What Makes a Data-Driven Culture?
Michael Hatfield from BRG's Global Applied Technology (GAT) team discusses how to approach data-driven culture and the analytical tools that can help empower organizations to make effective, data-driven business decisions.
TRANSCRIPT
MJ 00:00 Hi, everyone, this is Michael Jelen from the Global Applied Technology Podcast. The GAP Team, as we call ourselves, is a globally distributed team of software engineers, data scientists, graphic designers, and industry experts who serve clients through our products built atop the BRG DRIVETM analytics platform. We're helping some of the world's largest and most innovative clients and governments transform raw data into actionable insights, drive efficiency through automation, and empower collaboration to improve business decisions. You can learn more about us, our products, and our team on our website, brggat.com. And if you have any questions or comments, please email us at gat@thinkbrg.com.
Today, I'll be speaking with Michael Hatfield, “Hattie,” an associate director in the Global Applied Technology team. We dug deep into what makes data-driven culture and the technology tools that empower organizations to win using data. I love discussing the multitude of analytics tools that organizations purchase that never seem to get them closer to effective, data-driven, business decisions. Please enjoy this conversation with Mike Hatfield.
Well, Hattie, I've always thought of you as the southern gentleman on the team, but you've also come to the table with one of the best work ethics I've ever seen. I know that started for you at a relatively young age, doing a pretty difficult job outside. I believe the only technology that you had was maybe a machete for that job. Would you be able to talk to me a little bit about your time as a tobacco farmer?
MH 01:23 Oh man, absolutely. And just so you know, it's either a tobacco knife, or most normal people would call it a hatchet, but that is absolutely the key technology. That and a spear. So, I worked on my grandfather's 60-acre tobacco farm as my very first job. Nothing but manual labor and sweat and effort. I can tell you it definitely ranged in terms of features and capabilities, starting from spearing tobacco and cutting tobacco, putting it on wagons and then, probably one of the more fun parts, was whenever it would rain, you have to get the tobacco into the barn. But when it rains, you actually get a break. And a break is getting into the barn, crawling up into the top of the barn, and moving the tobacco from the lowest point in the barn to the highest point in the barn, so you can make more room for more tobacco. And so, that cycle continues from about May 15 through whenever you're done. And a story for another day, but my uncle ran off the one big summer that I was kind of old enough to lead the charge. And I will tell you, 60 acres of tobacco for one guy and two part-time uncles—not the one that left—really takes a while and takes you into about October. So, a good solid—what is that?—six-seven months of manual effort, but I'm better for it.
MJ 02:45 Yeah, it sounds like that's definitely where you've cultivated that hard work.
MH 02:51 Well, it should be no surprise that I don't think anyone is born wanting to be interviewed on data-driven cultures per se, but I found my way there nonetheless. So quick background on myself. I grew up in Maryland in the United States and actually moved on from there to work at a prestigious Wendy's in fast food. Worked at another restaurant doing line cook and server work, because that's just what was available to me in my own backyard. And so, I've learned a lot through a lot of different trades.
But luckily, thanks to a loving family and just a great support system, went on to university at the University of Maryland and went from there and worked in DC at the World Bank's Credit Union. So I studied international business and Spanish, and did a lot of things with data, but didn't really know that would be part of my job. In that job at the World Bank's Credit Union, which is really retail banking services for a very international clientele, I learned a lot more about data, really started implementing it into my work.
But I got to tell you, I think the way that I came through tobacco farming all the way to the data component of my work there was really that I was always trying to find a new way to do things. And I was watching other people. I'm like, "They're really kind of messing it up for the rest of us." So, when I worked with other managers and leaders, they were great in their own respect, but nothing was data-driven in those first jobs. So that's how I think I got really excited about this, really pumped about it, and really made being data-driven a part of, at least, my culture. I don't know if it really stuck with other organizations, but that's how I moved into the role and I’m excited to talk about it. Actually, today, though, I did not expect that thirty-seven years ago.
MJ 04:36 I don't think anyone knows what they're getting themselves into, but this area seems like it's been discussed more and more recently. The idea of a data-driven culture or driving an organization to make decisions based on data seems to be a buzz that everyone's talking about right now. And so, at the risk of being a little bit controversial, I was hoping we could go a slightly different direction today than a lot of the other data-driven discussions around culture. But maybe, first, if you could just define a little bit about what we mean by a data-driven culture or how we approach that slightly differently or your view based on all your experience in that area?
