2026.10.09 | GP | Software | Qlik: Data Governance in AI Analytics

Primary:

Qlik Technologies Inc.

Associated:

Cloudera, Inc.

TIBCO Software International Inc.

Precisely Software Incorporated

Sisense Inc.

Alteryx, Inc.

Viewpoint:

Former Executive

Moderator: David Farrar (DF)

Frank Hernandez (FH), Former Americas Practice and Solution Leader for Professional Services - Qlik

SUMMARY

Overview

The discussion assessed Qlik’s transition from traditional licensing to cloud subscriptions and its long-term relevance as data infrastructure, analytics, governance, and AI converge. The expert has more than 25 years of technology experience, including roughly 20 years in data-related roles and prior experience at Qlik.

Qlik retains value in governance, analytics, data integration, and regulated reporting. However, hyperscale cloud and data-platform providers increasingly bundle these capabilities, reducing the need for standalone tools. Subscription migration has also created pricing pressure, customer scrutiny, and shorter renewals.

Key Insights

Qlik and other point-solution vendors face a substantial risk of being displaced as hyperscalers provide integrated ingestion, transformation, cataloging, quality, analytics, and AI. AI delivers the greatest value when models and agents operate close to the underlying data, favoring platforms such as Snowflake, Databricks, AWS, and Azure. Qlik is unlikely to be a primary choice for a new enterprise AI architecture, although existing customers may benefit from AI embedded within its products. One-year renewals are an important warning signal because customers may be buying time to rationalize technology or migrate. Multi-year commitments would indicate stronger retention. Qlik has meaningful advantages in government, security-sensitive deployments, smaller businesses, and highly formatted regulatory reporting. Its acquisition-led portfolio creates cross-selling opportunities, but product fragmentation, leadership changes, and employee turnover may slow innovation. The moderator repeatedly explored the expert’s view that Qlik could remain relevant through a unified AI strategy, stronger cloud-data partnerships, and an interoperability-focused catalog offering.

Competitive Landscape

10/11/2026 at 11:35 AM UTC

Company or category

Qlik

Snowflake

Databricks

Positioning

Advantages

Risks or weaknesses

Independent analytics, integration, and governance provider

Government credentials, broad portfolio, audit controls, regulated reporting

Pricing pressure, legacy products, fragmented AI strategy, hyperscaler competition

Integrated cloud data and AI platform

Native AI, cataloging, pipelines, quality controls, partner ecosystem

Migration still requires customer investment

Cloud data and AI platform

Integrated compute and AI capabilities across clouds

Not discussed in depth

AWS and Azure

Hyperscale infrastructure platforms

Bundled services, financial incentives, large services capacity

Potential customer lock-in

MicroStrategy and similar BI vendors

Specialist analytics providers

Strong precision reporting

Exposed to commoditization of general analytics

Systems integrators

Influence enterprise architecture decisions

Large hyperscaler practices and migration expertise

May steer spending away from niche vendors

Quantitative Insights

Metric

Significance

More than 25 years

Expert’s technology experience

Roughly 20 years

Experience in data-related disciplines

One-year renewals

Viewed as a leading churn-risk indicator

Three teams and interfaces

Illustrates legacy workflow complexity

30%–50% price increases

Could mask retention risk when attached to short contracts

Approximately $3 million

Illustrative migration cost that may deter equivalent platform switching

Hundreds of millions of dollars

Illustrative hyperscaler customer commitments that can support migration incentives

DF: Good morning, everyone. My name is David Farrar. I'm a moderator and senior credit analyst with Guidepoint Insights. Welcome to our call on Qlik. Where can this data governance and analytics provider continue to deliver premium value in an AI-focused analytics market? With our advisor today, Frank Hernandez. I want to remind clients

10/11/2026 at 11:35 AM UTC

on the call, you do have an opportunity to ask questions by emailing ask@guidepoint.com, and those questions will make their way to me and I'll be able to incorporate them. Before we proceed, I want to give Frank a couple minutes to talk about his background and career. Frank, please go ahead.

FH: Thank you, David. A little bit about me. I've spent a little over 25 years in the technology space, everything from large multinational outsourcing engagements as a practitioner with my days at IBM to really taking on the SaaS world very early on in the early 2000s, so spanning sort of SaaS platforms and then moved into the data space for about 20 years, everything from information controls and essentially controlling the movement of data across large enterprises. You think of the old news articles where you had to make sure that every penny was transacted right with some of our larger Wall Street banking firms and sort of had an audit trail tied to it. Spent several years in the BI sector, building out BI platforms and solutions for consumption by customers, then moved into the data governance and data engineering space for organizations that were looking at adopting. You know, standard tooling that will help them secure data, protect it, govern it, ingest it, transform it, and then serve it up to corresponding BI and other consumption layers via APIs and such. So that's a little bit about me.

