Unlocking the Power of Data A Prerequisite for Successful AI Implementation (Podcast Recording)

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Transcript 

Steven Tedjamulia:
Welcome to another exciting episode of AI Insights, your go-to podcast for all things artificial intelligence and machine learning. I’m your host, Stephen Tijamulia, and today we have a very special guest with us. Joining us is Rick Silva, the Vice President of Data Science at CompTIA. Rick, thank you very much for being here.

Rick Silva:
Thanks, Stephen. It’s great to be with you again. We used to know each other a while back, so it’s great to reconnect.

Steven Tedjamulia:
Oh, super. Very excited to have you here. You bring so many insights. I know from the quick discussions we had, we talked about unlocking your data as a prerequisite for AI. And that’s kind of what we’re going to talk about today. But before we get started, Rick, you have a very amazing background. You’ve done many things throughout your career. Why don’t you get started and give us a little bit more details about your journey in data science so that… the audience can know kind of your perspective of where you’re gonna come from as you’re answering these questions.

Rick Silva:
You bet. Yeah. So I’ve been in the industry for a few decades and I started off with a focus on international markets and international product development within a software company. And the interesting thing about that is when you go back a few years and you look at international, we are kind of in the cusp of the world is our doorstep. We can go everywhere. Back in the day, however, All questions were new questions, and there was no easy answers anywhere. That part probably hasn’t changed for a lot of what we do. Because of that, I had to rely a lot on the data that was around me. And I got pretty used to doing that to the point that I thought, you know what, let’s cling to the business data, let’s cling to the customer value. And I’ve started a data science journey through… Systems management companies, cybersecurity companies, been in FinTech and now EdTech. And it’s been wonderful. I think the opportunities in relation to data just continue to grow and I expect that they will.

Steven Tedjamulia:
So if you look at that journey of many years from where you started to now, how has it evolved? Right? How has it evolved and where do you think it’s going as a VP of data science? You sit there and you look back where it was started rudimentary to now to chat GBT and it telling you how to do things. Give us your perspective.

Rick Silva:
So I think early on, there were the scenarios of the data is around us, we just don’t have it in hand. And you had to do a lot of manual gathering of data. And you’ll find as I continue through here, some of these things are still very relevant today. And in fact, I think some of the work associated with working with data hasn’t changed. I mean, just roll up your sleeves and get the work done. But I think there was, data available, it was nearby, some of it was in hand, and then collecting it and being able to make sense of it was a key differentiator. As the years have gone by, data has become more available, systems have become more available, data is gathered together in more common places, and those data sets that are relevant to the problems we’re trying to solve not far away. So you’re not reaching for raw data too much anymore, although, again, that never changes. But there are large data sets that are continuing to be available. And as you mentioned, coming into the era of generative AI, it just ups the game. I mean, we continue to stand on taller and taller shoulders as we try to address the similar business needs to what we’re trying to accomplish.

Steven Tedjamulia:
And in your career, what kind of breakthrough innovations have you seen with customers using data and getting results? Is there any case studies you could share with us? As you said, people, they’ve gathered the data they’re gathering. There’s still more gathering to happen, but they’re trying to use it for valuable breakthrough results in their organization. What have you seen?

Rick Silva:
You know, there’s a project that comes to mind. I like to call it the Fire Distinguisher Project that I did working with a customer support team. This was kind of your typical scenario of lots of customers. You know, if you plotted those customers out, either by revenue or by size or by attention, you’d have a few very large customers hugging that y-axis. and then heading off into a long, long tail of smaller and smaller customers. Everybody is profitable, everybody’s doing well, it might seem, but oftentimes a business may get caught addressing the needs of the largest customers just because they’re large. And I got looking at this long tail scenario and I thought if we only had a way to identify issues that were coming up, even when you’re not hogging the attention of, you know, of whoever is trying to serve your needs. And I found a way to create this tool. Again, I call it the fire distinguisher. It’s identifying where is the spark? I mean, if you’re down the long tail, you’re the small customer of a very large enterprise, you might not feel like anybody knows about you and your worries get overlooked. we as individuals, as people do the same thing. So just using data and redefining what is normal across that entire range, I was able to abstract the size of the customer out to be able to see that any time that there were issues that were popping up, whether you were a large customer or further down that long tail, they would spike out of what we consider normal. And it gave us the ability to. proactively address needs that we’re constantly surfacing in an area that we weren’t resourced to address them at all. But by having a way to highlight the issues that we’re showing up, or at least customers that were having issues, we could make proactive calls to say, hey, what’s going on? We’ve noticed a few extra tickets come through, for example. and it really kind of evened out the game and it helped increase loyalty, decrease our churn rates.

Steven Tedjamulia:
And I love it. Where do you think, I can see a lot of data consolidation, multiple systems reporting on that side. Where do you think AI would take what you did in that project to the next level? Where do you see that solution and what you contribute in that project? Where do you see that happening in the next few years with AI?

