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Welcome to the Data Strategy Gurus podcast. In this show, we bring together the brightest minds in the world of data strategy, data management, artificial intelligence, and disruptive technologies.
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Thought leaders and experts share their insights, knowledge, and experience on how to stay ahead of the game in an ever-evolving data landscape.
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Whether you're a data professional, a business leader, or simply someone who is passionate about the power of data, this podcast is for you.
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So sit back, relax, and join us on a journey to explore the world of data, analytics, artificial intelligence, tech, and beyond. Hi, and welcome to the Data Strategy Gurus podcast.
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Once again, we're at the Qlik World Conference in Las Vegas, and we're joined by Kevin Ryan, who's the director of business intelligence of Wolters Kluwer.
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Uh, Kevin, uh, can you explain, uh, director of business intelligence- Sure... what you are doing at Wolters Kluwer? Sure. My job is, um, sort of two, two main priorities. One is to be, um,
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take advantage of, in an ad hoc fashion, any question that might come to my team related to sales, uh, marketing, product, uh, development. Um, basically get like an order taker.
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So if someone has a question about, um, turnover or a question about finances, they come to my team, and we process those requests. That's about half of the work that we do.
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The other half is much more strategic in nature, and that involves, um, me and my team helping to design and implement a data strategy that, that exists, that can survive and be, um, pragmatic across, um, all of the divisions within Wolters Kluwer.
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So, um, one's sort of very tac- tactical, very tactical, and the other one is more, more strategic in nature. Yeah, I'm interested in, in the technical part where we say we get these ad hoc questions- Mm-hmm...
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and sometimes you get the same question five times again, and you're trying to do- Sure... the self-service- Sure... business intelligence. Yeah.
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So how do you take these type of questions and can keep up to the speed of, of the questions that are- Right... coming in? Yeah, I mean, in, in a practical sense, um,
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you can't necessarily, um, engineer an automated solution for everything, so that does, that, that's a reality. Um, but I will say that when we do
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start clustering, I suppose, um, requests that come into my team that seem to be, you know, fairly repetitive, that's when we start automating.
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That's when we start really trying to push, um, push the analyst out from my team and more into, um, into our stakeholder organizations, and that really is philos- a philosophy that we're trying to implement around, um,
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around the citizen analyst, really. Mm. If, if cen- if BI is centralized, it's my opinion that it's, it's difficult scaling out to really, uh, uh, um, accommodate the strategic needs of a whole organization.
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So, um, whether that's technology or with process or training of individuals, um, it's something that we've tried very hard on my team to start implementing, and it's been quite successful.
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Um Yeah, so you start in a central way, capturing the questions, and when you're looking into scaling that one, you, you take that acumen with the business analyst and put them- Correct...
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because you had BICC's a while ago. Yep. People have been trying to centralize, decentralize- Yeah... all these motions, and- Yeah... nothing seemed to work in a certain way. Correct.
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For some organizations, the BICC did work because you have centralized the knowledge on, on how you set everything up on a technical level, but as well on the company, uh, industry level. Absolutely. No, and I think that
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that centralization versus non-centralization, you know, I think that it's, it gets to, you know, a, one of the real struggles that I do have in, around culture, um, in that right now the culture of my organization is that, um, analytics questions can be centralized, and there, a lot of the, uh, resistance that I find in my job, it's around creating that culture where folks are more curious about accessing data, answering their own questions, providing insights, and then the inherent learning that happens when you self-discover a, a, a problem in the data, or you find something in the data that is- Yeah...
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inherently yours. Um, the momentum that that brings to, to any individual or organization is, is really impressive. Yeah, an interesting point, the, the data culture as we these- Mm-hmm... days call it.
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Uh, what, what are you trying to set up to, to create that real data culture- Right... where there is no people look at data, "Oh, data, data. Oh, technology"- Yeah...
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and they kind of get, get afraid of, of get, doing something- Yeah... with the data and really getting the value out of that? Yeah. Um, you know, I, I don't remember the guy's name. He's a,
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he's a social influencer, but he draws the... He has this anecdote about, um, dissemination of information. Um, and what I have found is that
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putting things out there, letting people volunteer to be, uh, you know, I wanna learn more about data and analytics.
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I wanna s- you know, I'm, I'm a classic marketer, or I, you know, I'm in, I've been doing the same sales job for, for a very long time. Asking people to raise their hand, who wants to learn a little- Yeah...
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bit about SQL? Who wants to learn a little bit about, you know, how we onboard data sources? How do we then visualize data? Um, getting those early adopters- Yeah...