DF: Okay. So just for context, you were with Qlik from like 18 through the end of 25, right? And then you've joined Snowflake since then, right? That's your sort of current.

FH: That's correct. Yes.

DF: Okay. Yeah, it's a different context in terms of talking about like when you were there and what's going on now. So, you know, as far as to start off our conversation, why don't we just talk a little bit about sort of how the transition went or what you saw from the services side of the transition for clients from license to the subscription model, which, you know, has been going on for quite a while for Qlik. It's been a challenge. Everyone knows about it, but I just sort of like to hear what your experience was there and, you know, sort of how finished you felt like it was by the time you had left at the end of last year.

FH: Sure. So, when I think about the transition from what was initially like a user-based model, and there's going to be some variability due to acquisitions and mergers, but there was really this shift from a user-based pricing to more of a SaaS-based pricing as we moved from on-prem to the cloud. And so in order to meet the customer base that was looking at more cloud-native capabilities, the jump was such that, instead of looking at users and CPUs and the required infrastructure for these environments that were on-prem and historically in customer data centers, it was, "Hey, let's reduce that footprint from you no longer having to worry about building your own infrastructure, maintaining it, monitoring it, security around it, all these items." And so that really was the driving factor for shifting to more of a cloud-based pricing around consumption or, in some cases, there were some other models that were being considered depending on where in the portfolio you were adopting the technology.

DF: Mm-hmm. So the conversations that we've had, competitors and others have said basically that the switchover was relatively disruptive. You know, there was certainly some challenges in terms of clients, I don't know, getting price sticker shock. But did you feel like that by the time end of last year that that had been substantially completed or was there still the feeling that there were some clients who were maybe still needing to be converted or still fighting or still thinking about whether or not they were going to sign up for a short-term renewal to make a switch or were sort of on board with a longer-term use of Qlik on a SaaS basis.

FH: That's a great question. And so the experience is it's a multi-threaded challenge. So with the shift from pricing, you couple that with the hyperscalers and cloud native organizations that have built out their data platforms to embed more AI, more capabilities that are now equally as capable as the point vendors. When you start thinking about ETL, ELT, ingestion capabilities, data cataloging, and corresponding the analytics consumption layer, now the hyperscalers all have that capability out of the box. So when you start to really peel back the onion, you start to see that there's this

10/11/2026 at 11:35 AM UTC

increase in price that's happening, which is causing customers to say, should I be considering reducing duplicative costs because where my data physically lives? From an infrastructure standpoint, it now has that capability from the hyperscalers, and so that has presented a challenge where pricing is now being... if I could do it for equal or less, I'm going to look at that. And as a result of those items, you're going to obviously see churn. You're going to obviously see scenarios where I would want to commit to a longer or shorter renewal instead of doing a multi-year renewal. Now I'm seeing a spike in single-year renewals. And that, I think, becomes a challenge just collectively for organizations who have essentially been standalone and had capabilities that the hyperscalers didn't have. And now they're seen more as an aspect of the platform that they can get inherently where their data lives. So there are equal challenges around it, which is obviously creating challenges around renewals due to these pricing models. In fact, there are customers who are like, if we could revert back to the per-user pricing and give me the opportunity of avoiding duplicative payments, I might prefer that. So I know those conversations are something of interest that we're seeing customers in the industry looking for from these parallel solution providers for ETL, standalone data catalogs, or standalone BI platforms.

DF: Okay, so this is an interesting question for me and for the longevity of the point solution provider, which is... Where's their niche at this point, right? If I'm providing a premium product to a large or relatively large enterprise, relative to the complex technology infrastructure, data infrastructure, do they have a place in that? Or as you said, because the cloud providers or the data lakes, like the company we're with now, have all these offerings, does that effectively replace them? And this becomes like the whole SaaSpocalypse fear that the legacy provider is going to be replaced, but that's not always the case. Are there specific structures or reasons why the legacy point solution still makes more sense today and will likely continue to have more relevance going forward, or is the business model broken more than just a matter of cost or data architecture?