MH 05:12 Happy to do that. And I'm not sure if it'll be completely controversial or if it aligns somewhat or completely based on our readings and based on our own experiences, so I can only tell you what I know. I mean, when I talk about a data-driven culture, it's simply that organizations should certainly be putting data at the center of everything they do. And I think you'll hear that pretty much everywhere. I think the part that becomes controversial is, are people really achieving that inside of organizations? And are any organizations achieving it the way that they want to?
And so, the way that we've defined it—through our experience working in consulting professional services and bringing technology to that—is really making sure, one, that data is at the center of the conversation. And it's not just the conversation, though. What we mean is that when you go to a meeting or you're making a decision about capital expenditure and strategic decisions that you're making inside the organization, it's not just one pie chart that you're using to drive the decision. It's not just a pie chart plus your gut. It is being intense and intentional about using data to get to the right decision in the end. And we think, from our experience, that requires not only the data, but a very accessible way to get to the data that requires collaboration around data, engagement with the data. Yes, it requires the analysts that built up that work, but it also, of course, requires those leaders and those decision makers. And you've got to have a way to bring all of those people to the proverbial table even if that table is digital and that table is remote. But it's got to be at the center of what you do and not necessarily an afterthought or ancillary to the decision-making process. And so that's what we've seen really work very well at organizations we've worked with, some that we've probably wanted to work with, and some that we aspire to be.
MJ 07:01 Okay. So, it's something that encompasses the entire organization, and it's something that goes from the people that ingest data all the way up to the C-level, people that are making decisions based on that information. It seems like something that's very important to the organization and often differentiates good firms from bad firms or success from failure. Can you talk a little bit about why this is so important?
MH 07:24 Oh man, I could probably talk for days on that topic, but I think I'll start with the reason that it's super important. Right? And it's sort of obvious is that it's not the most exciting thing. It can be a little bit even boring to talk about a data-driven culture and constantly talk about data. I know there's some people like myself, and I'd argue yourself, that love to talk about it, that love to get in the weeds, that love to assess data and think about what it really means. But as an organizational topic for an organization with 1,000, 5,000, 15,000, 50,000 people in it, it's probably not the first thing that everybody does want to talk about. But the fact is it's incredibly valuable.
Read as much as you can on data-driven cultures or those that have been identified to lead with data; you'll find that they are typically more productive—depending on the metrics that are being used for that—they are performing better. When we're talking about publicly traded companies, they are performing better. So, no matter how you cut it, no matter how excited you get about it or bored you get about talking about it, there is value in having a data-driven approach to decision-making and just a general culture. If it translates into profits, organizationally, I'd want to be talking about it. I think that that's incredibly valuable.
I think, anecdotally, I'll peel back to my experience in the World Bank's Credit Union that I talked about before. So, here's what a bad experience looks like in terms of being data driven. When I started the job, I mentioned I always looked at other managers and leaders, and we just really weren't data driven, and I didn't even know what that was, but I knew we could do it better. And so, what I started doing was myself, right? I took the approach to bring data to the meetings, to bring data to the conversations, try to be more data-led when people asked a question. Not just what I thought about something, but give them the concreteness behind why I felt that and where the data was pointing us, or at least what data gave us insights and what we should really be talking about. And people liked that approach. They liked when I came to the meeting and had that approach.
But they didn't want to have that approach. They didn't necessarily interact with that data. They looked to the one analyst to bring that work. I was pretty junior at that time, so they start depending on other people. But those decision makers weren't really interacting with the data, querying it, interrogating it, myself, other people that have great insights on those things. And so it was never at the center of the discussions. And if I didn't show up to the meeting or other people like me that had a similar approach, you weren't going to have that data-driven discussion. And you started to see it would quickly fade away. It wouldn't be in the meetings; it wouldn't be in the decision-making. And you never got the right strategic and formulated approach to dig it in with data and feeling confident about those results and being intentional about those results. And so that was tough.
And I think some of the byproducts of that—you're talking about an Excel analysis being sent over an email and a static report, a PDF here, a PowerPoint there. “I don't have time to go find that information, just send me a quick PowerPoint.” So now you've got these static pieces of derivative content that are flying all around email inboxes. And I've got to assume that this resonates with someone or probably anyone that's actually listening to this. How many pieces of static derivative content they've had flying to their inbox, and a month later, it's either stale, it doesn't really help, it's not meaningful, it doesn't catch up to what the results are a month from now or, even in today's world—right?—tomorrow, the next hour, or the next even 30 minutes when we think about real-time data. And so that's just not a good experience from my perspective.
