What is the best Healthcare Data Warehouse Model for Your Organization?
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Join Steve Barlow as he addresses the strengths and weaknesses of each of the following three primary Data Model approaches for data warehousing in healthcare: 1. Enterprise Data Model 2. Independent Data Marts 3. Late-binding Solutions
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What is the best Healthcare Data Warehouse Model for Your Organization?
- 1. Creative Commons Copyright February 2014 Comparing Healthcare Data Warehouse Approaches: A Deep-dive Evaluation of the Three Major Methodologies
- 2. © 2013 Health Catalyst | www.healthcatalyst.com2 A Personal Experience with Healthcare 2 • Dear mother… • A trip to the doctor…
- 3. Healthcare Analytics Goal Why have an EDW? â—ŹIt is a means to a greater end â—ŹIt exists to improve: 1. The effectiveness of care delivery (and safety) 2. The efficiency of care delivery (e.g. workflow) 3. Reduce Mean Time To Improvement (MTTI) 3
- 4. Creative Commons Copyright 4 Three Systems of Care Delivery
- 5. © 2013 Health Catalyst | www.healthcatalyst.com Excellent Outcomes Poor Outcomes # of Cases Mean 1 box = 100 cases in a year Excellent Outcomes # of Cases Poor Outcomes Focus On Inliers (“Tighten the Curve and Shift It to the Left”) •Strategy. Identify best practices through research and analytics and develop guidelines and protocols to reduce inlier variation •Result. Shifting the cases which lie above the mean (47+%) toward the excellent end of the spectrum produces a much more significant impact than focusing on the adverse outlier tail (2.5%) 5 Population Health Management
- 6. Healthcare Analytics Adoption Model
- 7. Polling Question What level would you to the healthcare analytic solutions with which you are most familiar? (levels 1 – 8)
- 8. © 2013 Health Catalyst | www.healthcatalyst.com8 An Analyst’s Time Understanding the need Hunting for the data Gathering or compiling (including waiting for IT to run report or query) Interpreting data Distribution of data Waste Value-add Analyst’s or Clinician's Time Too much time spent hunting for and gathering data rather than understanding and interpreting data
- 9. © 2013 Health Catalyst | www.healthcatalyst.com9 HR – Desired State Authors Drillers Viewers Viewers Drillers Authors or Knowledge Workers Ideal User Distribution for Continuous Improvement • Authors or knowledge workers are scarce and in high demand – few users have both clinical knowledge AND access to tools and data • Large backlogs of analytic/report requests exist since underlying systems are too complex for the average user (users make analytic requests vs. self-service) • Create more knowledge workers by doing the following: • Expand data access (audit access vs. control access) • Simplify data structures (relational vs. dimensional) • Continue use of naming standards (intuitive vs. cryptic) • Providing better tools (metadata, ad hoc, etc.) • Promote shift in culture by rewarding process knowledge discovery rather than punishing outliers Typical User Distribution
- 10. © 2013 Health Catalyst | www.healthcatalyst.com© 2013 Health Catalyst | www.healthcatalyst.com Comparison of prevailing approaches
- 11. © 2013 Health Catalyst | www.healthcatalyst.comLess Transformation ProviderProvider PatientPatient Bad DebtBad Debt DiagnosisDiagnosis ProcedureProcedure FacilityFacility EncounterEncounterCostCost ChargeCharge EmployeeEmployee SurveySurvey House Keeping House Keeping Catha LabCatha Lab ProviderProvider CensusCensus Time Keeping Time Keeping More Transformation Enforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model 11 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW
- 12. © 2013 Health Catalyst | www.healthcatalyst.comLess Transformation ProviderProvider PatientPatient Bad DebtBad Debt DiagnosisDiagnosis ProcedureProcedure FacilityFacility EncounterEncounterCostCost ChargeCharge EmployeeEmployee SurveySurvey House Keeping House Keeping Catha LabCatha Lab ProviderProvider CensusCensus Time Keeping Time Keeping More Transformation Enforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model – Still need Subject Area Marts 12 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW DiabetesDiabetes SepsisSepsis ReadmissionsReadmissions
