Episode Transcript
[00:00:00] Speaker A: All right, diving right in. Today we're going deep on business analytics maturity.
[00:00:05] Speaker B: Ooh, I like that. Diving deep.
[00:00:07] Speaker A: You know, like, from just drowning in data to having that, like, clear vision of what to do.
[00:00:13] Speaker B: Absolutely. Going from data chaos to, you know, AI practically whispering the best decisions in your ear.
[00:00:19] Speaker A: Okay, Love it. And we've got a great guide for this, this deep dive. Right? A recent article from G2M Insights, the business analytics maturity model. A roadmap for sales and marketing executives.
[00:00:32] Speaker B: That's the one. And this roadmap, it really lays out a path, you know, with these five levels of maturity.
[00:00:37] Speaker A: Five levels. Okay.
[00:00:37] Speaker B: Yeah.
[00:00:37] Speaker A: So it starts with the beginning, then goes to developing, emerging, accelerating, and ends with leading.
[00:00:43] Speaker B: That's the journey. Yep. From fumbling in the dark to being a true leader.
[00:00:48] Speaker A: So paint me a picture. What's it like being stuck at that. That beginning stage?
[00:00:51] Speaker B: Okay, imagine a company where every decision feels like. Well, a total guess.
[00:00:56] Speaker A: Ouch.
[00:00:57] Speaker B: Yeah, data is all over the place. Spreadsheets galore, Each department guarding their information like. Like it's a secret treasure.
[00:01:04] Speaker A: And no real insights, just gut feelings driving everything.
[00:01:08] Speaker B: Pretty much. It's like trying to put together a puzzle, but with half the pieces missing and no picture to guide you.
[00:01:13] Speaker A: Sounds messy. Now, flip side. What about a leading company? What makes them different?
[00:01:18] Speaker B: A leading organization. They treat data like a strategic weapon, not a burden.
[00:01:25] Speaker A: Okay, I like that analogy.
[00:01:26] Speaker B: Right. Think of it as a perfectly organized library. You know, every piece of information is cataloged, easy to access, ready for analysis.
[00:01:34] Speaker A: So they've got systems in place. Right, Like a master data management system.
[00:01:38] Speaker B: Exactly. Everything's integrated, clean, consistent.
[00:01:41] Speaker A: And they actually use that data to make decisions at every level.
[00:01:44] Speaker B: They have a culture built around data driven decision making.
[00:01:47] Speaker A: So at that leading level, is AI actually making the decisions?
[00:01:51] Speaker B: Well, not quite. It's more like AI is this incredibly smart advisor.
[00:01:56] Speaker A: Okay.
[00:01:56] Speaker B: You see, providing insights, recommendations based on tons of data.
[00:01:59] Speaker A: But what kind of recommendations are we talking about?
[00:02:02] Speaker B: Imagine an AI that looks at thousands of customer interactions.
[00:02:06] Speaker A: Okay.
[00:02:06] Speaker B: And then it finds patterns that predict, say, churn risk. Then it suggests personalized strategies to keep those customers from leaving.
[00:02:14] Speaker A: Wow, that's powerful. But how do we even start getting there from point A to point Z on this roadmap?
[00:02:20] Speaker B: Well, the article points to data management and governance as the foundation.
[00:02:25] Speaker A: Right. Gotta get that data house in order.
[00:02:27] Speaker B: Exactly, because at the beginning stage, it's like building on shifting sand. The data's all over the place, inconsistent, unreliable.
[00:02:35] Speaker A: A recipe for disaster, for sure.
[00:02:38] Speaker B: But as a company matures, they start to see the light. They consolidate data, put clear processes in place, and they get tools for cleaning and integrating data.
[00:02:47] Speaker A: So it's like gradually replacing that shaky foundation with like solid bedrock.
[00:02:53] Speaker B: Perfect analogy.
[00:02:54] Speaker A: Now what about tools? What would a company at, say, the emerging stage be using?
[00:02:59] Speaker B: Hmm. At the emerging stage, you might see them using data warehousing solutions, implementing data quality rules, maybe even starting to look at master data management systems.
[00:03:08] Speaker A: So they're on the right track, but still a way to go to reach that leading level.
[00:03:11] Speaker B: Exactly. It's a journey, a step by step process.
[00:03:14] Speaker A: Okay, so we've got our data foundation in place. What's the next step up that maturity ladder?
[00:03:19] Speaker B: Reporting and dashboarding come next. Because at the beginning stage, you're stuck with these static reports.
[00:03:24] Speaker A: Right? Like looking in the rear view mirror.
[00:03:25] Speaker B: Yeah, they tell you what happened, but they're not helping you steer the ship.
[00:03:28] Speaker A: Like trying to navigate with an outdated map.
