WEBVTT - Smart Talks with IBM: Unlocking Data Strategy: Data Literacy for Better Business 0:00:00.040 --> 0:00:05.000 Hey everyone, it's Robert and Joe here. Today we've got 0:00:05.040 --> 0:00:06.960 something a little bit different to share with you. It 0:00:07.080 --> 0:00:10.680 is a new edition of the Smart Talks podcast series, 0:00:10.720 --> 0:00:14.319 which is produced in partnership with IBM. This season of 0:00:14.360 --> 0:00:18.640 Smart Talks with IBM is all about new creators, the developers, 0:00:19.040 --> 0:00:22.600 data scientists, c t o s, and other visionaries creatively 0:00:22.640 --> 0:00:27.120 applying technology and business to drive change. They use their 0:00:27.160 --> 0:00:30.640 knowledge and creativity to develop better ways of working, no 0:00:30.720 --> 0:00:34.840 matter the industry. Join hosts from your favorite Pushkin Industries 0:00:34.920 --> 0:00:38.560 podcast as they use their expertise to deepen these conversations. 0:00:39.040 --> 0:00:41.800 Malcolm Gladwell will guide you through this season as your 0:00:41.840 --> 0:00:45.000 host to provide his thoughts and analysis along the way. 0:00:45.320 --> 0:00:48.600 Look out for new episodes of Smart Talks with IBM 0:00:48.680 --> 0:00:52.040 every month on the I Heart Radio app, Apple Podcasts, 0:00:52.159 --> 0:00:55.480 or wherever you get your podcasts. And learn more at 0:00:55.520 --> 0:01:04.520 IBM dot com slash smart Talks. Hello, Hello, Welcome to 0:01:04.600 --> 0:01:08.440 Smart Talks with IBM, a podcast from Pushkin Industries, I 0:01:08.640 --> 0:01:13.280 Heart Radio and IBM. I'm Malcolm Gladmo. This season, we're 0:01:13.319 --> 0:01:17.800 talking to new creators, the developers, data scientists, ct o s, 0:01:18.080 --> 0:01:21.840 and other visionaries who are creatively applying technology in business 0:01:21.920 --> 0:01:26.160 to drive change. Channeling their knowledge and expertise, they're developing 0:01:26.160 --> 0:01:31.039 more creative and effective solutions, no matter the industry. Our 0:01:31.080 --> 0:01:35.520 guest today is Nicholas Renaut, Senior Data science and AI 0:01:35.640 --> 0:01:40.920 technical specialist at IBM. Nicholas's job is to help companies 0:01:41.080 --> 0:01:44.560 formulate a data strategy that streamlines the way they do 0:01:44.640 --> 0:01:50.160 business and prepares them to use sophisticated AI technologies. But 0:01:50.240 --> 0:01:52.800 beyond his day to day, Nick is also a content 0:01:52.880 --> 0:01:56.280 creator on YouTube, where his channel has over a hundred 0:01:56.280 --> 0:02:00.880 thousand subscribers. His videos explain computer science incepts in a 0:02:00.920 --> 0:02:04.560 way beginners can understand, and he often demonstrates how to 0:02:04.720 --> 0:02:09.280 use machine learning and data science to solve novel problems. 0:02:10.080 --> 0:02:13.440 On today's show, How Nicholas learned Data science from the 0:02:13.440 --> 0:02:17.679 bottom up, the fundamentals of data management, and how an 0:02:17.680 --> 0:02:24.079 innovative data strategy can help businesses create novels solutions, Nick 0:02:24.120 --> 0:02:28.880 spoke with Ronald Young Jr. Host of the Pushkin podcast Solvable. 0:02:29.440 --> 0:02:33.280 Along with being a frequent contributor to MPR, Ronald also 0:02:33.400 --> 0:02:37.639 hosts and produces the podcast Time Well Spent and Leaving 0:02:37.639 --> 0:02:44.440 the Theater. Okay, let's get to the interview. So tell 0:02:44.440 --> 0:02:47.200 me a little bit about how you got into data 0:02:47.240 --> 0:02:49.240 and when you found out like the power that it 0:02:49.280 --> 0:02:51.760 really harnesses. Do you have a story or anything that 0:02:51.840 --> 0:02:55.760 kind of like when you first piqued your interest in data. 0:02:56.680 --> 0:02:59.839 My first interaction with data and with coding was act 0:03:00.000 --> 0:03:03.320 really when I was around about eleven years old. So 0:03:04.400 --> 0:03:08.560 this was really just getting started with just looking at spreadsheets. 