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getting them bought into that culture, then they sort of, um... I don't know, it's something that I've just found to be,
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you know, it, it's starting to work well because eventually that momentum reaches a, a tipping point that, that I'm hoping, um- Yeah. You-...
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it should be much easier to change a culture than, than, than-Um, you know, than from, from scratch. So we're clo- we're getting there. We're getting there.
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Yeah, you're trying to find your champions within, within- Yeah... the company, in the business side if, well- Yeah... it's always, it's one company for me always. Yeah. Business IT, uh, try to not- Yeah...
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do that separation. And then, uh, try to find these people- Yeah... and, and help them in, in- Yeah... getting those, those, those skills then.
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Yeah, one point though, champions, from my experience, it usually involves a vice president or, or some- you know, someone with some kind of stature in their name.
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And the reality is that those people might support it, but to, to really build, to change a culture, you need to get people, you know, analysts level, manager level, um, employees that can get in with, get in with data, answer their own questions.
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Yeah. And that's how you really change that culture and slowly but surely, it's, it's, it's been working out. Yeah. And then you said as well, the, the strategic level, what, what, what you- Right... you're at?
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So, so that is less stakeholder, um, driven and more around, um, the hard problem after next. So right now, what we're trying to do, for example, is, you know, one, none of my stakeholders is asking for,
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um, segmentation based in customer lifetime value, for example. Um- Okay. Yep. But, but, um, it's an idea that we're working on,
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such that over the course, when, when the request comes from a stakeholder, the hard work's already done. So we're sort of like trying to foresee- Prepare, yeah... the next.
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Um, and it's just, you know, a lot of my job is engaging with stakeholders and understanding, and what I'm hearing now is that,
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you know, revenue might not be the best metric to be evaluating, um, a customer relationship. Are there ways to do differentiated treatment- Yeah...
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from a customer experience perspective, from a, um, a marketing perspective? So
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what that leads to in my mind is, you know, understanding, putting, being able to put, uh, you know, a, a, a metric on a specific customer and their, all their relationships and have that be able to differentiate how you treat that customer relative to other customers.
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So it's not being asked for right now, but that's, that's a lot of what we're trying to do is push, um, push, push the organization in areas where really advanced metrics and, and capabilities can answer hard questions that are coming- Yeah...
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down the pipe. So you're educating them. Well, educating is a hard word always- Sure... but to, to help them understand what you can do with data. Right.
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For me, it's kind of strange that, that a marketeer wouldn't know anything about customer lifetime value, but what you're, what you're saying, it is exactly like that.
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So I, sometimes it's a bit strange that the people from the tech side know more about that and, and can help them in such a way. Yeah. I, I think I f- I think what it is really is about understanding...
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Like if you've got a set of data and it, it contains usage data for a particular customer, um, you know, a stakeholder might look at that and, and be able to say, "Oh, that's interesting.
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That, that tells me what, what just happened. What just happened in the last quarter or the last month?" Where
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someone with understanding of technology can say, can look at that and say, "We can pull in other variables around retention." Mm-hmm.
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"And now we can do things like predicting, um, a customer's decision to retain or not retain based on a particular data signature in their usage-" Yeah... "or things like that." And that's, so that's how the kind of, um,
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you know, the technology, the technologists understanding our data kind of coordinate with our, with our stakeholders to come up with some, some interesting analytical solutions. Yeah.
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So yeah, they, they do understand what you can bring together, and that's not necessarily known by the people using- Correct... the data. Correct. Yeah. Okay. It, it, it's, um, it's just understanding, you know, really,
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you know, on a, on a time-based perspective, can you help me understand about the past or can you help me predict things about the future?
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And that's really what I'm trying to push towards is data is obviously in, you know, interesting being retrospective, but there's a ton of power- Yeah... in prospective and, and being able to be predictive.
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Um, and we're a B2B, um, firm, so it's a lot of the traditional playbooks kinda lack that idea of churn, the idea of cross, you know, cross-selling and upselling. But, um, it, it really is just, it's the same math.
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And if you have the data, you can, you can answer the same types of problems. And then again, it's about, um, getting a culture to adopt those, those recommendations and change for the better. Yeah.
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You mean, you mean in B2B, it's not the same when you talk about churn and, and, and everything. Correct. In B2C, it's, it's very prominent to do- Absolutely... cross-selling, upselling. Yes. Yeah.
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Yeah, I mean, it's, it's, you know, you know, a,
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a subscription business, a monthly subscription business like Netflix, something like that, they might have something like 4% per month where they have to, they're, they're const- so it's a constant, constant, um, problem.
