Your AI Strategy IS Your Data Strategy - Bruno Caldas on Building the Intelligent PMO - The Major Project Podcast

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(00:00):
.0019999999Welcome to the Major Project Podcast. 2 00:00:01,849.999 --> 00:00:03,570.0019999999 I'm your host, Orion Matthews. 3 00:00:03,610.001 --> 00:00:12,500.002 In this pod, we are learning from the people that work on projects over one billion dollars, trying to tease out those lessons and learnings from these big endeavors. 4 00:00:13,10.003 --> 00:00:15,600.001 My guest today is Bruno Caldas. 5 00:00:15,979.999 --> 00:00:20,130.003 He is a phenomenal individual, an incredible reporter. 6 00:00:20,159.999 --> 00:00:23,49.999 He's got an 18-plus year career. 7 00:00:23,100.002 --> 00:00:31,409.997 16 of them are in mining, and he is at the intersection of projects, finance, systems, and analytics. 8 00:00:31,490.001 --> 00:00:44,110.002 So he's pairing up the owner side discipline of governing capital projects with hands-on project controls and backs those with this sort of business intelligence and AI. 9 00:00:44,530.002 --> 00:01:02,050 So one thing about Bruno that's interesting is that we actually, uh, I was introduced to him by someone from, uh, a conference in Houston, and I was like, "I need to meet some of the most incredibly talented people, uh, that are out there in the data reporting space."

(01:02):
And so, uh, mutual contact friend said, "You gotta talk to Bruno. 11 00:01:06,570.003 --> 00:01:08,359.997 Bruno's amazing. 12 00:01:08,539.997 --> 00:01:11,879.998 The work he's doing at Rio Tinto is, is, is incredible." 13 00:01:12,170.001 --> 00:01:17,510.006 And yeah, so that's how I got to know Bruno, and I'm really excited to have you on. 14 00:01:17,510.006 --> 00:01:19,390.002 So Bruno, welcome to the pod. 15 00:01:21,464.002 --> 00:01:23,364.002 Thank you so much for this introduction. 16 00:01:24,424.002 --> 00:01:33,294.003 So yes, we've been talking since this, since we are being introducing, and, it's been a honor to be part of your podcast. 17 00:01:34,424.003 --> 00:01:39,584.001 Maybe you can take us a little bit through your career and tell us how you got into project controls. 18 00:01:39,784.003 --> 00:01:41,104.001 Just a little bit about your background. 19 00:01:42,474.001 --> 00:01:42,614.001 Yeah. 20 00:01:42,614.001 --> 00:01:46,124 So just a little bit of introduction before starting my career. 21 00:01:47,283.999 --> 00:01:53,964.002 Uh, so I was always a nerd guy i- in terms of computer. 22 00:01:54,854.002 --> 00:01:59,563.999 So when I was, like, 14 years old, I worked in a cyber cafe. 23 00:02:00,764.002 --> 00:02:04,943.999 So I, I built, like, 20 computers. 24 00:02:05,84.002 --> 00:02:06,323.997 So I started with hardware. 25 00:02:07,294.001 --> 00:02:11,463.997 Uh, the computers came in, only the piece to assemble. 26 00:02:12,343.999 --> 00:02:16,313.998 So I help it in, and then I start to work on the software piece.

(02:16):
.998So I was working there, like, not really at work 'cause I was in school at the time. 28 00:02:22,433.996 --> 00:02:28,823.997 I was working there Saturdays and Sundays, but it was a really nice, uh, experience. 29 00:02:29,744.001 --> 00:02:35,944 I was in contact with computers all the time, so the digital piece was always my, my passion. 30 00:02:37,93.999 --> 00:02:44,523.993 Um, I started my career So I have a bachelor in, mechatronics automation engineering. 31 00:02:45,843.995 --> 00:02:50,903.989 At the time, like this was, I conclude my bachelor in 2009. 32 00:02:51,53.994 --> 00:02:56,773.989 There was no AI, but you kind of learn everything to, to, to robots. 33 00:02:56,803.994 --> 00:03:03,133.986 Like, the true piece of a mechatronics engineer is mechanical engineering, electric and programming. 34 00:03:04,83.985 --> 00:03:09,63.992 So was, uh, start to have this first contact with programming. 35 00:03:09,63.996 --> 00:03:15,43.993 I know it's a background, but this is really start to see, oh, this is really nice. 36 00:03:15,43.993 --> 00:03:29,207.995 I think this is what I want to, for my career Um, so after I conclude my bachelor, 2009, I went to a oil and gas refinery project. 37 00:03:30,107.995 --> 00:03:33,57.994 That's my first billion-dollar project. 38 00:03:33,937.995 --> 00:03:36,277.995 But I was just starting as an intern. 39 00:03:37,287.994 --> 00:03:39,957.994 Uh, I was responsible for the Primavera P6. 40 00:03:41,537.994 --> 00:03:48,557.995 At the time, we had a full room of people just taking care of the Primavera P6 that was, scheduled. 41 00:03:48,557.995 --> 00:03:52,377.996 I believe it was 80,000 activities. 42 00:03:53,867.995 --> 00:04:09,427.996 Uh, and the was at the time, this was 2009, it was a local, uh, Primavera P6, so we are able to access all, all the same Primavera but in a local, network. 43 00:04:09,827.997 --> 00:04:11,167.996 Completely different from today. 44 00:04:12,427.996 --> 00:04:17,167.998 And we are working, of course, the critical path, all the activities. 45 00:04:17,997.996 --> 00:04:27,377.994 And I went to the site, uh, to learn more about oil and gas, was really amazing experience. 46 00:04:27,797.999 --> 00:04:36,565.999 So I started my career direct in, in, in project service, project controls And I decided to go all in on this journey. 47 00:04:37,465.999 --> 00:04:47,565.999 And the thing that really helped me since the beginning was the combination of my software skills with the engineering and the technical skills. 48 00:04:49,115.999 --> 00:04:55,765.999 Um, so I stayed there one year in the same company. 49 00:04:56,225.999 --> 00:04:58,175.998 I s- I was actually a, a contractor. 50 00:04:58,625.998 --> 00:05:03,25.997 I was not, I was not the, the oil and gas company itself. 51 00:05:03,85.999 --> 00:05:05,475.998 I was the PCN company at the time. 52 00:05:06,595.997 --> 00:05:10,395.996 And then they send me to a railroad project. 53 00:05:11,345.997 --> 00:05:16,75.996 Uh, it was actually a, a subway and a monorail. 54 00:05:16,205.998 --> 00:05:17,655.996 Was two projects at the same time. 55 00:05:18,495.999 --> 00:05:21,855.999 So we are building the schedule using MS Project. 56 00:05:23,115.997 --> 00:05:26,535.998 And I helped them with AutoCAD as well. 57 00:05:26,565.998 --> 00:05:28,596 So I was hired as a training engineering. 58 00:05:29,35.999 --> 00:05:36,665.997 The training engineering program in Brazil, you, you stay like six months in one role, six months another role, six months another role. 59 00:05:36,685.997 --> 00:05:40,265.995 So the o- the goal is to learn all the roles. 60 00:05:40,975.998 --> 00:05:42,755.995 So I stay a little bit with procurement. 61 00:05:43,386.002 --> 00:05:51,576.001 I stay in the engineering side doing AutoCAD for the, for the train stations, for the monorail. 62 00:05:52,316.002 --> 00:05:56,846.001 Uh, the contract also included the trains itself. 63 00:05:56,935.997 --> 00:06:04,765.998 So they built a factory for 25 subways and 25 monorails. 64 00:06:05,375.999 --> 00:06:11,105.995 Each, each monorail has like six, uh, cars, same for the subway. 65 00:06:11,105.995 --> 00:06:25,955.995 It was a really good experience as well because I learned engineering procurement, uh, scheduling again but now with MS Project instead of Primavera P6. 66 00:06:26,895.996 --> 00:06:33,655.996 Uh, we are also using at that time SolidWorks, which is the 3D drawing. 67 00:06:34,456.001 --> 00:06:37,156.001 So I did the full training of SolidWorks. 68 00:06:37,995.994 --> 00:06:46,765.991 I never really draw using SolidWorks but it was interesting to learn the 3D piece at the time. 69 00:06:47,85.998 --> 00:06:53,815.994 And as I mentioned to you, my, my strong was always- Uh, learn software pretty quick. 70 00:06:54,135.994 --> 00:06:56,55.994 I was learning everything by myself. 71 00:06:56,455.994 --> 00:07:00,875.994 At the time there was no internet, so we are doing PDF books. 72 00:07:01,335.994 --> 00:07:10,935.994 I was printing 400 pages and I was preparing the books all by myself and reading and doing the exercise in the computer. 73 00:07:11,725.993 --> 00:07:12,875.994 So it was a different time. 74 00:07:12,875.994 --> 00:07:20,115.993 It was still possible to learn by yourself without YouTube, but it was little bit harder. 75 00:07:20,115.993 --> 00:07:23,625.993 Like you need to read, I don't know, 400 pages. 76 00:07:24,715.993 --> 00:07:24,735.993 Yeah. 77 00:07:24,805.993 --> 00:07:26,785.992 But it was a good experience. 78 00:07:26,805.993 --> 00:07:30,545.99 If you want me interrupt me for questions? If not, I can- Yeah. 79 00:07:30,545.99 --> 00:07:31,565.993 So- Yeah. 80 00:07:31,575.993 --> 00:07:49,835.996 So that was Petrobras and then you went to Vale and started your mining career, is that right? One guy that I met on the refinery, he went to Guinea in Africa for a iron ore huge project there. 81 00:07:50,945.995 --> 00:07:57,885.994 And then he was looking for someone with Primavera P6 skills and also with English. 82 00:07:57,915.997 --> 00:08:00,915.997 My English at the time it was not the best, but it was good enough. 83 00:08:03,15.995 --> 00:08:06,715.995 So, uh, 2011 I received a proposal. 84 00:08:08,185.993 --> 00:08:21,115.99266667 Um, so the proposal was really good in terms of money because when you work, on the FIFO, fly in, fly out, you have a like 100% bonus. 85 00:08:21,115.99266667 --> 00:08:24,995.996 So it was like three times my s- my compensation at the time. 86 00:08:25,845.992 --> 00:08:29,75.996 So it was a no-brainer, uh, choose, choice. 87 00:08:30,75.996 --> 00:08:44,245.994 I, I, I didn't search the, the, the, the country in the internet 'cause I said, "I think if I'm gonna search, maybe I'm gonna see the, the other side of Africa, so let's just go and see what happens." 88 00:08:45,325.996 --> 00:08:46,455.994 This was 2011. 89 00:08:47,85.99 --> 00:08:50,605.994 I received like my onboarding July 4th. 90 00:08:50,705.993 --> 00:08:54,585.996 In July 7th I was already on a plane to Guinea, Africa. 91 00:08:55,845.992 --> 00:08:56,655.992 So this was... 92 00:08:56,695.998 --> 00:08:58,795.998 Maybe I can share, uh, my screen. 93 00:08:59,385.986 --> 00:08:59,815.986 Sure. 94 00:09:00,215.987 --> 00:09:02,635.997 Just to talk a little bit about this project. 95 00:09:02,635.997 --> 00:09:05,355.997 This is a $20 billion project. 96 00:09:06,465.997 --> 00:09:09,235.988 I feel I don't have the option to share. 97 00:09:09,675.997 --> 00:09:10,645.997 Or I have, yes.

