In this episode of Builders Wanted, we’re joined by Sumeet Arora, Chief Product Officer at Teradata. Sumeet shares his insights on the importance of speed and innovation in the fields of data analytics and AI, emphasizing how Teradata delivers impactful business results by transforming complex data challenges into actionable solutions. The discussion dives into product leadership principles, the balance between speed and reliability, and the evolving landscape of analytics.
In this episode of Builders Wanted, we’re joined by Sumeet Arora, Chief Product Officer at Teradata. Sumeet shares his insights on the importance of speed and innovation in the fields of data analytics and AI, emphasizing how Teradata delivers impactful business results by transforming complex data challenges into actionable solutions. The discussion dives into product leadership principles, the balance between speed and reliability, and the evolving landscape of analytics.
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Key Takeaways:
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“ I think it's equally important for people in my role to not just build a great product, but also build it fast. It has to be fast and excellent, both. And doing things faster in this era means that you have to also treat velocity as a product itself. It's almost like setting up the right system and then great things come out.” – Sumeet Arora
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Episode Timestamps:
*(02:06) - Defining the mission of a builder
*(03:12) - Velocity as a product
*(07:51) - The shift to invisible, frictionless analytics
*(23:04) - Lessons from product failures
*(34:28) - Quick hits
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Links:
Connect with Sumeet on LinkedIn
Connect with Kailey on LinkedIn
Learn more about Caspian Studios
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Sponsor
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0:00:09.2 Kailey Raymond: Welcome to Builders Wanted, the podcast for people shaping what's next in customer engagement, technology and innovation. Today's guest leads product in one of the most critical arenas for modern business, data analytics and trusted AI. I'm joined by Sumeet Arora, Chief Product Officer at Teradata, a company powering analytics across cloud, on prem and hybrid environments. Since joining Teradata earlier this year, Sumeet has brought deep experience in AI, SaaS and engineering from high growth product orgs. His focus: turning complex data challenges into real business impact at scale. Today, we'll dig into what it takes to lead product in a high stakes space where innovation meets enterprise expectations and why analytics is no longer just about insight, but action. Let's get into it.
0:00:58.1 Producer : This podcast is brought to you by Twilio, the customer engagement platform that helps businesses turn real time data into seamless personalized experiences. Engage customers on their terms across SMS, voice, email, WhatsApp and more. Power every interaction with AI so conversations feel natural, not robotic. Adapt in real time, delivering the right message on the right channel exactly when it matters. That's the power of Twilio. More than 320,000 businesses from startups to Fortune 500s trust Twilio to transform customer signals into conversations, connections and real revenue. Reimagine the way you engage with your customers. Learn more@Twilio.com.
0:01:46.8 Kailey Raymond: Sumeet, welcome to the show. I'm very excited to have you here.
0:01:51.5 Sumeet Arora: Kailey, it's just equally exciting for me to join you today.
0:01:55.1 Kailey Raymond: This should be really fun. As I understand it, you recently stepped into the role of CPO at Teradata and you've led product and engineering at other major analytics players. So, I'd love to learn from you to kind of set the tone. How do you define your mission as a builder right now?
0:02:11.8 Sumeet Arora: That question is close to my heart because that's what I say to my team, we are all builders. In many ways our job is to leave our industry better than the state it was in when we joined it. And that's kind of how I see it. I've joined Teradata about 191 days ago and the reason I stalk in days is because the world is moving so fast. Every day matters. But the reality is I want to make sure that we as a team change our industry in ways better than we have done, so far we have done it, but we continue that and we expand that pace. And that's how I define the mission of a builder.
0:02:50.7 Kailey Raymond: I love it. You're really thinking about like a growth mindset and I like that you're thinking in days because of the way that we're changing so fast. It's true. It does feel like the world changes way faster now than it did even two, three years ago. This question buddies up into that a little bit. You know, we're kind of talking about this growth mindset. What's a principle or philosophy that you think that you bring into product leadership? You have this global portfolio, lots of legacy, but you have this also need to innovate quickly. So, what's that philosophy that you bring?