0:03:08.560 --> 0:03:12.080 So my dad would come home and after working a 0:03:12.280 --> 0:03:16.919 nine or five job, he actually started working with investing 0:03:17.040 --> 0:03:21.239 in stocks and doing value based trading that way. I'll 0:03:21.280 --> 0:03:23.760 always remember I walked up to his desk one time 0:03:24.120 --> 0:03:25.959 and he said, Nick, if there's one thing that you 0:03:26.000 --> 0:03:29.639 should learn, I'm seeing all these people work on these 0:03:29.680 --> 0:03:36.040 things called macros in spreadsheets, and these people like wizards 0:03:36.080 --> 0:03:40.080 inside of my business. I know that you're still you're 0:03:40.080 --> 0:03:41.960 still in high school, but I really think you should 0:03:42.000 --> 0:03:45.080 learn this stuff. And I started doubling in some Excel 0:03:45.200 --> 0:03:49.440 spreadsheets and started just recording macros and tweaking stuff, and 0:03:49.440 --> 0:03:51.920 that that's where it all started. But from there, it's 0:03:51.960 --> 0:03:55.480 It's always been a recurring vein throughout my career that 0:03:55.520 --> 0:03:59.840 I've done some sort of wizardry with data, whether it 0:04:00.000 --> 0:04:03.320 a coding or business intelligence or data is it's it's 0:04:03.360 --> 0:04:07.120 always had a bit of a strain throughout throughout whatever 0:04:07.160 --> 0:04:10.080 I've done, whether they start ups or YouTube or or 0:04:10.400 --> 0:04:13.160 what I'm doing now at IBM. Your dad was right. 0:04:13.240 --> 0:04:15.720 Let me just say that, because as someone who's trying 0:04:15.760 --> 0:04:18.760 to put together a spreadsheet just to manage my personal finances, 0:04:19.160 --> 0:04:21.920 trying to look up the formula to actually bring a 0:04:22.040 --> 0:04:25.840 value from what what uhet to another is enough of 0:04:25.880 --> 0:04:28.680 a struggle for me. So I'm glad to do it. Really, 0:04:29.040 --> 0:04:33.240 it's like it's absolutely is uh so like knowing that 0:04:33.320 --> 0:04:35.400 you know, this was how you started getting into spreadsheets. 0:04:35.560 --> 0:04:38.160 You know you're looking at stocks and all of that. Um, 0:04:38.279 --> 0:04:41.159 can you talk to me about how you found out 0:04:41.400 --> 0:04:45.359 the importance of data literacy, how you begin to value 0:04:45.760 --> 0:04:49.640 understanding what the numbers meant and what power that could have. 0:04:50.680 --> 0:04:54.360 I got a cadet ship at one of the big 0:04:54.400 --> 0:04:57.720 four accounting firms and started out as an orditor there, 0:04:57.760 --> 0:05:01.960 which is pretty much day to focus. So I saw 0:05:02.040 --> 0:05:08.440 that these numbers ultimately fed into a significantly bigger picture, 0:05:08.520 --> 0:05:13.200 which was a formal annual report, and numbers being wrong 0:05:13.279 --> 0:05:17.160 in an annual report can move markets. Right. Those numbers 0:05:17.160 --> 0:05:20.359 need to be absolutely bang on. But I think that 0:05:20.520 --> 0:05:23.120 is sort of where it started. Where it really culminated 0:05:23.480 --> 0:05:26.920 was when I started doing some work at the Reserve 0:05:26.960 --> 0:05:31.640 Bank of Australia. And those numbers don't just impact the 0:05:32.680 --> 0:05:37.880 metrics for a particular organization, they impact the entire countries metrics. 0:05:37.880 --> 0:05:40.760 Getting those numbers wrong on a particular chart or getting 0:05:40.800 --> 0:05:44.600 them right on a particular chart can move entire organizations 0:05:44.640 --> 0:05:47.800 or can shift an entire country. It's kind of crazy 0:05:47.920 --> 0:05:53.520 what the value that doing things correctly with data has. 0:05:53.560 --> 0:05:56.840 So when you're presenting a metric, you have to ensure 0:05:56.960 --> 0:05:59.839 that you are portraying the appropriate message. It's not just 0:06:00.000 --> 0:06:04.440 about the raw number, because correlation does not necessarily imply causation. 0:06:04.480 --> 0:06:07.800 So understanding what it is that you're saying is so 0:06:07.800 --> 0:06:11.560 so important, and it is so much more powerful now 0:06:11.600 --> 0:06:14.520 that we've got so much more data available at our fingertips. 