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Our business is, you know, we retain, we retain 90 plus percent per year.
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So there's not the pressing imperative that's sort of built into your business where you need to, um, but it's obviously it's the retaining customers is the cheapest way to do, to do marketing, to grow your- Yeah...
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your footprint and things like that. So yeah, um, it, it's, it's amenable, it's amenable to, to, uh, all the great tricks of the trade, uh- Yeah...
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that, that exist in B2C, uh, you know, BI shops and things like that, [laughs] so. Yeah. And your, well, business intelligence, it feels like, uh, this is from ages ago.
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Uh, w- are you looking at artificial intelligence to, to apply that in, in your B2B space and at Wolters Kluwer? Yeah. I mean... So look, it, I have a pretty high threshold for what I would consider AI to be.
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Um, and I think for me, it's mostly around, um, a self-learning component, right?
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So being able to put-Say, a decision engine out into the market and have the decision, you know, engine surface recommendations for customers and then learn from when a, a recommendation was adopted or not.
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Um, so we are scratching the surface around that, I would say. Um, but you know, AI more so in, you know, finding, taking an entire corpus of data from, um, you know, a, uh,
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uh, our customer service agents- Mm-hmm... and, and putting that through a natural language processor. To being able to tell, you know, to really thin out the thematics, uh- Yeah...
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that, that are existing and then, you know, applying me- metadata to that and, and being able to analyze that. That's, that's I'd, I'd say the extent of the, um, the AI that we're, we're going for.
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But, you know, definitely trying to be, um, prescriptive in, in anything that we do. So finding out a customer that...
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We have, we've, uh, we have models called, uh, red alert models, which it's, um, it's a data-driven approach to proactively letting a sales rep know that they might have an issue. Is that AI? I don't know.
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It, it, it's, it's a, it's a, it's a, it's a, it's an evaluation of data and then making a recommendation and learning from that, so.
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Yeah, it's statistics and then the next level, it's AI, it's a model, and then you have your self-learning models- Yeah, yeah... like you say. It's, it's, it's, it's a thin line.
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I, I agree with you where you say very often people shout out, "It's AI," but it's kind of automated decision engine. It's... Right. Yeah. [laughs] Right. Right. Um. So, um,
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you know, I have, I have the utmost respect for folks that, uh, are actually building AI engines, and, and so I, I, I tend to be a little bit conservative of a- [laughs]... of applying, applying the label. Yeah, yeah.
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I'm, I'm, I'm following what you're saying, so I just sometimes challenge as well. Okay, AI, you know, just show me and, and what are you doing. Sure.
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So in, in that respect and in, in business intelligence solutions, and especially in AI where they think it's, it's, it's, it's smart enough for, for itself, it comes down to the data.
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What, what are the type, type of data problems, what you're facing on, on data quality level? Because AI is gonna be very helpful in, in, in supporting the data quality, uh, for toolings. Yeah.
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Not where we're just discussing AI at the end of the loop. Yeah. I look at it to help in the data management as well. No, I, I think that there's, you know, and I, I haven't seen a lot of...
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So, so whenever you do, when you ru- whenever you roll up your sleeves and you do kind of, um, programmatically trying to cleanse data, right? There's inherently a,
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a threshold with which you're willing to, um, say, um, I'm gonna, I'm gonna, I'm gonna crank up the interpretation of, of this algorithm.
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It's gonna change my data, and it might make some change in the data that I'm not gonna be able to find. But the balance of, of, of using a, an intelligent approach to a problem with data quality. Um,
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so in that regard, I think that there's a tremendous amount of potential for AI in data quality where it can sort of learn from, you know, an in- in an, a data set and make those modifications that need to be made, um, on a small scale.
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Um, we're fortunate enough that, you know, the data problems that we have are around, um, frankly classical organization of silos around how do you, how do you... What do you, um, you know, how do you define churn?
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How do you define retention- Yeah... one business to another? Yes. When is a customer, um, in that window of being, uh, a retention risk? Um, and those are easier problems to have- Mm-hmm...
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but you can't throw, uh, you can't throw technology at 'em. Yeah. No, it's really- Fortunately... the semantical meaning of, of, you say- Yeah... of, of your KPI, of, of what- Yeah... what churn means. Yeah.
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And do you use a kind of framework for that or anything that helps in getting that, that, that right? So we, so it, we take two approaches. Um, one,
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and, and really anything related to changing the, the data strategy of our organization, um, we tend to go top-down and set up, um, just committees of, you know, how, how are we gonna define a customer?
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How are we gonna... Who's gonna be responsible for making changes? Yeah.
