Insights from the Front Lines: How to Maximize Your Data Tech and AI Solutions

Video Synopsis

• Topmost Challenges Getting Maximum Value From Data Tech
• Business Value
• Clear Purpose
• Common Goals
• Embrace Change
• End-to-End Mindset
• Being Curious Drives Optimum Value

Transcript

Note: This transcript was created using speech recognition software. While it has been reviewed by human transcribers, it may contain errors.

Dora Boussias:

I’m so excited to be here and share the next 30 minutes with you all, where we’ll be talking about some hard-learned lessons, really, some insights from the front lines, as I call it. And it’s all about how do we get the most out of our data, technology, and AI solutions that we are implementing through organizations. So before we dive into that, let me just get a quick moment to share who am I, why should you even listen to me. Just to let you know, I’ve actually been in this space for about 30 years, a long time, always data driving or contributing to global enterprise cross-functional initiatives around data. And I’ve also done about 20 of my 30 years in the business, I’ve also done enterprise architecture data and analytics. And I drive with business value in mind, I think you’ll see that comes through as we go through what we’ll be speaking about today. Change is a big part because it’s really driving transformation. Those of us in the data space, I think everybody will recognize how much it is driving change with the organization and working with people. And I just want to also say that currently I work for Striker, a wonderful organization. However, everything I say is on behalf of myself, and I’m drawing from the career with exposure to financial services, healthcare, insurance, education, and retail earlier on in my career. 

Topmost Challenges Getting Maximum Value From Data Tech

So let’s dive into it. And here’s what I was wondering a few months back when I actually gather what we’ll talk about today. I was thinking, how do we focus on what really matters? You know, there’s ever increasing data, there’s data proliferating everywhere, technology, infinite technology solutions, AI is advancing so rapidly every day, the only constant it seems is change. So what I was thinking is, how do we actually innovate, because we need to innovate. We need to not be stuck where we are, but embrace a lot of the newness and the change, I would think, innovate, but do it ethically. And that is hard to do. How do we innovate ethically, while elevating the stakeholder experience? And when I say stakeholder experience, I’m referring to our customers, or our patients, depending what, you know, what industry we work in. I’m also referring to people that we’re doing business with, like our suppliers, other stakeholders, but even the people internally in the organizations. So innovate ethically, really elevate that stakeholder experience, and again, stay focused on what really matters. Because especially in this time and age, with so much happening, it feels like there’s a tsunami of information hitting us, so many things happening, how do we thrive in organizations by focusing and executing on what really matters? 

So back in February, February 5th, is the date of my asking ChargePT to see what it would say. The exact question was, what are the topmost challenges in getting maximum value from our data technology and AI investments? And this is what ChargePT came back with. Data quality and governance, we can’t get away from it, right? To really get the most out of what we’re implementing, it needs to be data that we can trust, that it’s relevant to what we’re trying to solve for, and it’s reliable, and we can get to it. I mean, I’ve seen things, for example, nevermind data, just even a simple question as to who can tell me something about these data attributes. I’ve seen examples in situations where somebody asked a question, and it was not one, not two, not three, but 15 emails later, they even got access to the person that had a little bit of that expertise specific to the data attributes that they were looking at at the case. So having access to right, trustworthy, relevant data, and how we manage and govern that, obviously it’s a challenge out there. And I think all of us being in the data space, it’s no surprise there. 

We talk about the skills shortage and the talent gap. And ChargePT obviously contributed that as one of the top challenges, but I want to add another dimension to this. Yes, we need talent. And if you’re a leader out there or wherever, no matter actually your own organization, but especially if you’re a leader, I would say it’s not enough to just have good talent. It is also important to match the right skills for the right role, the right talent for the right role. And we’ll talk a little bit more about that. Bullseye right in the middle, I love this, alignment to business objectives. I said earlier, a long time in this space and in this career, and this is one of those biggest pitfalls that I see we’re doing. And it is a biggest challenge when we’re not aligned with the business objective. And again, we’ll talk a little bit more about that. Resistance to change. I think this will resonate how many times you try to bring in, implement a new project, a new capability, be it a new technology, a new business capability, but people are just comfortable with what they’ve known and the pushback is real. 

