WEBVTT - Smart Talks with IBM - Unlocking Data Strategy: Data Literacy for Better Business 0:00:04.400 --> 0:00:07.800 Welcome to Tech Stuff, a production from I Heart Radio. 0:00:11.840 --> 0:00:14.480 This season of Smart Talks with IBM is all about 0:00:14.600 --> 0:00:18.480 new creators, the developers, data scientists, c t o s, 0:00:18.560 --> 0:00:23.280 and other visionaries creatively applying technology in business to drive change. 0:00:23.800 --> 0:00:26.680 They use their knowledge and creativity to develop better ways 0:00:26.680 --> 0:00:30.280 of working, no matter the industry. Join hosts from your 0:00:30.320 --> 0:00:33.880 favorite Pushkin Industries podcasts as they use their expertise to 0:00:33.960 --> 0:00:37.480 deepen these conversations, and of course Malcolm Gladwell will guide 0:00:37.520 --> 0:00:39.839 you through the season as your host and provide his 0:00:39.920 --> 0:00:43.000 thoughts and analysis along the way. Look out for new 0:00:43.040 --> 0:00:45.480 episodes of Smart Talks with IBM on the I Heart 0:00:45.560 --> 0:00:49.159 Radio app, Apple Podcasts, or wherever you get your podcasts, 0:00:49.360 --> 0:00:55.760 and learn more at IBM dot com slash smart talks. Hello, Hello, 0:00:55.800 --> 0:00:59.840 Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, 0:01:00.160 --> 0:01:04.720 I Heart Radio and ib M. I'm Malcolm Glamo. This season, 0:01:04.880 --> 0:01:09.160 we're talking to new creators, the developers, data scientists, ct 0:01:09.319 --> 0:01:13.080 os and other visionaries who are creatively applying technology in 0:01:13.160 --> 0:01:17.400 business to drive change. Channeling their knowledge and expertise, they're 0:01:17.440 --> 0:01:21.880 developing more creative and effective solutions, no matter the industry. 0:01:22.640 --> 0:01:26.760 Our guest today is Nicholas Renaut, Senior Data science and 0:01:26.920 --> 0:01:32.119 AI technical specialist at IBM. Nicholas's job is to help 0:01:32.160 --> 0:01:36.240 companies formulate a data strategy that streamlines the way they 0:01:36.240 --> 0:01:41.200 do business and prepares them to use sophisticated AI technologies. 0:01:41.840 --> 0:01:44.120 But beyond his day to day, Nick is also a 0:01:44.160 --> 0:01:47.600 content creator on YouTube, where his channel has over a 0:01:47.680 --> 0:01:52.520 hundred thousand subscribers. His videos explain computer science concepts in 0:01:52.560 --> 0:01:56.240 a way beginners can understand, and he often demonstrates how 0:01:56.280 --> 0:02:01.040 to use machine learning and data science to solve novel problems. 0:02:01.840 --> 0:02:05.200 On today's show, How Nicholas Learned Data science from the 0:02:05.240 --> 0:02:09.440 bottom up, the fundamentals of data management, and how an 0:02:09.480 --> 0:02:15.840 innovative data strategy can help businesses create novels solutions, Nick 0:02:15.919 --> 0:02:20.639 spoke with Ronald Young Jr. Host of the Pushkin podcast Solvable. 0:02:21.200 --> 0:02:25.079 Along with being a frequent contributor to NPR, Ronald also 0:02:25.160 --> 0:02:29.400 hosts and produces the podcast Time Well Spent and Leaving 0:02:29.400 --> 0:02:36.200 the Theater. Okay, let's get to the interview. So tell 0:02:36.240 --> 0:02:38.960 me a little bit about how you got into data 0:02:39.000 --> 0:02:41.000 and when you found out like the power that it 0:02:41.040 --> 0:02:43.520 really harnesses. Do you have a story or anything that 0:02:43.639 --> 0:02:47.520 kind of like when you first pique your interest in data. 0:02:48.440 --> 0:02:51.960 My first interaction with data and with coding was actually 0:02:52.040 --> 0:02:56.359 when I was around about eleven years old, So this 0:02:56.520 --> 0:03:00.320 was really just getting started with just looking at spread sheets. 