FH: So in thinking about that, what I want to guide today's audience around is... it's going to vary based on persona and who you're talking to. If I'm an executive who is looking at avoiding vendor lock-in and it's a more of a cost-driven perspective, that's going to be valuable to me to say these niche technologies and point solutions are very valuable because it allows me to avoid putting all my eggs into one basket. So that's 1 perspective. When I look at it as, at the end of the day, especially when I think about all my years in services. We're delivering outcomes to consumers within these organizations so they can deliver business transformational changes and make decisions based on the data. Those end consumers want to have access to data and these insights, you know, very quickly. And for 25 years, you always hear about shadow IT and organizations not being able to fulfill the demands of the business. And generally, it's because of all of the point solutions. And what I'll describe is in the typical life cycle of eventually providing some sort of insights to a consumer within an organization, you're going to want to extract and ingest data from the source system. And that's the first step, and that's usually historically been 1 tool. So you got to have someone trained in that tooling to be able to properly take that data from source and ingest it. Then you have to go through a phase of transformation where you're going to put in some data governance, some data quality. That's another tool generally that will do this. So potentially now you're looking at 2 tools. Sometimes it's 1 depending on where in the market you are, right? Usually mid-market might have 2 tools, 1 like a web-based tool for just ingestion and then an ETL tool for the transformation. Now the next phase of that is making that data analytic ready. And so in that phase, now you're going to another interface, you know, the database systems, right, the Snowflakes of the world, and you're saying we're going to aggregate this data, we're going to add business logic to it, it's another UI, and then you essentially make that, you know, sort of as analytic ready as you can, but you're still not done because then there's another handshake to the BI tool. And now the BI tool needs to own sort of the last mile that really personalizes that insights and how they're going to be presented to that consumer. So there's additional development being done at the BI tool, yet another interface. And then you think about the cost of doing that, not only from a software technology perspective, but the resources and you have sort of separate teams; you may have a data engineering team, you're going to have a BI team. And so now these are highly cost processes that are being built. So with those costs and the delay in getting those insights to customers, those consumers are very willing to cut out the middlemen, the niche

10/11/2026 at 11:35 AM UTC

solutions. Because what's going to be of value to me is really looking at a scenario where I can get data very quickly, right? And in the hyperscaler world, you think of zero copy architecture, right? Where 2 organizations like Snowflake and Salesforce have a relationship for zero copy of data, so there's no need for ETL anymore, there's no need for ingestion there. Now all of a sudden that relationship starts to cut out, you know, again, these niche solutions. You think about understanding more around the cost of egress charges. When you start thinking about these additional storage fees of being able to have to send data out to a niche solution product, right, to do some of these ETL processes and then send it back into the database, now there's additional charges for that. So the hyperscalers are making it less, you know, or warranting less of a need to adopt or continue to maintain these niche solutions because of these capabilities. And then on top of that, you start thinking about, you know, the way they separate costs, right? Like they separate storage costs from compute costs. And so there's this next level of cost optimization. That happens. So it's really putting a strain on the niche solution providers like Qlik to be able to, whether it's the ETL capability that they have, the data cataloging capability, or even the insights and analytics side to stay competitive when the hyperscalers are making it that much more easy to deliver data. That just accelerates by several factors when you start thinking about the AI capability and when you start thinking about how you can leverage native built-in AI capabilities to build these end-to-end solutions. When I first described the legacy way, you get a data engineer, you get a database person, you get a BI person, you get 3 different teams. On 3 different UIs and now AI through natural language prompting can be brought in, they can write what they're looking for, and it can go out and build these things. The acceleration has been amazing, you know, when you start thinking about how organizations are leveraging AI. And so that, again, more reasons that it's presenting challenges for the standalone niche providers.

DF: Okay, so does that make the... well, I guess the question there is, because this has already been happening. Sorry, that's the question one way. So this has been happening, right, as people have moved to the cloud. Does the hybrid environment versus, say, a full cloud model, which there's very few people who have a full cloud model, but does that make a difference in terms of using a third-party point solution or at this point has that effectively been sidelined as an issue for the cloud providers?

FH: So it's a great question. And when we start thinking about where we are in this hybrid approach, what we're seeing is the market is shifting to adopting multi-cloud hybrid architectures that are on-prem and cloud, leveraging Iceberg capability. And so for those in the audience, I'll try to simplify it. Essentially, that is shifting the importance of the data catalog, where historically the data catalog was more of a pure governance play. Like, let me understand lineage of my data. Let me understand risk. Let me understand who owns my data, where is it at, etc. Now all of a sudden the fight in the landscape is who can win the data catalog because that is now a core component of not only interoperabilities from a strategy standpoint to allow hybrid, on-prem, multi-cloud architectures, but enabling and enriching AI from a context perspective.

DF: Right.