(09:10):
Okay. 99 00:09:12,325.991 --> 00:09:13,305.991 Screens. 100 00:09:13,715.991 --> 00:09:13,935.991 Yes. 101 00:09:17,591.996 --> 00:09:26,861.996 Mm-hmm so this is Guinea, northwest of Africa. 102 00:09:27,411.996 --> 00:09:27,921.996 Mm-hmm. 103 00:09:28,21.996 --> 00:09:34,751.996 Uh, the capital is Conakry, and the mine was actually here, one, two, three, four. 104 00:09:35,811.997 --> 00:09:38,941.995 So the Simandou project was running two companies at the time. 105 00:09:39,731.996 --> 00:09:46,961.995 Mm-hmm half was Vale and half was Rutindo, the company that I am today, so it's kind of coincidence. 106 00:09:46,961.995 --> 00:09:49,21.996 So that covers a lot of miles then. 107 00:09:49,21.996 --> 00:09:50,741.996 Those mines aren't that close. 108 00:09:51,101.993 --> 00:09:58,591.993 Yes, yes a couple of hundred miles apart for each or, or more? I think, yeah, it's around 150 kilometers. 109 00:09:58,641.993 --> 00:09:59,71.993 Okay. 110 00:09:59,781.993 --> 00:10:02,241.993 So we went direct here to the mines. 111 00:10:03,841.994 --> 00:10:20,532.001 Um, here, of course, there's no infrastructure, so the first thing that we had to do is building some schools, hospitals, roads, even a camp for the people that's gonna work. 112 00:10:20,532.001 --> 00:10:33,501.994 So we need to develop the cities around because we also need the locals to be trained to work with us, to have a good salary, and I think that was the biggest thing that I saw. 113 00:10:33,511.996 --> 00:10:46,082 Like, the first month that I arrived in Africa, the, all the houses are not developed, and after almost two years there, we saw, like, the community growth. 114 00:10:46,951.999 --> 00:10:48,471.996 They were building houses. 115 00:10:48,961.997 --> 00:10:58,42.002 Everyone was growing 'cause at the time, I think w- uh, 70% of the labor was local, so we are developing a whole country. 116 00:10:59,391.998 --> 00:11:05,392.003 And this is here, we also have the full railroad until the port in Conakry. 117 00:11:06,361.999 --> 00:11:22,331.992 So we are actually almost in, I don't know, 50% of the country, we had some constructions, and this, the railroad also has tunnels, bridges, so it's very complex.

(11:22):
.008So you built the railroad then too? Yes. 119 00:11:25,402.007 --> 00:11:26,322.006 As part of the project? Yes.
Wow. 121 00:11:27,852.004 --> 00:11:34,482.002 So- The railroad and the port for a mining company to build a railroad, is that common, It's, it's- out of... 122 00:11:35,122.005 --> 00:11:35,622.005 Yeah. 123 00:11:35,861.996 --> 00:11:36,221.996 Yeah. 124 00:11:36,251.995 --> 00:11:39,331.998 So when we talk about iron ore, we are talking about million tons per year. 125 00:11:41,391.994 --> 00:11:47,331.995 So the four, one, two, three, four combined is 120 million tons per year. 126 00:11:48,172.005 --> 00:11:48,312.005 Mm. 127 00:11:48,342.003 --> 00:11:49,712.005 So you need to build a railroad. 128 00:11:50,241.997 --> 00:11:56,922.003 So for iron ore, it's kind of, when you are always doing an iron ore project, you're always doing the logistic as well. 129 00:11:57,966.003 --> 00:11:59,826.003 And that's includes also the port. 130 00:12:00,666.003 --> 00:12:00,866.003 Yes. 131 00:12:01,846.003 --> 00:12:06,586.003 So you built the port- And- The railroad goes all the way through that country, basically. 132 00:12:06,996.003 --> 00:12:07,656.003 Correct. 133 00:12:07,906.003 --> 00:12:08,346.003 Yeah. 134 00:12:08,356.002 --> 00:12:13,486.002 And the railroad, um, there's also the option to, to... 135 00:12:13,596.003 --> 00:12:16,526.003 for people, the passengers. 136 00:12:16,586.003 --> 00:12:18,276.003 It's not just iron ore. 137 00:12:19,186.003 --> 00:12:22,586.003 So they are using the railroads for other things as well. 138 00:12:22,706.002 --> 00:12:26,966.004 So this was a 20 d- billion-ish project. 139 00:12:26,966.004 --> 00:12:37,96.004 Like, what was the hard part of the project when you showed up? Like, how did you intersect this, and what do you think were the big challenges on getting a project like this one done? My... 140 00:12:37,596.003 --> 00:12:42,846.001 I think the biggest challenge for me at the beginning was the malaria mosquito. 141 00:12:43,746.004 --> 00:12:44,106.004 Mm. 142 00:12:44,116.003 --> 00:12:52,756.001 So because here's malaria is so strong, like, if you have malaria, you can die in 48 hours. 143 00:12:53,936.002 --> 00:13:04,316.002 So we are in a country very hot, using long shirts all the time, passing the repellent every two hours.

(13:04):
.001We also are sleeping in what we call, uh, net, uh, just to protect against the, the, the malaria mosquito. 145 00:13:15,405.999 --> 00:13:18,776.003 So the biggest, the first big challenge was the malaria. 146 00:13:18,786.004 --> 00:13:26,506.004 So if you have a, a headache, you need to do malaria test, because malaria can kill you in, in 48 hours. 147 00:13:26,986.001 --> 00:13:27,766.003 But we had it... 148 00:13:27,835.999 --> 00:13:31,616.005 If, if you identify malaria, you just take the correct medicine, you are fine. 149 00:13:33,176.003 --> 00:13:38,45.999 So the first big challenge for me was to understand this new disease. 150 00:13:39,6.005 --> 00:13:45,426.001 Um, and this was the strongest level of malaria that can kill in 48 hours. 151 00:13:45,725.999 --> 00:13:53,196.002 In north of Brazil, there are a little bit malaria, but there is a weak malaria that it's, it's not a big problem. 152 00:13:53,545.999 --> 00:13:56,396.002 So here, this was the first big challenge.

(13:57):
.002And now talking about the construction, the biggest challenge was if you need an extra equipment, like a excavator, usually- Mm-hmm takes three months to arrive on site. 154 00:14:08,706.002 --> 00:14:08,836.002 Yes. 155 00:14:08,836.002 --> 00:14:09,146.002 Oh, wow. 156 00:14:09,566.003 --> 00:14:12,596.001 So we had to do all the prep. 157 00:14:13,405.999 --> 00:14:18,415.996 The, the primavera was with three months to arrive a new excavator for me. 158 00:14:20,236.001 --> 00:14:24,586.005 So we had to do a different preparation. 159 00:14:24,586.007 --> 00:14:27,75.993 We had to start ordering everything. 160 00:14:27,75.997 --> 00:14:38,690.004 We had to start the procurement earlier- So is that like everything is a long lead item basically, uh- Yes, because we didn't have a railroad. 161 00:14:39,620.004 --> 00:14:42,440.003 We didn't have a good, research. 162 00:14:42,490.002 --> 00:14:45,920.003 We didn't have EPCM companies close. 163 00:14:46,510.003 --> 00:15:01,520.002 Like we ha- um, we actually brought two EPCM companies from Brazil, the, the number two at the time, and they had other projects in Africa but very far like s- like Mozambique. 164 00:15:02,770.003 --> 00:15:05,710.003 So it was very hard to bring equipments. 165 00:15:06,850.004 --> 00:15:25,814.004 So we arrived early because we had to do the planning three months in advance Uh, and a good thing about MB, I was able to learn everything about, I don't know, about civil work since the beginning because we started with civil works, earthquake, uh, earth, earthworks. 166 00:15:27,594.004 --> 00:15:30,284.004 And then we start civil, mechanical, electrical. 167 00:15:31,164.005 --> 00:15:31,964.005 And I was... 168 00:15:33,224.004 --> 00:15:48,834.006 As I was responsible for the primavera, I was, going to the site, uh, twice a week with some civil engineer, uh, with, uh, 65, 50 years old just to learn everything. 169 00:15:49,644.002 --> 00:15:51,214.006 It was a really good experience. 170 00:15:51,854.005 --> 00:15:57,584.003 And as we are in the middle of the jungle, there was nothing to, to socialize. 171 00:15:57,594.003 --> 00:15:59,754.002 There was no bars, restaurants. 172 00:16:00,844.003 --> 00:16:03,174.005 There was not even an option to spend money. 173 00:16:03,194.005 --> 00:16:16,594.006 Like, all the money that we are gaining, the only way to spend was, a station, a gas station that you can buy, um, Heineken, Coca-Cola. 174 00:16:16,644.004 --> 00:16:18,394.005 So I was... 175 00:16:18,394.005 --> 00:16:19,494.004 So we are living there.

(16:19):
.002The s- still flying fly outs was 40 days in site, 10 days in Brazil. 177 00:16:25,794.002 --> 00:16:31,804.001 So the four days site we are spending $0- Wow because, because there is no place to spend dollars. 178 00:16:33,904.006 --> 00:16:36,184.002 So then just sort of fast-forwarding a little bit here. 179 00:16:36,184.005 --> 00:16:39,014 So you're in Vale for 10 years. 180 00:16:39,24.001 --> 00:16:43,734.005 You started in Guinea, then USA- Brazil uh, got, Yeah. 181 00:16:43,734.005 --> 00:16:46,964.004 And then USA, Brazil, and then Canada. 182 00:16:47,554.003 --> 00:16:47,794.003 Yes. 183 00:16:47,794.003 --> 00:16:57,644.004 And through that you were basically a project controls engineer, project control specialist, then FP&A analyst- Yeah um, and then interim manager. 184 00:16:58,243.998 --> 00:16:58,733.998 What... 185 00:16:58,923.999 --> 00:17:00,174.001 How did your... 186 00:17:00,374.003 --> 00:17:09,284.001 what job did you like the most there, and what kind of other projects were you working on? You know, maybe just give us- Yeah a little bit of a, a run through of Vale.