MJ 11:16 Yeah, absolutely. And I think we're at an unusual time right now in the growth of data in organizations where over the past ten years or so, there's been an explosion in the amount of information that we're taking in in an organization. Some of that's organized very well in structured systems. But as you mentioned, there are a lot of derivative content and Excel sheets and PowerPoints that are just being floated around. So, it seems to me like we've got so much information, it's perhaps not very organized and centralized. And then, we've recently seen an explosion with many different products that aim to be a single silver bullet to be able to analyze that information or convert raw data into something that we can use in organizations. But what I've noticed is that it seems like every organization buys three, four, five, six of these different software packages, and it doesn't really solve the problem at all. So can you talk a little bit about what you've seen in terms of these trends or how we've arrived at this situation where we've got tons of data and tons of tools, but we can't seem to make sense of any of it?
MH 12:17 For sure. So, I love that you hit on that. It means a lot to me, because that certainly resonates with me both in my experience of what I've read and what I see. And that's exactly the piece that you raised around five or six systems being purchased. And what I have immediately seen, because I think that I personally have lived on both sides, from being an analyst and then really working on development and delivery around analytics and decision-making, is there's five or six systems or those piece of software that are bought with the builder in mind. And that's not a knock on the builders, and that's not a knock on that procurement of those different components of software. What it is a knock on is that we're kind of forgetting about those decision makers and the people that have to experience that data.
So, we bring in a new self-service analytics tool, and people start using that to build up their own charts. And you've even got some really cool things. Again, I'm not knocking knees. More recently, I was looking at even more intuitive self-service analytics. And I won't drop any names, but it's a piece of software out there. You, basically, type in what you're thinking that you want to see from the results, right? And if it's hooked up to the right data, the machine learning and AI layer is going to, essentially, look at, "Okay, you want the average sales for the last six months in a certain division on a certain product." And it'll spit back out a chart. And that's pretty cool. And I can see that being valuable. And yet, I still would say if you're a bigger enterprise and you've got five or six systems that you're using or already using or your analysts are using in terms of analytics, you're probably not going to overhaul and bring in yet another one with just a slightly different feature set per se. And even if you bring it in, you've got things spread across all of these different pieces of software.
And so, I go back and, again, I look at those decision makers, not the builders, and ask: What is their user experience? How are they interacting with these tools and components? And from my experience being one and also talking to a lot of them, they say, "Oh man, my experience is that I've got stuff everywhere. I don't even know where to go. I think I have some financial reports in this one ERP system. I think I have the customer data that I want in this other marketing software. I think I have some operational things being built by another team. I don't know where to go, and so I often just end up not using any of it."
And so that's what I'm really keyed in on, or at least motivated by. Can't say that problem is solved yet, but that's a problem in the space that I haven't seen solved by introducing more and more and more building tools that are great, but they're still leaving decision makers probably a little bit confused, overwhelmed, and not really knowing where to go to work with the best data that they can at the right time for the right problem.
MJ 15:16 Do you think that that may be a result of the legacy way that we've structured organizations in the past with different job functions to be siloed? For instance, if the finance team needs analytics, they have their own financial analysts, they have their own software to handle that, and they run all of their different queries and build visualizations in a silo. Where the marketing team may be using a very similar piece of software, may have analysts with a very similar skillset, but because they're looking at a different usage of that information, there's no communication between the two teams. And in general, it seems like management at the top level may have to look at five or six different charts from completely different areas created in, perhaps, different ways. Is that something that you've noticed at working with different clients? And have you seen examples of where that function has been centralized?
MH 16:04 I think I've seen both, working backward. So, I do think I've seen both. I have seen it centralized, and I've seen it really disparate. I don't know if it's a function of just the old way of doing and formatting a business in a hierarchy in different business units. I wouldn't try to comment on where it all stemmed from, but I think it's okay that a marketing team would use specific tools and a finance team would use other tools, because a lot of your functional area is different in that way. I guess what's not different is self-service analytics or analytics in general, right? All of those are going to have a reporting function. So, I don't take any credit away from any of those teams that they have functional business area analysts doing that work, but I think it's more—as I said, I can't tell you where it stemmed from, but I see where it's evolved to today. And even if they're using the same tools in terms of self-service analytics—think of the Tableaus as the Power BIs of the world. And there's so many others, but those are just two really big ones in terms of market share. If you think of those, it's more that the marketing team is building what they need at any given time, or they have an analyst, especially in these big organizations. And the same for finance.