- 13. © 2013 Health Catalyst | www.healthcatalyst.com Bill of Materials Conceptual Model 13 Product Supplier Order Customer Typical Analyses •Counts •Simple aggregations •By various dimensions
- 14. © 2013 Health Catalyst | www.healthcatalyst.com Star Schema Conceptual Model 14 Fact (Transaction) Dimension 1 (Product) Dimension 4 (Location) Dimension 2 (Date) Typical Analyses •Transaction counts •Simple aggregations •By various dimensions Dimension 3 (Purchaser)
- 15. © 2013 Health Catalyst | www.healthcatalyst.com EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) OncologyOncology DiabetesDiabetesHeart Failure Heart Failure RegulatoryRegulatory PregnancyPregnancy AsthmaAsthma Labor Productivity Labor Productivity Revenue CycleRevenue Cycle CensusCensus PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Dimensional Data Model Redundant Data Extracts Less TransformationMore Transformation 15 Vertical Summary Data Marts
- 16. © 2013 Health Catalyst | www.healthcatalyst.com Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary Financial Source Marts Financial Source Marts Administrative Source Marts Administrative Source Marts Departmental Source Marts Departmental Source Marts Patient Source Marts Patient Source Marts EMR Source Marts EMR Source Marts HR Source Mart HR Source Mart DiabetesDiabetes SepsisSepsis ReadmissionsReadmissions Less TransformationMore Transformation FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) Human Resources (e.g. PeopleSoft) Human Resources (e.g. PeopleSoft) Adaptive Data Warehouse
- 17. © 2013 Health Catalyst | www.healthcatalyst.com Classic Star Schema Deficiencies • Resolution of many many-to-many relationships • Not as much about counts of transactions • More about: • Events • States of change over time • Related states (e.g. co-morbidities, attribution) 17
- 18. © 2013 Health Catalyst | www.healthcatalyst.com Sample Diabetes Registry Data Model 18 Diabetes Patient Typical Analyses • How many diabetes patients do I have? • When was there last HA1C, LDL, Foot Exam, Eye Exam? • What was the value for each instance for the last 2 years? • What are all the medications they are on? • How long have they been taking each medication? • What was done at each of their visits for the last 2 years? • Which doctors have seen these patients and why? • List of all admissions and reason for admission? • What co-morbid conditions do these patient have? • Which interventions (diet, exercise, medications) are having the biggest impact on LDL, HA1C scores? Procedure History Vital Signs History Current Lab Result Lab Result History Office Visit Exam Type Exam History Diagnosis History Diagnosis Code Procedure Code Lab Type
- 19. © 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com19 Measurement System Exercise Webinar
- 20. © 2013 Health Catalyst | www.healthcatalyst.com The Enterprise Shopping Model Produce Meat Dairy Dry Goods __ Apples __ Pears __ Tomatoes __ Carrots __ Beef __ Ham __ Chicken __ Pork __ Milk __ Eggs __ Cheese __ Cream __ Pasta __ Flour __ Sugar __ Soup __ Celery __ Banana __ Melon __ Grapes __ Turkey __ Sausage __ Lamb __ Bacon __ 2% Milk __ Half & Half __ Yogurt __ Margarine __ Baking soda __ Rice __ Beans __ B. Sugar E n t e r p r i s e S h o p p i n g M o d e l Your Shopping List Eggs Flowers Tires Dry cleaning Additional purchases
- 21. © 2013 Health Catalyst | www.healthcatalyst.comLess Transformation ProviderProvider PatientPatient Bad DebtBad Debt DiagnosisDiagnosis ProcedureProcedure FacilityFacility EncounterEncounterCostCost ChargeCharge EmployeeEmployee SurveySurvey House Keeping House Keeping Catha LabCatha Lab ProviderProvider CensusCensus Time Keeping Time Keeping More Transformation Enforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model (Technology Vendors) 21 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW
- 22. © 2013 Health Catalyst | www.healthcatalyst.com Using a dimensional model in Healthcare is kind of like shopping for data like this … 22