(17:09):
.004So I started my career on the major projects. 188 00:17:13,244.007 --> 00:17:15,904.001 After this one I have the S11D. 189 00:17:16,964.008 --> 00:17:18,684.01 The S11D, very similar project. 190 00:17:19,824.008 --> 00:17:22,992.006 Um- So we had the mine. 191 00:17:23,822.006 --> 00:17:36,152.006 The D is actually the body D of the iron ore mine, and we have a full railroad until, uh, São Luís, 1.5 192 00:17:36,152.006 --> 00:17:37,382.005 kilometers port. 193 00:17:38,132.006 --> 00:17:38,942.006 Very similar. 194 00:17:39,332.007 --> 00:17:43,192.006 So here I stay, like, four years, fly in, fly out as well. 195 00:17:43,692.007 --> 00:17:48,842.006 But this was, like, three weeks in the headquarter in Rio de Janeiro and one week here. 196 00:17:48,892.007 --> 00:17:51,192.006 Sometimes two weeks here and two weeks in Rio. 197 00:17:52,432.006 --> 00:17:55,632.007 And I was walking the full railroads, the 1.5 198 00:17:56,592.008 --> 00:17:57,732.005 thousand kilometers. 199 00:17:58,632.005 --> 00:18:05,292.007 So every night I was sleeping in a different city just to see the progress of the railroad. 200 00:18:05,942.007 --> 00:18:09,172.005 And these are something that we implemented here in Africa as well. 201 00:18:09,192.007 --> 00:18:19,342.005 So every Friday we are doing a drone picture from the same site, like So we have the full railroad, a lot of pictures Friday. 202 00:18:20,32.007 --> 00:18:26,2.005 So we are able to update our Primavera just looking into these high, high-quality pictures. 203 00:18:27,82.009 --> 00:18:29,362.008 We are building reports from those pictures. 204 00:18:29,812.005 --> 00:18:31,862.006 So it was really nice that we implemented this. 205 00:18:31,912.011 --> 00:18:36,862.008 We actually started this in, in Africa, so I brought this to this project as well. 206 00:18:37,362.008 --> 00:18:45,962.011 Was that your idea then, to use the drone footage? At the time in Africa, the drone drone was, I don't know, $50,000. 207 00:18:46,92.012 --> 00:18:46,972.008 Was really expensive. 208 00:18:47,92.012 --> 00:18:52,672.008 And like four years later, we were able to pay, I don't know, $10,000. 209 00:18:52,712.007 --> 00:18:53,502.009 Was really cheap. 210 00:18:53,562.013 --> 00:18:57,662.012 Today, I don't know, you can buy for $500 maybe. 211 00:18:57,662.012 --> 00:18:57,832.012 Hmm. 212 00:18:57,832.012 --> 00:19:02,832.011 So at that time it was, was way expensive, and we did some training. 213 00:19:03,562.013 --> 00:19:12,52.012 We trained the local teams to learn how to, to pilot the drone just to take the pictures every Friday. 214 00:19:12,52.012 --> 00:19:20,757.0105 We are also doing videos, uh, once we com- 'cause this has 50 bridges, 1.5 215 00:19:20,757.0105 --> 00:19:20,767.0105 kilometers. 216 00:19:20,767.0105 --> 00:19:28,252.006 So every time you conclude a new bridge, we do a drone video and then we show this video on the headquarter. 217 00:19:28,832.01 --> 00:19:30,162.008 So it's a really nice experience. 218 00:19:30,442.011 --> 00:19:31,282.009 I like this part. 219 00:19:32,576.013 --> 00:19:35,166.013 Here I was doing the performance reporting. 220 00:19:36,36.013 --> 00:19:40,86.013 But my role here, I was the eye of the CPO. 221 00:19:40,116.013 --> 00:19:47,126.013 So as the CPO was in the headquarter, I was going here, seeing everything, doing my own reports. 222 00:19:47,136.012 --> 00:19:54,676.013 So he had his own reports to compare to the EPCN reports and, and make sure everything was correct. 223 00:19:54,766.012 --> 00:20:04,416.013 I was also doing, Primavera, but on the macro level, not going into the, the task was, was really nice experience as well. 224 00:20:05,416.012 --> 00:20:07,846.012 So here we are starting the Amazon forest. 225 00:20:08,816.014 --> 00:20:08,966.014 Okay. 226 00:20:08,966.014 --> 00:20:11,796.012 So there is a, a, a particularity here. 227 00:20:12,476.013 --> 00:20:19,746.012 So when we are start, we are joining the Amazon forest, the railroad needs to be elevated, 'cause you cannot mess... 228 00:20:19,806.014 --> 00:20:22,206.014 you need the- the animals need their own space. 229 00:20:23,366.015 --> 00:20:26,296.015 So the railroad starts to be very elevated here. 230 00:20:27,166.015 --> 00:20:30,956.013 But it's a small piece that is on the Amazon forest. 231 00:20:32,456.012 --> 00:20:35,296.014 So for a project- like, 15, 20 billion. 232 00:20:35,306.01 --> 00:20:55,530.012 At that scale of CapEx, like, how does that change how you plan, communicate, control, and then how do you deal with the executive pressure for a project that big? So here we have a, a particularity that is possible to do in a project like in this size. 233 00:20:56,270.012 --> 00:20:59,490.012 So we have like 1,000 kilometers of railroad. 234 00:21:00,190.011 --> 00:21:04,330.012 You can divide it in 50 kilometers scope. 235 00:21:05,180.012 --> 00:21:07,720.012 Sorry to say kilometers, because I'm not used to miles. 236 00:21:07,720.012 --> 00:21:20,650.011 50 kilometer scope by EPCM company, and then the companies that are performing better, we, we give more kilometers, we give more contracts. 237 00:21:21,760.011 --> 00:21:27,490.011 So because of that, we are able to be- have a really good performance on the railroad. 238 00:21:29,100.011 --> 00:21:33,980.01 Uh, and this was one of the benchmark projects that we, we have. 239 00:21:34,880.01 --> 00:21:41,650.013 Uh, one of the projects here, there is, we call the, a railroad from the S11D to the old railroad. 240 00:21:42,270.009 --> 00:21:47,290.01 This one was the benchmark project that I, that I worked with the, in my entire career. 241 00:21:47,330.011 --> 00:21:56,690.009 So we delivered on budget, on time, and we actually got all the lesson learned from this project and start to apply in other projects. 242 00:21:57,250.012 --> 00:21:57,270.012 All right. 243 00:21:57,270.012 --> 00:22:02,450.016 Tell me about lessons learned a little bit, because that's something that sometimes gets skipped. 244 00:22:03,0.018 --> 00:22:07,220.014 You said that- Mm-hmm you had lessons learned and you managed to apply them. 245 00:22:07,240.016 --> 00:22:20,220.016 How did you actually do that? So because railroad projects are very similar It's not that hard to, to do benchmarking. 246 00:22:20,480.016 --> 00:22:23,310.016 Like you are doing 100 kilometers of railroad here. 247 00:22:25,520.016 --> 00:22:33,820.015 What were the, the, the best things that you applied that you can apply to the other, uh, sessions? For instance, the management. 248 00:22:34,110.016 --> 00:22:45,490.016 Like at the time the dashboards was Excel, and we started doing some dashboards for the, for the railroad and for the port as well. 249 00:22:46,360.016 --> 00:22:52,490.016 So we are able to see the performance that was outstanding, and then send a team there. 250 00:22:52,500.014 --> 00:22:54,600.015 It's why this performance is outstanding. 251 00:22:55,880.015 --> 00:22:57,600.014 Ah, because we did this and this. 252 00:22:57,610.014 --> 00:22:59,960.016 So, so let's bring this to the others. 253 00:23:01,610.014 --> 00:23:07,490.014 So- And you were kind of one of the dashboard wizards if- Yeah they were lucky to have you. 254 00:23:07,490.015 --> 00:23:12,720.015 Um- There's so few people in the industry that have that skill to bring it all together. 255 00:23:12,830.015 --> 00:23:13,820.014 Would you say that was... 256 00:23:14,740.015 --> 00:23:21,320.018 You know, how did you do that for a project this big w- working in Excel? Yeah, I have a funny history. 257 00:23:21,330.014 --> 00:23:38,380.016 So when I was in SLN in the port, which is in São Luís, I s- I met a guy that was doing a dashboard in Excel, and I saw that fascinating, fascinate. 258 00:23:39,80.018 --> 00:23:42,80.018 The Excel at the time was really heavy and slow. 259 00:23:43,240.018 --> 00:23:46,740.018 But I, I sit with him the whole afternoon. 260 00:23:46,790.018 --> 00:23:48,220.017 I asked him to explain. 261 00:23:49,100.017 --> 00:23:50,970.018 He sent me this Excel later. 262 00:23:51,700.018 --> 00:24:07,346.018 I was able to do reverse engineering on that Excel, and that's how I started to do dashboards Um, so I started Excel, you know, it's really hard. 263 00:24:08,46.018 --> 00:24:12,576.018 Like, if you start adding more data, start to get really slow. 264 00:24:13,606.017 --> 00:24:18,246.017 The computers at the time, it was not that fast like the ones that I have n- now. 265 00:24:18,796.017 --> 00:24:23,536.017 So I remember that the dashboard was really heavy and hard to share. 266 00:24:24,36.017 --> 00:24:34,266.018 But we are using some pictures from the Excel to build our monthly reports, and that's how we, we start to do dashboards. 267 00:24:35,946.017 --> 00:24:55,790.021 And then- do you think that that made a difference for establishing trust with leadership? Or, you know, how did, how did the reporting fit in to the overall sort of project trust-building with executives? I always like to be very visual. 268 00:24:56,950.021 --> 00:25:09,730.021 Uh, when I start to do the drawing thing, I saw that people in, in the headquarter really wanted to see my weekly reports with the high-quality pictures on the side. 269 00:25:10,600.02 --> 00:25:14,630.02 And then I start to grow this report, like the monthly version of the report. 270 00:25:15,540.021 --> 00:25:18,340.02 I start to do some dashboards, some charts. 271 00:25:19,770.021 --> 00:25:23,100.02 Uh, I was doing Excel and printing on the... 272 00:25:23,100.02 --> 00:25:25,970.021 At the time it, we had two monthly reports. 273 00:25:26,130.021 --> 00:25:38,800.021 Was one monthly report with 200 pages Word, PDF that will be shared with the whole team, and had one more executive version with three pages, just what the leadership needs to see. 274 00:25:40,80.018 --> 00:25:47,190.019 So in, have only three page, you need to be more visual, adding some charts. 275 00:25:47,530.019 --> 00:25:52,350.018 Then you saw that people were enjoying, so I started to, to like. 276 00:25:52,350.019 --> 00:25:54,60.021 And I'm very visual as well. 277 00:25:54,940.019 --> 00:26:02,700.023 I, like, it's easier to see some dashboards instead of reading 200 PDF pages. 278 00:26:03,550.023 --> 00:26:07,740.023 At the time we didn't had AI to summarize a 300-page PDF. 279 00:26:08,540.023 --> 00:26:10,280.019 It was all, yeah. 280 00:26:11,240.018 --> 00:26:15,90.02 Don't know if everyone was reading that report at the time. 281 00:26:16,220.021 --> 00:26:23,510.016 So how do you deal with bad data then? You're bringing in all this information and it's in Excel and you were generating the reports. 282 00:26:23,510.018 --> 00:26:40,554.021 Was it at this point you were QA-ing it yourself or, you know, how did you kind of sort through the signal to noise on all that information? Yeah, I, I think to answer these questions maybe talk about my next position. 283 00:26:40,584.021 --> 00:26:51,844.021 So after almost two years in Africa, four years in north of Brazil, I was ready to go to a, a portfolio level. 284 00:26:52,774.021 --> 00:26:57,114.021 So I, I, I said, "I've already lived in Africa and north of Brazil. 285 00:26:57,144.02 --> 00:27:00,754.02 I would like to stay here on the headquarter a little bit." 286 00:27:01,334.021 --> 00:27:11,634.019 So I got a position on the FP&A, and I was responsible for the whole CapEx, uh, management for copper and nickel. 287 00:27:12,284.021 --> 00:27:25,664.019 That's wh- when I started to work with Vali Ca- Vale Canada, 'cause at the time, the copper and nickel, still today, headquarter is here in Toronto, like three blocks from the CN Tower that you see here. 288 00:27:26,224.02 --> 00:27:26,684.019 Mm-hmm. 289 00:27:27,374.021 --> 00:27:46,296.02 Uh, so when I started to work in the FP&A, the main source was SAP So I start to go really deep in SAP, and I was responsible to teach the things in the copper and nickel mines how to use SAP. 290 00:27:47,566.021 --> 00:27:53,416.021 And as soon as I was got more mature, my SAP knowledge, start to do trainings. 