0:06:14.520 --> 0:06:16.159 It's really easy to go and grab a bunch of 0:06:16.200 --> 0:06:18.480 metrics and go, hey, I'm gonna grab this data from 0:06:18.520 --> 0:06:20.240 over here, grab that data from over here for a 0:06:20.360 --> 0:06:23.479 measured together. Hey, look, these two lines follow the same trend. 0:06:23.480 --> 0:06:25.880 They must be related. Do you find yourself ever looking 0:06:25.880 --> 0:06:29.440 at data points and saying those the how do I 0:06:29.480 --> 0:06:31.400 don't understand this chart? Why did they Where did they 0:06:31.480 --> 0:06:33.400 pull this from? Do you find yourself doing that a 0:06:33.440 --> 0:06:36.240 lot of your regular life? Oh? Yeah, that There's there's 0:06:36.240 --> 0:06:38.840 some great charts out there as well that you always see, 0:06:38.880 --> 0:06:42.040 and they plut like the number of Nicolas Cage movies 0:06:42.279 --> 0:06:46.200 against the g d P of Bolivia or something, and 0:06:46.279 --> 0:06:49.240 it's like, well, they're going in the same direction. They 0:06:49.320 --> 0:06:53.200 must have some relationship. But people can really quickly look 0:06:53.240 --> 0:06:55.680 at a picture and go and make an assumption about 0:06:55.760 --> 0:06:58.760 what that is saying without actually interpreting. Hey, are these 0:06:58.760 --> 0:07:02.279 on the same scales? The what time period is being displayed? 0:07:02.360 --> 0:07:05.520 What am I actually looking at here? And I find 0:07:05.560 --> 0:07:07.560 myself doing this more and more often when I just 0:07:07.600 --> 0:07:10.280 see a child on my hold on, Let's just not 0:07:10.320 --> 0:07:13.040 make any assumptions. What is this chart actually trying to say? 0:07:13.120 --> 0:07:16.680 What is it actually trying to portray? Because you can 0:07:16.840 --> 0:07:19.400 lie with statistics if you know what you're doing. It 0:07:19.560 --> 0:07:24.440 is they're so powerful and people can gloss over them 0:07:24.480 --> 0:07:26.880 so quickly. We've got attention spends that is so much 0:07:27.160 --> 0:07:30.200 shorter of these days that it can be very very 0:07:30.960 --> 0:07:36.280 easy to take away the wrong message. So you also 0:07:36.360 --> 0:07:40.880 produce content across various platforms, including YouTube and your personal blog. 0:07:41.760 --> 0:07:44.080 Uh as a content creator, how did you get started 0:07:44.120 --> 0:07:47.160 in that field and what type of content are you creating? Yeah, 0:07:47.200 --> 0:07:51.480 that's a crazy story, right. So I always wanted to 0:07:51.520 --> 0:07:53.920 get into tech and said, hey, I'd really really like 0:07:54.560 --> 0:07:57.720 to work for IBM. I saw what they were doing 0:07:57.720 --> 0:08:00.960 with Watson, and I'm like, why people were talking about 0:08:01.040 --> 0:08:04.880 this more? And I had no affiliation with with IBM 0:08:04.920 --> 0:08:07.080 at the time, and I'm like, well, this is so cool. 0:08:07.120 --> 0:08:09.840 There used to be this thing called or this service 0:08:09.880 --> 0:08:14.040 available and that the cloud platform called Personality Insights, and 0:08:14.320 --> 0:08:16.840 you could plug in a little bit of text and 0:08:17.040 --> 0:08:20.720 from that piece of text, it would analyze that particular 0:08:20.760 --> 0:08:24.120 person's personality based on the Big five personality traits. And 0:08:24.120 --> 0:08:26.800 there actually used to be this demo app where you 0:08:26.840 --> 0:08:28.840 could hook it up to a Twitter account, so I 0:08:28.840 --> 0:08:33.959 could pass through Oprah's Twitter account or Lebron's Twitter account 0:08:34.080 --> 0:08:37.160 and it would actually analyze their profiles. And this is 0:08:37.240 --> 0:08:42.240 so cool. It was nuts, and I was like, and 0:08:42.320 --> 0:08:43.839 a lot of people don't know how to use this. 0:08:44.080 --> 0:08:46.360 So that was quite possibly one of the first two 0:08:46.440 --> 0:08:51.199 toils that I made on YouTube, and I actually used 0:08:52.320 --> 0:08:54.760 a bunch of videos that I made following after that too. 0:08:56.440 --> 0:08:59.240 Finally land a job at IBM. I actually spammed a 0:08:59.240 --> 0:09:01.319 bunch of links in my resume and my couple that 0:09:01.440 --> 0:09:03.800 I was like, Hey, I'm already working with this stuff 0:09:03.840 --> 0:09:07.960 