FH: So for those in the audience that may think about, you know, the role of catalog, there's a lot of business logic, like policies and things like that, that also get embedded into it. Now, all of a sudden, you know, AI and the agents are able to leverage that to provide more context sensitive, personalized insights as a result of that. So it's able to do it at a domain level, multi-domain levels, across clouds, hybrid on-prem clouds. So it's really making a difference as that Iceberg strategy or interoperability strategy is growing. So it's not something that's adopted everywhere, right? But it is a pretty large component of what we're seeing in the marketplace when we start thinking about filling the gap for, you know, multi-cloud, hybrid, on-prem, cloud architectures.

DF: So does Qlik provide that in any, or any of the other point solutions for that matter, in any specific value add way versus say the, you know...

FH: Yeah, Qlik does have a data catalog, and that was with the merger with Talend. They have a catalog solution that

10/11/2026 at 11:35 AM UTC

is available. At the time when I was there, you know, Iceberg was still fairly new and being adopted from an interoperability perspective, so I don't know if they've shifted to leverage the catalog capability to be part of the foundations for AI and an interoperability play, but that would absolutely be of value for them as a differentiator in the marketplace, especially for, like I said, those organizations that are looking at avoiding vendor lock.

DF: Is being on the outside as they are, are you able to... well, I guess we could talk about how, depending upon what AI implementation you're using, you may or may not benefit or be limited by the catalog or data quality. The reason I ask it, and I'm hoping to ask in the right way, but I'll sort of clarify a little bit, is the company is making the pitch that they want to be the source for your trusted data for AI, right? So the provider of creating that trust. Does their position in technology, does that resonate as a viable service, deliverable service, or is that something that may be a good marketing pitch and may have, you know, a last mile problem that they have to overcome to be that provider.

FH: Yeah, so I'll look at it again from a couple of different personas.

DF: Yeah.

FH: When I, in my early days in working in what I described as in the data control space, we catered a lot to the audit and auditor function, right, to ensure every penny was accounted for. Now you can reconcile that and understand it. One of the biggest things that auditors and folks in the information control space, very large financial organizations and health care organizations, et cetera, really appreciate is an external process and technology providing that extra layer of trust to the processes that are internal. So by having a third-party ETL, these third-party capabilities that aren't inherently built into the business process, right, where the data sits, is you give it the ability of providing information controls that aren't governing themselves. Right. So again, through the auditor's perspective, I prefer automated third-party technologies to provide me with a trust to say, yes, that ETL split transaction happened. And I'll just give an easy example, right? You make a payment to your credit card. It's $100. When they receive the payment, they've got to split it up. They've got to say your high interest goes to, you know, part of that $100, your lower interest, right? Principal, right? So they split that up. So as those transactions get split up, how are they ensuring that that split is meeting compliance needs, right? So when we start to think about the needs of Dodd-Frank or Sarbanes-Oxley, now, all of a sudden, the third-party tool reconciling that process really worked and that the split happened appropriately because you're supposed to apply the primary of that payment to the higher fees that can be done independently from the process that creates a level of trust for that persona. When we start to think about trust in the results, the consumers of that data, they really don't see the plumbing, right? We turn on the water faucet, water comes out, you don't really understand everything that comes, all the plumbing that goes into the house and then into the streets where the water is providing to the households and the entire neighborhood. But what the consumers are looking for is really the quicker insights that are trusted, but how are they going to be trusted, right? Like what's enabling trust? We start to think about the semantic layer where there's business logic to help us all define revenue the same way. So that way I'm seeing revenue numbers, you're seeing them. Maybe there's some security policies inside that I can only see it for North America. You might only see it for a region. So there may be some differences, but it's calculated the same for everyone. And we're able to really link that and have trust and confidence in that data, especially when we start thinking about public companies and their obligations thereafter. So, what provides trust there is more about the behind the scenes governance of that data. Is it accurate? Is it consistent? Is it reliable? And are we able to do all these things, incorporate data quality rules, build out the semantic layers, enable agents that are being monitored, right, for schema drift and changes to the environment so they could proactively let someone know something is changing. And it's not just AI agents are being built to go rogue. This is where the hyperscalers are really excelling to be able to deliver trusted data because they're able to accomplish all those things within their platforms. So I think trust from a consumer perspective, I wouldn't think of it as, oh, if you've got this third-party tool versus everything being built in a single platform or hyperscaler environment or even multi-cloud. How you architect it isn't as important as the end results. And am I getting trusted data? Are we defining it right? And so all the dependencies that are being

10/11/2026 at 11:35 AM UTC

architected behind the scenes really become DF: All of these are less visible to the consumer, although they're equally important. Right. Okay. So to me, it sounds like if I'm looking to do an AI implementation and I'm hiring a consultant or someone or, essentially, your platform, Qlik is not going to, doesn't sound like they'd be on the list of the architecture that you're putting in for a new AI type implementation. Because like you said, if I'm looking for answers, right, if I'm looking for the ability to ask my AI chatbot for analytics and for recommendations or however, it would be part of the hyperscale or wherever the data is sitting. Architecture would be determining or making sure that the quality was there in terms of results. Does that sound accurate?