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But then the other part that we do is, um, again, that sort of grassroots decentralized approach, is we're creating a lot of pilots where, specifically around data quality and data stewardship, where, you know, those decisions where you can't have, you know, an elegant AI, um, driven solution to data quality, you can outsource it to your business analysts- Yes...
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and have them, and bring them into the fold of, of data quality, you know, and making it more of a team sport. We've done a lot of work in that regard with our, with our partners at Talend, um, around,
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you know, using their, their kind of non-code based, drag and drop based- Mm-hmm... uh, solutions to quality to be able to do, you know, outsourcing some of the stuff to, to stakeholders.
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Because that to me is, is a concern in, in that, um, you know, to scale, to scale my organization or the data strategy- Mm-hmm...
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across the whole division, um, it's gonna need the, the amount of people that you have to do it is gonna have to stay lean.
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And it's gonna have to be very efficient, and it's gonna have to rely on, on stakeholders to participate because, you know, doubling the data responsibilities of a, of a BI team- Yeah. [laughs]...
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you know, it's difficult to then dig- to then say, "Well, I need you to double the size of my team as well." So we're sort of preparing for that as well. Yeah, just to fix the data quality issues. Yeah. Oh, for sure.
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[laughs] Yeah, for sure. So you're using Talend already. Mm-hmm. Uh, you're using QlikView. What do you think about now they're coming together? Um, will that make your life easier, um- So-... processing things?
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What I love about-QlikView a-and, and Talend. It's... To do... So I'm, I'm essentially trying to build a data strategy for an entire...
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Help, help build a d- a data strategy for a h- an entire enterprise. Mm-hmm. QlikView is an amazing product because it allows you to make a quick buy versus build decision. You know, if you have
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several different data sources and you just need BI out of the box- Mm-hmm... they are there for... They are there to support those organizations.
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And I think that it's a, it's a, it's a brilliant model to follow, um, because at the end of the day, like, once data is in a lake or a warehouse, it's agnostic technology, right? Yeah.
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So how it got from a source system into a data warehouse, Qlik, Talend, uh, Power BI, Azure, you know, writing, writing custom scripts in Python, it, it doesn't really matter.
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But once it's there, and it's well-documented, that's so... That to me is, is really where there's a cool place for a company like Qlik to exist right now, 'cause I think that's the...
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There's tons of, tons of, um, directors of BI like me out there that are struggling with, you know, FTP sites and struggling with email attachments and things like that, you know. [laughs] Yeah.
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You know, they're not, they're not massive, you know, Fortune 50, Fortune 500 companies, but they're of scale that they have data. Yeah. And that's where Qlik is, is a fantastic...
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Just walking around the floor and seeing some of the demos that they have right out of the box, that's the, that's the brilliance of their, of their positioning, I think. Yeah, and I see the partnership what they have.
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Uh, I, I saw some, some, some partners as well doing version control- Yeah... on QlikView, as well on Cognos and Power BI. Yeah. And then you think, yeah, they're doing...
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They know what is missing, but that is tackled by the partnerships, so. No, it is. It really is. And, and I think that, you know, I... There wasn't...
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Companies like Qlik didn't exist when I started my career, where you could just say, "Just get, procure the, procure some funds,
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you know, write the check for the software, a little bit of the services, and you could be up and running with visualizations in a matter of weeks." Yeah. It just, that, that to me is a, is a very cool,
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very cool differentiator, I think, for, um, for lots of different organizations out there, um, for sure. Yeah. Great one. Um, Kevin, besides data, music connects us as well. What is your favorite type of music or band?
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[sighs] Um, so that's a very, very big question. Um, you know, I think right now I'm listening to a lot of, um, Grateful Dead. I'm listening to a lot of Dead & Company.
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Trying to decide whether to go see Dead & Company in Philadelphia- Okay... in June. Um, but I, I run the gamut. We listen to hip hop in my house. We listen to- Okay... classic rock. Um, you know,
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we listen to, you know, uh, dan- mot- dance, you know, you name it. We're really... But right now, heavy rotation with Grateful Dead and Dead & Company- Yeah... I'd say. Okay. Great one.
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Kevin, it was nice talking to you. Very nice talking to you. Thanks for your time. And- Thank you very much... thank you very much. All right. Okay. Cheers. Cheers. Thank you very much.
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Thank you for joining us on this awesome podcast. As senior executives, data and analytics architects, and AI professionals, your time is valuable and we appreciate you choosing to hang out with us.
