ERP024 - Data Science w/ Alastair Woolcock

Welcome to Evolve Radio where we explore the evolution of business and technology. So if you're in technology or business, you've probably heard the term big data. What does big data even mean anyway? Today I'm joined by Alister Wolcock to talk about data science. We uncover the science of data analytics and how data is unlocking market-changing insights at all levels of business. This is the real guts, the real underbelly of what big data is actually leveraged for in business. Even if you're a small business trying to understand social media's impact on your marketing efforts or you're a large enterprise organization trying to understand client behavior, data can be an incredibly powerful tool. And today Alister and I talk about the growing industry of data science and how businesses are digging through this mountain of data to find relevant insights that could give them an advantage in the market. If you enjoy the show, be sure to subscribe on iTunes, Stitcher, or wherever you get your podcast from. Also, be sure to check out the web page evolvedt.com/podcast for show notes, links to my guests and to check out previous episodes. Also, be sure to check out the web page evolvedt.com/podcast for show notes. links to my guests and to check out previous episodes. Now, let's get started. Joining me on the podcast today, I have Alister Wolcock, and Alister is a principal consultant at Credara, as well as founder and business partner at Kabu Global. Thanks for coming on, Alister. Thanks so much, Todd, appreciate joining you today. So, Alister and I have a long history, known each other and worked in the past. Uh, definitely one of the smarter characters I've worked with, um, amazing sales executive, amazing, uh, executive business, uh, executive as well. And, uh, Alister, you and I have had a shared passion for data and analytics, uh, through throughout the years, and, uh, I first recognized sort of your insight in this when you were building a data analytics and trading platform, uh, under Trafalger. Um, do you want to give us a little sense of kind of your technical history and your business history and what's led you to where you are currently in your career? Yeah, absolutely, Todd, you know, what you referenced there on the uh trading platform. You know, today doesn't seem as revolutionary, but when we first stepped into that, that was in 2008. And, uh, our basis thesis was this, could we build and a black box, that could do intraday trading, uh, on the live markets of NYSE and S&P and everything else, um, in a single click technology. And we did, we went about, we endeavored and we built that black box, um, and traded live for the next uh four years. using those programs and it was an algorithmic trading model, um, that uh we built out. And you know, incredible piece of technology that proved both rewarding and technically challenging as those things do. Uh, we underestimated some data sets and did well on others. Uh, we underestimated some data sets and did well on others. Um, and uh, yeah, we moved several million dollars worth of capital over those those four years on the Trafalger model. Um, outside of that, then we uh endeavored and took that into largely into uh the general data space. And that was uh with the parlay into what is uh Credara, the firm that I'm at now. And uh we've used that very successfully to drive our initiatives with companies like Starbucks and Brinker and other firms like that. Uh where we've really taken a digital strategy and tied in their back-end data delivery and e-commerce platforms. And really customer information and now extrapolated that into revenue producing models and uh and we're quite excited about where that can go. And then on the Kabu side, we bring in the data science capability, which is a super hot component in the market. That I think everybody's been to wrap their heads around as we continue to hear what are effectively right now industry buzzwords. Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Uh Yeah, and that's a one of the things that we definitely wanted to touch on is the the term big data gets thrown around a ton. And even as we were chatting before we started recording, big data doesn't really mean anything. It's such an overused marketing term that unfortunately the capabilities and what that that future technology will offer often get lost in the noise of that. You suggested you like the term data science better, can you explain to us why that makes a a better terminology for what you're trying to do in the market? Yeah, absolutely, because the to me, you know, big data, it's it's just a different segment, um, that supports the greater construct of data science. So what I mean by that is, most people that big data strategies are really leveraging BI analyst work, a lot of reporting, dashboarding, optimization type things, they're doing data warehouses. All of which are essential underpinnings to a a data science strategy for a company. However, what just putting in a data lake or a data warehouse and doing reporting and dashboarding, it it can give you uh information, but it doesn't correlate necessarily to a business outcome. And that is why I think when you think of where big data is going, you could break it into six primary categories of, you know, customer insights. Uh the product components, uh some efficiency, digital marketing, operational excellence. Or your products and services that you can bring to market. And and when you think of bringing things to market, you don't typically think of data warehouses. So iterating from just the conversation of having a big data strategy to a data science strategy is an essential pivot for companies. As they think of how to create revenue, retain customers or or bring new things to market. And I think there's some phenomenal examples of that, um, of recent history. And even back into the 1990s. So you hit on something that I I talk a lot about when I'm working with clients. I'm a huge fan of dashboarding and data analytics in general and what insights that can give you to the business. But what I often warn people against is that those dashboards and those reports, they won't give you any answers. They can often give you great questions to continue to ask. But people often think that they're going to be able to have some magical insight or that the data is going to point blank tell them something that they didn't know. And quite often that isn't the case, it's more of a, I have a theory, I want to test some data, do some review and some analysis. Now what is the data tell me and therefore what should I continue to ask? Is that sound similar to your approach? It does, simply stated for me, Todd, the, you know, IT, uh, works around, pivots around how to optimize or present data back to say an executive team or marketing teams or anybody else to make a decision. Um, whereas data science is more akin to a scientific process. Right, you want to start with a hypothesis and you want to prove that right or wrong. Just like we did at Trafalger back in the day, our hypothesis was, could we build a system that would do intraday trading in a single click technology because we knew that was monetizable and we knew that would be a powerful tool set for the market. So it's really thinking outside the box and it is um, coming up with your hypotheses and then proving those right or wrong and knowing that a wrong proof point is as powerful as proving something correctly in some cases. 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