And they're living in the same software, the same general place, but they're not realizing that they should collaborate, or they should share that information back and forth. And so, they're not actually taking the time to cross-pollinate their ideas, bring certain things together. And so then, you asked, again, why is that? Have they really brought decision makers into the fold? Decision makers say, "I need to know more about X." And even they often empower their teams to, "Yeah, tell me what I should know about X or Y." I don't think that they're brought into the process of what else is out there that I could know. What else could I peruse? What else could I benefit from across these different business areas in our organization or our clients' organization? It may be a good example.
So, I think, now, in more SAS-focused businesses, those lines blur a lot more. Oftentimes, a product team, basically, blurs the lines with marketing, which blurs the line with sales. And so, as those lines get blurrier, they're much better at knowing the information that you have across all of these different business units. And I can't say that that is the same in large organizations that I'm thinking of, really, at the enterprise level.
So there's an opportunity there to, again, not only bring decision makers into how these decisions get made in terms of what we're building, where we're putting it, and how it all works together; but I think there's a huge opportunity just for them to give a bit more feedback on how they think they need to use information and maybe what they wish that they can know if they can know everything, right? So, a little bit more aspirational in terms of how they think about being data driven.
MJ 19:00 And one thing I think I've noticed that has worked very well for a handful of companies is along the same lines, where if we're blurring the lines between different functions, perhaps we should blur the lines between the role that people have with analytics. It shouldn't be something that's segregated to an analyst team only, but rather a level of data literacy that enables everyone to collaborate and discuss things to run and build their own sort of analysis. I wouldn't say everyone has to be at the same level as a wizard on any of these different self-service BI platforms, but just simply being able to speak the language of data seems to be very useful in a handful of companies that are able to do that and build that into the culture at every level of the organization seem to be able to iterate a little bit faster.
Can you talk about how we use this data, or what is the core purpose here? So, if we're talking about being data driven or having a data-driven culture, obviously, the metric we're going for is results. So, what is that job to be done to get us from these different platforms into something that translates directly into performance for the organization?
MH 20:07 I think there's a lot to unpack there. I'll go all the way back to the point that—I mean, I love that you brought up data literacy, and I certainly subscribe to that and love that you brought up that it shouldn't just be that super wizard or very analyst driven when you think of data-driven cultures. Where I'll be controversial is that I do think, no matter what tools we put in the toolkit and what we're talking about in today's enterprise-level hierarchy, I do think it's going to continue to be analysts that do build a lot of the dashboards, the analytics, and the general content that's consumed by other decision makers, supervisors, managers and on in the hierarchy. And so, I very much believe in data literacy. I believe in self-service and democratization of data. I just don't think it's going to naturally change where a VP of sales is going to consistently and constantly build up their own reports and start from scratch. And the reason being is because the real job to be done there—you're right—is to create value and, ultimately, drive results.
What does that mean for them? They've got a lot of results to drive at that level, and time-to-value is just of the utmost importance. So, to get to time-to-value faster—my hypothesis—is not going to be for a VP of sales in an enterprise organization to go make their own charts or graphs. They're going to need to delegate and hold other people responsible and trust teams of people. And that's going to require really easy access to the data that they need, and it's going to require a lot of collaboration around what they need, questions that they have of it because you're not always going to get it right on the exact first try, right? And so, no matter what tools you're working with, bringing those decision-makers into this experience more understands about that accessibility, the seamlessness, the engagement, the collaboration, and the ability for that to be that easy to get to the value very quickly, which makes those people want to be a part of that process over and over, right? I'm going to come back to that team, and I'm going to come back to those analytics. I'm going to come back to the table on developing the analytics or enhancing them, asking the right questions of those things. And so that's what, in my mind, is really the job to be done.
And that's the problem to be solved that all the self-service tools in the world aren't necessarily going directly at. They're just giving you a different or faster way to build a bar chart, but they're not bringing the collaboration and engagement and the analytics to the center of the decision-making, which is really what we're talking about when we say build a data-driven culture.
MJ 22:47 That makes perfect sense. And I think, in many ways, that dovetails with the vibe that our team is trying to send out to the world. Obviously, a good chunk of us, yourself included, started from an analytics background, where we were handed large swaths of data, given a specific problem to solve, and then we'd lock ourselves in a room for a certain period of time, come back with an answer. Over the past ten, fifteen years, that's changed radically as the complexity of business problems has escalated. And now, it's almost impossible to make a decision without a cross-functional team of—maybe there's a lawyer, maybe there's a subject-matter expert, perhaps there's an accountant and a data scientist—all looking at this information at the same time to help turn that into something that provides value to the business. So, I think what you're saying here, integrating everyone and creating this collaborative environment is certainly the future of fast decision-making using data. So, I think that that's absolutely perfect and that seems to be the way forward.