and I could do it. And the person that hired me, 0:09:08.200 --> 0:09:11.560 she actually said that that was like such an amazing 0:09:11.600 --> 0:09:16.040 way to portray what what you love about what you do. 0:09:16.360 --> 0:09:19.240 That that that had such an influencing factor in actually 0:09:19.320 --> 0:09:21.920 getting the job. But yeah, I did it because one 0:09:22.000 --> 0:09:24.120 the tech was so cool and I thought it was 0:09:24.160 --> 0:09:29.480 so interesting and so powerful, and yeah, eventually that helped 0:09:29.520 --> 0:09:32.000 me land that job. So you do a lot of 0:09:32.240 --> 0:09:36.319 tutorials where you're you're breaking down complex topics to kind 0:09:36.360 --> 0:09:40.000 of a wider audience. Why is that important for you 0:09:40.080 --> 0:09:43.320 to do? Yeah? I think one of the amazing things 0:09:43.760 --> 0:09:46.240 about knowledge is it's one of the things that you 0:09:46.280 --> 0:09:50.000 can give away and never lose, right. And I think 0:09:50.200 --> 0:09:54.800 one of the trickiest things about the whole data science 0:09:54.960 --> 0:09:59.120 and machine learning field is that it can be pretty 0:09:59.160 --> 0:10:05.320 tricky to get started, and sometimes we get hung up 0:10:05.360 --> 0:10:09.280 with learning from the bottom up right and there's nothing 0:10:09.320 --> 0:10:12.920 wrong with learning fundamentals and learning foundations and really getting 0:10:12.960 --> 0:10:15.920 stuck in. But in order to stick with something, you 0:10:16.000 --> 0:10:18.720 have to find it interesting. So if you can see 0:10:18.760 --> 0:10:21.080 the end result and then work your way back up 0:10:21.160 --> 0:10:24.360 and work out how that's worked, then it is so 0:10:24.440 --> 0:10:27.360 much more attractive because you get that instant gratification and go, hey, 0:10:27.400 --> 0:10:30.640 I've just built this machine learning app that is able 0:10:30.679 --> 0:10:33.440 to decode sign language. It's so cool. Now I'm going 0:10:33.480 --> 0:10:35.479 to go and work out the tech behind it. Admittedly, 0:10:35.520 --> 0:10:37.560 not everyone goes and works out the tech behind it, 0:10:37.600 --> 0:10:40.280 but what I'm trying to do is make it so 0:10:40.360 --> 0:10:44.440 that more people can get involved and get started with it. Lately, 0:10:44.480 --> 0:10:48.280 I've been doing these things called code that challenges, and 0:10:48.400 --> 0:10:50.680 they're kind of crazy, right, but I love doing them. 0:10:51.080 --> 0:10:55.400 So I have to build entire machine learning or data 0:10:55.440 --> 0:10:59.960 science applications without looking at any reference code, stack over 0:11:00.000 --> 0:11:05.560 a flow, or looking at any documentation within fifteen minutes. 0:11:05.720 --> 0:11:09.280 So it is literally just like a trial by fire. 0:11:09.320 --> 0:11:11.319 I'll have my phone, I'll set a time, and I'm like, 0:11:11.360 --> 0:11:14.360 all right, guys, we're on. Like the edit is literally 0:11:14.440 --> 0:11:17.800 just coding NonStop and me explaining on the go. But 0:11:17.840 --> 0:11:20.440 it allows people to see and explain my thought process 0:11:20.559 --> 0:11:23.560 as I'm developing it. UM, that's obviously super fun, right, 0:11:23.600 --> 0:11:27.120 because it's highly engaging and it shows people that, hey, 0:11:27.200 --> 0:11:31.640 you can get started in this relatively quickly. Nicholas is 0:11:31.679 --> 0:11:34.440 a kind of person whose passion for data science is 0:11:34.480 --> 0:11:38.320 so great it spills over from his professional life onto 0:11:38.360 --> 0:11:41.920 his YouTube channel. But when he's not making videos, he's 0:11:42.000 --> 0:11:45.520 using that same expertise to help his clients make their 0:11:45.559 --> 0:11:50.440 businesses work better. At IBM, Nicholas works with businesses to 0:11:50.559 --> 0:11:54.079 formulate a data strategy, preparing them to get the most 0:11:54.160 --> 0:11:58.520 out of technology like machine learning or deep learning. He 0:11:58.559 --> 0:12:01.760 explained to Ronald Wife, thinking critically about the data it 0:12:01.840 --> 0:12:06.400 generates can help a company run more efficiently. So