[00:03:31] Speaker B: Exactly. You need a gps, not a history book. So as you move up, reporting goes from static to dynamic.
[00:03:37] Speaker A: Okay, so we're talking interactive dashboards, real time data.
[00:03:41] Speaker B: Right. And eventually those. Those AI powered dashboards that. That actually help predict where you need to go.
[00:03:47] Speaker A: That's incredible. But I'm sure some companies are hesitant to embrace all this. What holds them back?
[00:03:53] Speaker B: There are a few roadblocks. Yeah, Some struggle with data silos. You know, departments hoarding their data, the data hoarders. Right. Others lack the technical expertise to implement these advanced analytics solutions.
[00:04:07] Speaker A: Makes sense. And I bet company culture plays a huge role too.
[00:04:10] Speaker B: Absolutely. That's a big one. Because to reach the leading stage, you need a major mindset shift.
[00:04:16] Speaker A: So at the beginning, it's all gut feelings, no trust in the data.
[00:04:20] Speaker B: Exactly. Decisions based on intuition or whoever shouts the loudest.
But to get to that leading level, you need leaders who champion data.
[00:04:30] Speaker A: Okay, so leaders need to walk the walk, not just talk the talk.
[00:04:34] Speaker B: Exactly. They need to model that data driven decision making and create a culture where. Where everyone sees the value of data.
[00:04:41] Speaker A: So it's not just about having the tools, it's about having the mindset.
[00:04:44] Speaker B: Exactly. It's about making data driven decisions the norm, not the exception.
[00:04:49] Speaker A: Love that. Okay, so we've got a solid data foundation, sophisticated reporting, a data driven culture. What's next on this journey to analytics nirvana?
[00:04:57] Speaker B: Well, that's where we venture into the exciting realm of predictive and prescriptive analytics.
[00:05:02] Speaker A: Ooh, I like the sound of that. But I guess we'll have to wait for part two to dive into that.
[00:05:06] Speaker B: Stay tuned. Welcome back to the Deep Dive. Last time Remember, we were just about to explore that exciting world of predictive and prescriptive analytics.
[00:05:15] Speaker A: Right, right. Where data becomes like a crystal ball instead of that rearview mirror.
[00:05:18] Speaker B: Exactly. It's about anticipating the future, not just reacting to the past.
[00:05:23] Speaker A: So walk me through it. How do these predictive and prescriptive analytics look different at those different maturity levels?
[00:05:30] Speaker B: Okay, so at the very beginning, the beginning stage, predictive analytics is practically non existent.
[00:05:36] Speaker A: Companies are just like stuck in the past.
[00:05:39] Speaker B: Pretty much they're reacting to what already happened, not. Not looking ahead.
[00:05:43] Speaker A: So how do they start dipping their toes into these, these more advanced analytics?
[00:05:48] Speaker B: Well, as they move into that developing stage, they start experimenting with basic statistical analysis, you know?
[00:05:55] Speaker A: Okay, so like baby steps.
[00:05:56] Speaker B: Exactly. They might use those regression models to, to understand the relationships between different variables. Or they might try try forecasting some basic trends.
[00:06:05] Speaker A: Got it, got it. What about emerging stage? What kind of cool stuff happens there?
[00:06:10] Speaker B: That's where things get interesting. Emerging companies, they're embracing more sophisticated predictive modeling techniques.
[00:06:16] Speaker A: Okay, so like what are they actually doing?
[00:06:18] Speaker B: They might be using machine learning, you know, to predict things like customer churn or identify sales opportunities, or even, even optimize those marketing campaigns.
[00:06:29] Speaker A: Ah, so that's where AI really starts to come into play.
[00:06:31] Speaker B: Exactly. And then as they move into the accelerating stage, that AI becomes even more deeply integrated into their operations.
[00:06:39] Speaker A: Okay, so, so what does that look like in practice?
[00:06:42] Speaker B: Well, they might use AI powered tools to personalize customer experiences or even automate those decision making processes.
[00:06:50] Speaker A: Wow, AI is calling the shots.
[00:06:52] Speaker B: Well, not exactly calling the shots, but definitely helping to inform those decisions.
[00:06:56] Speaker A: So by the time they hit that leading stage, they're practically fortune tellers.
[00:07:01] Speaker B: Well, not quite fortune tellers, but they're definitely leveraging the power of, of those predictive and prescriptive analytics to make really informed decisions.
[00:07:09] Speaker A: Can you give me an example? Something concrete?
[00:07:11] Speaker B: Sure. The G2M Insights article mentions a company that used AI to analyze sensor data from their manufacturing equipment.
[00:07:20] Speaker A: Okay.
[00:07:20] Speaker B: They were actually able to predict potential failures before they happened.
[00:07:25] Speaker A: Wow, that's amazing.