291 00:27:53,446.02 --> 00:27:57,26.02 So I trained around three hundred people for SAP. 292 00:27:58,336.021 --> 00:28:06,406.021 At the time it was SAP PS, product system, FM, funds management, MM, which is the procurement. 293 00:28:07,596.019 --> 00:28:14,406.02 And once I was got more mature of SAP, I thought, "Oh, I think it- it's missing some fields here. 294 00:28:15,256.019 --> 00:28:21,616.021 Can we customize SAP?" So I, I had st- start meetings with IT, how can improve SAP. 295 00:28:22,336.021 --> 00:28:28,636.02 So w- I was part of a group that was constantly improving, SAP customizing. 296 00:28:29,856.021 --> 00:28:34,326.018 And we also did s- at the time the company did some merges and acquisition. 297 00:28:34,646.018 --> 00:28:36,86.018 Not, not merge, just acquisition. 298 00:28:36,936.019 --> 00:28:41,36.022 So we had to implement SAP on the, the new acquired companies. 299 00:28:41,266.021 --> 00:28:44,76.02 I was part of this as well. 300 00:28:44,776.023 --> 00:28:51,116.02 And then twenty seventeen came and Microsoft launched Power BI, and that's changed everything. 301 00:28:51,126.022 --> 00:28:56,676.021 So I was, was already with a good, a good data knowledge. 302 00:28:57,546.02 --> 00:29:05,96.017 I was already doing some dashboards in Excel, but I was a- already doing some PowerPoint presentations with charts. 303 00:29:06,646.018 --> 00:29:18,106.021 And then when Microsoft came with Power BI, and was already included in our Microsoft, Office three six five, so it was already free for the whole company. 304 00:29:18,746.021 --> 00:29:22,646.02 So, uh, I started to look some videos. 305 00:29:22,716.015 --> 00:29:27,216.015 At the time it was, like, one professor in the whole YouTube. 306 00:29:28,476.017 --> 00:29:34,116.021 So it's good that, that I, uh, speak English because in the beginning it was just English. 307 00:29:34,836.018 --> 00:29:40,806.018 And then some, uh, training in Portuguese was starting as well. 308 00:29:40,816.021 --> 00:29:41,206.019 So I... 309 00:29:41,216.015 --> 00:29:45,756.02 Since the beginning, I was the SME for Power BI. 310 00:29:46,376.019 --> 00:29:51,226.024 I started to implement, uh, in this F&A team. 311 00:29:52,136.013 --> 00:30:09,876.025 And then the, my director at the time, she was always with her iPad, her tablet, and she was, "Oh, can you do some dashboards for me that I can bring to my meetings in my tablet?" So that was my first real sponsor. 312 00:30:11,16.018 --> 00:30:16,126.008 So I did some, dashboards that she, she can use in the meetings. 313 00:30:16,216.015 --> 00:30:21,506.011 She can access in her iPad and comment, "Oh, this is the numbers." 314 00:30:22,596.02 --> 00:30:27,486.008 So this was, like, my, my first sponsor back in two thousand eighteen. 315 00:30:28,912.018 --> 00:30:29,132.018 Yeah. 316 00:30:29,422.018 --> 00:30:31,262.018 You know, this is an interesting point. 317 00:30:31,312.018 --> 00:30:38,112.018 I'm, I'm curio- I'm gonna loop back to SAP for a second, but, um, and then I wanna ask you a question on the Power BI too. 318 00:30:38,792.018 --> 00:30:46,862.018 For SAP, people listening to this, you sort of casually mention, "Oh, and then we roll it out, 300 people training," you know, mergers. 319 00:30:47,142.017 --> 00:30:56,442.018 Like, these are big endeavors that are- Yes very painful for a lot of orgs, and maybe somebody is listening to this going, "I'm about to do this SAP implementation. 320 00:30:56,492.017 --> 00:30:57,802.017 Maybe I should quit my job." 321 00:30:58,372.016 --> 00:31:19,948.018 Um, what advice do you have, if you've done these successfully, for SAP, say, during an M&A deal where you're gonna roll it out? Like, what made it work for you, if it did at all? Maybe, maybe your advice is not to use SAP I think mostly they had a lot of manual reporting. 322 00:31:21,328.018 --> 00:31:26,718.018 I remember a team that were spending the first 15 days of the month building reports. 323 00:31:27,918.018 --> 00:31:38,228.017 So we were paying very expensive senior analyst specialists to instead of bringing sites from the data, just creating reports. 324 00:31:39,348.017 --> 00:31:43,68.018 So this was one of the, the first problems that I saw. 325 00:31:43,668.017 --> 00:32:10,760.018 And with Power BI it was easy to automate the reporting, and then we, we have those brilliant minds, specialists that can go to the numbers and bring insights how we can improve our operation, or how we can improve our project management, how we can- Use this data to improve the company, uh, the productivity of the projects and the operation as well. 326 00:32:11,820.018 --> 00:32:24,140.018 So I show them the value of, of, what we can do with a standard SAP with all the data coming really good. 327 00:32:24,200.019 --> 00:32:24,480.019 Yeah. 328 00:32:24,600.019 --> 00:32:27,0.019 You know, the ex- the garbage in, garbage out. 329 00:32:27,240.018 --> 00:32:31,530.019 So let's treat our SAP well. 330 00:32:32,340.019 --> 00:32:34,160.018 Let's focus on SAP. 331 00:32:34,160.018 --> 00:32:40,820.017 Each team maybe have someone with all the knowledge to make sure all the data here is good. 332 00:32:42,150.016 --> 00:32:44,210.017 And this is very valuable for the headquarter. 333 00:32:44,230.018 --> 00:32:53,420.018 If you are receiving a good data from all of your sites, from all of your projects, you can do amazing analysis in your headquarter. 334 00:32:54,280.017 --> 00:32:55,870.019 And I think that was the next step. 335 00:32:55,870.019 --> 00:33:00,140.018 So once we organize it, all our data, uh... 336 00:33:00,250.019 --> 00:33:03,470.016 So SAP was implementing the company in 2015. 337 00:33:04,80.017 --> 00:33:07,170.015 So after 2016, all the data was reliable. 338 00:33:07,990.02 --> 00:33:11,190.019 So I build a huge historical data from 2016. 339 00:33:12,440.017 --> 00:33:20,820.019 Uh, and then we are using the historical data for the budgeting process as well, like the five years rolling forecast. 340 00:33:21,940.017 --> 00:33:26,250.015 And we actually had the historical data to support us. 341 00:33:27,40.016 --> 00:33:32,810.02 And was not just billion-dollar projects, was the full, uh, portfolio of projects. 342 00:33:32,830.017 --> 00:33:35,920.02 That includes R&D, sustaining. 343 00:33:36,590.019 --> 00:33:49,680.018 So we are talking about 5,000 projects per year, CapEx of $10 to $50 billion per year when you combine 100% of the portfolio. 344 00:33:51,40.016 --> 00:33:52,190.015 Well, and so I have a... 345 00:33:52,410.018 --> 00:33:59,630.013 So this one you were FP&A, so you're on the- Yeah finance side and, uh, you're reporting to the board. 346 00:34:00,310.02 --> 00:34:08,760.018 But- Yeah one question I have for you, 'cause we talk to a lot of people that are on the project side, and project controls is different than finance, right? You have a CFO- Yeah and a PMO. 347 00:34:09,530.014 --> 00:34:14,180.021 So you were on the CFO side, so but you've been on the project side. 348 00:34:14,180.021 --> 00:34:14,440.017 Mm-hmm. 349 00:34:14,440.017 --> 00:34:31,334.019 So how did seeing projects through the finance lens change how you thought about project controls? So I was hired to be the CapEx side of the FP&A because of my project background. 350 00:34:32,464.019 --> 00:34:40,654.019 So before, like, like, on the finance side, you have a, a, a variance of, I don't know, a b- a million dollars in a project in January. 351 00:34:42,14.019 --> 00:34:46,44.019 So if you, if you don't have a, a project background, you're gonna see the variance. 352 00:34:46,114.018 --> 00:34:51,94.019 Ah, you're gonna talk with the guy who's gonna explain to you, and you're just gonna put on the report. 353 00:34:51,384.018 --> 00:34:57,194.018 You're not gonna question- Mm-hmm because you don't understand projects enough to question. 354 00:34:57,404.017 --> 00:35:03,774.017 So it's, it Maybe it's gonna The difference is, uh, tax rate that you can explain 'cause you are finance. 355 00:35:04,144.019 --> 00:35:11,944.019 But if the difference is, oh, my EPCM contractor created a claim, a claim because of this and that, and that's the, the difference. 356 00:35:12,864.016 --> 00:35:20,414.017 So my first role was to question the big variance 'cause this is what the CFO wants to see. 357 00:35:20,444.018 --> 00:35:27,494.018 Like, why we are spending 10% more in February or why we have spent 20% less. 358 00:35:28,64.021 --> 00:35:38,114.019 What's happening? So this was the, like, I, I was on the F&A side, but I was bringing a lot of my project background. 359 00:35:39,484.019 --> 00:35:49,44.019 And an- another thing completely different that I learned on the F&A was the funds management and also the effects rate management. 360 00:35:49,914.018 --> 00:36:00,994.018 Because we had, at the time, projects were in Canada, in Brazil, Tunisia, in New Caledonia, so we have different FX rates. 361 00:36:01,954.019 --> 00:36:08,444.018 Uh, you have the funds management SAP, which you load budget by project. 362 00:36:09,824.019 --> 00:36:13,364.02 And then you have these FX variants that you need to take care. 363 00:36:13,364.02 --> 00:36:17,414.017 You need to do the, reallocation between projects. 364 00:36:17,414.018 --> 00:36:29,604.017 So if your project is not performing, another project's performing really well or maybe there is a change management or a claim that needs an extra $10 million, you need to find a donor. 365 00:36:30,114.015 --> 00:36:35,44.017 So this was a complete different level of how you see the projects. 366 00:36:35,854.016 --> 00:36:42,124.015 Because when I was on site, the focus was deliverable, deliver on budget and on time. 367 00:36:42,974.019 --> 00:36:43,104.014 Mm-hmm. 368 00:36:43,114.019 --> 00:36:46,944.012 And then we are on finance, and we are seeing the whole portfolio. 369 00:36:47,774.015 --> 00:36:54,634.017 You also need to make sure all the projects that are performing are receiving the budget. 370 00:36:54,914.014 --> 00:36:58,634.015 And if you have an FX rate issue, you need to fix. 371 00:36:59,664.014 --> 00:37:03,164.018 So we are-- I was seeing the other side of the coin. 372 00:37:03,824.018 --> 00:37:13,284.012 Also really important, and I would say the, the company was very mature in the data, uh, management. 373 00:37:13,294.011 --> 00:37:19,234.012 Like, after two years there, I thought they were really mature in their SAP process. 374 00:37:20,434.018 --> 00:37:27,94.012 Uh, I thought they were really good in observing the, the, the acquisition company. 375 00:37:28,414.014 --> 00:37:32,394.015 And I think this, this I learned a lot with them. 376 00:37:32,744.016 --> 00:37:36,524.012 And that's what's really helped me to get a position in Canada. 377 00:37:37,734.021 --> 00:37:40,282.6836667 So- Yeah. 378 00:37:40,332.6836667 --> 00:37:43,292.6836667 I'm gonna try and pin you down, on a, on a little battle here. 379 00:37:43,292.6836667 --> 00:37:46,902.6836667 So PMOs have been on the rise, I would say. 380 00:37:46,902.6836667 --> 00:37:53,282.6826667 Some folks argue that the pro- the PMO should report at a higher level. 381 00:37:53,312.6826667 --> 00:38:02,612.6826667 So sometimes PMOs report through maybe the CFO suite, maybe a PMO reports directly, uh, and peers with the CFO. 382 00:38:02,632.6826667 --> 00:38:26,552.6836667 Like, where do you think the, the best place for a PMO is within an organization? Is it peering with the CFO suite 'cause they're different but- Mm-hmm friends, or would it be one level down? My experience with PMO, it's always reported to CPO or the CTO, 'cause we have some technical aspects. 383 00:38:27,972.6846667 --> 00:38:46,156.6856667 Uh, when this big billion-dollar projects, the company needs to have a really mature project management system And that's why usually the PMO is with this, or the CPO or the CTO. 384 00:38:47,346.6856667 --> 00:38:56,186.6866667 Uh, all the new projects needs to follow the project management system, in the oil and gas and mine. 385 00:38:56,196.6856667 --> 00:39:00,226.6856667 We have, uh, the projects are by phase, yeah. 386 00:39:00,226.6856667 --> 00:39:05,656.6856667 We start with concept, pre-feasibility, feasibility, execution, which is the construction. 