FH: You nailed it, right? That's exactly it. The closer AI and what gets built, when I think of AI and ML, I'm thinking the agents, machine learning models, the closer they are to the data, they have the additional context to increase their accuracy and proficiency in their responses. I will not say that, yeah.

DF: That if I am a current customer of Qlik, do I not realize the value of their AI built within their tools to accomplish what their functions are faster are extremely beneficial.

FH: But I'm not buying an AI solution thinking Qlik. Again, you're going to expect AI in all of your solutions across the enterprise estate because the function that technology provides will be accelerated by that platform's capabilities. But when you think about the pure data landscape itself, where the data sits, that's going to give you the most context and value for building out the AI agents and machine learning and those capabilities that are going to really allow you to generate revenue or automate and do everything else as far as modernization with AI comes into play.

DF: Okay, so if I'm thinking about this, there's 2 sides of the business, right? For Qlik, there's this analytics side, right? The sense side, and then there's the Talend sort of data integration side. And there's also a significant difference in terms of customer size or customer type, and we can get into the discussion of whether or not you have a lot of overlap given it was a merger and how much cross-sell actually occurred. But if I'm seeing declines in the Talend side of the business, would you tie that directly to just the modernization of moving more to the cloud, modernization going to a more focus on the quality of data coming through the hyperscaler cloud or data lake provider versus, say, a need for that high financial services level of auditability? I mean, is that why you're seeing the declines in Talend and is that where that part of the business is? You know, except for that sort of more niche specific uses around like financial services where you'd see that really continue to attrition away because of lack of need effectively.

FH: Well, the needs are there, but... When the capabilities are commoditized, right? Like I can get those within my Snowflake environment, right? My platform provides that. And so the hyperscalers are, you know, more of a... we'll give you access to everything. We're just going to charge you for what you use. I'm not being limited to, hey, I'm only licensed for the BI product or the ETL product or the data catalog. So that shift is an aspect of it.

DF: Yeah.

FH: When I think about market segmentation, there's going to be a large disparity between doing business with governments. The ability for Qlik to do business with federal, state organizations and the kind of extra security that they have has really been something that is very impressive. And so, there aren't a lot of companies in their space that can say, "I can do the kind of business I do with major federal and state organizations," like they do. And to some extent, I would double-click or recommend that if there's anyone looking at it, is to understand what the strategy is for continued support and growing the government sector because I think they have a competitive edge. Then you get into small and mid-sized business. If I had to think about small and mid-sized business, I think the price sensitivity that they tend to have caters to what Qlik can do because they can provide you with something that is fiscally very appealing, right? And it will warrant them to sort of separate operations from their database to their ETL and BI layers. But where it starts to really blur the lines is enterprise and large enterprise. When you start thinking about large multi-

10/11/2026 at 11:35 AM UTC

billion dollar organizations, their spend and they're being challenged all the time to reduce costs for overlapping technologies. So there is significant rationalization opportunities that are out there. And then you think about, because you alluded to the Accentures and the Big Four, the major SIs in the world, take a look at where their alliances are. And what I mean by alliances: where are they building COEs and they have hundreds, if not thousands of practitioners aligned with technologies? That's going to be an indicator of where the growth is because organizations that are building practices around Snowflake, as an example, it's because there is significant growth happening in there and they're influencing the market and the decisioning, right? If you're bringing in an Accenture to talk about a data strategy, are they likely to talk about Qlik as part of that ecosystem? Are they likely to talk about the hyperscale of technologies, right? And so they're influencing these buying decisions based on that, which helps the hyperscalers grow. So there's an aspect of... double-clicking on the partner relationships that the niche providers have because that's going to help understand: are they going to continue to grow or are they starting to stall?

DF: So if I'm going to use or go with an SI and I'm going to do this transformation, does that, from your perspective, inherently say that Qlik is going to be replaced or that there's logically for a large enterprise a reason for Qlik to be retained over a long term for a particular application or for particular use cases?

