WEBVTT 1 00:00:00.000 --> 00:00:03.510 (upbeat calm music) 2 00:00:03.510 --> 00:00:05.160 To compete in a fast-changing 3 00:00:05.160 --> 00:00:08.020 business environment, organizations have to constantly 4 00:00:08.020 --> 00:00:10.010 learn how to do things better. 5 00:00:10.010 --> 00:00:12.843 Like executing key-business processes more efficiently 6 00:00:12.843 --> 00:00:16.400 or crafting smarter strategies for success. 7 00:00:16.400 --> 00:00:19.000 Of course each individual an the organization 8 00:00:19.000 --> 00:00:21.990 is responsible for continually strengthening their skills 9 00:00:21.990 --> 00:00:24.360 and knowledge, but collective learning 10 00:00:24.360 --> 00:00:26.510 is where the real action is. 11 00:00:26.510 --> 00:00:28.800 What is collective learning exactly? 12 00:00:28.800 --> 00:00:30.650 And why does it matter more than ever 13 00:00:30.650 --> 00:00:32.600 for organizations today? 14 00:00:32.600 --> 00:00:36.720 line:15% Collective learning is the understanding of how teams 15 00:00:36.720 --> 00:00:39.200 line:15% cities, organizations, and nations learn 16 00:00:39.200 --> 00:00:41.400 line:15% It's the acquisition of knowledge not by individuals 17 00:00:41.400 --> 00:00:42.670 but by groups of people. 18 00:00:42.670 --> 00:00:44.560 Companies stay in business 19 00:00:44.560 --> 00:00:48.260 because they have some knowledge that is in some way unique 20 00:00:48.260 --> 00:00:50.130 that allows them to participate in a market 21 00:00:50.130 --> 00:00:52.730 that few other people can participate, that allows them 22 00:00:52.730 --> 00:00:54.900 to produce a product, that few that people can produce 23 00:00:54.900 --> 00:00:57.720 and therefore they have some sort of temporal monopoly. 24 00:00:57.720 --> 00:01:01.070 Acquiring that knowledge, it's vital for companies. 25 00:01:01.070 --> 00:01:03.320 As it turns out, you can measure how much 26 00:01:03.320 --> 00:01:07.110 collective learning is taking place at the levels of teams, 27 00:01:07.110 --> 00:01:10.500 cities, and even entire industries and countries, 28 00:01:10.500 --> 00:01:12.980 but only if you use the right metrics. 29 00:01:12.980 --> 00:01:15.690 I've begun to study how knowledge accumulates 30 00:01:15.690 --> 00:01:18.500 at the collective level, how much knowledge that the city 31 00:01:18.500 --> 00:01:22.420 of Berlin has vis-à-vis Paris or Tokyo or New York. 32 00:01:22.420 --> 00:01:25.520 And I developed two measures that can help us understand 33 00:01:25.520 --> 00:01:29.770 how knowledge moves and what is the intensity of knowledge 34 00:01:29.770 --> 00:01:31.460 that exist in a location. 35 00:01:31.460 --> 00:01:33.890 The first one is quite simple and it's an idea 36 00:01:33.890 --> 00:01:36.500 that is now known as relatedness. 37 00:01:36.500 --> 00:01:39.570 This is the distance, you know, in the knowledge that exists 38 00:01:39.570 --> 00:01:42.490 in a city and the knowledge that is required by an industry. 39 00:01:42.490 --> 00:01:45.690 So, it would answer the question, you know, how far is Miami 40 00:01:45.690 --> 00:01:50.660 from the biotech sector? Or how close is Tokyo, you know, 41 00:01:50.660 --> 00:01:54.250 from the musical instrument manufacturing industry? 42 00:01:54.250 --> 00:01:56.190 And what you can do is you can look at all of the industries 43 00:01:56.190 --> 00:01:57.780 that are present in a location. 44 00:01:57.780 --> 00:02:00.230 You can see how related they are to the industries 45 00:02:00.230 --> 00:02:01.750 that are not yet present. 46 00:02:01.750 --> 00:02:04.630 And those measures are very predictive of the industries 47 00:02:04.630 --> 00:02:08.220 that a city or a country are gonna enter or exit 48 00:02:08.220 --> 00:02:09.290 in the future. 