MH 23:46 Let me tell you, I think that you covered that more aspirational and—I mean, you just made me excited, if every decision were to be made the way that you just talked about, with cohesion and collaboration across different teams. You've got the right people on the decision at the right time, which, by the way, you can blame COVID, or you can give credence to the last eighteen to twenty-four months of just changing the way that we work and being distributed, where we catalyzed that process of doing that a lot more asynchronously.
But I'm going to go out on the more negative limb and just say that what you just described is the exemplar. I can't say it's happening every day on some of the most important decisions happening in some of the most important companies on Earth, frankly. And that is what keeps me—I wouldn't say—up at night. It's actually what keeps me motivated to find the opportunity out of that, right? How many decisions are getting made where I didn't quite get to bring the right accountant in at the right time, and I didn't quite get the intel from a manager that's on the ground on this particular area? And why not? Because I couldn't get the right meeting together or can't get there, and I just had to make a decision given the time I had given what I have.
So that's where, again, we keep thinking, how do we solve that problem and solve it better and bring technology into it? And that's why I think data-driven cultures are not easy. But do they really have to be that hard? You think about customer experience software, you think about anything customer facing, you think about all these different types of software out in the industry that have just blown up over the last, let's call it, two to five years, and they're all focused around an experience. They all focus on particular problems. And they really bring that experience into the center of it. And so, the technology doesn't solve the problem by itself, but giving people the right process, giving them a good platform to do that work and do it faster and bring the right people at the right time, that sounds like it's something that could really make this not all about teaching data literacy and not all about educational courses and not all about manual intervention, right? And so that's where I get excited. But sometimes, I have to paint the cynical picture of it for just a moment, but I certainly aspire to what you ascribe to.
MJ 26:02 Hear you 100 percent and seeing the same trends that you're seeing out there. I do often ask myself, why is this so hard? But it's a problem that everyone's dealing with, so clearly, it is. And there is some barrier there. You mentioned technology here, and I know you've been largely talking about culture up to this point and ways that we can change the data-driven focus of individuals, but maybe talk to me a little bit about what kind of technology solutions could solve this, because I feel like there's an opportunity here to be able to streamline this process and make it easier to collaborate and give these tools to people. But what does that look like, and how do we do that practically?
MH 26:40 Sure. I'll take a step back. Something that really resonated with me more recently, and I think it finds its way into trying to solve this problem—I was listening to Scott Belsky, the chief product officer at Adobe. And I probably won't get the quote exactly right. His topic was consumerization in the enterprise space. And he constantly was hitting on this concept around there being a new expectation for elegance and simplicity. It's suddenly really high. Before enterprise users have things, right? They just used the product, because they were told to use lest the decision that was made. But now, you've got a lot of these different tools—and let's take analytics as an example: self-service things cropping up, people using things that they want to use, maybe in small business units or just small teams. And I think that's really awesome. I bring that up because more of the first part of that. It's that expectation for elegance and simplicity. I think that decision makers should expect a much easier process of using data and getting to the heart of it. And I think that decision makers from supervisory-level in a business unit, all the way up to C-suites at enterprise-level organizations, I think they would they would think that it should be easier. They're willing to put data at the forefront of a decision, but they probably would say it's not the easiest thing to do in the world.
So, I think what technology looks like, to start helping with that, I think they would expect technology to help with that problem to some degree, right? It'd be like, "Why isn't there an app for that?" Right? And so, maybe it's not a specific app, but it's being driven by that user experience. The technology would bring some of those things that I've said before into the forefront. So, collaboration should really be key. I've got to get to the right people at the right time, whether that's the analysts that built the start of the analytics that I'm going to use, or it's a line manager that deals with this subsection of data that I've drilled down to that I find very interesting that could drive a lot of value for my organization. I need to talk to them. I need to bring them in on it. And it doesn't require a big meeting. And it doesn't require bringing everyone into the same place at the same time. It can happen using some technology that allows collaboration to be a lot easier and a lot more asynchronous. I think that's a huge part of it from an engagement perspective. That's why I keep mentioning a really solid user experience, right?