there's 0:12:06.440 --> 0:12:10.040 a quote that you've used in your presentations say their 0:12:10.080 --> 0:12:13.240 firms are trying to become insights driven, but only one 0:12:13.320 --> 0:12:17.240 third report succeeding. What is the role of creativity in 0:12:17.280 --> 0:12:21.080 the successful one third and how are you at IBM 0:12:21.120 --> 0:12:24.840 helping to increase that number. I remember going to a 0:12:24.880 --> 0:12:29.080 talk by our previous CEO, and she said that there's 0:12:29.200 --> 0:12:32.880 a large number of organizations that are just experimenting with 0:12:33.040 --> 0:12:36.240 random acts of digital so they're just testing out some 0:12:36.280 --> 0:12:39.400 of these news technologies are saying kind of what's possible. 0:12:39.960 --> 0:12:43.040 But the ones that are truly being successful are the 0:12:43.120 --> 0:12:47.840 ones that are getting there, that data ready, that data 0:12:47.880 --> 0:12:50.800 strategy in play. They're the ones that are starting to 0:12:50.840 --> 0:12:54.000 collect their data. They're starting to get it ready and organized. 0:12:54.240 --> 0:12:56.400 They're starting to take a look at it and starting 0:12:56.440 --> 0:13:00.720 to iterate and prototype and in a st ructured manner, 0:13:00.760 --> 0:13:05.280 they're starting to roll this stuff out. The journey to 0:13:05.640 --> 0:13:10.640 get something as sophisticated as machine learning into production is 0:13:10.760 --> 0:13:13.520 a lot more difficult than I think people realize because 0:13:13.920 --> 0:13:18.360 you're now building a box that has its own rules. 0:13:18.440 --> 0:13:22.280 You haven't defined those rules yourself, So how do you 0:13:22.360 --> 0:13:25.320 explain that when something goes right? But how do you 0:13:25.360 --> 0:13:29.120 explain when something goes wrong? And having governance around that 0:13:29.320 --> 0:13:33.440 is absolutely critical, which is really whether the data strategy 0:13:33.520 --> 0:13:36.319 does come into play. So let's let's get into a 0:13:36.640 --> 0:13:40.319 more business focused data strategies. Why is it so important 0:13:40.360 --> 0:13:43.440 to have a data strategy in place to fuel AI 0:13:43.520 --> 0:13:46.960 modeling and how does data literacy play a role in 0:13:47.080 --> 0:13:52.240 getting value from these models. We've got algorithms left, right 0:13:52.280 --> 0:13:54.360 and center these days, but I think the thing that 0:13:54.400 --> 0:13:56.959 people forget is that you can't use any of these 0:13:57.000 --> 0:14:02.160 algorithms unless you've got data. So ensuring that you have 0:14:02.440 --> 0:14:07.280 a structure in place too one, collect your data, to 0:14:07.880 --> 0:14:12.839 organize it, three, analyze it, and then or infuse to 0:14:13.000 --> 0:14:17.040 machine learning or deep learning into it is absolutely critical 0:14:17.080 --> 0:14:19.120 because if you don't collect it, you can't do anything 0:14:19.120 --> 0:14:21.840 with it. If you don't organize it, you can't discover 0:14:22.000 --> 0:14:24.640 what you've actually got, what the quality looks like. You 0:14:24.680 --> 0:14:26.640 don't analyze it, you don't know whether or not you 0:14:26.680 --> 0:14:29.960 can trust it. Um and then he infused is always 0:14:29.960 --> 0:14:32.600 like the icing on the cake, right to the machine learning, 0:14:32.600 --> 0:14:35.680 the deep learning, all the cool buzzwords that people throw around. 0:14:36.240 --> 0:14:41.440 That is like the last step, and it is always 0:14:41.480 --> 0:14:44.600 the coolest step. But you can't ever get to that 0:14:44.680 --> 0:14:47.720 last cool step unless you've gone through that the hard 0:14:47.760 --> 0:14:51.600 work that that's come before. Let's like expand a little 0:14:51.640 --> 0:14:55.280 bit on the pain points for companies when they're developing 0:14:55.360 --> 0:14:58.160 or implementing a data strategy. What do those pain points 0:14:58.160 --> 0:15:03.720 look like? Honestly, the biggest pain point that I see organizations, 0:15:03.760 --> 0:15:06.720 actually the top two that I see them coming back 0:15:06.760 --> 0:15:11.800 to over and over again, is collecting