Rick Silva:
Yeah, I think there’s something that we’ll probably come back to in our discussion a little bit later on. It’s kind of the behavioral analytics of anybody, any of your customers, any individuals that you’re working with. So in the scenario that I talked about, you know, being able to identify an issue is one thing and that’s like a snapshot in time. And I think a lot of the AI issues begin that way. It’s like, can we discover things we’ve never seen before? And you realize that you can. But then over time you begin to have this ability to look at how are things trending? How is loyalty? What is the trend for loyalty for an individual or an organization who may be a customer of yours? So I think going forward, what we have the ability to do is to use the benefit of time, kind of that rear view mirror. Here’s data we’ve never seen before. What do we do with it? But you’ve taken that step. You’ve implemented the AI and set up those areas of memory, right, the AI memory. What have we done in the past? We saw this as a company. We made these recommendations. And then what happened, right? Remember that you made a recommendation. Remember who accepted and who didn’t, what their actions were, and to begin to see over time. How do people behave? All that leads into looking at today’s behaviors and being able to predict what’s going to happen next.

Steven Tedjamulia:
And do you also see where AI would be able to write the reports for, you know, management to take action or actually communicate and send emails to clients? You see that it could eventually get there in

Rick Silva:
Sure,

Steven Tedjamulia:
an automated

Rick Silva:
I mean…

Steven Tedjamulia:
way based on the data?

Rick Silva:
Yeah, yeah. You know, there’s, I mean, I look at what can AI do for us? And there’s two sides. One is reduce our costs. And these are activities that we’re already doing within our organizations. If you’re already trying to reach out to people via email, great. We can do that in a more targeted way. Can we create content to facilitate, to speed up the outreach through whatever channel you’re using, including, as you say, email? Yes, you can. So you can decrease costs for your internal operations and the activities that are currently going on. In fact, I think that’s one of the greatest ways to begin, is to say, what are we currently investing in with our people, with our time, our money? What are we doing today that we can either speed up or be more targeted, have greater focus, and to use our current level of resourcing greater traction. The flip side of that is the revenue side. It’s can we change product development to be able to create value that we couldn’t even see there was a need for before and go down the area of delivering things to the market that are new and innovative.

Steven Tedjamulia:
Let me ask you a question there, because our audience includes a lot of C-suite and executives such as yourself. We’re trying to figure out, what do I do with AI to your point? Maximize revenue, lower costs, make things more efficient. There’s a lot of things they could be doing. And where you sit in executive level, how do you manage and educate the executive team on what can be done with AI, what should be done now, what should wait? What kind of playbook do you have for that? And it may be a little bit off script from what we talked about, but as you brought that up, I thought that would be really good insights because people are trying to figure out what should I do right now, right? How have you framed it for your team?

Rick Silva:
Yeah, that’s a tricky one because we’re this is quite a transformation. I mean if you look back over time There’s probably no time in history where we’re going through so much data transformation and the ability to have data To help us make decisions The problem is somewhere along that chain you get to the human brain Who’s who’s trying to take in even greater amounts of data to make a better decision or to make it faster? um So in the end There’s, it’s really, really easy to get a gap between people who’ve made decisions, people who’ve run businesses, people who’ve looked out onto the horizon and charted new paths ahead and the ability that we have today and the resources to lean on. And you can open up a gap that looks a lot like, well, I don’t trust the data or… this doesn’t make sense to me or I’ve never seen this before or how could you possibly arrive at that conclusion? And I think that there’s a lot of might in taking those small steps to build that trust. So like I said, start with what you’re doing today and you know, an existing process that you have and make it better and get the create the value out of that create the wins from Um, I think it’s, it’s going to be easy for us to, to generate lots of things using AI and have a lot of people standing around scratching their heads going, it looks beautiful, but what do I, like, can I trust this thing? And I think one, you have to keep, you have to build trust. Two, it has to be explainable. And I’m going to add in a quick third. There’s a lot of folks out there that feel like, you know, AI or data science. is like a magic wand. And if you just have some of that good stuff, the problem will go away. And it’s still, it’s hard work. It’s math and it’s science and it’s business and the science of business. And it can be understood and it can be explainable. And just the way that we used to make business decisions before, if we have enough understanding, we can make solid business decisions going ahead also.

Steven Tedjamulia:
No, I like that point. I mean, I face that as well in day to day where if you take your executive team and you use AI and jump too far ahead, that’s great, but I don’t know the accuracy of the data. We’ve never done this before. There’s a lot of skepticism, potentially, of what I call it to say, well, this is really work. But if you kind of start small, like you said, and iterate and show success and tell success stories, very similar to early days of data science, then the belief starts happening, right? And you get more accurate and then you start merging the gap. So I love what you just said there. Skilling, it’s a little different, but like training your team and you come from the training background, providing skills in this new world of AI, machine learning. What’s your advice to managers who are, you know, trying to get their team skilled up so they can be more effective in this area.

