarized Measures The business intelligence utilized at this level needs to match these characteristics The measures delivered to these decision makers must be highly summarized In many cases, each measure is represented, not by a number, but by a status indicator showing whether the measure is in an acceptable range, is starting to lag, or is in an unacceptable range These highly summarized measures are known as Key Performance Indicators r nt pe e Up gem a an M s, er ag s an er , M ad ns Le rso up pe ro re G Fo and l ve nt -le e id em M ag an M Highly Summarized KPIs Summarized Data with Drilldown Data Mining Detail-level Data Concrete Measures Figure 2-2 Concrete measures at each level of the organization 17 18 Delivering Business Intelligence with Microsoft SQL Server 2008 r nt pe e Up gem a an M s, er ag s an er , M ad ns Le rso up pe ro re G Fo and l ve nt -le e id em M ag an M Latency OK Weekly-Monthly Latency Requirements Hourly-Daily Latency Requirements Timing of the Foundation and Feedback Information Figure 2-3 Timing of the foundation and feedback information at each level of the organization Definition Key Performance Indicators (KPIs) are highly summarized measures designed to quickly relay the status of that measure They usually reflect the most vital aspects of the organization KPIs are used to provide these high-level decision makers with a quick way to determine the health of the essential aspects of the organization KPIs are often presented as a graphical icon, such as a traffic light or a gauge, designed to convey the indicator’s status at a glance We discuss KPIs in greater detail in Chapter 10 of this book Higher Latency Because these upper-level decision makers are dealing in long-term policies and direction, they not need up-to-the-minute business intelligence Another way to state this is to say they can have more latency in their business intelligence These decision makers need to see downward trends in time to make corrections They not need to see the daily blips in the organization’s operation Definition The latency of business intelligence is the amount of time between the occurrence of a transaction and the loading of that transaction’s information into the business intelligence system C h a p t e r :  M a k i n g t h e M o s t o f W h a t Yo u ’ v e G o t Mid-Level Mid-level decision makers are managing the operation of departments and other working units within the organization They are setting short-term goals and doing the planning for the functioning of these areas Mid-level decision makers are still at a level where they should not be in the details of day-to-day processes Summarized Measures with Drilldown These mid-level decision makers need business intelligence that is still summarized, but they often need to drill down into this information to get at more detail Therefore, these decision makers can utilize printed reports, along with interactive systems, allowing data-led discovery These decision makers can also make use of information from data mining Some Latency Acceptable Because these decision makers are closer to the everyday functions, they may require business intelligence with less latency In some cases, they may need to see measures that are updated daily In other cases, these decision makers are looking for trends discernable from weekly or monthly loads The Broad Base At the broad base of our business intelligence pyramid are the forepersons, managers, and group leaders taking care of daily operations These people are setting daily operational goals and making decisions on resource allocation for the next week, the next day, or perhaps the next shift They are planning the next sales campaign or maybe just the next sales call These decision makers usually need business intelligence systems with high availability and high responsiveness Measures at the Detail Level These decision makers are dealing with the details of the organization’s operations They need to be able to access information at the detail level In some cases, the work groups these decision makers are responsible for are small enough that they can see the detail for the work group directly without being overwhelmed In other cases, measures need to be summarized, but drilldown to the detail level will probably be required These decision makers may utilize some forms of data mining to help discern trends and correlations in daily information 19 20 Delivering Business Intelligence with Microsoft SQL Server 2008 Low Latency Because these low-level decision makers are managing day-to-day operations, they need to react quickly to changes in feedback information For this reason, they can tolerate little latency In some cases, these decision makers require data that is no more than one day old, one hour old, or even less Maximum Miniatures, Inc Throughout the remainder of this book, Maximum Miniatures, Incorporated serves as the basis for all of our examples Maximum Miniatures, or Max Min, Inc., as it is referred to by most employees, manufactures and sells small, hand-painted figurines It has several product lines, including the Woodland Creatures collection of North American animals; the Mythic World collection, which includes dragons, trolls, and elves; the Warriors of Yore collection, containing various soldiers from Roman times up through World War II; and the Guiding Lights collection, featuring replica lighthouses from the United States The miniatures are made from clay, pewter, or aluminum Max Min markets these miniatures through three different channels It operates five of its own “Maximum Miniature World” stores dedicated to selling the Max Min product line Max Min also operates MaxMin.com to sell its products online In addition, Max Min sells wholesale to other retailers Business Needs Max Min, Inc has experienced rapid growth in the past three years, with orders increasing by over 300 % This growth has put a strain on Max Min’s only current source of business intelligence, the printed report Reports that worked well to support decision making just a few years ago now take an hour or more to print and even C h a p t e r :  M a k i n g t h e M o s t o f W h a t Yo u ’ v e G o t longer to digest These reports work at the detail level with little summarization Max Min’s current systems provide few, if any, alternatives to the printed reports for viewing business intelligence In addition, Max Min, Inc is facing tough competition in a number of its product areas This competition requires Max Min to practice effective decision making to keep its competitive edge Unfortunately, Max Min’s current business intelligence infrastructure, or lack thereof, is making this extremely difficult Because of these issues, Max Min has launched a new project to create a true business intelligence environment to support its decision making This project includes the design of a data warehouse structure, the population of that data warehouse from its current systems, and the creation of analysis applications to serve decision makers at all levels of the organization The new business intelligence platform is based on SQL Server 2008 After an extensive evaluation, it was decided that the SQL Server 2008 platform would provide the highest level of business intelligence capability for the money spent SQL Server 2008 was also chosen because it features the tools necessary to implement the data warehouse in a relatively short amount of time We will examine each step of Max Min’s implementation project as we learn about the various business intelligence tools available in SQL Server 2008 Before we begin, let’s take a quick look at Max Min’s current systems Current Systems Max Min has five data processing systems that are expected to serve as sources of business intelligence (see Figure 2-4) Manufacturing Automation System (Comma-delimited Text File) Order Processing System Point of Sale (POS) System (MS SQL Server DB) (XML Files) Business Intelligence for Max Min, Inc MaxMin.com Online System (MS SQL Server DB) Figure 2-4 Sources of business intelligence at Max Min, Inc Accounting System (MS SQL Server DB) 21 ... to make business intelligence available throughout our organizations 11 12 Delivering Business Intelligence with Microsoft SQL Server 2008 Business Intelligence and Microsoft SQL Server 2008 Fortunately,... system, without the prior written permission of the publisher ISBN: 97 8-0 -0 7-1 5494 5-5 MHID: 0-0 7-1 5494 5-5 The material in this eBook also appears in the print version of this title: ISBN: 97 8-0 -0 7-1 5494 4-8 ,... in daily information 19 20 Delivering Business Intelligence with Microsoft SQL Server 2008 Low Latency Because these low-level decision makers are managing day-to-day operations, they need to