FH: Yeah, this is a great, it puts a smile on my face. I really love what I do. I'm excited about the continuous change in the data landscape. If you're not providing more of an end-to-end play across the entire data landscape, you're more than likely going to be consumed. And so if you're an organization and I work for MicroStrategy, which is another analytics firm, and it was a while ago, so I know things have changed there. But if you were a pure play analytics firm, you're really nervous at this point because the hyperscalers are making it really easy, like I alluded to before, for the time to insights to be at its most accelerated that I've ever seen in the last 25 plus years with AI being embedded natively in them. For those who are following Snowflake and its Cortex capability, I mean, every day I wake up and I'm super excited about the kind of transformational things we're doing and how quickly we're able to do that because of the AI being built in and covering the entire landscape. So, yes, there is a very big risk that the point solutions are going to be consumed or something along those lines. There is a challenge before them in the marketplace where it's hard for them to differentiate themselves. And that gets magnified even more for organizations like Qlik. It's very well publicly documented the M&A that they've had. And with all of the mergers that they've had, if you just look over the last 10 years, you'll see the number of mergers and acquisitions coming together. And those are all for good reasons. And the reality is there's also a potential negative effect when looking back that I would imagine some industry analysts can really weave this together. But as you bring all these companies together in a siloed way, there's often a change in functional leadership or just a lot of attrition within the product organization. And so when you have that kind of scale of growth and you have that kind of employee churn, it's been public that the CEO recently changed. What happens? You stall on innovation. And when you stall on innovation, the hyperscalers don't. They have almost limitless budgets to be able to continue to grow and supersede the capabilities or significantly close the gap. And that's what will happen.

DF: Well, where do you see the biggest risk for Qlik in terms of, we'll say, product staleness? Is it a case where on the analytics side, it's stale or maybe I'm not asking that question correctly, but like you said, you've had a lot of products brought together. You have a lot of products that are still used by clients that may or may not be being updated or modernized or have a product roadmap. I mean, how much of the Qlik Analytics, Sense, View, or Qlik Sense, QlikView side of the business is, would you say it's exposed to being replaced by capabilities, not necessarily part of a larger architecture issue, but just the capabilities aren't what I want or what I need? So at the time, things could have changed in the last year or so, is there, you know, really AI being incorporated into their platforms?

FH: Yeah. Any of the QlikView stuff will never have that, right? It's just on-prem and they just can't, there's a good population of customers that, you know, for a variety of reasons, won't get off of QlikView, but that's legacy technology. It won't be able to have... it's on-prem technology, so it won't be able to have the AI capabilities. When

10/11/2026 at 11:35 AM UTC

you think about Qlik Cloud from an analytics standpoint, again, incorporating AI into that at the time wasn't where it needed to be.

DF: Mm-hmm.

FH: So that is probably, again, when I start thinking about what Qlik Sense Cloud does as far as it has the ability of ingesting and scripting data, quasi ETL-ish, they can do a little bit of lineage to serve up the dashboards and then be able to generate dashboards. Like, any one of us can go into Google Gemini or some of these AI tools and say, build me a website, right? You get an HTML file and you get a fully functioning website in an hour, right? And so, build me some sort of insights or dashboards or things like that. That is where the hyperscalers have really blown... I want to say, organizations that are more niche players away because they just weren't at the same level of innovation when it comes to that. And that's Qlik Sense. I would say the same thing when it comes to the data cataloging capability. And because of AI, the importance and dependency on the data catalog has grown significantly for interoperability and AI purposes. That's a missed opportunity. I don't know if they... again, if they've changed and looked at it a different way. And then on the legacy, Attunity, replication, Talend, ETL, data quality, right, these are again, just the gaps have closed pretty significantly as far as capability in the marketplace and the connectors and how easy they are to build. I mean, you could even go in and say to an AI agent these days, build me a custom stored procedure that's going to go out and get data now. And there's a battle between custom ad hoc stuff that can be governed within a platform; that's still an issue. So, I think of the innovation to really accelerate each of their platforms leveraging AI is definitely an opportunity that would have helped create more stickiness or longer-term stickiness. But when you weren't there and you didn't have that, and all of a sudden you get an organization like Snowflake saying, I can help you build out your pipelines, I can help you catalog your data, I can help you incorporate data quality into trust factors, and then I can help you present the insights and you have the ability of doing that with AI, they really accelerate it; it becomes really challenging at that point.

Now, what I will say is there's been at least now a need for, you'll hear the term, there's different kinds of BI and analytics. So the use cases, you get self-service analytics, you get users that want their normal dashboards and stuff. Those are all being served up very well. Where I think the BI capabilities like Qlik, like a MicroStrategy will continue to flourish for some time is what I'm going to describe as pixel perfect reporting. If I've got to build a 10K report or whatever, something like that that's heavily regulated has to be a specific format, those organizations are still ahead of the game.