and organizing their data. 0:15:12.160 --> 0:15:19.720 So let's say, for example, you've got a manufacturing type organization, 0:15:20.880 --> 0:15:24.800 and what they want to do is they want to 0:15:24.880 --> 0:15:31.360 improve the production quality on a particular manufacturing line. So ideally, 0:15:32.240 --> 0:15:34.760 if they see that they've got defective products on the 0:15:34.800 --> 0:15:37.040 manufacturing line, they want to get rid of those sooner 0:15:37.160 --> 0:15:38.760 rather than later because they don't want to be shipping 0:15:38.800 --> 0:15:42.200 him out to the customer going through the whole warranty 0:15:42.240 --> 0:15:45.280 and claims process that just costs a ton of money. 0:15:45.480 --> 0:15:48.320 So they're like, well, it would be great to use 0:15:48.360 --> 0:15:51.480 some computer vision or some deep learning to detect when 0:15:51.480 --> 0:15:53.880 we've got defects on the product line, and then we 0:15:53.920 --> 0:15:57.720 can grab those and rip them out. Somebody along the 0:15:57.720 --> 0:16:00.320 line is like, great, let's go and do it. The 0:16:00.360 --> 0:16:03.200 first stumbling block that you're going to trip up at is, 0:16:03.560 --> 0:16:07.040 hold on, do you have any images of defective products 0:16:07.120 --> 0:16:10.320 from example cameras that are looking at that production line. 0:16:10.880 --> 0:16:13.520 So if you haven't gone and collected images of that 0:16:13.680 --> 0:16:17.520 or video of that, there is no way in hell 0:16:17.600 --> 0:16:20.360 that you can actually go and build that system to 0:16:20.560 --> 0:16:27.200 improve your organizational productivity. So knowing well in advance what 0:16:27.320 --> 0:16:31.240 data you're likely to need is absolutely critical. It is 0:16:31.600 --> 0:16:36.560 the first step in the data science life cycle. So collecting, understanding, 0:16:36.880 --> 0:16:41.360 and exploring your data is the absolute first step. The 0:16:41.480 --> 0:16:45.320 second one is a little bit more interesting. So let's say, 0:16:45.320 --> 0:16:49.120 for example, you sort of want to get in on 0:16:49.160 --> 0:16:52.440 the craze that is data science or machine learning, and 0:16:52.520 --> 0:16:57.200 you bring on a data science team. The next biggest 0:16:57.240 --> 0:17:00.320 stumbling block that I find a lot of organizations trip 0:17:00.400 --> 0:17:03.400 up on is discovering their data. They've got a ton 0:17:03.440 --> 0:17:06.879 of data, but nobody knows what they've got. So being 0:17:06.920 --> 0:17:11.040 able to find, search, discover, rate, review, and rank that 0:17:11.119 --> 0:17:16.000 information is paramount because you'll have people come in and 0:17:16.000 --> 0:17:20.560 go okay. So a line managers approached me and said 0:17:20.640 --> 0:17:22.600 that we want to take a look at our top 0:17:22.600 --> 0:17:26.160 performing customers and we want to build a retention strategy 0:17:26.240 --> 0:17:30.120 so we're not losing customers anymore. Well, your data scientists 0:17:30.160 --> 0:17:31.879 is then going to go, well, do we have data 0:17:31.920 --> 0:17:34.720 of customers that have left previously. If you can't easily 0:17:34.800 --> 0:17:37.359 search and find out what you've got, that makes it 0:17:37.400 --> 0:17:42.040 pretty hard to go and build those models. So collecting, organizing, 0:17:42.080 --> 0:17:46.919 and discovering really absolutely critical, but that they can be 0:17:46.960 --> 0:17:49.720 a little bit tricky to handle in a large number 0:17:49.720 --> 0:17:53.840 of organizations. What kind of supporting technology and new solutions 0:17:54.359 --> 0:17:58.199 do we need to meet growing data management issues? It 0:17:58.280 --> 0:18:01.760 really comes down to a few things. So ensuring that 0:18:01.800 --> 0:18:04.240 you can one collect the types of data that you're 0:18:04.280 --> 0:18:07.600 looking at. So I think when people think of data, 0:18:07.640 --> 0:18:10.119 they're always thinking of hate it's just going to be 0:18:10.200 --> 0:18:12.560 a bunch of spreadsheets. It might just be stuff that 0:18:12.600 --> 0:18:15.679 we can throw into a database, But there is so 