387 00:39:06,476.6846667 --> 00:39:09,206.6856667 Then we have the commissioning and the operation. 388 00:39:10,586.6846667 --> 00:39:15,966.6856667 So, uh, the PMO needs to support all those phase. 389 00:39:16,236.6866667 --> 00:39:17,616.6836667 Those phase are complete different. 390 00:39:17,626.6856667 --> 00:39:20,926.6836667 Like we start with exploration, same as oil and gas. 391 00:39:22,266.6856667 --> 00:39:26,276.6876667 So we start with financial modeling, capital location. 392 00:39:26,906.6846667 --> 00:39:33,506.6866667 If the project has a good MPV and IRR to go to, to the next phase. 393 00:39:34,926.6896667 --> 00:39:38,446.6856667 So this is something that I learned a lot in the FP&A phase. 394 00:39:38,516.6826667 --> 00:39:43,216.6836667 But I think the, the CFO needs to be really involved. 395 00:39:43,336.6816667 --> 00:39:47,146.6776667 I think that my next position kinda showed this a little bit. 396 00:39:48,596.6846667 --> 00:39:57,816.6776667 So when I was on the FP&A, and I was doing the budget cycle for, uh, Copper and Nickel in Canada. 397 00:39:58,906.6816667 --> 00:40:06,976.6786667 They sent me here to Toronto to stay like, uh, one month here, helping them to do the, the full budget cycle for the next five years. 398 00:40:08,486.6836667 --> 00:40:14,166.6806667 And then when I arrived here, I start to do the bud-budget cycle with Power BI. 399 00:40:15,476.6816667 --> 00:40:17,196.6766667 So I create some scenarios. 400 00:40:18,186.6836667 --> 00:40:23,796.6816667 We had a cap of budget, so I, I was able to create a capital location scenarios. 401 00:40:23,816.6806667 --> 00:40:31,986.6806667 What would be the best projects for the next five years, and what would be the next project for the next year, which would be the focus. 402 00:40:33,216.6896667 --> 00:40:38,526.6816667 And then during the meeting, I was with the CFO here in Toronto, the CFO of Copper Nickel. 403 00:40:39,506.6826667 --> 00:40:43,956.6806667 Then I showed him the presentation that I did for the, the budget cycle. 404 00:40:44,706.6796667 --> 00:40:46,706.6776667 I opened the Power BI on the screen. 405 00:40:47,646.6826667 --> 00:40:52,786.6816667 At the time it was physical meetings, was not, uh, was in-person meeting. 406 00:40:53,536.6816667 --> 00:41:08,90.6866667 Then I start to show the scenarios, and then I come back to my presentation and recommend the scenario for him based on data And then he, he said on the, on the middle of the meeting, he said, "Oh, we need this guy here." 407 00:41:08,610.6866667 --> 00:41:16,290.6866667 And that's how I was hired for the, for the company here on the headquarter in Toronto. 408 00:41:17,490.6856667 --> 00:41:28,580.6856667 And this meeting changed, it completed my life because I got a, a work permit now to move to, to Canada. 409 00:41:29,650.6866667 --> 00:41:46,550.6876667 I was doing what I loved, which was, uh, PMO with Power BI, and I was implementing Power BI, Power BI on, on, on here in, in Canada. 410 00:41:47,210.6856667 --> 00:41:51,210.6876667 And all the other things we are seeing, I say, "Oh, I would like this in my team as well. 411 00:41:51,210.6876667 --> 00:41:52,880.6856667 I would like this in my department as well." 412 00:41:53,780.6856667 --> 00:41:57,0.6876667 So it's kinda like w- went viral in the company. 413 00:41:57,640.6866667 --> 00:42:02,580.6856667 Everyone was seeing the dashboards, uh, the presentation, and they were really interesting. 414 00:42:03,740.6846667 --> 00:42:11,160.6826667 So my position here in Toronto was to build a PMO for Corporate Liquor. 415 00:42:12,660.6826667 --> 00:42:24,130.6876667 And a different type of PMO, like with everything on a dashboard, a one-stop shop thing so you can see the risk management of your projects. 416 00:42:24,130.6876667 --> 00:42:28,710.6826667 You can filter your projects and see, the cost, the schedule. 417 00:42:29,190.6816667 --> 00:42:38,730.6816667 So we are building the full one-stop shop, like a project control tower for the whole portfolio, and this was really nice. 418 00:42:38,770.6836667 --> 00:42:45,660.6806667 That was my first experience as a manager, so I had a team around seven people. 419 00:42:46,960.6786667 --> 00:43:03,336.6836667 Uh, had some consulting as well that, that I was leading Um, and then I started to implement a, a investment committee, and the main client for the investment committee was the CFO. 420 00:43:04,436.6836667 --> 00:43:12,776.6826667 So coming back to your question, I think the At the time, the PMO was reporting to the CTO. 421 00:43:14,236.6836667 --> 00:43:18,36.6826667 The CTO in mining is not technology, is technical. 422 00:43:18,696.6826667 --> 00:43:19,146.6836667 Mm-hmm. 423 00:43:19,286.6826667 --> 00:43:33,36.6826667 And we had this, uh, investment committee every month with the CFO, and we had another investment committee on a quarterly basis with the global CFO from the headquarter in Brazil. 424 00:43:34,516.6796667 --> 00:43:37,476.6826667 Uh, and then I implemented this investment committee. 425 00:43:38,506.6816667 --> 00:43:42,956.6816667 Investment committee we are bringing, the performance of the projects. 426 00:43:43,666.6816667 --> 00:43:50,536.6816667 We are talking a little bit about the historical data, the budget cycle, project approval. 427 00:43:50,816.6826667 --> 00:43:56,846.6836667 So it was a very successful investment committee and I was responsible. 428 00:43:58,436.6816667 --> 00:44:02,66.6776667 It was around 24 meetings per year at two per month. 429 00:44:02,966.6806667 --> 00:44:06,606.6806667 Was a lot of work just to run this investment committee. 430 00:44:06,606.6806667 --> 00:44:18,596.6806667 But I think that was the time when the whole company was seeing my work, uh, and was, was amazing to be recognized like everyone. 431 00:44:19,626.6846667 --> 00:44:23,796.6836667 They asked me to implement similar to other products. 432 00:44:24,786.6806667 --> 00:44:30,716.6806667 And, uh, the company at the time, the iron ore was always the top one. 433 00:44:30,746.6796667 --> 00:44:35,466.6806667 So everything that iron ore was doing, the other products was copying. 434 00:44:36,6.6816667 --> 00:44:48,516.6826667 So for the first time, the iron ore was seeing what's, what the base metals copper, nickel product group was doing, and I was doing a benchmark project to teach them how to do the same. 435 00:44:49,496.6796667 --> 00:44:53,876.6806667 So this was one of my biggest accompl- accomplishment, accomplishments. 436 00:44:54,776.6776667 --> 00:45:00,946.6836667 And this was a whole combination 'cause I was using some slides from the Power BI in the investment committee. 437 00:45:00,946.6836667 --> 00:45:05,46.6786667 The investment committee was way based on data. 438 00:45:05,526.6856667 --> 00:45:12,916.6836667 Of course, we had some other topics, but I usually start bringing a lot of data and graphics and charts. 439 00:45:14,236.6776667 --> 00:45:20,986.6856667 Uh, that time I started to study storytelling, so how I can build storytelling in my presentation. 440 00:45:22,216.6726667 --> 00:45:26,626.6726667 And this was one, uh, was really good. 441 00:45:27,66.6726667 --> 00:45:30,576.6716667 That's how I got my, my next job as well. 442 00:45:30,586.6726667 --> 00:45:34,586.6726667 So, what was, would you say, you set up a PMO from scratch. 443 00:45:34,626.6716667 --> 00:45:35,716.6726667 That's no small feat. 444 00:45:36,246.6716667 --> 00:46:00,622.6726667 let's say someone's listening now about to set up a PMO for a multi-billion dollar org, um, what was the hardest thing about setting one up from scratch, and what's a tip that you would have for the audience on how to do it? So because I, I have a big system background, I started with the system piece. 445 00:46:01,472.6726667 --> 00:46:04,952.6726667 So as I explained to you, the SAP was very mature at the time. 446 00:46:05,712.6726667 --> 00:46:15,972.6726667 So the first piece of the PMO was to do the cash and cost performance, and that's includes, as I explained to you, the historical data. 447 00:46:15,982.6716667 --> 00:46:21,802.6716667 So we have like 10 years, uh, on the past plus 10 years in the future. 448 00:46:21,862.6726667 --> 00:46:23,362.6716667 So it was like a 20 years. 449 00:46:24,182.6726667 --> 00:46:27,862.6726667 My dashboards you can s- you are able to see 20 years of data. 450 00:46:28,972.6736667 --> 00:46:29,812.6706667 It was really good. 451 00:46:30,732.6716667 --> 00:46:35,562.6726667 That's why you have a really strong data foundation. 452 00:46:35,572.6716667 --> 00:46:36,462.6696667 It's really important. 453 00:46:36,472.6736667 --> 00:46:44,132.6736667 It's really hard nowadays to have 20 years of data available, but it's, it's really good. 454 00:46:44,672.6706667 --> 00:46:46,882.6706667 So we s- I started with my... 455 00:46:47,812.6696667 --> 00:46:53,472.6696667 I would say where I was more in my comfort zone, which was the cost and cash. 456 00:46:54,222.6696667 --> 00:46:56,662.6696667 And then we started to go piece by piece. 457 00:46:56,662.6716667 --> 00:46:59,512.6696667 The company already had a project management system in place. 458 00:47:00,812.6686667 --> 00:47:06,502.6706667 So the first thing is we simplified because the, it's m- was more item R oriented. 459 00:47:06,942.6736667 --> 00:47:11,422.6686667 And as I explained to you the beginning of the podcast, item R is half logistic. 460 00:47:11,472.6726667 --> 00:47:14,112.6736667 So it's, it's railroad and port as well. 461 00:47:14,832.6736667 --> 00:47:15,692.6706667 Mm-hmm. 462 00:47:15,692.6706667 --> 00:47:18,162.6716667 Copper and nickel you don't really have railroad and port. 463 00:47:18,332.6736667 --> 00:47:23,242.6726667 We are talking about, uh, kilotons of, of nickel and copper. 464 00:47:23,272.6686667 --> 00:47:29,550.6736667 We are not talking about million tons- I don't know, it's like four hundred million tons per year. 465 00:47:30,190.6736667 --> 00:47:31,790.6736667 The whole three fifty. 466 00:47:32,930.6736667 --> 00:47:37,470.6736667 And like it's, it's complete different commodities. 467 00:47:38,390.6736667 --> 00:47:45,730.6726667 So the focus was the mine and the, the big difference were the smelters and refineries and the mills. 468 00:47:46,710.6726667 --> 00:47:50,680.6726667 So we need to learn more about the processing plant and also the byproducts. 469 00:47:51,780.6736667 --> 00:47:54,660.6736667 When you extract copper, you have gold as byproduct. 470 00:47:55,630.6726667 --> 00:48:00,370.6726667 When you extract nickel, you have copper and, and cobalt as byproduct. 471 00:48:01,130.6726667 --> 00:48:05,330.6726667 So you are generating multiple products, multiple commodities in a single mine. 472 00:48:06,550.6746667 --> 00:48:08,330.6716667 So it's little bit different. 473 00:48:08,630.6726667 --> 00:48:10,30.6726667 You need to, to learn. 474 00:48:10,450.6716667 --> 00:48:16,40.6726667 So the project management system needs to be adapted for, for the, the product group. 475 00:48:17,100.6736667 --> 00:48:20,860.6736667 And this is, was also an opportunity to simplify. 476 00:48:21,750.6716667 --> 00:48:23,850.6716667 So we had a team doing that as well. 477 00:48:23,850.6736667 --> 00:48:28,480.6736667 So first got the project management system in place. 478 00:48:28,870.6736667 --> 00:48:29,560.6736667 Simplify. 479 00:48:30,20.6716667 --> 00:48:34,798.1729167 We did the systems with Power BIs, SAPs. 480 00:48:34,798.1729167 --> 00:48:41,270.6746667 Then we were able to get, the physical data as well from the Primaveras and bring it to this Power BI. 481 00:48:42,690.6696667 --> 00:49:00,530.6686667 Uh, we had-- We got our risk management team to standardize the risk management process, and then we are able to also create a Power BI that can see all the risk and opportunities for the, for the, the portfolio projects. 482 00:49:02,230.6736667 --> 00:49:07,970.6736667 the next step was to create, uh, prioritization projects for the, for the future projects. 483 00:49:09,270.6746667 --> 00:49:10,440.6696667 So the level... 