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Don't forget to spread the word on social media, and let's continue to drive innovation in the industry together. Thanks for listening, and we'll catch you on the next episode. [outro music]
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Welcome to the Data Strategy Gurus podcast. In this show, we bring together the brightest minds in the world of data strategy, data management, artificial intelligence, and disruptive technologies.
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Thought leaders and experts share their insights, knowledge, and experience on how to stay ahead of the game in an ever-evolving data landscape.
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Whether you're a data professional, a business leader, or simply someone who is passionate about the power of data, this podcast is for you.
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So sit back, relax, and join us on a journey to explore the world of data, analytics, artificial intelligence, tech, and beyond. Hi, and welcome to the Data Strategy Gurus podcast.
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Once again, we're at the Qlik World Conference in Las Vegas, and we're joined by Kevin Ryan, who's the director of business intelligence of Wolters Kluwer.
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Uh, Kevin, uh, can you explain, uh, director of business intelligence- Sure... what you are doing at Wolters Kluwer? Sure. My job is, um, sort of two, two main priorities. One is to be, um,
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take advantage of, in an ad hoc fashion, any question that might come to my team related to sales, uh, marketing, product, uh, development. Um, basically get like an order taker.
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So if someone has a question about, um, turnover or a question about finances, they come to my team, and we process those requests. That's about half of the work that we do.
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The other half is much more strategic in nature, and that involves, um, me and my team helping to design and implement a data strategy that, that exists, that can survive and be, um, pragmatic across, um, all of the divisions within Wolters Kluwer.
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So, um, one's sort of very tac- tactical, very tactical, and the other one is more, more strategic in nature. Yeah, I'm interested in, in the technical part where we say we get these ad hoc questions- Mm-hmm...
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and sometimes you get the same question five times again, and you're trying to do- Sure... the self-service- Sure... business intelligence. Yeah.
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So how do you take these type of questions and can keep up to the speed of, of the questions that are- Right... coming in? Yeah, I mean, in, in a practical sense, um,
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you can't necessarily, um, engineer an automated solution for everything, so that does, that, that's a reality. Um, but I will say that when we do
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start clustering, I suppose, um, requests that come into my team that seem to be, you know, fairly repetitive, that's when we start automating.
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That's when we start really trying to push, um, push the analyst out from my team and more into, um, into our stakeholder organizations, and that really is philos- a philosophy that we're trying to implement around, um,
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around the citizen analyst, really. Mm. If, if cen- if BI is centralized, it's my opinion that it's, it's difficult scaling out to really, uh, uh, um, accommodate the strategic needs of a whole organization.
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So, um, whether that's technology or with process or training of individuals, um, it's something that we've tried very hard on my team to start implementing, and it's been quite successful.
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Um Yeah, so you start in a central way, capturing the questions, and when you're looking into scaling that one, you, you take that acumen with the business analyst and put them- Correct...
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because you had BICC's a while ago. Yep. People have been trying to centralize, decentralize- Yeah... all these motions, and- Yeah... nothing seemed to work in a certain way. Correct.
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For some organizations, the BICC did work because you have centralized the knowledge on, on how you set everything up on a technical level, but as well on the company, uh, industry level. Absolutely. No, and I think that
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that centralization versus non-centralization, you know, I think that it's, it gets to, you know, a, one of the real struggles that I do have in, around culture, um, in that right now the culture of my organization is that, um, analytics questions can be centralized, and there, a lot of the, uh, resistance that I find in my job, it's around creating that culture where folks are more curious about accessing data, answering their own questions, providing insights, and then the inherent learning that happens when you self-discover a, a, a problem in the data, or you find something in the data that is- Yeah...
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inherently yours. Um, the momentum that that brings to, to any individual or organization is, is really impressive. Yeah, an interesting point, the, the data culture as we these- Mm-hmm... days call it.
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Uh, what, what are you trying to set up to, to create that real data culture- Right... where there is no people look at data, "Oh, data, data. Oh, technology"- Yeah...
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and they kind of get, get afraid of, of get, doing something- Yeah... with the data and really getting the value out of that? Yeah. Um, you know, I, I don't remember the guy's name. He's a,
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he's a social influencer, but he draws the... He has this anecdote about, um, dissemination of information. Um, and what I have found is that
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putting things out there, letting people volunteer to be, uh, you know, I wanna learn more about data and analytics.
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I wanna s- you know, I'm, I'm a classic marketer, or I, you know, I'm in, I've been doing the same sales job for, for a very long time. Asking people to raise their hand, who wants to learn a little- Yeah...
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bit about SQL? Who wants to learn a little bit about, you know, how we onboard data sources? How do we then visualize data? Um, getting those early adopters- Yeah...