DF: As I look at the last sales numbers or revenue numbers that they put out, they said they had a modest decline, which is always difficult to frame given it's a subscription model and you have renewal periods. The conversation we've had has been that, look, 3-year, 1-year, depending upon the way the customer is looking at retaining the company, but I guess what I would try to understand is, with the companies that are either coming up for renewal or the ones that went through in the last couple of years, is there a pig in the python from prior years? From, say, '24 that is coming up in '27, that would be highly likely to be looking at making the move or already making the move. I guess I'm trying to think about how concerned would you be that the coming cycle or renewal period is going to be very heavy on short-term renewals dealt with maybe higher pricing because they're not looking for the discounts over the longer years. Are we in that position where you're on a tipping point between renewal and pricing, or do you think that there's another set of years as folks slowly make their implementations around AI and use someone like Snowflake to redesign their whole data structure and data processing and data quality? I guess when we start thinking about the renewal process in general, there are opportunities for the longer-term customers to take advantage of the transition to now having access to all of the portfolio.

FH: Versus, you know, I was just licensed to use Qlik Sense or Talend. So that unification of the offering becomes advantageous upon renewal time because you could say, you're going to have, you know, the ability of leveraging anything within our portfolio and sort of making it stickier for them as a technology within that customer's pipeline.

10/11/2026 at 11:35 AM UTC

But I would say any organization who, you know, is kind of going through and feeling some of the changes with increased costs, tech debt, rationalization projects that are happening within the customer organizations, there is going to be a leading indicator around 1-year renewals. And so, you know, you might be able to say, hey, I can get an increase of 30% on average. And show that, hey, we're making money as an organization because we're, you know, 30% to 50% increases. But if it's short-term, right, 1-year commitments, that to me is a leading indicator of risk. And so the growth in 1-year renewals might often mean, and I see this from customers, "Give me a year so I can work on my, you know, evolving my strategy and look at potentially moving away." That's often what I see customers saying in this market. So if you're looking at multi-year opportunities, I'd see that as a positive sign. But an increase in 1-year renewals, even if they're larger on average, I would just want...

DF: You know, anyone who's interested in that to really double click on that to understand what kind of risk indicators that may mean. So you obviously made the switch over to a more modernized or larger or newer company. Have you been seeing customers accelerate that move to 1 year, say this year versus last year when customers are just talking about... I mean, has there been an uptick in terms of customers pushing forward on that data transformation? Because the story has been the last several years is there's lots of talk, but not as much motion. So I'm curious if that motion has been accelerating, because it used to be in the beginning of the year it was the office of the CFO doesn't want to understand it, but they don't want to necessarily implement it. But has that changed from your mind for a lot of the laggards who had sort of started making the process? Have they accepted that they can get the trust, except for that sort of pixel perfect level, that they can get the data and analytics that they want from cloud and these large AI vendors, or are they still skeptical and delaying that sort of final decision?

FH: I almost think of it as there is heavy influence from hyperscalers to challenge every renewal, and I'll... you know... kind of think about it in a way of the partnerships between major organizations, right? Hyperscalers, you start to think about... you know, some of the stuff that's in the news where company A is committed to spending with AWS $hundreds of millions. And when you start to think about those kinds of partnerships and investments, you know, the underlying aspect of these public companies, right? When they are public, you often see that. The commitment is because they're going to drive additional workloads within their environment. So if I'm AWS, I've got an incentive because you're going to be bringing me essentially more business, whether it's through your platform or directly to me. And so those commitments often allow for incentives, right? And I think of it as, you know, when we start to think about the evolution of what is it going to take to get me off of a technology to another competing technology. If I'm a CFO, if I'm just doing like for like... I'm going from Azure to AWS... it's going to cost me $3 million to lift and shift because I'm seeing better pricing for storage and compute, right? Is it worth that $3 million or whatever it is to be able to make that migration? If it's like for like, as a CFO or someone who's an economic stakeholder, it's not something that I'm motivated to do. If I'm getting net new value that's going to generate some business outcomes and add some value, I'm more interested in doing that. When I go from being more interested to very interested in leaving one technology to the other is now when that.

DF: David Farrar, Guidepoint.com, www.guidepoint.com.

FH: Company says, "I'll also invest services time. My services organization will have some sort of investment in seeing your success by your commitment." And now you start to look at the negotiation of, if you commit to us for 3 years, I'll also invest my services organization to help with that migration to reduce your investment. Now CFOs and economic stakeholders see that as really appealing. And that is where the hyperscalers, because of their deep pockets, because of their strategies around finding new areas of consumption from competitive technologies is really, you know, an interesting motion that's being seen. And I see the same thing happening for Qlik, you know, and other technologies in the marketplace.

DF: But it does, and just to sort of drill down a little bit on the type of customer, are you seeing that same behavior out of some of the more highly regulated or highly security-critical?