Analytics, if you've dealt with them—and so many people have, right? If you've used self-service or if you've even been the consumer of it, I can tell you, and I won't go back to some major names in the analytics and self-service space, but my experience using those—it is not really a user experience at all. I have to fumble my way to the right tools. I have to dig down on certain things. When I want to collaborate with my team, it's not really there in the analytics. And I find that to just be a huge miss. So, I think anything that brings the collaboration and a real easy user experience and accessibility to those things to the forefront, I think that's going to help increase the cadence of data-driven decision-making. I think it's almost going to make it a little bit more enjoyable, because you reduce the friction, you get to the value faster. You're going to want to do that again. So those are really the main things. We should keep it that simple, because what comes from that in terms of features, right, and workflows that you can do, sky's the limit. But if we can't get the right people collaborating and engaged with the analytics and putting the analytics in the center of the conversation, the rest is going to fall flat, just like it probably has today in many self-service tools.
MJ 30:20 Perfect. I vibe with you on that. So, I feel like the easy access, the collaboration, the asynchronous ability to discuss things with your other stakeholders, and a solid user experience to make all of this very easy and accessible through a web portal seems like the way forward, or certainly a way to make this a little bit easier on people. I know this is a problem you've been working with directly with our dev team and going out and speaking to customers and clients about their experience here, doing user interviews and trying to develop a product that fits this need. Can you talk to me a little bit about that journey and some of the things you've learned, and how you've been able to incorporate them into products that we're able to offer to our clients?
MH 31:01 Sure. So, I think what I've been spending the most time doing and trying to reformulate, maybe, in my head, is the process about letting our users out there in the world, and our clients help decide what this journey looks like and shape it a lot more. So, taking a step back and not asserting what it is that I know about this, which I've probably done for the last thirty minutes, but in terms of product development and helping users, we really just need to hear what they think about this process, right, and where their pain points are. And so, I've enjoyed over the last, I'll call it, eighteen months—I guess we've been doing it all the time that we've been working in consulting. But in the last eighteen months, really focusing on trying to hear user feedback, internal and external, right? Our own users are also users. But I want to talk to end users that are dealing with the analytics that are working with them and trying to figure out where their pain is. And I've really just enjoyed taking that perspective. It starts to unlock so much value when you don't hypothesize and then make that hypothesis the truth, but instead maybe have a guess and then fill it in and follow the real feedback, right, from users. So, I've loved doing that to build out the product.
And I think what we've really centered on is what we've talked about, and that's the reason I brought this things to the forefront. I think most of the end users that we've talked with and the personas that we've built up around this are basically saying we've got plenty of tools, we're not really using them; or we've got plenty of tools, but we don't know the other tools that we could have or what a good tool would look like. And that collaboration piece is missing. I'll look at the data, and I'll pull something great out of it, but I wait until the next meeting with my team to go over the results to either ask questions of it or reformulate something or make the decision. And they're wondering, why couldn't we just make the decision without being in a room together? Why couldn't we solve this a little bit faster? They're asking the questions of why can't I get the right information from the right people at the right time?
And so that's what's really been driving how we're building out our solution, which we call the solution Symphony. The shameless plug here is that, yes, we think that we're starting to solve this problem. The non-shameless part is, I think this problem has legs. I think this problem has miles to go in terms of solving it, and I think it can be really different in three and five years based on the way that content is constantly changing and the way that people are building analytics are constantly changing. But right now, the things that we've fundamentally done have been to try to bring that engagement, that collaboration into that single portal and allow the right things to happen at the right time.
And from a feature perspective, one of the big things we had to do—you mentioned it before. But five or six different tools being used to build analytics, right? They all have a different user experience. So, bringing those into one collective, unified portal, and that collaboration and that engagement and interaction with the analytics at the center of decision-making. When it's exactly the same whether you're using Tableau, whether using Power BI, that decreases the learning curve so much. That decreases some of the functional knowledge that you have to work with it, to interact with other people. So, we're really just trying to decrease the friction to use those analytics and make it look consistent and feel consistent every time, no matter where the tools live or how they're built. And that has really driven time-to-value, and that's really driven user engagement. And I think those two things to start are going to get us farther down the road. So, I'm excited to think about all the things that we can do as we get more and more user feedback. But right now, reducing that friction and increasing the collaboration and increasing the engagement with the analytics is proving out to mean something to customers.
MJ 35:03 So, it sounds like this technology solution that we're moving toward—and again, I agree with you. I think there's miles to go down this road of increasing the overall ability for organizations to make decisions using data. But it sounds like the first part here is ingesting all of this information from many different sources across the organization into a single location just to give people a single place they can go to look at data and collaborate on it. So, once you've got that information into one place, it seems like it's great for teams to use whichever tools they prefer. If marketing wants to use Tableau and finance wants to use Power BI, completely fine. Let people use the tools that they want and they're familiar with to build the analytics that they need to function on a day-to-day basis. But then, bringing those analytics into the same platform—so I guess, are you saying we wrap those analytics into this technology platform and allow people to view them through the same portal with the same unique permissions and things like that to ensure that people at a managerial level are able to see things from all the different teams that they would need to?