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getting them bought into that culture, then they sort of, um... I don't know, it's something that I've just found to be,
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you know, it, it's starting to work well because eventually that momentum reaches a, a tipping point that, that I'm hoping, um- Yeah. You-...
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it should be much easier to change a culture than, than, than-Um, you know, than from, from scratch. So we're clo- we're getting there. We're getting there.
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Yeah, you're trying to find your champions within, within- Yeah... the company, in the business side if, well- Yeah... it's always, it's one company for me always. Yeah. Business IT, uh, try to not- Yeah...
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do that separation. And then, uh, try to find these people- Yeah... and, and help them in, in- Yeah... getting those, those, those skills then.
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Yeah, one point though, champions, from my experience, it usually involves a vice president or, or some- you know, someone with some kind of stature in their name.
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And the reality is that those people might support it, but to, to really build, to change a culture, you need to get people, you know, analysts level, manager level, um, employees that can get in with, get in with data, answer their own questions.
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Yeah. And that's how you really change that culture and slowly but surely, it's, it's, it's been working out. Yeah. And then you said as well, the, the strategic level, what, what, what you- Right... you're at?
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So, so that is less stakeholder, um, driven and more around, um, the hard problem after next. So right now, what we're trying to do, for example, is, you know, one, none of my stakeholders is asking for,
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um, segmentation based in customer lifetime value, for example. Um- Okay. Yep. But, but, um, it's an idea that we're working on,
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such that over the course, when, when the request comes from a stakeholder, the hard work's already done. So we're sort of like trying to foresee- Prepare, yeah... the next.
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Um, and it's just, you know, a lot of my job is engaging with stakeholders and understanding, and what I'm hearing now is that,
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you know, revenue might not be the best metric to be evaluating, um, a customer relationship. Are there ways to do differentiated treatment- Yeah...
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from a customer experience perspective, from a, um, a marketing perspective? So
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what that leads to in my mind is, you know, understanding, putting, being able to put, uh, you know, a, a, a metric on a specific customer and their, all their relationships and have that be able to differentiate how you treat that customer relative to other customers.
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So it's not being asked for right now, but that's, that's a lot of what we're trying to do is push, um, push, push the organization in areas where really advanced metrics and, and capabilities can answer hard questions that are coming- Yeah...
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down the pipe. So you're educating them. Well, educating is a hard word always- Sure... but to, to help them understand what you can do with data. Right.
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For me, it's kind of strange that, that a marketeer wouldn't know anything about customer lifetime value, but what you're, what you're saying, it is exactly like that.
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So I, sometimes it's a bit strange that the people from the tech side know more about that and, and can help them in such a way. Yeah. I, I think I f- I think what it is really is about understanding...
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Like if you've got a set of data and it, it contains usage data for a particular customer, um, you know, a stakeholder might look at that and, and be able to say, "Oh, that's interesting.
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That, that tells me what, what just happened. What just happened in the last quarter or the last month?" Where
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someone with understanding of technology can say, can look at that and say, "We can pull in other variables around retention." Mm-hmm.
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"And now we can do things like predicting, um, a customer's decision to retain or not retain based on a particular data signature in their usage-" Yeah... "or things like that." And that's, so that's how the kind of, um,
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you know, the technology, the technologists understanding our data kind of coordinate with our, with our stakeholders to come up with some, some interesting analytical solutions. Yeah.
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So yeah, they, they do understand what you can bring together, and that's not necessarily known by the people using- Correct... the data. Correct. Yeah. Okay. It, it, it's, um, it's just understanding, you know, really,
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you know, on a, on a time-based perspective, can you help me understand about the past or can you help me predict things about the future?
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And that's really what I'm trying to push towards is data is obviously in, you know, interesting being retrospective, but there's a ton of power- Yeah... in prospective and, and being able to be predictive.
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Um, and we're a B2B, um, firm, so it's a lot of the traditional playbooks kinda lack that idea of churn, the idea of cross, you know, cross-selling and upselling. But, um, it, it really is just, it's the same math.
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And if you have the data, you can, you can answer the same types of problems. And then again, it's about, um, getting a culture to adopt those, those recommendations and change for the better. Yeah.
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You mean, you mean in B2B, it's not the same when you talk about churn and, and, and everything. Correct. In B2C, it's, it's very prominent to do- Absolutely... cross-selling, upselling. Yes. Yeah.
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Yeah, I mean, it's, it's, you know, you know, a,
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a subscription business, a monthly subscription business like Netflix, something like that, they might have something like 4% per month where they have to, they're, they're const- so it's a constant, constant, um, problem.