484 00:49:10,730.6696667 --> 00:49:15,670.6736667 When you have underground mines, like the nickel mines are two point five deep. 485 00:49:16,720.6716667 --> 00:49:19,670.6706667 One mine here, two hours from Toronto in Sudbury. 486 00:49:20,910.6746667 --> 00:49:25,450.6716667 So there is also this complexity of underground mines as well. 487 00:49:26,750.6706667 --> 00:49:31,930.6716667 Um, and this makes a lot of sustaining projects. 488 00:49:32,530.6766667 --> 00:49:36,730.6766667 So the sustaining projects and the growth budget was pretty much the same. 489 00:49:36,800.6676667 --> 00:49:40,300.6736667 I remember one billion dollars per year to be on this. 490 00:49:40,570.6726667 --> 00:49:43,760.6776667 In growth projects, you have like five to ten projects. 491 00:49:43,940.6676667 --> 00:49:45,750.6686667 In sustaining, you have two thousand. 492 00:49:46,60.6776667 --> 00:49:47,760.6686667 R&D, another two hundred. 493 00:49:49,634.6756667 --> 00:49:58,804.6756667 So we are able to create a process to capture new projects to, uh, they need to answer some questions and then create a prioritization process. 494 00:50:00,94.6756667 --> 00:50:10,734.6746667 This was one of the big things of the PMO, the prioritization process, 'cause we are the benchmarking the company at the time, and we implement it on the other products as well. 495 00:50:12,454.6756667 --> 00:50:13,614.6756667 And do you believe... 496 00:50:13,804.6756667 --> 00:50:16,204.6756667 I mean, it sounds like you did it by the book. 497 00:50:16,254.6746667 --> 00:50:25,764.6766667 You started with data as your foundational piece before really taking the practice and governance model forward. 498 00:50:25,774.6746667 --> 00:50:25,794.6736667 Mm-hmm. 499 00:50:26,534.6736667 --> 00:50:37,764.6726667 And with proper data and good storytelling, that sort of gave you the cover to then go in and build out the rest of the function sort of one at a time. 500 00:50:37,764.6726667 --> 00:50:50,792.6756667 Is that kind of what I'm hearing in terms of your- Yeah the approach? Because the data, the SAP was very mature, we are able to show incredible dashboards to our sponsors. 501 00:50:50,802.6756667 --> 00:50:55,162.6756667 So we are able to say, "Oh, look, this PMO has a lot of potentials. 502 00:50:56,152.6756667 --> 00:51:03,742.6756667 Can I have more people to help me?" So I kinda used what was mature to build something that... 503 00:51:04,272.6746667 --> 00:51:08,142.6756667 to get more sponsors, 'cause to build a PMO, you need sponsors. 504 00:51:08,142.6766667 --> 00:51:11,792.6766667 Otherwise, you have a very small team and you focus in a very small scope. 505 00:51:13,172.6766667 --> 00:51:17,512.6746667 So to be bigger, you need to, to show some work. 506 00:51:17,512.6746667 --> 00:51:22,612.6766667 So my recommendation is start with your comfort zone. 507 00:51:23,472.6746667 --> 00:51:30,422.6766667 Build something really nice that people will like, will see and say, "I would like this in my department as well. 508 00:51:30,422.6766667 --> 00:51:43,342.6746667 Can, can you help? Can you support with that?" So my recommendation is start with, with what is more mature, and then you go to the piece that needs more work. 509 00:51:44,622.6756667 --> 00:51:44,892.6756667 Yeah. 510 00:51:45,652.6746667 --> 00:51:54,72.6766667 So most mining orgs, I would say, I'll probably upset some people, and tell me if I'm wrong, but I think they're generally laggards with technology. 511 00:51:54,502.6756667 --> 00:52:10,342.6746667 Why do you think mining is behind on the technology adoption curve compared to, say, like healthcare or other major industries? I would say for operation, they are very mature. 512 00:52:10,362.6696667 --> 00:52:11,392.6706667 They are very ahead. 513 00:52:12,142.6766667 --> 00:52:18,702.6696667 Because when you increase the technology of the operation, you can save million of dollars. 514 00:52:19,772.6736667 --> 00:52:20,852.6756667 Just give an example. 515 00:52:21,932.6696667 --> 00:52:27,882.6706667 The iron ore, let's, let's round it to 400 million tons. 516 00:52:28,622.6716667 --> 00:52:36,672.6726667 So if you reduce $1 on the whole process, on the whole cost, you are increasing $400 million on the EBITDA. 517 00:52:37,352.6756667 --> 00:52:37,732.6756667 Wow. 518 00:52:37,852.6756667 --> 00:52:47,192.6716667 So we have, in iron ore, a lot of technology products, autonomous AI. 519 00:52:47,192.6716667 --> 00:52:51,652.6656667 I would say that the operation piece of the mining components are very mature. 520 00:52:52,562.6666667 --> 00:52:54,872.6756667 They are using AI since 2023. 521 00:52:54,902.6786667 --> 00:52:58,832.6676667 They are having an AI team trying to find solutions. 522 00:52:59,502.6696667 --> 00:53:03,672.6756667 There are some mines that use autonomous, uh, trucks. 523 00:53:04,782.6756667 --> 00:53:08,872.6646667 Uh, and those autonomous trucks, you can gather a lot of data from the sensors. 524 00:53:08,952.6656667 --> 00:53:19,32.6636667 And with this, you can use an AI to give insight and increase productivity, decrease the diesel if the truck is diesel, if the truck... 525 00:53:19,462.6756667 --> 00:53:22,222.6736667 But now we have some electric trucks as well. 526 00:53:22,222.6736667 --> 00:53:24,912.6676667 I think the future of the mining will be electric. 527 00:53:26,402.6786667 --> 00:53:31,522.6786667 Another one, I would say the Brooklyns project that I've been. 528 00:53:31,992.6786667 --> 00:53:33,512.6786667 I would like to share my screen again. 529 00:53:33,532.6786667 --> 00:53:34,462.6786667 Mm-hmm. 530 00:53:35,12.6776667 --> 00:53:37,332.6786667 Cause I think this is amazing. 531 00:53:38,932.6786667 --> 00:53:42,202.6776667 So that's 11D. 532 00:53:42,942.6776667 --> 00:53:54,922.6786667 It opens- I think I need to share my whole screen 'cause there's multiple Green chill. 533 00:53:55,32.6786667 --> 00:53:55,352.6786667 Okay. 534 00:53:56,172.6786667 --> 00:54:02,434.6786667 So this project Is truckless. 535 00:54:04,354.6786667 --> 00:54:19,104.6786667 Okay? What truckless means? Means every time the mine, uh, develop, the conveyor belts walk together. 536 00:54:20,564.6786667 --> 00:54:21,24.6786667 Wow. 537 00:54:21,184.6776667 --> 00:54:26,904.6786667 And this is 100 million tons per year capacity, completely truckless. 538 00:54:27,94.6766667 --> 00:54:29,14.6786667 So no diesel, no CO2. 539 00:54:30,324.6786667 --> 00:54:36,664.6786667 Um, and this is amazing, like you can see the conveyor belts. 540 00:54:37,684.6776667 --> 00:54:43,154.6766667 Here on this picture you see the conveyor belts, going to the processing plant. 541 00:54:45,54.6776667 --> 00:54:56,914.6786667 And this was the project that I worked on North from Brazil Uh, so look, this is the conveyor belts coming from the mine to the plant Wow. 542 00:54:56,924.6786667 --> 00:55:10,514.6786667 So this is an example of how technology influence the operation, and I think the operation is maybe five years ahead of the corporate, let's call us corporate. 543 00:55:11,284.6786667 --> 00:55:16,854.6796667 'Cause they have the dollar return very quick if they invest in technology on the operations. 544 00:55:17,434.6796667 --> 00:55:17,634.6796667 Mm-hmm. 545 00:55:17,634.6796667 --> 00:55:18,404.6786667 That's the big difference. 546 00:55:19,444.6796667 --> 00:55:27,584.6796667 From the project side, as we use a lot of EPCM companies, we kinda rely of their technology. 547 00:55:28,154.6786667 --> 00:55:31,834.6786667 That's one of the, major aspect of your question. 548 00:55:32,844.6756667 --> 00:55:44,584.6766667 The second aspect is the security, like the company need to be 100% sure, like if you implement a new AI, new... 549 00:55:44,624.6746667 --> 00:55:48,604.6806667 I don't know why the big companies doesn't have cloud yet. 550 00:55:49,104.6786667 --> 00:55:56,444.6786667 'Cause they have a six, one month year process just to make sure it's safe, data will not leak. 551 00:55:57,694.6826667 --> 00:56:01,804.6776667 So I would say the EPCM companies is one thing. 552 00:56:01,814.6776667 --> 00:56:05,744.6816667 The second is the security, the data. 553 00:56:06,964.6786667 --> 00:56:15,954.6756667 Um, but I think the companies are maybe one year behind because of the steps, but I think it's catching up. 554 00:56:17,534.6786667 --> 00:56:24,724.6766667 But when I talk with my colleagues from the operation side, they had an AI team with 10 people since 2023. 555 00:56:24,724.6766667 --> 00:56:25,294.6806667 Yeah. 556 00:56:25,294.6806667 --> 00:56:28,4.6816667 So it's completely different from corporate. 557 00:56:28,84.6816667 --> 00:56:28,104.6816667 Wow. 558 00:56:28,204.6766667 --> 00:56:28,674.6766667 Yes. 559 00:56:29,304.6756667 --> 00:56:32,804.6756667 'Cause they are more focused- But how do you- Yeah on, on increase the operation. 560 00:56:33,944.6776667 --> 00:56:39,684.6746667 So basically, the areas where there's the highest ROI investing technology, that's where you're seeing it. 561 00:56:40,154.6766667 --> 00:56:52,264.6756667 But where do you see sort of the business intelligence layer going in the mining sector? You know, we have, like you said, Power BI is an amazing tool, and now AI is kind of entering into the space. 562 00:56:52,274.6716667 --> 00:57:05,954.6786667 What are you seeing over the next few years? Is there a transformational wave coming or business as usual maybe, just optimizing? Yeah, I think the AI is changing completely the way you work. 563 00:57:07,714.6666667 --> 00:57:20,404.6726667 We, I, I am doing AI immersions every month, and sometimes what you are studying January, it's outdated in March. 564 00:57:20,714.6666667 --> 00:57:24,954.6746667 Like things are changing so quick that you need to keep doing, keep studying. 565 00:57:25,704.6716667 --> 00:57:28,114.6806667 So now it's part of my routine to study AI. 566 00:57:30,154.6686667 --> 00:57:38,494.6716667 I would say I'm trying to be an, a subject matter expert for AI, my company. 567 00:57:39,754.6746667 --> 00:57:52,894.6766667 Uh, I would say I'm putting a lot of hours this, and I'm very confident- I'm organizing some AI training sessions with the team, so I, I will be teaching them the fundamentals. 568 00:57:53,824.6766667 --> 00:58:06,264.6756667 'Cause today we see a lot of people doesn't understand how a token works, uh, doesn't understand the difference of ChatGPT, SOL, Terra, and, Luna. 569 00:58:07,174.6766667 --> 00:58:12,854.6766667 Same for the three versions of Cloud, Fable, Summit, and Op- Op- Opus. 570 00:58:14,874.6756667 --> 00:58:15,224.6756667 Opus, yeah. 571 00:58:15,614.6756667 --> 00:58:20,264.6766667 so I've been studying not just the... 572 00:58:21,524.6756667 --> 00:58:24,464.6796667 I also built a full team of agents. 573 00:58:25,554.6786667 --> 00:58:29,964.6776667 Uh, remember that we were talking about the, the project management systems. 574 00:58:30,574.6786667 --> 00:58:30,904.6776667 Mm-hmm. 575 00:58:30,904.6776667 --> 00:58:43,764.6786667 So what if you have an agent for risk, another agent for, uh, social and communities, another for health and safety? So this is something that I've been building at this moment. 576 00:58:44,564.6766667 --> 00:58:45,674.6796667 Not fully implemented. 577 00:58:45,684.6796667 --> 00:58:50,354.6786667 It's more about me training, and deciding the next steps. 578 00:58:51,464.6816667 --> 00:58:54,884.6766667 The companies are creating a lot of AI positions. 579 00:58:54,914.6786667 --> 00:59:01,994.6796667 If you filter on LinkedIn you see that this is completely changing the market, I think. 580 00:59:03,134.6796667 --> 00:59:10,694.6786667 And the good thing is my background in Power BI is really useful right now because I have the data background. 581 00:59:11,664.6786667 --> 00:59:15,994.6776667 I learned, uh, metadata architecture, which is really useful. 582 00:59:17,224.6756667 --> 00:59:21,534.6796667 Uh, and I know all the data foundation. 583 00:59:21,554.6776667 --> 00:59:29,24.6776667 Like y- if you don't understand, if you are really good in AI and you are not good with data foundation, doesn't mean anything. 