0:03:24.7 Sumeet Arora: Yeah, there's probably a lot of thoughts that cross my mind on that topic. But the one thing that I do really focus on, Kailey, is velocity. I think people expect innovation, but they expect it faster. And I think speed is a big deal in our industry, in the tech industry. And so, I learned this from some of my previous roles. I think it's equally important for people in my role to not just build a great product, but also build it fast. It has to be fast and excellent both. And doing things faster in this era means that you have to also treat velocity as a product itself. And so, product managing velocity in your organization, going through the entire life cycle, looking at how fast the team makes decisions, how are we able to change those decisions if we have to? How do we measure? How do we learn from what we measure? How do we kind of remove bottlenecks in the whole system in terms of idea to customer value? I think that product managing velocity is one thing I do worry about as well. It's almost like setting up the right system and then great things come out, great innovation comes out. So, I think that part is important to me.
0:04:46.6 Kailey Raymond: I love that, I've never heard that before. Thinking about velocity as a product, I think that's so, so smart. And it's almost like, you know, time to value. We talk about when we're shipping products to external customers, making sure that they're onboarding quickly and we're no friction whatever. But you're also talking about it, building those processes internally and how you make sure that you're building that structure to drive that innovation fast and to kind of push your team to be able to execute, so astute, so smart. Teradata obviously has this wide range of customers from on prem, these like heavy workflows to Cloud and SaaS. How do you think about leading product strategy across such a diverse landscape?
0:05:30.8 Sumeet Arora: Yeah, the thing is there is different ways for different sectors and different deployment environments to absorb innovation. And that also includes the speed at which our customers can consume that. So, I do keep that in mind. However, I don't want our innovation to be the least common denominator. That would be not good. I don't want my engineer shackled by constraints of the slowest moving customer or the ones who are maybe not wanting to change as much. I want them to be indexed to almost being faster than the fastest moving customers. Right? To think proactively and pick the best ideas and bring them to market faster. So, your question is a great question, which is like, how do you balance these two worlds? And I think the way we design the system is we design the system for speed, for unshackling our developers, but at the same time have the ability to take that innovation, package it, make sure it's very high quality, make sure it's very high reliability, it's very secure, and also deliver it where needed at the pace at which it can be consumed. So, we can kind of do both fast and slow at the same time.
0:06:44.8 Sumeet Arora: And the way to do that, Kailey, is some real strong engineering discipline. For example, maintaining a unified code base, like as soon as you try to diverge, it tries to create more problems for the engineers, because they have to think different worlds. So we try to keep the fundamental building blocks the same. We pick the best ideas that maybe are common across and help a broader cause implement those, try to stay away from one offs. So, those are the types of disciplined things that we do to keep sanity in this world, which is very diverse in its needs.
0:07:17.7 Kailey Raymond: That's so interesting. I've talked to my team a lot about this as well. Marketing, obviously a little bit different, but the concept is the same, which is that structures and processes often allow you to speed up. So, slow down to speed up in some ways, making sure that you have the infrastructure in place to be able to then really hit go and not have to reinvent the wheel every single time. I'm sensing a theme for this conversation, Sumeet, which is structure breeds speed and velocity, which are important to you. Which brings me to the analytics landscape in general. Shifting incredibly fast. From your view, what do you think the biggest shift is happening right now around how companies consume data or embed analytics into their operations.
0:08:07.7 Sumeet Arora: First is I definitely believe that data platforms, analytics platforms are better when they're invisible. Like they are almost like the oil in your engine. It does the job and you barely see it. Right? The more frictionless we are, the better off I think our customers are going to be and the world is going to be. The general philosophy is we want data driven decisions, information driven decisions everywhere. Actions that are based on decisions and decisions that are based on facts and data. And we want to make sure that at that point of decisioning, whether it's a human making the decision or an agent making a decision or a piece of software making a decision, doesn't matter. At that point you need to make that decision, the right insight is available to you. And I think that is where the industry as a whole, whether it's the data platform or the analytics piece, is evolving to actually, and I also call it as the push versus pull, Kailey. A lot of the systems of technology are based on a pull mechanism that, hey, we'll come, we'll ask a question, we'll get an answer, we'll look at a dashboard, we'll get an answer, we'll go to this app and analytics will be embedded in there and we'll get an answer.
0:09:24.9 Sumeet Arora: But what about the situation where the systems push the insights to you right when you need it, knowing that you need it, that is frictionless for me, that means the system was invisible and it just served you and you made good decisions and you took great action. So, I think that's where this whole thing is going in my opinion.