0:18:15.880 --> 0:18:18.199 much more out there. Right, there's video, how do we 0:18:18.240 --> 0:18:21.600 store that? How do we hold that? There is images, 0:18:21.840 --> 0:18:26.320 there's natural text. Like we're just talking about ensuring that 0:18:26.400 --> 0:18:29.119 you've got appropriate processes in place to be able to 0:18:29.200 --> 0:18:34.600 store holding catalog that I think is absolutely critical. We 0:18:34.680 --> 0:18:37.960 talked a little bit about data cataloging and the need 0:18:38.000 --> 0:18:41.520 to be able to search and discover that data. That 0:18:41.800 --> 0:18:44.879 is absolutely paramount. Once you've got it collected, how do 0:18:44.880 --> 0:18:50.040 you find it? What is IBM's unique approach to facilitating 0:18:50.080 --> 0:18:56.040 access to data within companies. So one of the biggest things, 0:18:56.119 --> 0:18:58.520 and one of the my favorite things that I get 0:18:58.560 --> 0:19:02.159 to work with, is a particular tool set, right, and 0:19:02.200 --> 0:19:04.440 this tool set is called cloud Path for Data. So, 0:19:04.840 --> 0:19:08.560 without getting too pitchy, that the absolutely amazing thing about 0:19:08.560 --> 0:19:12.000 This is that those stages that I was talking about, right, 0:19:12.040 --> 0:19:15.840 So collect, organized, analyze, and infused. It actually helps facilitate 0:19:15.880 --> 0:19:20.480 each one of those stages. Right. So you can actually collect, store, 0:19:20.680 --> 0:19:23.119 and hold your data in a secure and government place. 0:19:23.720 --> 0:19:27.200 You've got data catalog in capabilities which allows you to search. 0:19:27.280 --> 0:19:30.360 Like one of my favorite things is that you might 0:19:30.400 --> 0:19:32.040 have a data set. Right, So I might be a 0:19:32.119 --> 0:19:34.640 data scientist, and then we might have another data scientist 0:19:34.680 --> 0:19:37.600 on the team. I can have a data set inside 0:19:37.600 --> 0:19:39.840 of there, and I can actually rank it and add 0:19:39.840 --> 0:19:42.280 comments and go, hey, just be wary of this column 0:19:42.280 --> 0:19:44.320 with lot certain features that you need to be mindful of, 0:19:44.920 --> 0:19:50.359 and that provides additional metadata understand what is what my 0:19:50.440 --> 0:19:53.000 data actually looks like and and things that I should 0:19:53.000 --> 0:19:58.400 be mindful for. So I'm I'm Joe employee. How can 0:19:58.640 --> 0:20:03.440 data be helpful to me? Great question? So, I mean 0:20:03.520 --> 0:20:07.160 data is impacting everyone, right, whether you you like it 0:20:07.320 --> 0:20:11.879 or not. Um and more often than not, what you're 0:20:11.880 --> 0:20:16.280 going to find is that you can improve whatever it 0:20:16.359 --> 0:20:18.440 is that you do by by looking at that data, 0:20:18.520 --> 0:20:23.560 whether it's let's take an organization out of it. If 0:20:23.600 --> 0:20:26.920 you use sleep trackers, you can begin to see when 0:20:27.200 --> 0:20:30.360 you're sleep, or when you're getting good quality sleep versus 0:20:30.600 --> 0:20:33.199 when you're getting bad quality sleep. If you start to 0:20:33.200 --> 0:20:37.240 collect additional data points like hey, am I drinking enough 0:20:37.280 --> 0:20:41.119 water during the day? Am I doing certain things like 0:20:41.160 --> 0:20:43.320 looking at my phone just before I go to bed? 0:20:43.359 --> 0:20:47.160 Are these things influencing my sleep? And is that causing 0:20:47.400 --> 0:20:51.480 a negative impact on my quality of life? So that's 0:20:51.560 --> 0:20:54.520 taking a broader view of it. But when you step 0:20:54.600 --> 0:20:59.080 into a team or a business view, data can can 0:20:59.440 --> 0:21:02.320 make your life for billion times easier. If you know 0:21:02.520 --> 0:21:05.840 that there's a particular issue in a system earlier on 0:21:06.040 --> 0:21:09.159 in a data pipeline, before something crosses your desk, you 0:21:09.240 --> 0:21:11.240 might go and say, hey, look, if we just changed 0:21:11.280 --> 0:21:14.520 how we collected these pieces of information, if we just 0:21:14.640 --> 0:21:17.000 transformed what we actually did with it, this is going 0:21:17.040 --> 0:21:20.000 to streamline my entire workflow and and help me out. 0:21:20.400 --> 0:21:23.400 But not only that, Right, So I work a little 0:21:23.440 --> 0:21:27.080 bit with the automation team, and they're really big on 0:21:27.280 --> 0:21:30.359 robotic process automation. Let's say you're doing something each and 0:21:30.400 --> 0:21:34.640 every single day. You're copying a far from here to there. 