But the other challenge, according to Chad Gipity back in February, is ethical and regulatory compliance. No surprise there, right? Think about GDPR. Think about CCPA, the California Consumer Act, the EU AI Act these days, right? There are real ethical considerations and bias in our data that could make it challenging for us to get good trustworthy answers and direction from the systems we implement based on the data that it leverages, right? And there are real regulations and compliance that we need to draw by it with. I have heard of stories, I mean, that was a few years back, but one we cannot really explain that machine learning models, that the company actually shut down what they thought was good because it was just not explainable and they couldn’t take the risk. So there are cases and these are challenges and we’re putting a lot of emphasis and investment organizations are into doing that. 

Business Value

That’s what Chad Gipity thought. But when I look back, I wanted to add another dimension and bring to you what I thought over the last 30 years are things that we know, but we kind of skip right over. So I packaged that into five key principles. I actually heard on the keynote earlier this morning, Mark also mentioned some of these things, but I would say that these are so critical that we really underestimate them. And let me take you through that and what I mean. The five key principles, starting with business value. Like I was saying earlier too, when I introduced myself. With 30 years in this business, I’ve seen many different technologies. The hype these days, it’s all about large language models and generative AI. The hype 15 years ago could have been Hadoop. Hype around data lake versus data lake house. There is a lot of hype depending how things have changed where we are, which really always stays constant. And we can’t get away from what’s really important for our organizations. It’s not implementing the latest and greatest technology, although don’t get me wrong. This is good. This is, we need to use the new tools, including gen AI. We need to embrace. We just want to make sure that we use the right technology and the right tool for the right purpose, which means we need to understand what problem are we trying to solve. 

A lot of what’s happening in our organizations is that we just get all about the hype, the shiny object as Garner calls it. I call it buzz, just something else that is buzz worthy and it changes, it seems almost from day to day. So we need to understand that it is not about doing technology for the sake of technology. It’s not even about the data. You know, I published an article on CI review, the magazine a couple of years ago now, and it was about the top pitfalls on our way to implementing a successful data strategy. And the very first one is that data strategy really is not about the data, but it is about the business. So are we adding value? What pain point are we trying to solve for? What new revenue stream are we trying to create or enhance? What risk are we trying to mitigate to safeguard our organization so it’s a thriving business? We can’t really do that without focusing on it. And we’ll talk a little bit more about that. But being curious, implementing design thinking and how to go about it, it’s always a great practice for us to find that true north and making sure that we’re really focusing, again, on what matters before we even start implementing things. So business value, we skim over it at times. We don’t go on it deep enough. 

Clear Purpose

But the clear purpose, it’s also important because sometimes the goals are fuzzy. They’re not necessarily tied to stakeholder value. So those objectives, again, the objective is not to implement the latest newest technology, but making sure that it’s connected back to real, tangible business value. And while we’re doing this, when we have a clear purpose, it also means that we insist on clear roles and expectations. That’s one of the ways that we can do that. How many times have you perhaps walked away from a meeting and you weren’t sure what you were supposed to do going from there? I’ve seen situations where two weeks later somebody emails and say, “Hey, do you have that research done that we spoke about at the meeting?” Now, two weeks later, that’s a long time. We’re wasting time here, even if it’s three days later, right? And the person says, “Well, no, I didn’t know what I was supposed to do because I thought you were doing that.” I wish I was alive in the room and I could ask you, leave comments, please, let me know what you think. I’ve seen it so many times in my career where we just go in circles sometimes. This is all about going back to the basics. This is not rocket science, but those basics that are so instrumental and yet we underestimate the criticality and we skip right over. And then we keep spinning cycles sometimes versus, again, how do we get the most out of our solutions, right? Having that clarity of roles and expectations, having the right people in the right role, that is important. 

I’ll give you just another little example, what I’m saying about having the right people in the right role and having repeatable ways of working as well, right? With cadence, with clarity and all of that. But there was a time in my career that I joined this organization and there was this gentleman that was great, bright, competent, and he was put in charge of driving the data governance strategy for the organization, implementing it. Great gentleman, great guy, competent, more as a day-to-day operations manager. Now the visionary leader that you think that you would need in that role to really make sure that you would communicate what the company was trying to do effectively to the executives. Because we can’t do anything without getting that funding. And executives need to buy in, both to prioritize the project or the program or the phase of a bigger roadmap that we’re trying to implement, to prioritize it and fund it. So you need the right people in the right role. That initiative, obviously, for that company, which was big, just failed. And in my view, that was a big reason why we were not focusing on the right things, looking at the, understanding the business, again, and having the right people to drive the right parts of that initiative. So back to the basics, no rocket science, but extremely important. 