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Our business is, you know, we retain, we retain 90 plus percent per year.
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So there's not the pressing imperative that's sort of built into your business where you need to, um, but it's obviously it's the retaining customers is the cheapest way to do, to do marketing, to grow your- Yeah...
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your footprint and things like that. So yeah, um, it, it's, it's amenable, it's amenable to, to, uh, all the great tricks of the trade, uh- Yeah...
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that, that exist in B2C, uh, you know, BI shops and things like that, [laughs] so. Yeah. And your, well, business intelligence, it feels like, uh, this is from ages ago.
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Uh, w- are you looking at artificial intelligence to, to apply that in, in your B2B space and at Wolters Kluwer? Yeah. I mean... So look, it, I have a pretty high threshold for what I would consider AI to be.
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Um, and I think for me, it's mostly around, um, a self-learning component, right?
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So being able to put-Say, a decision engine out into the market and have the decision, you know, engine surface recommendations for customers and then learn from when a, a recommendation was adopted or not.
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Um, so we are scratching the surface around that, I would say. Um, but you know, AI more so in, you know, finding, taking an entire corpus of data from, um, you know, a, uh,
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uh, our customer service agents- Mm-hmm... and, and putting that through a natural language processor. To being able to tell, you know, to really thin out the thematics, uh- Yeah...
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that, that are existing and then, you know, applying me- metadata to that and, and being able to analyze that. That's, that's I'd, I'd say the extent of the, um, the AI that we're, we're going for.
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But, you know, definitely trying to be, um, prescriptive in, in anything that we do. So finding out a customer that...
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We have, we've, uh, we have models called, uh, red alert models, which it's, um, it's a data-driven approach to proactively letting a sales rep know that they might have an issue. Is that AI? I don't know.
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It, it, it's, it's a, it's a, it's a, it's a, it's an evaluation of data and then making a recommendation and learning from that, so.
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Yeah, it's statistics and then the next level, it's AI, it's a model, and then you have your self-learning models- Yeah, yeah... like you say. It's, it's, it's, it's a thin line.
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I, I agree with you where you say very often people shout out, "It's AI," but it's kind of automated decision engine. It's... Right. Yeah. [laughs] Right. Right. Um. So, um,
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you know, I have, I have the utmost respect for folks that, uh, are actually building AI engines, and, and so I, I, I tend to be a little bit conservative of a- [laughs]... of applying, applying the label. Yeah, yeah.
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I'm, I'm, I'm following what you're saying, so I just sometimes challenge as well. Okay, AI, you know, just show me and, and what are you doing. Sure.
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So in, in that respect and in, in business intelligence solutions, and especially in AI where they think it's, it's, it's, it's smart enough for, for itself, it comes down to the data.
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What, what are the type, type of data problems, what you're facing on, on data quality level? Because AI is gonna be very helpful in, in, in supporting the data quality, uh, for toolings. Yeah.
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Not where we're just discussing AI at the end of the loop. Yeah. I look at it to help in the data management as well. No, I, I think that there's, you know, and I, I haven't seen a lot of...
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So, so whenever you do, when you ru- whenever you roll up your sleeves and you do kind of, um, programmatically trying to cleanse data, right? There's inherently a,
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a threshold with which you're willing to, um, say, um, I'm gonna, I'm gonna, I'm gonna crank up the interpretation of, of this algorithm.
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It's gonna change my data, and it might make some change in the data that I'm not gonna be able to find. But the balance of, of, of using a, an intelligent approach to a problem with data quality. Um,
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so in that regard, I think that there's a tremendous amount of potential for AI in data quality where it can sort of learn from, you know, an in- in an, a data set and make those modifications that need to be made, um, on a small scale.
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Um, we're fortunate enough that, you know, the data problems that we have are around, um, frankly classical organization of silos around how do you, how do you... What do you, um, you know, how do you define churn?
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How do you define retention- Yeah... one business to another? Yes. When is a customer, um, in that window of being, uh, a retention risk? Um, and those are easier problems to have- Mm-hmm...
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but you can't throw, uh, you can't throw technology at 'em. Yeah. No, it's really- Fortunately... the semantical meaning of, of, you say- Yeah... of, of your KPI, of, of what- Yeah... what churn means. Yeah.
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And do you use a kind of framework for that or anything that helps in getting that, that, that right? So we, so it, we take two approaches. Um, one,
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and, and really anything related to changing the, the data strategy of our organization, um, we tend to go top-down and set up, um, just committees of, you know, how, how are we gonna define a customer?
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How are we gonna... Who's gonna be responsible for making changes? Yeah.