[00:07:26] Speaker B: Right, so they could proactively schedule maintenance, you know, prevent those costly downtime situations, keep everything running smoothly.
[00:07:34] Speaker A: That's incredible. It really shows how powerful this stuff can be.
[00:07:37] Speaker B: Exactly. And prescriptive analytics takes it even further. Goes beyond just predicting to actually recommending actions.
[00:07:45] Speaker A: So it's like having like a personal advisor who not only tells you what's coming, but also says, hey, here's what you should do about it.
[00:07:52] Speaker B: Precisely imagine. Imagine a sales team that's armed with AI powered insights you know, telling them which leads to prioritize, what kind of messaging to use, even the best time to make that call.
[00:08:04] Speaker A: That's incredible. Like a cheat code. Code for business success.
[00:08:07] Speaker B: In a way, yeah. It's about using data to make smarter, more strategic decisions.
[00:08:12] Speaker A: But I'm curious, with all these advancements, what happens to those, those traditional sales and marketing teams? Do they become obsolete?
[00:08:19] Speaker B: Not at all. In fact, analytics empowers them to be more effective than ever before.
[00:08:23] Speaker A: So it's not about AI taking over, it's about AI augmenting human intelligence.
[00:08:29] Speaker B: Exactly. At the beginning stage, you know, sales and marketing often operate in silos.
[00:08:34] Speaker A: Right. They're not really talking to each other.
[00:08:36] Speaker B: Exactly. Like two ships passing in the night, you know, trying to reach the same destination, but taking totally different routes.
[00:08:42] Speaker A: Yeah, I could see how that would lead to a lot of missed opportunities.
[00:08:46] Speaker B: Absolutely. But as companies mature, those silos start to break down.
[00:08:51] Speaker A: So they're finally on the same page.
[00:08:52] Speaker B: Right. Data is integrated, strategies are aligned, and sales and marketing start working together seamlessly.
[00:08:59] Speaker A: It's like they finally got that shared map in a common language, you know?
[00:09:02] Speaker B: Exactly. And at the leading stage, predictive analytics becomes the, the glue that binds them together.
[00:09:09] Speaker A: Okay, give me an example.
[00:09:11] Speaker B: Imagine a system that predicts which, which marketing campaigns are going to generate those qualified leads, and then automatically routes those leads to the right sales rep with personalized insights, recommendations, the whole nine yards.
[00:09:25] Speaker A: Wow, that's amazing.
But all of this, it requires more than just technology. Right?
[00:09:30] Speaker B: You're absolutely right. The real transformation happens when data becomes ingrained in the company culture.
[00:09:37] Speaker A: It's about the mindset, not just the tools.
[00:09:39] Speaker B: Exactly. At the beginning stage, there's often a fear of data.
[00:09:42] Speaker A: You know, like people are afraid of what the data might reveal.
[00:09:45] Speaker B: Right. They might see it as a threat to their jobs or their intuition.
[00:09:49] Speaker A: So how do you shift from a culture of fear to one of data driven confidence?
[00:09:57] Speaker B: It starts with leadership. You know, leaders need to champion the use of data.
[00:10:01] Speaker A: Okay, so lead by example.
[00:10:03] Speaker B: Exactly. And create an environment where asking what does the data say? Is encouraged.
[00:10:09] Speaker A: So it's about fostering that curiosity, that openness. Right. Where data is a tool for learning and improvement.
[00:10:15] Speaker B: Precisely. And it's about building data literacy throughout the organization.
[00:10:18] Speaker A: Right. Not just the data scientists. Everyone needs to be comfortable with data.
[00:10:22] Speaker B: Exactly. And when that happens, when everyone's speaking the same language, you see a real transformation in how the organization operates.
[00:10:29] Speaker A: It's a powerful vision, but I'm sure some folks are thinking, okay, this all sounds great in theory, but how do I actually do this in my company?
Welcome back to the Deep Dive. We've covered a lot of ground, right? From data management to, you know, those sophisticated predictive analytics.
[00:10:50] Speaker B: Yeah, We've been deep in the data.
[00:10:51] Speaker A: Trenches, but now it's time to bring it all together. How do we actually put these insights into action?
[00:10:59] Speaker B: That's the key question, Right. It's not enough to just understand the theory. We got to make it work in the real world.
[00:11:04] Speaker A: Right. So let's get practical. Imagine a company stuck at that beginning stage, drowning in data. Where do they even start?
[00:11:11] Speaker B: Well, the first hurdle is often just acknowledging there's a problem.
[00:11:14] Speaker A: Oh, I bet that's harder than it sounds.
[00:11:16] Speaker B: It is. Because sometimes, you know, companies at that stage are. They're so used to doing things a certain way that they don't even realize there's a better way.
[00:11:23] Speaker A: Like they're lost but don't know it.