And then, that would then drive the collaboration aspect, where people can dynamically filter and look at data the same way that they would outside of a platform. But they're just one click away from saying, "Hattie, tell me why this is wrong?" Or "We should escalate this to the marketing team to have them look at it in more detail." And then from that point, it sounds like if we're having this conversation inside of the platform asynchronously, we're all able to come up with whatever the solution may be or interpret this through our cross-functional team and then implement that into some sort of business decision. And then, I presume we would repeat that cycle, go right back to the data, which is all in that same location, and just continue to make better and better decisions and iterate quickly as an organization. Is that kind of how you imagine things, at least in the current state?
MH 36:54 I think it's pretty darn close. And I think at least the first half of that, I was so happy to hear it, because I think what you were articulating, which would resonate throughout our conversation, was you started putting the decision makers in the center of this experience, right? And so, why do I talk about user experience so much? The same reason marketers talk about customer experience so much. These decision makers are a big customer of mine from our perspective in technology and product. When you started talking about how easy we make it to get to the Tableau marketing work or the Power BI finance work, when we put it all in one place, let's just face it, that has value for people. It's convenient. It's a little bit simpler. They can get to it a little bit faster. They don't have to figure out how to work the different things or go to different places to get it. That's putting the decision-maker in the center of it. The decision maker is going to want to ask questions and collaborate with those people.
It's not about the builders. The builders use what tools you need to. And you know what? I'm not even going to fight that. And I think in our past, our team has also analytics builders. We had a lot of professional services work around building analytics, and we still do it sometimes. But the fact is, BI and self-service has just taken off and proliferated over the last ten years. Everybody's using a self-service tool, or everybody's building something, or they've got a team, or someone has got a knack for building a dashboard, getting the analysis out that they want. Let's not fight that. But that problem that was solved by these self-service analytics and a lot of the other tools out there, whether you're writing code or drag and drop, that took care of the builders. Meanwhile, the decision makers that were supposed to consume it just don't know if they've been at the forefront of thought around how this gets interacted with and how the work happens. And that's what I think that you just articulated, and that's what I think we're saying. When we start to think about them, there's still a lot of work to do in how analytics gets served up, therefore, creating that data-driven culture—or at least facilitating it. I won't say creating, but at least facilitating the data-driven culture. And that's exactly what we're doing.
MJ 39:07 What do you see as the next step beyond what we've just discussed in terms of that the current state and the tools that are available and the best way that we can solve it now? Do you see in the future—I don't know—I'm just throwing these ideas out here. Do you see these analytics being more and more tightly connected to the operations of the business? So perhaps it may be possible, after we've come up with a solution to a problem that we've identified through these analytics, and we've discussed it within this cross-functional team, that we're able to, with a few clicks of a button, make changes to the underlying information or systems that we've got all this data, all the data coming from. So for instance, if we're looking at a marketing analysis of which is a good way of selling a certain product, A or B, or doing AB testing and things like that, and we identify an issue—do you see us moving the systems or the ability to alter and tweak different decisions closer to that analytics layer so that we could wrap it up, come up with our solution, and then say, "Go, implement"? Or what other areas do you see this expanding into in the future?
MH 40:15 A great question. And I will lead with saying I'm going to let user feedback drive our decision-making around that—is what you just articulated possible and feasible. I think, yes, for sure, based on some of the things that we've heard. And a huge kudos to the Global Applied Technology team, I have to say this is a perfect opportunity for it. We can build absolutely anything. So, I think what you just talked about, sure, that's possible. I think you could name another one hundred things that are possible. We've got the talent, and we've got the approach to product building and facilitate user feedback. So let us do absolutely anything in the long tail of this work. As we both agree, there's miles to go. I think yours is one concept, and we'll see if that's where users take us.
Another thing that I've, maybe, heard just broad strokes up so far—so I'll paint it as a picture—is how this actual collaboration and engagement continues to happen, and I just still don't think that's necessarily in the analytics building layer. And so, what I mean by that is, how do we collaborate now? We're talking about mostly writing comments, questions, tagging people and certain things, tagging content. I can see with other tools that are surfacing and things that are happening in the AI and ML layers of other products. I mean, how about being able to—we talked about the asynchronous part—to quickly just videorecord yourself in the platform next to the data that you're looking at and, simultaneously, not just have to write about, but you can just talk about what you're seeing and ask those questions at your global team. I think that that has incredible value.