- 23. © 2013 Health Catalyst | www.healthcatalyst.com23
- 24. © 2013 Health Catalyst | www.healthcatalyst.com The Dimensional Shopping Model 24 Dairy Dry Goods __ ½ cup of butter __ ½ cup milk __ 2 eggs __ 1 cup white sugar __ 1 ½ cups all-purpose flour __ 2 teaspoons vanilla extract __ 1 ¾ teaspoon baking powder Dimensional Shopping Model - Cake Trip #2 to the Store How many recipes to do you need to make? Trip #1 to the Store Dairy Dry Goods __ 4 eggs __ 2 c shortening __ 1 c sugar __ 2 c brown sugar __ 2 t baking soda __ 2 t vanilla __ 1 t salt __ 4-5 c all-purpose flour __ 4 cups chocolate chips Dimensional Shopping Model - Cookies
- 25. © 2013 Health Catalyst | www.healthcatalyst.com EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) OncologyOncology DiabetesDiabetesHeart Failure Heart Failure RegulatoryRegulatory PregnancyPregnancy AsthmaAsthma Labor Productivity Labor Productivity Revenue CycleRevenue Cycle CensusCensus PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Dimensional Data Model Redundant Data Extracts Less TransformationMore Transformation 25 Dimensional Data Model (Healthcare Point Solutions)
- 26. © 2013 Health Catalyst | www.healthcatalyst.com The Adaptive Shopping Model 26 A d a p t i v e S h o p p i n g M o d e l __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ __ ______________ Store: _____________________________ Additional Get eggs Buy flowers Get tires rotated Pick up dry cleaning • Buy a Christmas tree • Baking Powder • Baking Soda • Buy a new couch • Get oil change • Chocolate Chips • Buy paint and painting supplies • Buy yarn and knitting supplies • Vanilla extract • Buy a set of pots and pans And Even More Initial List • Apples • Tomato Soup • Flour • Milk • Turkey • Lettuce • Sugar • Beans • Hot dogs • Banana • Noodles • Yogurt
- 27. © 2013 Health Catalyst | www.healthcatalyst.com Shopping List Revisited 27 Additional Get eggs Buy flowers Get tires rotated Pick up dry cleaning Once you are home can you make these recipes? Cake: 1 cup white sugar 1 ½ cups all-purpose flour 2 teaspoons vanilla extract 1 ¾ teaspoon baking powder ½ cup of butter ½ cup milk 2 eggs Cookies: 1 cup (2 sticks) butter, softened 2 large eggs 3/4 cup white sugar 2 1/4 cups all-purpose flour 1 teaspoon vanilla extract 1 teaspoon salt 1 teaspoon baking soda 2 cups chocolate chips • Buy a Christmas tree • Baking Powder • Baking Soda • Buy a new couch • Get oil change • Chocolate Chips • Buy paint and painting supplies • Buy yarn and knitting supplies • Vanilla extract • Buy a set of pots and pans And Even More Initial List • Apples • Tomato Soup • Flour • Milk • Turkey • Lettuce • Sugar • Beans • Hot dogs • Banana • Noodles • Yogurt
- 28. © 2013 Health Catalyst | www.healthcatalyst.com Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary Financial Source Marts Financial Source Marts Administrative Source Marts Administrative Source Marts Departmental Source Marts Departmental Source Marts Patient Source Marts Patient Source Marts EMR Source Marts EMR Source Marts HR Source Mart HR Source Mart DiabetesDiabetes SepsisSepsis ReadmissionsReadmissions Less TransformationMore Transformation FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) Human Resources (e.g. PeopleSoft) Human Resources (e.g. PeopleSoft) Adaptive Data Warehouse
- 29. © 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com29 Late-binding Deeper Dive
- 30. © 2013 Health Catalyst | www.healthcatalyst.com Data Modeling Approaches 30 Early Binding Late Binding Corporate Information Model Popularized by Bill Inmon and Claudia Imhoff Corporate Information Model Popularized by Bill Inmon and Claudia Imhoff I2B2 Popularized by Academic Medicine I2B2 Popularized by Academic Medicine Star Schema Popularized by Ralph Kimball Star Schema Popularized by Ralph Kimball Data Bus Popularized by Dale Sanders Data Bus Popularized by Dale Sanders File Structure Association Popularized by IBM mainframes in 1960s Reappearing in Hadoop & NoSQL File Structure Association Popularized by IBM mainframes in 1960s Reappearing in Hadoop & NoSQL