0:03:00.360 --> 0:03:03.880 So my dad would come home and after working a 0:03:04.080 --> 0:03:08.680 nine or five job, he actually started working with investing 0:03:08.840 --> 0:03:13.040 in stocks and doing value based trading that way. I'll 0:03:13.040 --> 0:03:15.520 always remember I walked up to his desk one time 0:03:15.880 --> 0:03:17.720 and he said, Nick, if there's one thing that you 0:03:17.760 --> 0:03:21.399 should learn, I'm seeing all these people work on these 0:03:21.440 --> 0:03:27.800 things called macros in spreadsheets, and these people like wizards 0:03:27.840 --> 0:03:31.840 inside of my business. I know that you're still you're 0:03:31.880 --> 0:03:33.720 still in high school, but I really think you should 0:03:33.760 --> 0:03:36.880 learn this stuff. And I started doubling in some Excel 0:03:36.960 --> 0:03:41.200 spreadsheets and started just recording macros and tweaking stuff, and 0:03:41.240 --> 0:03:43.520 that that's where it all started. But from there, it's 0:03:43.720 --> 0:03:47.240 it's always been a recurring vein throughout my career that 0:03:47.280 --> 0:03:51.680 I've done some sort of wizardry with data, whether it 0:03:51.760 --> 0:03:55.080 be coding or business intelligence or data views. It's it's 0:03:55.160 --> 0:03:58.880 always had a bit of a strain throughout throughout whatever 0:03:58.920 --> 0:04:02.240 I've done, whether based start ups so YouTube or what 0:04:02.320 --> 0:04:05.160 I'm doing now at IBM. Your dad was right. Let 0:04:05.160 --> 0:04:07.600 me just say that, because that's someone who's trying to 0:04:07.600 --> 0:04:10.520 put together a spreadsheet just to manage my personal finances. 0:04:10.960 --> 0:04:13.720 Trying to look up the formula to actually bring a 0:04:13.800 --> 0:04:17.640 value from what what uhet to another is enough of 0:04:17.640 --> 0:04:20.480 a struggle for me. So I'm glad to do. Really 0:04:20.600 --> 0:04:24.799 is it's like it's absolutely is uh so like knowing 0:04:24.839 --> 0:04:27.120 that you know, this was how you started getting into spreadsheets. 0:04:27.360 --> 0:04:29.920 You know you're looking at stocks and all of that. Um, 0:04:30.040 --> 0:04:32.960 can you talk to me about how you found out 0:04:33.160 --> 0:04:37.120 the importance of data literacy, how you begin to value 0:04:37.520 --> 0:04:41.400 understanding what the numbers meant and what power that could have. 0:04:42.440 --> 0:04:46.160 I got a cadet ship at one of the big 0:04:46.160 --> 0:04:49.480 four accounting firms and started out as an orditor there, 0:04:49.520 --> 0:04:54.040 which is pretty much data focus. So I saw that 0:04:54.880 --> 0:05:00.240 these numbers ultimately fed into a significantly big at PA Chill, 0:05:00.360 --> 0:05:04.960 which was a formal annual report, and numbers being wrong 0:05:05.040 --> 0:05:08.920 in an annual report can move markets, right, those numbers 0:05:08.920 --> 0:05:12.159 need to be absolutely bang on. But I think that 0:05:12.279 --> 0:05:14.920 is sort of where it started. Where it really culminated 0:05:15.240 --> 0:05:18.680 was when I started doing some work at the Reserve 0:05:18.720 --> 0:05:23.400 Bank of Australia. And those numbers don't just impact the 0:05:24.440 --> 0:05:29.640 metrics for a particular organization, they impact the entire countries metrics. 0:05:29.680 --> 0:05:32.560 Getting those numbers wrong on a particular chart, or getting 0:05:32.560 --> 0:05:36.360 them right on a particular chart can move entire organizations, 0:05:36.440 --> 0:05:39.560 or can shift an entire country. It's kind of crazy 0:05:39.680 --> 0:05:45.200 what the value that doing things correctly with data has. 0:05:45.320 --> 0:05:48.640 So when you're presenting a metric, you have to ensure 0:05:48.760 --> 0:05:51.640 that you are portraying the appropriate message. It's not just 0:05:51.720 --> 0:05:56.200 about the wrong number, because correlation does not necessarily imply causation. 0:05:56.279 --> 0:05:59.520 So understanding what it is that you're saying is so 0:05:59.520 --> 0:06:03.360 so important, and it is so much more powerful now 0:06:03.360 --> 0:06:06.280 that we've got so much more data available at our fingertips. 