584 00:59:29,324.6776667 --> 00:59:30,584.6786667 You will not deliver anything. 585 00:59:32,806.6806667 --> 00:59:40,106.6806667 Yeah, the CEO of Microsoft, I think last year, said that your AI strategy and your data strategy are the same strategy. 586 00:59:40,566.6806667 --> 00:59:43,846.6806667 Like, they can't really be separated, and I, I believe that. 587 00:59:43,846.6806667 --> 00:59:50,126.6806667 I think some people, if you throw AI- Yeah at bad data, it will give you beautiful reporting. 588 00:59:50,616.6796667 --> 00:59:57,556.6806667 It's almost worse because bad data with maybe Power BI- Yeah or Excel, it kind of looks bad. 589 00:59:57,566.6806667 --> 00:59:57,646.6796667 Mm-hmm. 590 00:59:57,646.6806667 --> 01:00:02,546.6786667 You can tell it's wrong, whereas AI will smooth it out and make it look like it's correct. 591 01:00:02,586.6796667 --> 01:00:02,966.6796667 Yeah. 592 01:00:03,346.6806667 --> 01:00:07,26.6806667 It's even more important to have your data fundamentals, dialed in, I think. 593 01:00:08,246.6796667 --> 01:00:24,936.6766667 That's what I'm gonna try to do the next months, to build a data fundamentals training for the teams here so I can make them to think more about to treat all of their tools the way they need to be treated. 594 01:00:26,386.6796667 --> 01:00:30,506.6796667 You cannot think that the tool is a problem for you. 595 01:00:30,506.6796667 --> 01:00:40,866.6756667 You need to input the, the best data that you have using, following the governance standard, and this is gonna be key. 596 01:00:41,156.6796667 --> 01:00:49,586.6776667 And this is what, what we learned with Power BI, right? If the data was terrible, you see all the graphs and charts will not make sense. 597 01:00:51,16.6806667 --> 01:00:58,36.6796667 So this is something that was part of our routine since 2017 to look at all the reports. 598 01:00:58,46.6796667 --> 01:00:59,606.6786667 So this number doesn't make sense. 599 01:00:59,656.6796667 --> 01:01:00,936.6776667 Let's come back to the data. 600 01:01:01,516.6806667 --> 01:01:06,356.6766667 So you are learning and learning how to improve the data foundation. 601 01:01:06,736.6746667 --> 01:01:12,16.6776667 And as I had good, uh, mentors, especially on the SAP side. 602 01:01:12,466.6776667 --> 01:01:28,366.6736667 I had an amazing mentor from Brazil that taught everything about SAP, and then I had to replicate this to copper nickel and business, so I was teaching then data foundation at the time. 603 01:01:29,486.6746667 --> 01:01:33,476.6776667 This is gonna be key for this AI revolution, in my opinion. 604 01:01:35,106.6766667 --> 01:01:35,426.6766667 Yeah. 605 01:01:35,426.6766667 --> 01:01:46,176.6806667 So you, you do, you teach about BI at Northwestern University in their MBA program, and now you're talking about rolling out maybe a corporate training plan around data. 606 01:01:47,16.6726667 --> 01:01:58,196.6756667 What's your strategy there? Because my experience in the project space is that maybe data, the data science, the level of data sophistication varies a fair bit. 607 01:01:58,336.6806667 --> 01:02:04,566.6756667 Some people don't have any fundamentals, and other people might be a little bit further ahead. 608 01:02:04,576.6706667 --> 01:02:10,166.6796667 But maybe the average is n- not nearly where you're at, I would say. 609 01:02:10,216.6796667 --> 01:02:34,748.6836667 So how, what, what's your approach there, and how, how much do people need to level up their understanding of data, do you think, um, to kind of meet this new AI world? Uh, what I'm doing right now, first focus on how to use AI token, how the AI works, how you train an AI, the tokens. 610 01:02:35,768.6836667 --> 01:02:39,318.6836667 As my company, we have an internal AI, the data doesn't leak. 611 01:02:39,968.6836667 --> 01:02:42,608.6836667 But you need to understand the limits. 612 01:02:43,518.6826667 --> 01:02:49,188.6826667 What kind of data we need to, uh, anonymate to use the AI. 613 01:02:49,738.6836667 --> 01:02:52,158.6826667 So it start with fundamentals, explaining. 614 01:02:53,638.6836667 --> 01:02:56,398.6826667 Let's say, let's use ChatGPT as an example. 615 01:02:57,328.6836667 --> 01:03:03,808.6836667 So you have the ChatGPT Sol, which is similar Fable, which is, let's say, a Ferrari. 616 01:03:05,108.6826667 --> 01:03:09,568.6836667 And you see a lot of people using a Ferrari to go to the groceries. 617 01:03:09,778.6846667 --> 01:03:11,188.6826667 No, you are using the wrong AI. 618 01:03:12,148.6836667 --> 01:03:19,18.6866667 You can use a Sonnet or a ChatGPT Luna just to go to the groceries. 619 01:03:19,428.6826667 --> 01:03:21,158.6836667 You don't need to use a Ferrari. 620 01:03:22,358.6836667 --> 01:03:34,408.6846667 So it starts with the basic stuff, then people start to see you as an AI SME in the company, and they start to do more questions, get more involved. 621 01:03:35,208.6816667 --> 01:03:37,418.6856667 There are people that are really afraid of AI. 622 01:03:39,18.6866667 --> 01:03:43,158.6846667 I see people think when I open the, the ChatGPT Codex, I use the VS code. 623 01:03:43,158.6866667 --> 01:03:48,548.6856667 They say, "I, I don't wanna learn any coding. 624 01:03:49,978.6856667 --> 01:03:53,448.6826667 I, I just want to go to this level of AI knowledge." 625 01:03:54,428.6826667 --> 01:04:01,408.6816667 So I try to, to teach them just until the MDs. 626 01:04:01,408.6816667 --> 01:04:05,88.6856667 I think the MDs, the markdown are very important today. 627 01:04:05,948.6796667 --> 01:04:15,428.6826667 So I explain them the fundamentals, the chatbot, the different of web, desktop, then go to cowork Codex. 628 01:04:16,358.6836667 --> 01:04:26,438.6846667 And then I teach them how to build any skill to create an MDs, because I'm gonna need their MDs to build my agents, to build by... 629 01:04:26,548.6856667 --> 01:04:32,678.6796667 So I'm usually teaching everyone, I would say level one to level five. 630 01:04:33,618.6776667 --> 01:04:34,68.6776667 Yes. 631 01:04:34,178.6756667 --> 01:04:39,138.6876667 And the level six skills, level seven, build a team of agents. 632 01:04:39,528.6816667 --> 01:04:42,18.6836667 Level eight, I don't know, cowork Codex. 633 01:04:42,748.6836667 --> 01:04:52,968.6836667 Say the l- las- last, last level for AI for me is the VS code when we start to getting skills from GitHub. 634 01:04:52,998.6826667 --> 01:04:57,478.6786667 So this part I keep to myself, because they don't wanna learn. 635 01:04:57,488.6726667 --> 01:04:59,708.6816667 They are happy with what I'm, I'm teaching them. 636 01:05:01,168.6816667 --> 01:05:02,338.6816667 But I think this is good. 637 01:05:02,338.6816667 --> 01:05:04,698.6816667 Uh, this is where I want for my career. 638 01:05:05,708.6816667 --> 01:05:16,868.6816667 I want to support everyone with the, until the MD, the markdowns level, and the rest I would like to do by myself. 639 01:05:16,898.6816667 --> 01:05:17,58.6816667 Yeah. 640 01:05:18,348.6816667 --> 01:05:36,916.6816667 Like, what is an AI use case that is genuinely saving you time today? So today to build a new Power BI, I usually If I am on my personal computer, I connect with cloud. 641 01:05:38,36.6806667 --> 01:05:43,546.6836667 I just do it for my personal Power BIs and I don't I do a full analysis. 642 01:05:44,646.6826667 --> 01:05:56,36.6816667 Uh, I talk with the cloud, like dimensions, the factors, uh, how I can optimize the dashboard, what I've been missing. 643 01:05:56,36.6826667 --> 01:06:01,986.6826667 I also ask him to suggest more charts and analysis that I've been missing. 644 01:06:03,66.6816667 --> 01:06:11,366.6816667 Today we can add HTML charts in Power BI, so we can ask the AI to support with that. 645 01:06:12,226.6806667 --> 01:06:15,786.6826667 Of course, I do a brainstorming a lot with AI. 646 01:06:17,76.6826667 --> 01:06:21,46.6806667 So I, I have someone to discuss with, like we are doing here. 647 01:06:21,216.6816667 --> 01:06:31,786.6806667 I do my own podcast with my AI just to discuss about the project and see what I'm missing, uh, what I think about my storytelling. 648 01:06:32,856.6816667 --> 01:06:34,106.6826667 So those kind of things. 649 01:06:34,436.6836667 --> 01:06:37,566.6796667 The storytelling for dashboards products are easy. 650 01:06:37,566.6806667 --> 01:06:43,946.6806667 We start on the portfolio level until we go to the final project level, discipline level. 651 01:06:43,946.6806667 --> 01:06:49,166.6786667 Uh, so I use a lot for Power BIs. 652 01:06:49,946.6786667 --> 01:06:54,156.6736667 AI is also very helpful for my presentations. 653 01:06:55,286.6746667 --> 01:06:59,806.6776667 Uh, on the corporate side there are little bit of restrictions. 654 01:06:59,806.6796667 --> 01:07:01,976.6736667 So we need to learn. 655 01:07:02,736.6796667 --> 01:07:09,156.6786667 Sometimes I need to create an ex- an Excel database with anonymous data. 656 01:07:09,896.6756667 --> 01:07:14,36.6776667 Instead of site, it's gonna be site 01. 657 01:07:14,36.6776667 --> 01:07:17,416.6746667 Instead of project name two, it's gonna be project 002. 658 01:07:19,386.6746667 --> 01:07:21,366.6736667 So this is useful as well. 659 01:07:22,466.6756667 --> 01:07:24,596.6786667 I use every day for brainstorming. 660 01:07:25,6.6766667 --> 01:07:28,576.6796667 I discuss with AI before starting a new task. 661 01:07:28,846.6726667 --> 01:07:29,36.6726667 Yes. 662 01:07:31,246.6816667 --> 01:07:33,696.6746667 So then, you know, that's a really great example. 663 01:07:33,696.6786667 --> 01:07:43,316.6786667 It sounds like you're using agents to sort of give you advice on how well your end product is going to match. 664 01:07:43,316.6786667 --> 01:07:44,106.6686667 Mm-hmm. 665 01:07:44,516.6706667 --> 01:08:24,712.6806667 Um, so you're kind of using it as a coach or a sounding board, and then also to just build some of these tools- Mm-hmm So this kind of ties into a question I have around sort of if you've got a PMO that has AI sort of increasingly running itself, like, what are people gonna be doing in five years? In this project space, do you think, in the PMOs? I think for the project control side we will have a kinda new way of managing projects compared to the old way. 666 01:08:26,32.6806667 --> 01:08:33,92.6806667 Uh, and the new way you have the integrated, intelligent and predictive. 667 01:08:34,232.6796667 --> 01:08:39,2.6806667 Today we have a lot of manual, disconnected and reactive. 668 01:08:40,592.6786667 --> 01:08:45,112.6816667 Uh, today we have a problem that data is in silos. 669 01:08:46,142.6816667 --> 01:08:52,522.6806667 Like you, you don't really know the information of a project South America, another project in Australia. 670 01:08:54,382.6786667 --> 01:08:56,342.6766667 You cannot do any benchmarking. 671 01:08:56,402.6796667 --> 01:09:02,402.6806667 You cannot say, "Can you help me?" Let's get it back to an example of the, the, the railroad. 672 01:09:03,522.6786667 --> 01:09:09,172.6766667 So now if I had everything loaded in AI, I can talk to the AI and do quicker. 673 01:09:09,192.6766667 --> 01:09:27,812.6806666 So which kilometer of my railroad, which miles are my benchmark and why they are different? What I can implement it from this benchmark from the others? So I think the AI's gonna change completely the way we, we structure our projects. 674 01:09:28,842.6806666 --> 01:09:33,742.6806666 We will have AI agents to support you. 675 01:09:34,832.6806666 --> 01:09:45,412.6796666 Uh, and then once you load the historical data, maybe you load competitor data as well, you'll be able to do internal benchmarks. 676 01:09:46,102.6826666 --> 01:09:49,542.6816666 I think for project estimate would be a big thing. 677 01:09:50,502.6806666 --> 01:10:34,246.6816666 Imagine you do your project estimating, get the data from all your lesson learned of your projects- Mm-hmm so this is gonna change completely how we estimate projects 'cause I would say 50% of our projects that doesn't go well, the schedule on the cost, it's the project estimating can be a big factor To be able to, through a medallion architecture, get raw data from different products using the mining as example from different iron ore, copper, uh, different regions, South America, North America, Australia. 