- 31. © 2013 Health Catalyst | www.healthcatalyst.com Origins of Early vs Late Binding • Early days of software engineering ● Tightly coupled code, early binding of software at compile time ● Hundreds of thousands of lines of code in one module, thousands of function points ● Single compile, all functions linked at compile time ● If one thing breaks, all things break ● Little or no flexibility and agility of the software to accommodate new use cases 31
- 32. © 2013 Health Catalyst | www.healthcatalyst.com Origins of Early vs Late Binding • 1980s: Object Oriented Programming ● Alan Kay, Universities of Colorado & Utah, Xerox/PARC ● Small objects of code, reflecting the real world ● Compiled individually, linked at runtime, only as needed ● Agility and adaptability to address new use cases • Steve Jobs: NeXT Computing ● Commercial, large-scale adoption of Kay’s concepts ● Late binding – or as late as practical – becomes the norm ● Maybe Jobs’ largest contribution to computer science 32
- 33. © 2013 Health Catalyst | www.healthcatalyst.com Data Binding in Analytics ● Atomic data can be “bound” to business rules about that data and to vocabularies related to that data ● Vocabulary binding in healthcare – Unique patient and provider identifiers – Standard facility, department, and revenue center codes – Standard definitions for sex, race, ethnicity – ICD, CPT, SNOMED, LOINC, RxNorm, RADLEX, etc. ● Binding data to business rules – Length of stay – Patient attribution to a provider – Revenue and expense allocation and projections to a department – Data definitions of general disease states and patient registries – Patient exclusion criteria from population management – Patient admission/discharge/transfer rules 33
- 34. © 2013 Health Catalyst | www.healthcatalyst.com Analytic Relations The key is to relate data, not model data 34 High Value Attributes About 20 data attributes account for 90% of healthcare analytic use cases Core Data Elements Charge Code CPT Code Date & Time DRG code Drug code Employee ID Employer ID Encounter ID Sex Diagnosis Code Procedure Code Department ID Facility ID Lab code Patient type Patient / member ID Payer / carrier ID Postal code Provider ID Vocab in Source System 1 Vocab in Source System 2 Vocab in Source System 3 Highest value area for standardizing vocabulary
- 35. © 2013 Health Catalyst | www.healthcatalyst.com Data Analysis Six Points to Bind Data 35 Source Data Content Source System Analytics Customized Data Marts Visualization Others HR Supplies Financial Clinical Academic State Academic State Others HR Supplies Financial Clinical QlikView, Tableau Microsoft Access Web Applications Excel SAS, SPSS et al. InternalExternal 1 2 3 4 5 6 Research Registries Operational Events Clinical Events Compliance Measures Materials Management Disease Registries Business Rule and Vocabulary Binding Points Low volatility = Early binding High volatility = Late binding
- 36. © 2013 Health Catalyst | www.healthcatalyst.com Binding Principles & Strategy 1. Delay Binding as long as possible…until a clear analytic use case requires it 2. Earlier binding is appropriate for business rules or vocabularies that change infrequently or that the organization wants to “lock down” for consistent analytics 3. Late binding in the visualization layer is appropriate for “what if” scenario analysis 4. Retain a record of the bindings from the source system in the data warehouse 5. Retain a record of the changes to vocabulary and rules bindings in the data models of the data warehouse 36
- 37. © 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com37 Thank you!
- 38. © 2013 Health Catalyst | www.healthcatalyst.com Questions 38 • To dive in deeper, click on links below: EDW Platform Population Health Healthcare Analytics Adoption Model Understanding Binding • Contact us to learn more about our solutions www.healthcatalyst.com/company/contact-us
Hinweis der Redaktion
- To help move to a more standardized system with more consistent and predictable outcomes, we have identified three areas of care delivery that need a systematic approach—an analytic system, a deployment system, and a content system.