Common Goals

So let’s talk about common goals. Just like that image to the left, you know, if everybody is not marching towards common goals, that team is not going to reach the destinations, not going as effectively, efficiently as they could. They’re not going to win that race. And when I say common goals, it’s both about consistently understanding what the goal is. When we hear something, everybody, we have different images and pictures in our heads that we relate what we listen, what we’re hearing to. So when we get a conclusion, everybody gets to it a different way. We interpret it differently. So taking a moment to make sure that everybody understands what the goal is, and then we marching across to that same goal. That means in an organization, like I said, my role has been global level roles, cross-functional cross-system for a long time now. So so many times I’ve seen efforts in organizations where great projects, but there’s dependencies, except this project doesn’t know about the project, and we don’t coordinate. Or even within the same initiative, different teams, like the build team works different than the data team works different than the business team. It can’t really be that successful that way. We need to talk to each other. We need to make sure that we are marching again towards that common goal, understanding the same, and we are working as one team. It’s not the data versus the tech team versus the data science team versus the business team. One team, we go further together. I absolutely believe in that, and I’ve seen it in practice, how it works, where we actually are intentional about this and driving that partnership, communication, and collaboration across our teams for those common goals. 

Embrace Change

When you’re standing still, you’re actually going backwards. This is what a friend of mine said a while back. Standing still is actually going backwards. It’s kind of like that image on your screen, right? That person’s standing still in front of the train. When that train leaves, the train goes forward, and although that person is standing still, it’s almost as if they’re going backwards. And change, like we already know, it’s a way of life. Might as well embrace it. It is the only constant, right, the very first slide that I shared with everyone here. Infinite data solutions, technology changing, rapid AI advances, constant change. I’ve worked and I’ve seen teams and organizations over my career where organizations have grown up in silos. Teams, people, you know, they know their systems, they know the processes. As we get emotionally connected to the different technologies or to the different processes, why should anyone change their habits, right? But the thing is, and this is, again, why we need to put the right people in the right role, because in embracing change, it’s not going to happen just by itself. So how do we drive it by explaining to people what’s in it for them, but from the other perspective to each one of us, I think it’s important to understand that it’s going to happen with, without us. So how do we become part of the solution rather than being left behind even when we’re standing still? 

End-to-End Mindset

So let me take you to the fifth principle. I want to talk a little bit about the end-to-end mindset and I think this is a concept, a principle and a practice that people forget a lot about. We get very much into the, what do I have to do within my project? It’s a little bit more of a project versus product mindset, right? With, like I said, 20 of my 30 years having done enterprise architecture, that is business process and data and technology and solution and obviously going really deep in the data world, what I’ve realized is that there’s always, always downstream impact. And what I’ve realized is that we can’t really put resilient systems out there, which for me, it’s one of those key drivers, right? It is not enough to just build solutions and implement them and roll them out. Because if we don’t take a moment to really think about, is this the right thing? And are we setting up a solid foundation? So many times I’ve seen in my career, when we go live with something we’re implementing and six months later, all of a sudden we have to go back and rewire things because we didn’t take enough understanding of who else depends on this. It could be that we’re building the data for a particular dashboard. It could be, for example, that we’re implementing, I don’t know, something in CRM, right? But then not understanding that, hey, two, three, four steps down the process, somebody else will need to look at this data that we’re capturing here in our CRM, and they will perhaps need to know some diverse indicator, for example, for our suppliers or our customers or whatever it is, right? Knowing that ahead of time, it didn’t mean that we built for it, but we designed for it. 