0:06:06.279 --> 0:06:07.960 It's really easy to go and grab a bunch of 0:06:07.960 --> 0:06:10.080 metrics and go, hey, I'm going to grab this data 0:06:10.120 --> 0:06:11.880 from over here, and grab that data from over here 0:06:11.920 --> 0:06:14.640 for a measured together. Hey, look, these two lines follow 0:06:14.680 --> 0:06:16.800 the same trend. They must be related. Do you find 0:06:16.839 --> 0:06:20.480 yourself ever looking at data points and say those this, 0:06:20.920 --> 0:06:22.720 How do I don't understand this chart? Why did they 0:06:22.720 --> 0:06:24.560 where did they pull this from? Do you find yourself 0:06:24.560 --> 0:06:27.160 doing that a lot of your regular life. Oh yeah, 0:06:27.200 --> 0:06:29.680 that There's there's some great charts out there as well 0:06:29.760 --> 0:06:31.880 that you always see, and they they plot like the 0:06:31.960 --> 0:06:36.040 number of Nicolas Cage movies against the g d P 0:06:36.520 --> 0:06:39.760 of Bolivia or something, and it's like, well, they're going 0:06:39.800 --> 0:06:42.720 in the same direction. They must have some relationship. But 0:06:43.320 --> 0:06:45.680 people can really quickly look at a picture and go 0:06:46.200 --> 0:06:48.640 and make an assumption about what that is saying without 0:06:48.680 --> 0:06:51.880 actually interpreting. Hey, are these on the same scales? Are 0:06:51.880 --> 0:06:54.560 they what time period is being displayed? What am I 0:06:54.600 --> 0:06:58.120 actually looking at here? And I find myself doing this 0:06:58.160 --> 0:06:59.920 more and more often when I just see a child 0:07:00.080 --> 0:07:02.799 them like, hold on, let's just not make any assumption. 0:07:03.040 --> 0:07:05.279 What is this chart actually trying to say? What is 0:07:05.320 --> 0:07:09.000 it actually trying to portray? Because you can lie with 0:07:09.080 --> 0:07:12.720 statistics if you know what you're doing. It is they're 0:07:12.760 --> 0:07:16.720 so powerful and people can gloss over them so quickly. 0:07:16.720 --> 0:07:19.600 We've got attention spans that are so much shorter these 0:07:19.680 --> 0:07:24.720 days that it can be very very easy to take 0:07:24.720 --> 0:07:29.400 away the wrong message. So you also produce content across 0:07:29.640 --> 0:07:33.960 various platforms, including YouTube and your personal blog. UH as 0:07:34.000 --> 0:07:36.200 a content creator, how did you get started in that 0:07:36.240 --> 0:07:38.920 field and what type of content are you creating? Yeah, 0:07:38.960 --> 0:07:43.240 that's a crazy story, right. So I always wanted to 0:07:43.280 --> 0:07:45.680 get into tech and said, hey, I'd really really like 0:07:46.320 --> 0:07:49.480 to work for IBM. I saw what they were doing 0:07:49.480 --> 0:07:52.760 with Watson, and I'm like, why are people talking about 0:07:52.800 --> 0:07:56.640 this more? And I had no affiliation with with IBM 0:07:56.680 --> 0:07:58.840 at the time, and I'm like, well, this is so cool. 0:07:58.880 --> 0:08:01.600 There used to be this thing or or this service 0:08:01.640 --> 0:08:06.160 available on the cloud platform called Personality Insights, and you 0:08:06.200 --> 0:08:09.040 could plug in a little bit of text and from 0:08:09.080 --> 0:08:12.920 that piece of text, it would analyze that particular person's 0:08:12.960 --> 0:08:16.080 personality based on the Big five personality traits. And there 0:08:16.080 --> 0:08:18.880 actually used to be this demo app where you could 0:08:18.960 --> 0:08:20.880 cook it up to a Twitter account, so I could 0:08:21.080 --> 0:08:25.960 pass through Oprah's Twitter account or Lebron's Twitter account and 0:08:25.960 --> 0:08:29.960 it would actually analyze their profiles. And this is so cool. 0:08:30.880 --> 0:08:34.360 It was nuts, and I was like and a lot 0:08:34.360 --> 0:08:36.400 of people don't know how to use this. So that 0:08:36.440 --> 0:08:38.720 was quite possibly one of the first true toils that 0:08:38.760 --> 0:08:44.560 I made on YouTube, and actually used a bunch of 0:08:44.640 --> 0:08:48.920 videos that I made following after that too. Finally land 0:08:48.920 --> 0:08:51.360 a job at IBM. I actually spammed a bunch of 0:08:51.400 --> 0:08:54.040 links in my resume and my coverle that I was like, hey, 0:08:54.080 --> 0:08:56.880 I'm already working with this stuff and I could do it. 0:08:57.240 --> 0:09:00.959 And the person that hired me, she actually said that 0:09:00.960 --> 0:09:05.640 that was like such an amazing way to portray what 0:09:05.640 --> 0:09:08.760 what you love about what you do, that that that 0:09:08.840 --> 0:09:11.679 had such an influencing factor in actually getting the job. 0:09:11.720 --> 0:09:14.400 But yeah, I did it because one the tech was 0:09:14.600 --> 0:09:16.840 so cool and I thought it was so interesting and 0:09:16.880 --> 0:09:22.040 so powerful, and yeah, eventually that helped me land that job. 0:09:22.640 --> 0:09:25.240 So you do a lot of tutorials where you're you're 0:09:25.280 --> 0:09:29.440 breaking down complex topics to kind of a wider audience. 