- Thank you Steve Based on feedback from people like yourselves, we have designed a short analogy that compares Data Warehousing to Shopping. We use this analogy to highlight, in a non-technical way, the differences between the three models Steve just described.
-  Let’s start by looking at the Enterprise Shopping Model displayed on the screen. Notice that it is well organized and highly structured. Next I am going to put up on the screen a list of items you need to get at the store. I want you to map those items to the Enterprise Shopping Model. While doing that imagine you get a call from your significant other asking you to also get the following items. Okay, now that you have attempted the exercise let’s take a quick poll. Describe your experience with that exercise Worked great Frustrating Stopped trying I can see that many of you were frustrated even though the model was designed to have everything you needed and to make creating a shopping list easier. It lacked flexibility and it could not be easily adjusted to address the new requirement of making non-food purchases.
- This shopping approach is much like the Enterprise Data Model which works very well in industries like retail and banking where what you need to capture is much more standardized and stable over time. Why this model breaks down in healthcare is because Medical knowledge is always expanding and changing so it is impossible to anticipate what the new data will look like and how it could fit into a model. Additionally, concepts like Length of Stay or Readmission Rates may have different definitions
- Now let’s look at the Dimensional Shopping Model. In this model, instead of a shopping list, we have specific recipes that we need to create which are Chocolate Chip Cookies, a Cake and Apple Pie. Prior to the webinar you were sent a link to a short video that depicted this approach. Let’s take a quick poll, Did you watch the video prior to the start of the webinar?
- For those of you that were not able to watch the video, I will high spot what was depicted. So we start with our shopper getting a call from the school board to bring cookies to the board meeting. She goes to the grocery store to get exactly what she needs to make the cookies, 2 cups of shortening, 4 cups of flour, 4 eggs and so on. Now imagine our shopper returning home to bake the cookies and getting a second call requesting she bring a cake too. So back to the store she goes to purchase many of the same ingredients. She returns home to get yet another call and the video ends as the exasperated shopper heads to the store for the third time.
- So this Dimensional Shopping Trip started out fine for our shopper, getting just exactly what she needed but as soon as she added another recipe and another recipe she was making redundant trips to the store.
-  This shopping approach is like the Dimensional Data Model which starts out really great with a couple impressive point solutions for a few departments. But as the demand for analytics grows the model starts to become a mass of redundant data feeds from the source systems to multiple applications. So for organization’s that are looking for an enterprise data strategy verses a few point solutions, they would quickly become encumbered by the Dimensional Data Model. Spending all their time designing trips to the store.
- Now let’s explore our final model, the Adaptive Shopping model. In this exercise you are given a shopping card with a simple structure that allows you to indicate the store you are shopping and the items you need. Now using the same shopping list we saw earlier, imagine how you would fill out your Adaptive Shopping Model. Likely you selected a store you were familiar with and then you started organizing the items in a way the reflected the layout of the store. Now when we add new items to the shopping list, if they don’t fit within your first shopping list you can just take another card and so one.
- Now let’s think about bringing all those items back to your house. Could you still make the recipes? You can and you can do it without all the redundant trips to the store.
-  This shopping approach is like Catalyst’s Adaptive Data model. The cards represented source systems that are brought into the Data Warehouse and these are going to match the transactional system exactly so it doesn’t take a lot of effort to bring that data into the warehouse. There is no manipulating or forcing the data into a model. Additionally, you have all the data you need for the analytic applications you know you know and for the requests that will come in the future. You can build all of these without having to go back out to the source for more data. This approach works really well in healthcare because the data requirements are typically not well understood at the outset. With the Adaptive approach you can build incrementally adding source systems as you need them.  Time for another poll question. Which shopping approach would you prefer? Enterprise Dimensional Late-Binding  Now no analogy works perfectly, but hopefully it will help you remember the characteristics and differences between each model.  A final poll question, Did this analogy help you better understand the differences between the three models? Yes No I already understood the differences