[00:11:24] Speaker B: Exactly. So you gotta shine a light on the issue. Do an honest assessment of your data practices. Ask the tough questions, like, where is our data? Who can access it? Is it even reliable?
[00:11:35] Speaker A: You know, so step one. Awareness.
[00:11:37] Speaker B: Exactly. Get everyone on the same page about where you stand.
[00:11:41] Speaker A: Then what?
[00:11:42] Speaker B: Then look for those quick wins. You don't have to do everything at once. Start with a small manageable project.
[00:11:48] Speaker A: Something to build momentum.
[00:11:50] Speaker B: Right. Maybe focus on improving data quality in one department or setting up a simple reporting dashboard to track a key metric.
[00:11:59] Speaker A: Something to show the value of data.
[00:12:01] Speaker B: Exactly. Prove it works. Then you can build from there.
[00:12:04] Speaker A: Okay, that's great for beginners. What about companies a little further along? Say, those in the developing or emerging stage?
[00:12:12] Speaker B: Well, they often hit a wall with data silos. Different departments, you know, they have their own systems, their own metrics, their own way of doing things.
[00:12:20] Speaker A: Like trying to solve a puzzle with pieces from different boxes.
[00:12:23] Speaker B: Perfect analogy. Now, breaking down those silos is crucial.
[00:12:26] Speaker A: How do you do that?
[00:12:27] Speaker B: You might need a data governance framework. Clear roles for who owns what data, or even invest in tools that can integrate data from all those different sources.
[00:12:37] Speaker A: So it's about collaboration, getting everyone working together.
[00:12:40] Speaker B: Exactly. Another common challenge is a lack of data literacy. You know, not everyone knows how to work with data effectively.
[00:12:47] Speaker A: Makes sense. You can have the best data, but if nobody knows what to do with it, it's useless.
[00:12:52] Speaker B: So invest in training, development, maybe some data literacy courses for everyone, specialized training for key roles, or even a mentorship program.
[00:13:03] Speaker A: So make everyone a data expert in their own way.
[00:13:05] Speaker B: Right. And then as companies move towards those higher levels, like exc Accelerating and Leading. They often struggle with scaling their analytics.
[00:13:12] Speaker A: They've got the basics down, but now they need to go big.
[00:13:15] Speaker B: Exactly. Think about it like. Like baking a cake. You've mastered the recipe, but now you need to bake a hundred cakes.
[00:13:22] Speaker A: 100 cakes. That's a lot of baking.
[00:13:23] Speaker B: Right. So you need to industrialize the process, invest in more robust infrastructure, automate those data pipelines, develop standardized models.
[00:13:31] Speaker A: You know, so it's about going from one off projects to a more.
A more repeatable, scalable system.
[00:13:37] Speaker B: Exactly. And then at the very top, that leading stage, the challenge becomes staying ahead, maintaining that momentum.
[00:13:44] Speaker A: All right, you've climbed the mountain. Now how do you stay on top?
[00:13:47] Speaker B: You got to foster a culture of continuous learning. You know, encourage people to explore new technologies, experiment, challenge the status quo.
[00:13:55] Speaker A: Always be learning.
[00:13:56] Speaker B: Exactly. It's a journey, not a destination, right?
[00:13:59] Speaker A: Absolutely. So I guess the takeaway is this data driven thing. It's a process.
[00:14:03] Speaker B: It is. And the journey's worth it. The payoff is huge. You know, better decisions, improved outcomes, a real competitive edge.
[00:14:12] Speaker A: It's about embracing the power of data.
[00:14:14] Speaker B: And using it to create a better future for your company and maybe even for the world.
[00:14:20] Speaker A: Love that. Well, thank you for taking us on this deep dive. I think our listeners have a much clearer picture of what it means to become truly data driven.
[00:14:29] Speaker B: It's been my pleasure. And remember, it's a marathon, not a sprint. Be patient, be persistent, and never stop learning.
[00:14:36] Speaker A: Couldn't have said it better myself.
[00:14:37] Speaker B: Yeah.
[00:14:38] Speaker A: And for our listener who want to learn more, be sure to check out that G2M Insights article, the business analytics maturity Model. We'll link it in the show notes. Thanks for joining us on the deep dive.
In this episode, we break down the Business Analytics Maturity Model and what it means for sales and marketing executives looking to elevate their data strategy. From organizations relying on spreadsheets and siloed reporting to those leveraging AI-driven decision-making, we explore the five stages of analytics maturity and how businesses can progress along this curve. We’ll discuss the challenges of fragmented data, the transition to predictive analytics, and the power of prescriptive insights in optimizing business outcomes. Learn how G2M Insights helps organizations build scalable analytics capabilities and foster a data-driven culture. Whether you’re just starting your analytics journey or […]
