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These are the user uploaded subtitles that are being translated: 1 00:00:00,880 --> 00:00:06,860 All right so to kick off this entire section about creating data models I figured it would only be appropriate 2 00:00:06,860 --> 00:00:08,680 to start with a simple question. 3 00:00:08,870 --> 00:00:11,870 What exactly is a data model. 4 00:00:11,870 --> 00:00:18,290 Now consider relationships view that looks like this got three different tables here a product lookup 5 00:00:18,290 --> 00:00:22,010 table and two data tables sales and returns. 6 00:00:22,010 --> 00:00:25,080 This alone is not a data model. 7 00:00:25,130 --> 00:00:31,550 What we're looking at here is a collection of independent tables which share no connections or relationships. 8 00:00:31,580 --> 00:00:37,520 So think of each of these tables as its own little island with no idea that the other two even exist 9 00:00:37,520 --> 00:00:38,570 in the model. 10 00:00:38,570 --> 00:00:44,090 And what that means is that if you were to try to do something like this breaking down of fields from 11 00:00:44,090 --> 00:00:50,870 the sales data like order quantity or IFIELD from the returns table like return quantity and break those 12 00:00:50,870 --> 00:00:57,290 quantities down by field from the product lookup table you're going to get the same grand total in every 13 00:00:57,290 --> 00:00:58,700 single row of the table. 14 00:00:58,850 --> 00:01:05,000 And again that's because both the sales and the returns data tables have no idea that there even is 15 00:01:05,000 --> 00:01:11,090 a product lookup table that exists in the universe let alone how to break down those quantity values 16 00:01:11,450 --> 00:01:13,760 by individual product names. 17 00:01:13,760 --> 00:01:21,520 So if this is not a data model then what is well let's consider that same structure that same relationships 18 00:01:21,520 --> 00:01:22,300 view. 19 00:01:22,300 --> 00:01:26,770 Except now we have relationships connecting the tables together. 20 00:01:26,770 --> 00:01:34,540 This is a data model and again it's a data model because the tables are now connected via relationships 21 00:01:34,900 --> 00:01:41,950 based on a common field or key which in this case is the product key column and without diving into 22 00:01:41,950 --> 00:01:49,330 the specifics just yet what this means is that if we revisit that same analysis looking at order quantity 23 00:01:49,420 --> 00:01:56,650 and return quantity by product names now we see the correct values because now the sales data table 24 00:01:57,040 --> 00:01:59,810 knows that the product lookup table exists. 25 00:01:59,830 --> 00:02:05,260 In fact it's connected to that product table based on the fact that they share a common product key 26 00:02:05,680 --> 00:02:11,040 and the same story holds for the returns data and its relationship to the product lookup. 27 00:02:11,050 --> 00:02:17,740 So as a result we're able to take any field from the product lookup table and filter and segment values 28 00:02:17,950 --> 00:02:19,760 from those two data tables. 29 00:02:19,780 --> 00:02:25,600 Now you'll see that same familiar Grand Total row at the bottom but now we get accurate numbers for 30 00:02:25,600 --> 00:02:27,610 each individual product. 31 00:02:27,610 --> 00:02:34,600 So this is obviously a relatively simple example but it's this core concept of joining and blending 32 00:02:34,600 --> 00:02:41,650 our data together using table relationships and common keys that will be the focal point of this entire 33 00:02:41,650 --> 00:02:43,130 section of the course. 34 00:02:43,330 --> 00:02:49,480 And it's this core concept that's going to enable us to do some really interesting really sophisticated 35 00:02:49,480 --> 00:02:51,070 analyses. 36 00:02:51,070 --> 00:02:54,440 So let's shift gears open up our venture works file. 37 00:02:54,520 --> 00:02:59,050 I want to show you what this looks like in the power be-I environment. 38 00:02:59,150 --> 00:02:59,490 All right. 39 00:02:59,490 --> 00:03:04,020 So once you opened up the Adventure Works report file you're going to want to make sure that you're 40 00:03:04,020 --> 00:03:08,990 using the latest version after the homework assignment from the last section. 41 00:03:09,120 --> 00:03:13,270 So you should see a product category and a product subcategory table. 42 00:03:13,380 --> 00:03:15,100 We're going to go into the report for you. 43 00:03:15,100 --> 00:03:21,270 Now for the first time there is going to be an entire deep dive section covering all of these visualization 44 00:03:21,270 --> 00:03:22,440 and reporting tools. 45 00:03:22,620 --> 00:03:28,290 But for now we're going to use one type of visual in particular called The Matrix which looks like this 46 00:03:29,390 --> 00:03:32,220 and The Matrix acts kind of like a pivot table. 47 00:03:32,260 --> 00:03:39,040 You can analyze values from a numerical field and break those values down by dimensions placed in rows 48 00:03:39,100 --> 00:03:40,150 or columns. 49 00:03:40,150 --> 00:03:45,730 So in this case our values are going to come from or A.W. sales table pacifically the order quantity 50 00:03:45,730 --> 00:03:46,810 field. 51 00:03:46,810 --> 00:03:53,350 So let's click and drag that order quantity to values and you'll see the total quantity from that column 52 00:03:53,770 --> 00:03:55,210 populate here in the matrix. 53 00:03:55,210 --> 00:03:57,820 Eighty four thousand one hundred seventy four. 54 00:03:57,820 --> 00:04:02,830 Now we want to break down this quantity or analyze it in a more detailed way. 55 00:04:02,830 --> 00:04:08,110 Our only option is to use fields that exist within this same sales table. 56 00:04:08,110 --> 00:04:14,560 So product keys for instance can be placed on the rows and now we see those Order quantities broken 57 00:04:14,560 --> 00:04:16,760 down at the product key level. 58 00:04:16,780 --> 00:04:23,440 So that's all well and good but we're extremely limited by the scope of that table and to show you what 59 00:04:23,440 --> 00:04:23,980 I mean. 60 00:04:23,980 --> 00:04:30,640 Let's go ahead and remove that product key Let's go into the product table itself which contains all 61 00:04:30,640 --> 00:04:37,030 sorts of interesting information about those products the price the name the description the color and 62 00:04:37,030 --> 00:04:44,220 let's say we want to break down those quantities by product name when we drag that field into rows. 63 00:04:44,230 --> 00:04:50,500 We get that same grand total over and over and over and over and over again which is the exact same 64 00:04:50,500 --> 00:04:53,140 issue that we saw earlier on the slide. 65 00:04:53,290 --> 00:04:58,810 The sales table has no idea that a product lookup table even exists as a result. 66 00:04:58,810 --> 00:05:04,390 It has no way to break down those quantity values based on any field in the product table. 67 00:05:04,390 --> 00:05:10,750 So this limitation is exactly why we need to build out a data model and exactly why we're going to spend 68 00:05:10,750 --> 00:05:12,840 so much time learning how to do that. 69 00:05:12,850 --> 00:05:14,430 In this section of the course. 70 00:05:14,710 --> 00:05:15,780 So stay tuned. 71 00:05:15,790 --> 00:05:16,340 More to come. 7578

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