Take that a step farther and take that immediately translated, be reading over that from a natural language processing perspective and see where the decision-making is actually going. If anyone's been following what's happening in the sales landscape, there's two main products: ones called Gong once called Course.AI. These things really are supposed to listen to your conversations and help you know how to sell better the next time and, basically, tell you when you're successful, when you're not. I can see a huge opportunity, whether it's listening or reading the text that we're putting around analytics and decision-making. When are we having great conversations? What are the conversations stalling? Who is at the center of really strong conversations that have led to profitable decision-making? Number of things can happen in that space. So, I just think that, generally, the things that aren't going to change collaboration and engagement with teams and with data, how that happens and how we make that really easy to do, I think could be really fascinating in the future.
Last thing I'd add to that is, also, continue to follow a broader approach and connectivity to other systems, right? So, I don't think that we think our Solution Symphony is going to be the end-all be-all, and you need to only ever use that. What we're actually conditioning people for is [to] use whatever tools you're comfortable with to build, and if you use other tools for the final approval process or decision-making process or what you find in the data; if you need to task certain people out to for research, discovery, actioning; if you want to improve your revenue results by 10 percent, who are you actually assigning that to and tying it to the data points that you were looking at when you made that decision? Maybe that's not all going to be a result or a feature of Symphony. So, we need to be thinking thoughtfully about how we tie out to other workflow systems and work with other products that are out there that people are using broadly, because you really can't win anymore if you just try to say we're going to be the product or the solution that you log into and literally everything is made there. But what you can do is connect to other systems and just be thoughtful about that ecosystem.
MJ 44:16 I think it was very well said. We're at this point in the data-driven life cycle where we've got so many great tools, and I think it's really when you bring all those tools together, as you mentioned, that we're able to achieve the extreme benefits of each of the different individual components. Once you're able to couple machine learning and AI with great interactive visualizations and subject-matter expertise, that's the goal of this entire process right from the get-go. It's to take the human attention, which is so limited, from our human experts that are trying to run businesses and make decisions, and allow technology to be a force multiplier for those people and for those teams. So, I really do see this as an extension of business decision-making over the next couple of years as we bring all this information together and just make it easier and easier for individuals to do their job a little bit better.
Hattie, it's been a pleasure speaking with you today and going through a pretty difficult-but-shouldn't-be-so-difficult topic of data-driven culture. After all the things we've talked about, coming up with a portal, giving people easy access, collaborating, good UX to leverage technology and create it as a force multiplier for human attention and expertise in an organization, what is it that you want to leave people with, or what's the main takeaway here?
MH 45:35 So, you know what? I'll be comical in that the main takeaway is we think that we're starting to solve this problem pretty well, so we'd love to hear from people that would agree with the way that we phrased the problem. But beyond that, in all seriousness, I would leave people with the plea to think about the decision makers and the people that have to consume the data and go talk to them in your organization. Go see what they have to say about their experience, not because it will bring you to, necessarily, the same conclusion as I've brought today, but because I think they really have been left out of the conversation. They get to demand what bar chart they look like or they get to demand what a dashboard should have in it because they need results. But the actual interaction of analytics at the center of their decision-making—not a PDF, not an email with a quick Excel chart in it—their actual experience working with reusable data so that we can actually build the system out, be more efficient, not waste the time of those decision makers, as well as the analysts as well as the managers, the supervisors, all the people that are part of that decision-making process.
If you ask the decision makers what their actual experience has been today working with analytics, I think it will really uncover some interesting problems to solve in each of your respective organizations. So go talk to those people the same way we're seeking feedback from clients and existing users and new prospective users. I would ask people to do that because either my hypothesis is terribly wrong or decision makers have really been left out of this and we've focused so much on the building, we forgot about what happens after we build. And I urge everyone to take a look at that, and I think you'll find profound findings from it.
MJ 47:28 Thank you. Well, decision makers, please reach out to us. Let us know your experience if it's similar, if it's different, ways that we could improve, what we're trying to build over here. And, Hattie, thank you so much for making the time to speak to me. It was such a pleasure and looking forward to chatting very soon.
MH 47:44 Good times. Thank you.
MJ 47:45Thanks. The views and opinions expressed in this podcast are those of the participants and do not necessarily reflect the opinions, position, or policy of Berkeley Research Group or its other employees and affiliates.