0:09:30.040 --> 0:09:33.240 Why is that important for you to do? Yeah? I 0:09:33.280 --> 0:09:37.080 think one of the amazing things about knowledge is it's 0:09:37.160 --> 0:09:38.680 one of the things that you can give away and 0:09:38.720 --> 0:09:44.079 never lose, right, And I think one of the trickiest 0:09:44.120 --> 0:09:48.120 things about the whole data science and machine learning field 0:09:48.280 --> 0:09:52.400 is that it can be pretty tricky to get started, 0:09:52.559 --> 0:09:57.960 and sometimes we get hung up with learning from the 0:09:58.000 --> 0:10:02.599 bottom up right, And there's nothing wrong with learning fundamentals 0:10:02.640 --> 0:10:06.200 and learning foundations and really getting stuck in. But in 0:10:06.320 --> 0:10:09.119 order to stick with something, you have to find it interesting. 0:10:09.240 --> 0:10:11.679 So if you can see the end result and then 0:10:11.760 --> 0:10:14.400 work your way back up and work out how that's worked. 0:10:15.080 --> 0:10:17.439 Then it is so much more attractive because you get 0:10:17.480 --> 0:10:20.040 that instant gratification and go, hey, I've just built this 0:10:20.120 --> 0:10:23.920 machine learning app that is able to decode sign language. 0:10:23.960 --> 0:10:25.720 It's so cool. Now I'm going to go and work 0:10:25.720 --> 0:10:28.080 out the tech behind it. Admittedly, not everyone goes and 0:10:28.120 --> 0:10:30.920 works out the tech behind it, but what I'm trying 0:10:30.920 --> 0:10:33.880 to do is make it so that more people can 0:10:33.880 --> 0:10:36.680 get involved and get started with it. Lately, I've been 0:10:36.720 --> 0:10:40.640 doing these things called code that challenges, and they're kind 0:10:40.640 --> 0:10:44.160 of crazy, right, but I love doing them. So I 0:10:44.240 --> 0:10:48.359 have to build entire machine learning or data science applications 0:10:48.800 --> 0:10:54.360 without looking at any reference code, stack overflow, or looking 0:10:54.360 --> 0:10:59.600 at any documentation within fifteen minutes. So it is literally 0:10:59.640 --> 0:11:02.199 just a trial by fire. I'll have my phone, I'll 0:11:02.200 --> 0:11:04.440 set a time, and I'm like, all right, guys, we're on. 0:11:04.640 --> 0:11:08.520 Like the edit is literally just cording NonStop and me 0:11:08.640 --> 0:11:10.600 explaining on the go. But it allows people to see 0:11:10.840 --> 0:11:13.839 and explain my thought process as I'm developing it. Um. 0:11:13.880 --> 0:11:17.800 That's obviously super fun, right because it's highly engaging and 0:11:17.800 --> 0:11:20.120 it shows people that, hey, you can get cited in 0:11:20.200 --> 0:11:24.720 this relatively quickly. Nicholas is the kind of person whose 0:11:24.720 --> 0:11:28.080 passion for data science is so great it spills over 0:11:28.200 --> 0:11:31.959 from his professional life onto his YouTube channel. But when 0:11:32.000 --> 0:11:35.520 he's not making videos, he's using that same expertise to 0:11:35.559 --> 0:11:40.200 help his clients make their businesses work better. At IBM, 0:11:40.400 --> 0:11:44.880 Nicholas works with businesses to formulate a data strategy, preparing 0:11:44.880 --> 0:11:47.880 them to get the most out of technology like machine 0:11:47.960 --> 0:11:52.080 learning or deep learning. He explained to Ronald why thinking 0:11:52.200 --> 0:11:55.480 critically about the data it generates can help a company 0:11:55.720 --> 0:11:59.079 run more efficiently. So there's a quote that you've used 0:11:59.080 --> 0:12:02.960 in your presentation say their firms are trying to become 0:12:03.000 --> 0:12:07.360 insights driven, but only one third report succeeding. What is 0:12:07.400 --> 0:12:11.320 the role of creativity in the successful one third and 0:12:11.559 --> 0:12:14.199 how are you at IBM helping to increase that number. 