0:21:34.800 --> 0:21:37.359 You're grabbing some information from a website, You're throwing it 0:21:37.400 --> 0:21:39.840 into a form and you have to do that twenty 0:21:39.840 --> 0:21:43.199 times a day. There are tools that can automate that 0:21:43.359 --> 0:21:45.919 entire process for you, and they're smart. They're not just 0:21:46.000 --> 0:21:48.160 looking at where you're clicking on the page. They're looking 0:21:48.160 --> 0:21:51.200 at what applications you're opening. They're looking at what fields 0:21:51.200 --> 0:21:55.000 you're pulling data out of. You can automate those entire workflows. 0:21:55.040 --> 0:21:57.320 That means that you don't have to do that repetitive 0:21:57.840 --> 0:22:00.040 kind of boring work that you don't really want to. 0:22:00.000 --> 0:22:02.960 You can palm that off and do the very bot 0:22:03.000 --> 0:22:04.919 and do the stuff that you actually really want to 0:22:04.920 --> 0:22:08.360 get involved in. As Nicholas said, the way a company 0:22:08.440 --> 0:22:11.800 leverages this data has an impact on every level of 0:22:11.840 --> 0:22:15.400 the business. Data informs how we do our jobs day 0:22:15.440 --> 0:22:18.600 to day and how we plan for the future. Having 0:22:18.640 --> 0:22:21.600 an open mindset about data makes it easier for a 0:22:21.640 --> 0:22:25.800 business to come up with creative solutions. In the next 0:22:25.800 --> 0:22:29.960 part of their conversation, Ronald asked Nicholas how data science 0:22:30.040 --> 0:22:34.159 and creativity come together. So let's talk a little bit 0:22:34.160 --> 0:22:36.240 more about creativity. We talked a little bit about your 0:22:36.240 --> 0:22:38.400 YouTube channel, UH and how you use that to help 0:22:38.400 --> 0:22:42.240 people get started with data science. What does creativity mean 0:22:42.280 --> 0:22:45.000 to you? And do you see your work as creative. 0:22:45.800 --> 0:22:49.680 I definitely say my work as creative, and I think 0:22:50.320 --> 0:22:57.440 creativity is truly thinking outside of the box and looking 0:22:57.640 --> 0:23:02.359 at just different ways of doing things. I think the 0:23:02.359 --> 0:23:06.040 biggest thing that I try to embody is having an 0:23:06.040 --> 0:23:11.600 open mindset and really never being willing to shut something 0:23:11.640 --> 0:23:15.280 down or not look at a particular solution or option, 0:23:16.400 --> 0:23:20.320 because you really never know where a particular solution might 0:23:20.320 --> 0:23:22.480 come from. If you look at where some of the 0:23:22.520 --> 0:23:27.560 advancements in that the medical field are coming from, it's 0:23:27.600 --> 0:23:34.040 because they're being open to new ideas, new materials, new ingredients, 0:23:34.040 --> 0:23:37.680 new recipes, new technologies. Having an open mindset really helps 0:23:37.720 --> 0:23:41.679 improve that that that ability to solve complex problems. And 0:23:41.720 --> 0:23:44.919 I think for me, creativity is really just having that 0:23:45.040 --> 0:23:47.400 that open mindset. Tell me a little bit about how 0:23:47.480 --> 0:23:51.119 you approach novel problems. What do you do when you 0:23:51.160 --> 0:23:56.840 get stuck? I think the most important thing I really 0:23:56.880 --> 0:24:01.159 like when I push myself to do something that I've 0:24:01.240 --> 0:24:04.639 personally never done before, and a lot of the time 0:24:04.960 --> 0:24:10.840 that yields new solutions to problems that that that might 0:24:10.840 --> 0:24:14.119 be really difficult to solve. It doesn't necessarily need to 0:24:14.160 --> 0:24:17.080 be using this particular set of techniques. It's what else 0:24:17.119 --> 0:24:19.920 can we do to solve this problem? And sometimes like 0:24:20.000 --> 0:24:22.159