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But then the other part that we do is, um, again, that sort of grassroots decentralized approach, is we're creating a lot of pilots where, specifically around data quality and data stewardship, where, you know, those decisions where you can't have, you know, an elegant AI, um, driven solution to data quality, you can outsource it to your business analysts- Yes...
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and have them, and bring them into the fold of, of data quality, you know, and making it more of a team sport. We've done a lot of work in that regard with our, with our partners at Talend, um, around,
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you know, using their, their kind of non-code based, drag and drop based- Mm-hmm... uh, solutions to quality to be able to do, you know, outsourcing some of the stuff to, to stakeholders.
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Because that to me is, is a concern in, in that, um, you know, to scale, to scale my organization or the data strategy- Mm-hmm...
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across the whole division, um, it's gonna need the, the amount of people that you have to do it is gonna have to stay lean.
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And it's gonna have to be very efficient, and it's gonna have to rely on, on stakeholders to participate because, you know, doubling the data responsibilities of a, of a BI team- Yeah. [laughs]...
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you know, it's difficult to then dig- to then say, "Well, I need you to double the size of my team as well." So we're sort of preparing for that as well. Yeah, just to fix the data quality issues. Yeah. Oh, for sure.
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[laughs] Yeah, for sure. So you're using Talend already. Mm-hmm. Uh, you're using QlikView. What do you think about now they're coming together? Um, will that make your life easier, um- So-... processing things?
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What I love about-QlikView a-and, and Talend. It's... To do... So I'm, I'm essentially trying to build a data strategy for an entire...
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Help, help build a d- a data strategy for a h- an entire enterprise. Mm-hmm. QlikView is an amazing product because it allows you to make a quick buy versus build decision. You know, if you have
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several different data sources and you just need BI out of the box- Mm-hmm... they are there for... They are there to support those organizations.
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And I think that it's a, it's a, it's a brilliant model to follow, um, because at the end of the day, like, once data is in a lake or a warehouse, it's agnostic technology, right? Yeah.
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So how it got from a source system into a data warehouse, Qlik, Talend, uh, Power BI, Azure, you know, writing, writing custom scripts in Python, it, it doesn't really matter.
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But once it's there, and it's well-documented, that's so... That to me is, is really where there's a cool place for a company like Qlik to exist right now, 'cause I think that's the...
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There's tons of, tons of, um, directors of BI like me out there that are struggling with, you know, FTP sites and struggling with email attachments and things like that, you know. [laughs] Yeah.
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You know, they're not, they're not massive, you know, Fortune 50, Fortune 500 companies, but they're of scale that they have data. Yeah. And that's where Qlik is, is a fantastic...
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Just walking around the floor and seeing some of the demos that they have right out of the box, that's the, that's the brilliance of their, of their positioning, I think. Yeah, and I see the partnership what they have.
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Uh, I, I saw some, some, some partners as well doing version control- Yeah... on QlikView, as well on Cognos and Power BI. Yeah. And then you think, yeah, they're doing...
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They know what is missing, but that is tackled by the partnerships, so. No, it is. It really is. And, and I think that, you know, I... There wasn't...
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Companies like Qlik didn't exist when I started my career, where you could just say, "Just get, procure the, procure some funds,
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you know, write the check for the software, a little bit of the services, and you could be up and running with visualizations in a matter of weeks." Yeah. It just, that, that to me is a, is a very cool,
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very cool differentiator, I think, for, um, for lots of different organizations out there, um, for sure. Yeah. Great one. Um, Kevin, besides data, music connects us as well. What is your favorite type of music or band?
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[sighs] Um, so that's a very, very big question. Um, you know, I think right now I'm listening to a lot of, um, Grateful Dead. I'm listening to a lot of Dead & Company.
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Trying to decide whether to go see Dead & Company in Philadelphia- Okay... in June. Um, but I, I run the gamut. We listen to hip hop in my house. We listen to- Okay... classic rock. Um, you know,
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we listen to, you know, uh, dan- mot- dance, you know, you name it. We're really... But right now, heavy rotation with Grateful Dead and Dead & Company- Yeah... I'd say. Okay. Great one.
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Kevin, it was nice talking to you. Very nice talking to you. Thanks for your time. And- Thank you very much... thank you very much. All right. Okay. Cheers. Cheers. Thank you very much.
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Thank you for joining us on this awesome podcast. As senior executives, data and analytics architects, and AI professionals, your time is valuable and we appreciate you choosing to hang out with us.
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Don't forget to spread the word on social media, and let's continue to drive innovation in the industry together. Thanks for listening, and we'll catch you on the next episode. [outro music]