678 01:10:35,36.6816666 --> 01:10:43,576.6816666 And then it will be able to have, uh, maybe the silver layer for our agents and the gold layer for your Power BI dashboards. 679 01:10:45,186.6816666 --> 01:11:03,106.6816666 And if you had a Power BI f- the whole portfolio in all disciplines, you have in a one-stop shop, and you also have the AI chat where you can discuss, you can create some benchmark templates for future projects. 680 01:11:04,146.6806666 --> 01:11:06,946.6796666 You can search by similarity. 681 01:11:07,46.6816666 --> 01:11:15,686.6816666 For instance, as I explained to you, the railroad is very similar, but in the mining, the projects are very different. 682 01:11:16,576.6806666 --> 01:11:26,596.6836666 So maybe they You have an AI agent to identify the similars, and then with the similars you can find the benchmarks. 683 01:11:28,106.6836666 --> 01:11:44,756.6836666 It seems like what I'm hearing is that over the last, you know, since Power BI came out, there was a push to combine your information, those that did it kind of y- have the story that you have, where you're like, "Look at this amazing stuff we've built." 684 01:11:45,36.6876666 --> 01:11:55,666.6936666 And then AI is now this new forcing function where it's, it's no longer sort of like optional cherry on top of a good organization is really well-integrated data. 685 01:11:55,666.6936666 --> 01:12:00,736.6896666 Now it is sort of that's moved into a state of, critical. 686 01:12:01,246.6896666 --> 01:12:03,656.6926666 Like that's go- that is the future. 687 01:12:04,96.6956666 --> 01:12:15,316.6876666 So data needs to get sort of combined properly, connected- Mm-hmm and then AI is going to be layered into that, which is kind of what I'm hearing you say. 688 01:12:15,606.6906666 --> 01:12:55,522.6906666 And sort of along those lines then, do you think that the PMO teams are gonna get smaller and just be really m- much sharper because AI and these tools are allowing them to be analysts, or do you think that the, the task of integrating this data is gonna make teams bigger and that they'll be more strategic in the future? Like looking at where the PMO sits five years and what the staffing kind of strategy will look like, what would you say? I would say the first step will need a lot of people to make sure, because companies have multiple data source. 689 01:12:55,872.6896666 --> 01:13:00,452.6906666 SAP, Carah, Procore, Confluent, Primavera. 690 01:13:02,12.6906666 --> 01:13:06,102.6896666 So make sure that the data foundation is there. 691 01:13:06,862.6906666 --> 01:13:08,932.6906666 It's gonna need a lot of people. 692 01:13:10,32.6906666 --> 01:13:14,862.6906666 Then build this AI, those medallion architecture. 693 01:13:15,542.6886666 --> 01:13:20,152.6916666 Understand the difference between regions, products, group. 694 01:13:21,382.6896666 --> 01:13:24,732.6906666 So you also need a, a big team for that. 695 01:13:25,722.6896666 --> 01:13:51,0.6906666 And once you put all the AI agents in place, uh, and maybe break the Power BI dashboard central tower, of course, you're gonna need people to make sure this is working, and you're gonna need more business intelligence people to add more dashboards as soon as they see the That makes sense, a new one. 696 01:13:52,590.6906666 --> 01:13:56,280.6896666 I think it will, will be, will decrease in the long term. 697 01:13:57,120.6906666 --> 01:14:09,100.6896666 But I think the short term and the midterm will probably keep the same or increase until the companies have this AI maturity in place. 698 01:14:11,30.6896666 --> 01:14:12,300.6886666 Yeah, I tend to agree with you. 699 01:14:12,300.6896666 --> 01:14:39,0.6896666 I think that, like, maybe, uh, I would be a, a little more aggressive thinking there's probably gonna be the need for a fairly substantial CapEx investment in, for some orgs to get their data unified- Mm-hmm in the PMO space, and then that might be over the next five years, and, and the implementation of AI, and then the sort of ROI will probably come, well, fairly- Mm-hmm quickly from that. 700 01:14:39,60.6886666 --> 01:14:43,990.6906666 But I would suspect the PMOs will grow or should grow. 701 01:14:43,990.6906666 --> 01:14:48,730.6926666 Like, if I was running a PMO, I would be trying to hire more people to sort out this data problem. 702 01:14:48,730.6926666 --> 01:14:55,410.6866666 I would try and not put it on my schedulers and my costies as, like, the next to-do that they have to do. 703 01:14:56,420.6886666 --> 01:14:57,30.6906666 I don't know if that would work. 704 01:14:57,30.6906666 --> 01:14:57,580.6896666 Yes. 705 01:14:58,250.6906666 --> 01:15:01,330.6926666 What do you think? I agree with you. 706 01:15:01,330.6926666 --> 01:15:08,50.6856666 I believe in the short term, the companies need to prioritize this AI revolution. 707 01:15:09,410.6866666 --> 01:15:14,750.6856666 They need to achieve this new way of managing projects. 708 01:15:16,100.6886666 --> 01:15:22,740.6866666 I think the companies has, can save million dollars in, when they put this in place. 709 01:15:23,990.6916666 --> 01:15:30,940.6916666 So in the short term, uh, I, I would be more aggressive and hire more people and make sure you have a strong data foundation. 710 01:15:31,710.6916666 --> 01:15:49,40.6856666 You build your AI meta architecture, AI refinery, your AI agents, then your control tower with the reporting and dashboards, and then you scale this to the whole company, all regions. 711 01:15:50,240.6916666 --> 01:16:05,410.6926666 So I would say the company should, uh, increase the PMO team to put this in place, and maybe once everything is in place, maybe I would say one to two years for imple- to implement that. 712 01:16:07,50.6896666 --> 01:16:09,800.6876666 Maybe they can start thinking about reducing the, the team. 713 01:16:10,430.6946666 --> 01:16:13,320.6876666 But at the same time there will be a risk if you reduce the team. 714 01:16:13,330.6886666 --> 01:16:16,700.6936666 Maybe you're not gonna keep the data foundation the same level. 715 01:16:18,766.6906666 --> 01:16:23,836.6906666 So this brings up an interesting point I wanna talk about with you, um, 'cause you've said this multiple times. 716 01:16:23,886.6906666 --> 01:16:32,876.6896666 When you're building out sort of a success story, you talk about starting small, proving it out, and then scaling it to the whole company. 717 01:16:34,326.6896666 --> 01:16:50,306.6886666 I have seen large data warehouse, data integration projects maybe from big five consultancies where they go in maybe at the CEO level, CFO level and they say, "Throw 20, 40 million at us and we're gonna do it for the whole company." 718 01:16:50,306.6896666 --> 01:17:02,776.6886666 Sort of a top-down approach, right? So they sort of try to approach this data integration problem by looking at everything and then solving it all, and then kinda pushing it down. 719 01:17:02,786.6886666 --> 01:17:09,186.6906666 You're talking about doing the PMO right starts kind of at this really fundamentally small level. 720 01:17:09,456.6906666 --> 01:17:15,706.6896666 You prove it out that way, have a win, and then that win starts to spread in the organization, so it's a bottom up. 721 01:17:16,276.6906666 --> 01:17:16,296.6906666 Yeah. 722 01:17:16,526.6906666 --> 01:17:19,526.6846666 So in my experience, the bottom up is more successful. 723 01:17:19,946.6886666 --> 01:17:37,456.6886666 It's- Mm-hmm but what would you say? Is it top-down initiatives are fine if you can get the funding? I usually starts with the framework, and if I have a good sponsor, the full CPO, CTO, I... 724 01:17:37,876.6886666 --> 01:17:46,76.6826666 With a big sponsor, I will build an MVP with a sample, like, 'cause the company's too big to do everything at the same time. 725 01:17:47,346.6816666 --> 01:17:53,256.6856666 So maybe in smaller company it's possible to, to do a, like a top-down approach, everything at the same time. 726 01:17:54,436.6856666 --> 01:18:13,936.6856666 But in a company that are in all time zones, need to work with people 4:00 AM, 3:00 AM who work with Australia, I would go small, do an MVP in maybe one business, one region, and then extend when it's proven. 727 01:18:15,156.6866666 --> 01:18:23,176.6876666 Even with the top-down approach, think it's too hard to, to do everything at the same time Yeah. 728 01:18:24,366.6876666 --> 01:18:43,176.6866666 Well, so, you know, thinking about our conversation today, what do you think would be sort of a number one takeaway that somebody listening to this thinking about their PMOs would you would like them to think about? Yeah. 729 01:18:43,176.6866666 --> 01:18:53,496.6866666 So the, the first thing, all the PMOs that I built, I had a data foundation team, some business intelligence people that understand data. 730 01:18:53,576.6866666 --> 01:18:56,276.6866666 Data, data was really important before AI. 731 01:18:56,836.6846666 --> 01:18:59,36.6876666 Now it's even more important. 732 01:19:00,96.6876666 --> 01:19:02,986.6856666 So focus on these people. 733 01:19:03,496.6846666 --> 01:19:08,166.6846666 Don't have 100% of your data foundation on the IT team. 734 01:19:08,836.6846666 --> 01:19:11,806.6866666 You need to have someone with this background in your team. 735 01:19:12,566.6846666 --> 01:19:22,456.6856666 There are a lot of people with project management background, project control that also have the, the data management piece. 736 01:19:23,566.6856666 --> 01:19:25,926.6866666 So build a, a strong data foundation. 737 01:19:26,626.6856666 --> 01:19:34,46.6826666 Starts with the system that are more mature, and then go system by system until you have everything. 738 01:19:34,536.6826666 --> 01:19:48,726.6826666 I think the ultimate goal for the company is have a one-stop shop for everything, for the dashboards, for the agents, uh, a chatbot that can help you, uh, new projects benchmark. 739 01:19:49,376.6866666 --> 01:19:52,26.6866666 So the final goal, be the one-stop shop. 740 01:19:52,526.6806666 --> 01:19:59,606.6846666 And now with this AI, AI refinery, maybe it's would be easier to integrate all of your source. 741 01:19:59,616.6836666 --> 01:20:01,446.6806666 So we have a big opportunity now. 742 01:20:02,46.6796666 --> 01:20:09,492.6786666 I think the, the companies need to explore this opportunity And be more aggressive as it may go. 743 01:20:12,412.6786666 --> 01:20:13,132.6786666 Thank you for that. 744 01:20:13,142.6786666 --> 01:20:19,292.6776666 That's, uh, the data foundation I think is the number one thing that you have underscored. 745 01:20:20,42.6786666 --> 01:20:20,192.6786666 Mm. 746 01:20:20,272.6786666 --> 01:20:34,722.6776666 And it sort of future-proofs you for where AI is going, and it also sounds like- Yeah um, probably just makes you more operationally advantageous even in the here and now. 747 01:20:34,772.6776666 --> 01:20:41,82.6766666 Yeah bruno, I really appreciate you coming on and sharing the story of your career. 748 01:20:41,472.6776666 --> 01:20:47,502.6796666 It's an amazing arc, and some of the things that you've done, you're a very modest person, but they are incredible. 749 01:20:47,622.6796666 --> 01:20:56,142.6796666 And getting to understand how you did them, the approach, and your vision on the future I think was extremely valuable. 750 01:20:56,152.6816666 --> 01:21:01,892.6816666 So thank you for coming on today, and all the time that you've given to the podcast. 751 01:21:02,92.6816666 --> 01:21:02,362.6816666 Thank you very much. 752 01:21:02,362.6816666 --> 01:21:04,692.6796666 It was an honor to be part of your podcast.

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Your AI Strategy IS Your Data Strategy - Bruno Caldas on Building the Intelligent PMO

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.css-14f5ked{margin:0;word-break:break-word;display:-webkit-box;-webkit-box-orient:vertical;box-orient:vertical;-webkit-line-clamp:2;overflow:hidden;}Your AI Strategy IS Your Data Strategy - Bruno Caldas on Building the Intelligent PMO