0:12:15.600 --> 0:12:19.120 I remember going to a talk by our previous CEO, 0:12:19.360 --> 0:12:22.559 and she said that there's a large number of organizations 0:12:22.600 --> 0:12:26.160 that are just experimenting with random acts of digital So 0:12:26.240 --> 0:12:29.559 they're just testing out some of these news technologies are 0:12:29.559 --> 0:12:33.120 seeing kind of what's possible. But the ones that are 0:12:33.120 --> 0:12:36.520 truly being successful are the ones that are getting there 0:12:37.840 --> 0:12:41.480 the data ready their data strategy in play. They're the 0:12:41.520 --> 0:12:44.080 ones that are starting to collect their data. They're starting 0:12:44.080 --> 0:12:46.960 to get it ready and organized. They're starting to take 0:12:47.000 --> 0:12:50.240 a look at it and starting to iterate and prototype 0:12:50.280 --> 0:12:53.640 and in a structured manner. They're starting to roll this 0:12:53.720 --> 0:12:59.360 stuff out. The journey to get something as sophisticated as 0:13:00.000 --> 0:13:03.719 machine learning into production is a lot more difficult than 0:13:03.760 --> 0:13:08.280 I think people realize because you're now building a box 0:13:08.480 --> 0:13:12.440 that has its own rules. You haven't defined those rules yourself, 0:13:12.720 --> 0:13:16.200 So how do you explain that when something goes right? 0:13:16.600 --> 0:13:19.360 But how do you explain when something goes wrong? And 0:13:19.400 --> 0:13:23.800 having governance around that is absolutely critical, which is really 0:13:23.800 --> 0:13:27.120 whether the data strategy does come into play. So let's 0:13:27.200 --> 0:13:31.400 let's get into more business focused data strategies. Why is 0:13:31.400 --> 0:13:33.720 it so important to have a data strategy in place 0:13:34.160 --> 0:13:37.680 to fuel AI modeling and how does data literacy play 0:13:37.679 --> 0:13:42.160 a role in getting value from these models. We've got 0:13:42.320 --> 0:13:45.520 algorithms left, right and center these days, but I think 0:13:45.559 --> 0:13:48.240 the thing that people forget is that you can't use 0:13:48.280 --> 0:13:53.400 any of these algorithms unless you've got data. So ensuring 0:13:53.440 --> 0:13:57.960 that you have a structure in place too one collect 0:13:57.960 --> 0:14:02.400 your data to organ is it three, analyze it, and 0:14:02.440 --> 0:14:07.600 then or infused to machine learning or deep learning into it. 0:14:07.679 --> 0:14:10.120 Is absolutely critical because if you don't collect it, you 0:14:10.160 --> 0:14:12.320 can't do anything with it. If you don't organize it, 0:14:12.720 --> 0:14:15.600 you can't discover what you've actually got, what the quality 0:14:15.640 --> 0:14:18.040 looks like. You don't analyze it, you don't know whether 0:14:18.120 --> 0:14:20.760 or not you can trust it. Um and then he 0:14:20.880 --> 0:14:23.080 infused is always like the icing on the cake, right 0:14:23.360 --> 0:14:25.640 to the machine learning, the deep learning, all the cool 0:14:25.720 --> 0:14:30.720 buzzwords that people throw around. That is like the last step, 0:14:31.120 --> 0:14:35.120 and it is always the coolest step. But you can't 0:14:35.400 --> 0:14:38.160 ever get to that last cool step unless you've gone 0:14:38.200 --> 0:14:42.280 through that the hard work that that's come before. Let's 0:14:42.400 --> 0:14:44.880 like expand a little bit on the pain points for 0:14:44.920 --> 0:14:49.040 companies when they're developing or implementing a data strategy. What 0:14:49.120 --> 0:14:52.360 do those pain points look like? Honestly, the biggest pain 0:14:52.440 --> 0:14:56.800 point that I see organizations actually the top two that 0:14:56.880 --> 0:15:00.000 I see them coming back to over and over again, 0:15:00.040 --> 0:15:05.920 and is collecting and organizing their data. So let's say, 0:15:05.960 --> 0:15:13.080 for example, you've got a manufacturing type organization and what 0:15:13.120 --> 0:15:18.560 they want to do is they want