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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,610 --> 00:00:00,870 All right. 2 00:00:00,880 --> 00:00:05,430 Next up we have the incredibly important topic of daks measures. 3 00:00:05,620 --> 00:00:12,750 Now by definition measures are daks formulas that are used to generate new calculated values. 4 00:00:12,760 --> 00:00:18,090 So couple important things to note about measurers like calculated columns. 5 00:00:18,090 --> 00:00:20,940 They reference entire tables or columns. 6 00:00:20,940 --> 00:00:28,170 So again no A-one style or grid references like you would use in Excel but unlike calculated columns 7 00:00:28,590 --> 00:00:32,550 the measure values aren't actually visible in your tables. 8 00:00:32,550 --> 00:00:39,080 In fact you can only see measured values within a visualisation like a chart or a matrix. 9 00:00:39,090 --> 00:00:44,670 So for those of you with Excel backgrounds think of measures the same way you'd think about a calculated 10 00:00:44,670 --> 00:00:49,940 field in an Excel pivot that calculated field doesn't exist anywhere else. 11 00:00:49,980 --> 00:00:55,300 You can't see it in tables you only see it within that Excel pivot table itself. 12 00:00:55,500 --> 00:01:01,530 Now measures and power be-I operate the same way you can only see them when you drag them into the values 13 00:01:01,530 --> 00:01:04,080 field of a visualization. 14 00:01:04,080 --> 00:01:10,170 So well-calculated calculated columns are evaluated based on row context measures are evaluated based 15 00:01:10,170 --> 00:01:12,180 on filter context. 16 00:01:12,180 --> 00:01:17,490 It's a little bit of a tricky concept to wrap your head around but what I mean by that is that these 17 00:01:17,490 --> 00:01:24,700 measures the values they take constantly recalculate whenever the fields are filter's around them change. 18 00:01:24,780 --> 00:01:31,350 So if you drag a new field or dimension into the row or column labels of a matrix or if you apply new 19 00:01:31,350 --> 00:01:37,350 filters to your report in each of those cases you're changing the filter context and you're changing 20 00:01:37,350 --> 00:01:40,050 the value that that measure should return. 21 00:01:40,050 --> 00:01:46,500 So to summarize as a rule of thumb you're going to use measures not calculated columns when a single 22 00:01:46,500 --> 00:01:49,070 row isn't going to be able to give you an answer. 23 00:01:49,080 --> 00:01:53,420 In other words when you need to aggregate and protip for remembering this. 24 00:01:53,580 --> 00:01:58,520 Remember that measures are used to create numerical calculated values. 25 00:01:58,550 --> 00:02:02,720 You can analyze in the values field of your report visuals. 26 00:02:02,880 --> 00:02:09,030 So if we actually look at the report pain in our adventure works file you'll see that we've actually 27 00:02:09,030 --> 00:02:10,870 already been using measures. 28 00:02:10,950 --> 00:02:13,680 We just didn't know we were using them quite yet. 29 00:02:13,680 --> 00:02:19,170 And the way we used measures was simply by dragging those fields into the values pane here. 30 00:02:19,470 --> 00:02:25,380 And you'll note that this little arrow tells us that we're looking at the sum of this particular numerical 31 00:02:25,380 --> 00:02:32,210 field order quantity and what we've done is actually create an implicit measure by doing that. 32 00:02:32,460 --> 00:02:37,250 So we're going to talk about implicit versus explicit measures in a bit but check it out. 33 00:02:37,250 --> 00:02:42,930 You've already got a head start on understanding how measures work so that we've done our intro to measures 34 00:02:43,050 --> 00:02:45,160 we've done our intro to calculated columns. 35 00:02:45,180 --> 00:02:50,100 Let's take a minute to compare the two side by side and then move on and get a little bit deeper into 36 00:02:50,100 --> 00:02:51,530 how daks really works. 4059

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