So thinking about the end-to-end business process, this is not about the data flows, you know, hop to hop to hop, just looking at the interfaces, right? And looking at the integrations. It’s about putting it into business context and really understanding how our business works. Only then we will be able to make sure that we’re actually solving a business problem or creating new opportunities or safeguarding ourselves. And understanding that, again, when we’re looking at the end-to-end, it’s not just about what we’re trying to do here because downstream, somebody else will also depend. And there’s never been a time that I haven’t seen that be the case. For resilient solutions, it’s what we keep on saying, right? Build once, build iteratively, understanding the impact doesn’t mean we build for everything. It doesn’t mean we take forever. We want to be agile, but sometimes, let me give you this example, maybe this resonates. If I want to build a house and I know that I need the ground floor this year, but I know it’s not until two years later, you know, my kids are going to come back and they’re going to, you know, maybe their grandkids come visit and I need a second floor, right? If I don’t take that into consideration to begin with, maybe my foundation is not strong enough because all I care about is just building my first floor. Or perhaps I’m putting the plumbing and electrical in a way that when I actually do have to build the second floor next year, the year after, well, I can do it, but it will cost me a lot more because I need to move the plumbing because it’s over here that I need that bathroom on the second floor. So again, I don’t need to build for it now, but I need to understand the implications and how everyone is going to use that product that we’re trying to build. So end to end, my setting point. 

Being Curious Drives Optimum Value

And here’s some tips, what has helped me over the years to make sure that I keep remembering and implementing, applying these principles in practice, right? Business value, clear purpose, consistent goals, embracing change and the end to end mindset. It really goes back to being curious and working with people. Start with a question. What is the business pain point or opportunity that we want to solve for? But here’s the thing. I’m sure we do that. What I’m trying to say here is don’t just take what you hear back as given, that’s it, right? Because it’s almost as if you need to peel the onion. It’s almost like asking those wise. What is the purpose? How is it going to help us? Who’s going to use this data that’s going to come out of the data hub, right? How are we going to use it? When we talk through things and you engage, we engage our stakeholders, business stakeholders that we’re trying to solve a need for, then we get into the conversation that it’s engaging and we’re really trying to understand and get deep down to what are we really trying to solve for or enable? So it’s not just a simple question. It’s really having that conversation, being curious and listening actively. 

But then also ask the question, do we all understand it clear and consistently? Like I was saying earlier, everybody gets to that interpretation a different way. If you say something to me, I have different pictures playing in my head to understand what you’re saying. Everybody does. So do that check, rephrase what we’re saying. Make sure it’s crystal clear and ask the question, do we actually think across the different stakeholders and the different teams, business and data and IT and different cross-functional teams, do we actually prioritize the needs across and coordinate and orchestrate? 

The next one is the biggest. We keep falling into this trap of talking technology and system. Again, it’s not about the technology. It’s about how do I keep my business thriving so we can be around and keep on implementing more great resilient solutions. So if I need to put a nail in the wall, I’m not going to talk about the screwdriver. It’s got a great benefits, great. But what I need is a hammer, not a screwdriver. So again, don’t fall into the trap about talking about technology, connect it back to business value. And talk about specifically relevant data. Years ago, I was part of talking with, you know, being part of a project where we were talking with our SIs and, you know, it was very specific, the attributes. Yes, we’ll bring a lot of data from the different legacies onto the data lake, lake house, right, creating analytics. But it was very specific. If we’re not specific, not only in our countries, but how we work with each other, we’re going to keep making many copies of our data and just not really focusing, executing on what’s really focused. So bring the relevant data so everything goes faster and we deliver value faster. And who downstream needs that? Like I was saying earlier, usually we get the ask from upstream, and we’re thinking about what upstream that asks for it, asks for it, needs, but who else downstream will depend on this? Let’s not forget that. 

And I’m going to leave you with one last thought. Underneath all of this, it’s really, as we’re using technology, great tools. It’s really working with each other, communicating, being clear, back to the basics is be curious. And when we are actively listening, and we’re fostering those collaborative relationships, and we foster the trust and credibility, what’s really underpinning all of this, it’s what I call empathetic leadership, respecting each other, working with each other, really listening to each other so that together as one team, we’ll put great resilient solutions together. Because no matter how technical your role, it is important to realize that at the end of the day, every business, it’s a people business. And no matter how technical your role, knowing and working on enhancing your communication skills, our communication skills, and focusing on what really matters is really what’s going to help us get the most from our solutions, and as well as in our careers and with our organization. And with that, I think we’re coming down to time too. This is the quick five principles that I wanted to share with everyone. Thank you so much for being here.