10/11/2026 at 11:35 AM UTC

FH: Yes.

DF: I think we all know who they are, right?

FH: Yes.

DF: It's the financial services institutions and, you know.

FH: Absolutely.

DF: So do you see the, if I'm thinking about the attrition or the churn, right, if I'm thinking about it across different verticals for Qlik, if that highly regulated section, right, the financial, we'll just call it financial services for lack of a better term, would you be expecting that part of their user base to potentially be seeing higher or lower renewal rates versus the other parts? Sorry, I'm not asking this question probably the right way, but if I'm thinking back historically and history has been those with the lower standards of Data security and quality were the first to start looking to move and the financial services were later. Then my loss levels would have been slightly different, right? Do you see that sort of financial services higher requirement customer catching up with the other side of it, or would you expect the trajectory to sort of remain as they are having for the last couple of years with all of them sort of moving in tandem?

FH: Yeah, I often think of it as, in my experience right now over 25 years, a lot of the FSI, they tend to have 5 of every technology or very core capability. A lot of that is, when you start to think about financial services, obviously there's a lot of M&A history in all of those that had their own technology, their own architectures. I feel like as we start to see the shift of AI being more dominant in these organizations, the driving factor is going to be, I'm going to make this investment in a hyperscale technology to get that access to AI because it's going to help me deliver whatever I'm trying to achieve first. And then, especially in these financial services organizations, so that they'll build a relationship with the hyperscalers for AI purposes. And then as you start to build things out over time, it won't be an immediate drop off, it's essentially saying now all of a sudden we should start considering a use case to leverage that hyperscalers ETL, BI capabilities.

DF: Well, from what I just thought through your description was I'm going to pilot the AI and then once I get comfortable with that, then I start looking at how I can expand it and that means I have to change the data or think about where the data is coming from and all that stuff we talked about earlier, right?

FH: Exactly.

DF: I mean, it's FH: like pilot DF: and FH: then data and then implementation, right?

DF: Yeah. So do you see an opportunity for Qlik to sort of inject itself? I mean, does the AI products that they have currently in the cloud product that they have, that they're offering to customers, is that adequate to say, except for those going through a full transformation, but to convince customers like, "Oh, I can get what I need here," I guess the good enough strategy? Or have they been eclipsed or are they not sort of on par enough to even participate in that sort of good enough level?

FH: I would say it depends on where they've evolved on the AI side. All right. If, what is the AI strategy? I almost think of it as, when you start thinking about, are they leveraging AI at an individual platform level, like, hey, in my Qlik Sense or AI in my Qlik Cloud, right, and it's really sort of standalone and not fully integrated looking at it across the entire ecosystem of capabilities, that may be an opportunity to close some of the gap, right? If it's very fragmented AI, but if

10/11/2026 at 11:35 AM UTC

they've changed their approach and said, hey, we're going to at scale leverage, you know, I'm hypothetical. I have no idea if this is the case, but if they said, hey, you know what, instead of it just being on AWS and Azure, it's going to be on Databricks or Snowflake that sits on AWS and Azure so it's multi-cloud so I could leverage the compute benefits of an AWS, excuse me, of a Snowflake, of a Databricks and that native inherently built AI models to make this more of a success I think that that moves the needle in that scenario and makes it more, but I don't know the current stance of their AI strategy.

DF: Yeah.

FH: So that would be important to wrap it up.

DF: But as a standalone... as an independent, right, without a sort of partnership, that's sort of not the direction they've been historically, right? I mean, they don't really have that level of integration into the other.

FH: Not historically, no.

DF: Yeah. So that would be, they'd have to make that shift to make that happen, right?

FH: Yeah, I mean, I think they would have to create some sort of alignment there, right, to say, hey, these are the technology partnerships that I want to have and it's going to help drive my AI strategy.

DF: All right. Well, listen, we're here at the end of the hour, so we'll go ahead and wrap up. But I really appreciate your time and your insights. Thank you very much. For our clients, if you have additional questions for Frank, please reach out to your salesperson. We can help you set up a consult. But thank you very much and everyone have a great day. Thank you. Thank you, Frank. Really appreciate it.

FH: Thank you.

DF: Have a good one.

FH: My pleasure.

DISCLAIMER: The statements or opinions expressed today are those of the Advisor and not Guidepoint, who disclaims all liability for the content provided. The Advisor may not disclose material nonpublic or confidential information or any information that would cause the Advisor to breach any duty or obligations. Guidepoint is not a registered investment advisor and the information provided is not intended to constitute investment advice.

10/11/2026 at 11:35 AM UTC