49 00:02:09.290 --> 00:02:11.510 And this is true for the products that our country 50 00:02:11.510 --> 00:02:14.190 is gonna export, the technologies in which a city is gonna 51 00:02:14.190 --> 00:02:16.680 patent, the research areas in which our university 52 00:02:16.680 --> 00:02:18.125 is gonna publish and so forth. 53 00:02:18.125 --> 00:02:20.340 (upbeat music) 54 00:02:20.340 --> 00:02:23.510 The second idea is a measure of all of the knowledge 55 00:02:23.510 --> 00:02:26.780 that would be containing a city and how that affects 56 00:02:26.780 --> 00:02:30.020 important macroeconomic outcomes, economic growth, 57 00:02:30.020 --> 00:02:31.830 you know and income inequality. 58 00:02:31.830 --> 00:02:34.240 These tools help you understand a couple of things. 59 00:02:34.240 --> 00:02:36.350 First, you know, you would know which activities 60 00:02:36.350 --> 00:02:39.650 are more likely to be successful at given the capabilities 61 00:02:39.650 --> 00:02:40.775 that you already have. 62 00:02:40.775 --> 00:02:43.358 (pensive orchestration) 63 00:02:44.460 --> 00:02:46.750 Every company can benefit by taking the time 64 00:02:46.750 --> 00:02:49.300 to understand how much knowledge is concentrated 65 00:02:49.300 --> 00:02:52.600 in a particular industry or geographic location. 66 00:02:52.600 --> 00:02:56.180 And how it's being built up through collective learning. 67 00:02:56.180 --> 00:02:57.240 Why? 68 00:02:57.240 --> 00:02:58.920 When you understand these things, 69 00:02:58.920 --> 00:03:01.570 you can make some pretty interesting predictions 70 00:03:01.570 --> 00:03:03.840 and you can use those predictions to make crucial 71 00:03:03.840 --> 00:03:06.610 business decisions like which markets your company 72 00:03:06.610 --> 00:03:09.800 should enter and which markets you should avoid. 73 00:03:09.800 --> 00:03:12.203 You can predict, you know, which are the markets 74 00:03:12.203 --> 00:03:14.270 that might grow in the future. 75 00:03:14.270 --> 00:03:16.890 You can also predict the success that each one 76 00:03:16.890 --> 00:03:19.220 of those markets might have in the sectors 77 00:03:19.220 --> 00:03:21.110 that you might be interested on entering. 78 00:03:21.110 --> 00:03:23.490 Leadership for a long time, you know, 79 00:03:23.490 --> 00:03:26.530 has been very intuitive and it's intuitive for a reason. 80 00:03:26.530 --> 00:03:29.380 You know, people that have been operating in a sector 81 00:03:29.380 --> 00:03:33.970 or an industry for a long time, they have good hunches 82 00:03:33.970 --> 00:03:36.530 about what works and what doesn't work 83 00:03:36.530 --> 00:03:38.640 because they have a lot of experience. 84 00:03:38.640 --> 00:03:42.110 As data becomes a more prevalent aspect of our lives 85 00:03:42.110 --> 00:03:44.620 and our work, it means that there's gonna be leaders 86 00:03:44.620 --> 00:03:46.500 that are gonna have looked at more data throughout 87 00:03:46.500 --> 00:03:48.560 their lives, and that's gonna give them a richer 88 00:03:48.560 --> 00:03:51.603 intuition and a richer set of experience to draw upon. 89 00:03:53.190 --> 00:03:55.590 Clearly collective learning can give companies 90 00:03:55.590 --> 00:03:59.010 some vital advantages, but to capture those advantages, 91 00:03:59.010 --> 00:04:01.870 companies need a new type of leader. 92 00:04:01.870 --> 00:04:04.180 What do these leaders do differently? 93 00:04:04.180 --> 00:04:07.600 How do they enable collective learning in their teams? 94 00:04:07.600 --> 00:04:10.180 And how do they help remove roadblocks to collective 95 00:04:10.180 --> 00:04:13.840 learning that arise as their organizations grow? 96 00:04:13.840 --> 00:04:17.180 In our next video, Cesar Hidalgo offers insights 97 00:04:17.180 --> 00:04:18.680 into these questions. 98 00:04:18.680 --> 00:04:21.670 So the most important ability, anybody needs to learn 99 00:04:21.670 --> 00:04:23.953 you know, is the ability to learn.