Rick Silva:
Yeah, something that I lean on a lot here is clarity in the problems you’re trying to solve. So there’s a lot of skill, there’s a lot of technology, there’s a lot of innovation, but you have to be very clear about the question that you’re asking and the problem you’re trying to solve. You can bring in a lot of people and think you’re going to solve some problem. If you haven’t defined the problem well. you’ll spend a lot of resources and you won’t deliver the value. So it’s not really a data topic. It’s mostly a business topic. Be really clear. The data angle to that though is to say, if we know exactly, think of the scientific method like what are we proving? What are we disproving? If you’re very, very precise on your hypotheses, your business hypotheses, then you know. one, what you’re solving, and two, when you’ve solved it. You’re able to measure when you’ve reached your goal. And too often, you want to make things better. But what does that mean? And how far do you go? So I think it’s really important to have clarity around the problem that you’re solving, and then to match that with the resources that you bring in, whether it’s analysts or ML ops, machine learning operations. DevOps, things like that.

Steven Tedjamulia:
I love it because it could be as you know what project and problems you’re trying to solve it could be a macro level or a bigger level that would let you know am I You know enhancing my team with more training and my bringing in new people and my outsourcing work, right? It gives you a lot of options to try to solve that problem, which may not only be a training problem

Rick Silva:
Can I add something in

Steven Tedjamulia:
Yeah, please

Rick Silva:
there? For what you just said, I’ve seen cases before where you have subject matter experts. So we have a diversity of organizations across the globe. And they’re all pursuing their own passions and trying to accomplish the goals of their organization. And there’s deep subject matter expertise in those organizations. That’s invaluable. to bring in a rigorous approach to data is extremely valuable also. If you can get both of those together, your subject matter expertise, along with data expertise, wonderful. That’s hard to do, but it’s possible to work hand in hand. And as you talk about training, so this, I thought of another example of a time a subject matter expertise approach and then a data approach and you can get the same results but you end up having a different scalability coming out the other end of that. Just remember to lean on the subject matter expertise as well as the data, marry them together if you possibly can.

Steven Tedjamulia:
And then you can scale. Is that

Rick Silva:
Exactly.

Steven Tedjamulia:
right? None of the scaling had it. That’s a great point. As we come to the end over here of this podcast, trying to keep it 20 minutes, is there anything else you’d like to say, giving any advice to our audience around AI, data science, your expertise that you shared?

Rick Silva:
Yeah, you know, one of the things is don’t try to boil the ocean. Take things one step at a time. Iterate massively. It’s really easy to bog down in a solution. But with the ability that we will have to share data across the industries and to be able to take advantage of one another’s hard work, I’ll put a pitch in there to say that take it that one step at a time. See the wins, lock those in place, go for the next one, lock it in place again. And we’re coming into a season, I think, where we have lots of gen AI solutions that are out there. And the key will be to take those and tailor those to our specific needs where we’re headed.

Steven Tedjamulia:
Great advice. Rick, it’s been an absolute pleasure having you at AI Insights today. Your insights and our close relationship is invaluable. Thanks for sharing with us your piece. Very much, thank you.

Rick Silva:
Thanks, Stephen.

Steven Tedjamulia:
And for the listeners, thank you for tuning in to this AI Insights episode. We will be publishing it on YouTube. We’ll be taking this, transcribing it, and creating an article for our AI Insights magazine. Rick, for sure, will spotlight you there and your efforts. And then we will continue posting to the community. So thank you for listening. And Rick, if you stay on, we can chat just a little longer. But thanks again, everyone, for coming and participating.

About Rick Silva

Rick Silva is an accomplished Executive Data and Analytics leader with a remarkable 20-year track record in crafting and leading data teams dedicated to providing customer-centric solutions. With a keen focus on proactive problem-solving, he has successfully integrated AI and ML methods into data strategies, transforming businesses towards innovation. Rick’s exceptional skill set encompasses worldwide strategic planning and market analysis, empowering him to excel in international markets. His energetic and innovative approach, combined with his adeptness in managing culturally-diverse, cross-functional teams, has consistently enabled him to navigate complexity and bring clarity to product and service development.

Rick Silva’s leadership journey showcases his impact across diverse roles and industries. As the Vice President of Data Science at CompTIA, he continues to drive data-driven excellence. His role as the Founder of Langerang LLC highlights his entrepreneurial spirit and sustained commitment to creating value. At MX, as the Head of Data Science, he demonstrated his prowess by leading teams to deliver AI and ML solutions that offered invaluable financial insights. His tenure at Symantec underscores his ability to innovate within security and technology realms, where he spearheaded strategic data discovery and the integration of machine learning models. Rick’s legacy also includes his remarkable tenure at Novell, where he orchestrated regional product management, engineering, and innovative analytics initiatives, solidifying his reputation as a visionary strategist. With his adeptness in decision sciences, team leadership, AI, and enterprise software, Rick Silva remains an influential force in shaping cutting-edge data-driven solutions.

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