to improve the 0:15:18.680 --> 0:15:24.200 production quality on a particular manufacturing line. So ideally, if 0:15:24.240 --> 0:15:27.360 they see that they've got defective products on the manufacturing line, 0:15:27.360 --> 0:15:29.200 they want to get rid of those sooner rather than 0:15:29.280 --> 0:15:31.040 later because they don't want to be shipping him out 0:15:31.080 --> 0:15:34.520 to the customer going through the whole warranty and claims 0:15:34.560 --> 0:15:38.440 process that just costs a ton of money. So they're like, well, 0:15:38.840 --> 0:15:41.480 it would be great to use some computer vision or 0:15:41.560 --> 0:15:44.240 some deep learning to detect when we've got defects on 0:15:44.280 --> 0:15:46.360 the product line, and then we can grab those and 0:15:46.440 --> 0:15:50.280 rip them out. Somebody along the line is like, great, 0:15:50.520 --> 0:15:53.360 let's go and do it. The first stumbling block that 0:15:53.440 --> 0:15:55.960 you're going to trip up at is, hold on, do 0:15:56.000 --> 0:16:00.440 you have any images of defective products from example cameras 0:16:00.520 --> 0:16:03.280 that are looking at that production line. So if you 0:16:03.360 --> 0:16:06.320 haven't gone and collected images of that or video of that, 0:16:07.440 --> 0:16:10.040 there is no way in hell that you can actually 0:16:10.080 --> 0:16:16.080 go and build that system to improve your organizational productivity. 0:16:16.160 --> 0:16:20.080 So knowing well in advance what data you're likely to 0:16:20.240 --> 0:16:24.320 need is absolutely critical. It is the first step in 0:16:24.360 --> 0:16:29.720 the data science life cycle. So collecting, understanding, and exploring 0:16:29.760 --> 0:16:33.880 your data is the absolute first step. The second one 0:16:34.160 --> 0:16:37.760 is a little bit more interesting. So let's say, for example, 0:16:38.880 --> 0:16:41.480 you sort of want to get in on the craze 0:16:41.520 --> 0:16:44.680 that is data science or machine learning, and you bring 0:16:44.680 --> 0:16:49.600 on a data science team. The next biggest stumbling block 0:16:49.680 --> 0:16:52.480 that I find a lot of organizations trip up on 0:16:52.760 --> 0:16:55.560 is discovering their data. They've got a ton of data, 0:16:55.600 --> 0:16:59.160 but nobody knows what they've got. So being able to 0:16:59.240 --> 0:17:03.520 find so to discover, rate, review, and rank that information 0:17:04.520 --> 0:17:09.400 is paramount because you'll have people come in and go okay. 0:17:09.480 --> 0:17:12.800 So a line managers approached me and said that we 0:17:12.840 --> 0:17:15.480 want to take a look at our top performing customers 0:17:15.600 --> 0:17:18.320 and we want to build a retention strategy so we're 0:17:18.359 --> 0:17:22.119 not losing customers anymore. Well, your data scientists is then 0:17:22.160 --> 0:17:24.359 going to go, well, do we have data of customers 0:17:24.359 --> 0:17:27.480 that have left previously. If you can't easily search and 0:17:27.520 --> 0:17:29.640 find out what you've got, that makes it pretty hard 0:17:29.680 --> 0:17:33.919 to go and build those models. So collecting, organizing, and 0:17:33.960 --> 0:17:38.800 discovering really absolutely critical, but that they can be a 0:17:38.800 --> 0:17:42.400 little bit tricky to handle in a large number of organizations. 0:17:42.920 --> 0:17:46.480 What kind of supporting technology and new solutions do we 0:17:46.560 --> 0:17:50.679 need to meet growing data management issues? It really comes 0:17:50.720 --> 0:17:53.679 down to two a few things. So ensuring that you 0:17:53.680 --> 0:17:56.520 can one collect the types of data that you're looking at. 0:17:56.680 --> 0:17:59.959 So I think when people think of data, they're always 0:18:00.119 --> 0:18:02.280 thinking of hate it's just going to be a bunch 0:18:02.320 --> 0:18:04.600 of spreadsheets. It might just be stuff that we can 0:18:04.600 --> 0:18:08.080 throw into a database. But there is so much more 0:18:08.160 --> 0:18:10.399 out there. Right, there's video, how do we store that? 0:18:10.480 --> 0:18:15.160 How do we hold that? There is images, there's natural text. 0:18:15.240 --> 0:18:19.159 Like we're just talking about ensuring that you've got appropriate 0:18:19.200 --> 0:18:22.400 processes in place to be able to store holding catalog 0:18:22.520 --> 0:18:27.000 that I think is absolutely critical. We talked a little 0:18:27.000 --> 0:18:30.359 bit about data cataloging and the need to be able 0:18:30.440 --> 0:18:35.280 to search and discover that data that is absolutely paramount. 0:18:35.359 --> 0:18:37.199 Once you've got it collected, how do you find it? 0:18:38.960 --> 0:18:42.680 What is IBM's unique approach to facilitating access to data 0:18:42.720 --> 0:18:48.440 within companies. So one of the biggest things, and one 0:18:48.480 --> 0:18:50.840 of the my favorite things that I get to work with, 0:18:51.160 --> 0:18:54.479 is a particular tool set, right, and this tool set 0:18:54.560 --> 0:18:57.480 is called cloud Path for Data. So, without getting too 0:18:57.480 --> 0:19:00.880 pitchy that the absolutely amazing thing about this is that 0:19:01.640 --> 0:19:05.720 those stages that I was talking about, right, so collect, organized, analyzing, infused, 0:19:06.200 --> 0:19:09.040 it actually helps facilitate each one of those stages, right, 0:19:09.520 --> 0:19:13.440 So you can actually collect, store, and hold your data 0:19:13.480 --> 0:19:16.960 in a secure and government place. You've got data catalog 0:19:17.080 --> 0:19:19.480 in capabilities which allows you to search. Like one of 0:19:19.480 --> 0:19:23.080 my favorite things is that you might have a data set, right, 0:19:23.119 --> 0:19:24.920 So I might be a data scientist, and then we 0:19:25.000 --> 0:19:28.040 might have another data scientist on the team. I can 0:19:28.080 --> 0:19:30.399 have a data set inside of there, and I can 0:19:30.440 --> 0:19:33.000 actually rank it and add comments and go, hey, just 0:19:33.040 --> 0:19:35.040 be wary of this column with lot certain features that 0:19:35.080 --> 0:19:38.879 you need to be mindful of, and that provides additional 0:19:38.960 --> 0:19:43.480 metadata understand what is what my data actually looks like 0:19:43.560 --> 0:19:47.120 and and things that I should be mindful for. So 0:19:47.359 --> 0:19:52.119 I'm I'm Joe employee. How can data be helpful to me? 0:19:53.160 --> 0:19:57.320 Great question? So, I mean data is impacting everyone, right, 0:19:57.359 --> 0:20:02.040 whether you you like it or not. Um and more 0:20:02.080 --> 0:20:04.520 often than not, what you're going to find is that 0:20:04.600 --> 0:20:09.000 you can improve whatever it is that you do by 0:20:09.080 --> 0:20:12.880 by looking at that data, whether it's let's take an 0:20:12.960 --> 0:20:17.080 organization out of it. If you use sleep trackers, you 0:20:17.119 --> 0:20:20.640 can begin to see when you're sleep or when you're 0:20:20.640 --> 0:20:23.919 getting good quality sleep versus when you're getting bad quality sleep. 0:20:24.240 --> 0:20:27.520 If you start to collect additional data points like hey, 0:20:27.840 --> 0:20:31.600 am I drinking enough water during the day, am I 0:20:31.720 --> 0:20:34.399 doing certain things like looking at my phone just before 0:20:34.480 --> 0:20:37.199 I go to bed? Are these things influencing my sleep? 0:20:37.680 --> 0:20:41.800 And is that causing a negative impact on my quality 0:20:41.840 --> 0:20:45.120 of life? So that's taking a broader view of it. 0:20:45.440 --> 0:20:48.560 But when you step into a team or a business view, 0:20:49.600 --> 0:20:52.959 data can can make your life a billion times easier. 0:20:53.440 --> 0:20:56.359 If you know that there's a particular issue in a 0:20:56.440 --> 0:20:59.960 system earlier on in a data pipeline, before something crosses 0:21:00.000 --> 0:21:02.359 your desk, you might go and say, hey, look, if 0:21:02.359 --> 0:21:05.159 we just changed how we collected these pieces of information, 0:21:05.640 --> 0:21:08.159 if we just transformed what we actually did with it, 0:21:08.400 --> 0:21:11.000 this is going to streamline my entire workflow and and 0:21:11.119 --> 0:21:14.439 help me out. But not only that. Right, So I 0:21:14.560 --> 0:21:18.080 work a little bit with the automation team, and they're 0:21:18.119 --> 0:21:21.320 really big on robotic process automation. Let's say you're doing 0:21:21.400 --> 0:21:25.080 something each and every single day. You're copying a far 0:21:25.359 --> 0:21:28.600 from here to there. You're grabbing some information from a website, 0:21:28.600 --> 0:21:30.919 You're throwing it into a form, and you have to 0:21:30.960 --> 0:21:34.000 do that twenty times a day. There are tools that 0:21:34.119 --> 0:21:37.040 can automate that entire process for you, and they're smart. 0:21:37.160 --> 0:21:39.399 They're not just looking at where you're clicking on the page. 0:21:39.440 --> 0:21:42.479 They're looking at what applications you're opening, They're looking at 0:21:42.480 --> 0:21:45.600 what fields you're pulling data out of. You can automate 0:21:45.640 --> 0:21:47.919 those entire workflows. That means that you don't have to 0:21:47.960 --> 0:21:51.200 do that repetitive, kind of boring work that you don't 0:21:51.240 --> 0:21:53.760 really want to do. You can palm that off and 0:21:54.000 --> 0:21:56.240 do the robot and do the stuff that you actually 0:21:56.240 --> 0:21:59.359 really want to get involved in. As Nicholas said, the 0:21:59.359 --> 0:22:02.359 way a company leverages this data has an impact on 0:22:02.560 --> 0:22:06.359 every level of the business. Data informs how we do 0:22:06.440 --> 0:22:08.960 our jobs day to day and how we plan for 0:22:08.960 --> 0:22:12.560 the future. Having an open mindset about data makes it 0:22:12.640 --> 0:22:16.240 easier for a business to come up with creative solutions. 0:22:17.040 --> 0:22:20.680 In the next part of their conversation, Ronald asked Nicholas 0:22:20.720 --> 0:22:25.520 how data science and creativity come together. So let's talk 0:22:25.520 --> 0:22:27.440 a little bit more about creativity. We talked a little 0:22:27.440 --> 0:22:29.760 bit about your YouTube channel, UH and how you use 0:22:29.800 --> 0:22:32.760 that to help people get started with data science. What 0:22:32.880 --> 0:22:35.879 does creativity mean to you and do you see your 0:22:35.880 --> 0:22:39.920 work as creative? I definitely say my work as creative, 0:22:40.240 --> 0:22:47.960 and I think creativity is truly thinking outside of the 0:22:48.000 --> 0:22:52.439 box and looking at just different ways of doing things. 0:22:53.119 --> 0:22:57.080 I think the biggest thing that I try to embody 0:22:57.240 --> 0:23:00.879 is having an open mindset and really in never being 0:23:00.880 --> 0:23:04.720 willing to shut something down or not look at a 0:23:04.760 --> 0:23:10.320 particular solution or option, because you really never know where 0:23:10.440 --> 0:23:13.280 a particular solution might come from. If you look at 0:23:13.600 --> 0:23:17.920 where some of the advancements in that the medical field 0:23:17.920 --> 0:23:21.679 are coming from, it's because they're being open to new ideas, 0:23:22.400 --> 0:23:28.240 new materials, new ingredients, new recipes, new technologies. Having an 0:23:28.240 --> 0:23:32.159 open mindset really helps improve that that that ability to 0:23:32.200 --> 0:23:35.800 solve complex problems. And I think for me, creativity is 0:23:35.800 --> 0:23:38.280 really just having that that open mindset. Tell me a 0:23:38.320 --> 0:23:42.080 little bit about how you approach novel problems. What do 0:23:42.160 --> 0:23:46.040 you do when you get stuck? I think the most 0:23:46.160 --> 0:23:51.480 important thing I really like when I push myself to 0:23:51.600 --> 0:23:55.479 do something that I've personally never done before, and a 0:23:55.520 --> 0:24:01.600 lot of the time that yields new solutions to problems 0:24:01.680 --> 0:24:04.760 that that that might be really difficult to solve. It 0:24:04.800 --> 0:24:07.640 doesn't necessarily need to be using this particular set of techniques. 0:24:07.720 --> 0:24:10.280 It's what else can we do to solve this problem? 0:24:10.359 --> 0:24:13.280 And sometimes like it'll be staring you in the face 0:24:13.359 --> 0:24:16.160 and you'll just have no idea until you go, hey, 0:24:16.160 --> 0:24:17.680 I'm going to throw everything out of the box and 0:24:17.920 --> 0:24:20.760