0:09:45.6 Kailey Raymond: It makes perfect sense to me and I mean, I'm just thinking about my day to day and how much better it would be if I just got a push notification of like, hey, this is trending downwards, maybe you should take a look at it as opposed to having to actively go in and push, put on all my right filters and try to make my own judgment and assessment like that information is available and it could very easily be shared with me proactively. I like that. Proactive versus reactive. It's right on your theme. You're staying on theme right now too. I also heard you say, agent, you've spoken about this idea of Trusted AI at scale in relation to Teradata. I think it's worth bringing up, you know, when we're talking about an analytics company, data in general. What does that idea of Trusted AI at scale look like in practice for enterprises?
0:10:39.7 Sumeet Arora: Yeah, well, in some ways it's actually about the brilliant basics because first we have to remember AI is a means to an end. It's not the end in itself. Like I look at all These billboards on 101 and 101 is a freeway here in the Bay Area through San Francisco and every billboard talks about how great someone's AI is. But the reality is at the end of the day, it's about the experience, any one of us in any business doesn't matter. It's the outcomes that we are enabling for our customers. It's the experience they are getting working with us. Those are the basics. And AI is a means to improving that, otherwise why would we do it right, right now to do that really well, trust is important because AI is only as good as the knowledge and the data it feeds off. And you know, we have spent Teradata has spent 45 years helping people deliver ROI for their use cases that require data. And if that data was not good, the use case failed. Same thing is applicable in this world. If the knowledge is not good, which means it's not trusted, that means it's not curated, it's not current, it's not modeled properly, then that is kind of ingredient number one towards trusted AI.
0:11:59.1 Sumeet Arora: And then of course there's a lot more beyond that because just like you and I, humans, when we get exposed to a lot of information, lot of knowledge, we actually get confused. I mean the same is true for AI agents. I mean, you give it too much TMI, it'll flop. So, you have to give it just the right worldview. So, we have to model this information, the data and unstructured, structured, all that stuff has to be properly modeled so that you give a worldview to the foundation models and the agent that is easier to grok. In that case, it becomes more explainable, it becomes more accurate, there's less hallucinations. So, there is a whole stack that you have to go through the core quality of the knowledge, the modeling. Then there is the agent layer itself where humans need to still be involved, absolutely. You have to audit, you have to train. So, there is a whole stack that needs to happen to deliver a trusted AI system. And I think it's a trusted human plus AI system, it's a control system. And I think that's what at Teradata, that's how we think about it. We think about AI with ROI and there is no ROI of, your AI can't be relied on. And that's kind of, this could be a conversation in itself, Kailey. But how we're thinking about this.
0:13:14.5 Kailey Raymond: Fully agree with you. I mean I think that there's been all these studies recently about like, okay, how much ROI is the AI that you're, the AI use cases that you're running actually producing. I think folks for the large part have been not necessarily focusing on the problems themselves, but focusing on the solution of AI itself. So, I'm liking kind of where you're headed with this. But it's a big modernization effort in many ways. There's a lot of data debt involved with a lot of these organizations. I know when we think about builders, we might think about like, greenfields. But the reality is, is that most enterprises have a lot of legacy systems. Not all the data is perfect. Some of it's really old. Not a lot of it doesn't connect together. So, how are product teams addressing that issue of data debt and modernization?
0:14:01.5 Sumeet Arora: Yeah, I think a few things. I'll make another comment on that topic. Besides the complexity of processes and in larger companies, there's acquisitions, there's a lot of stuff there. But even like when I talk about brilliant basics, it's the data and the knowledge being proper, but also actually knowing what you want. Measurable outcomes. Like if your outcomes cannot be measured, it's difficult to deliver ROI on that. So, I think that's. The brilliant basics for me includes, like, how are you actually measuring....
0:14:36.7 Kailey Raymond: What does success look like?
0:14:38.2 Sumeet Arora: What does success look like? Yeah, that's important. But you asked me a question on, how are product teams. I think the general computer science principle, which we have all learned, is if you have a complex problem at hand, you divide it into smaller pieces and you solve problems like each piece. And I think that's kind of what the industry is doing right now, which is, hey, let's focus on data quality. Let's look at duplicate documents and how we can govern that stuff. Let's extract the right entities and the right features from the documents. Let's put it all together. Maybe we'll... Let's go topic by topic. Let's not try to build a super agent. Let's build an agent that is an expert in small tasks, but it's really, really good at that. And then we can have agents that come and work with these agents. So, I think the industry is operating just like how you would by dividing a large complex problem into smaller pieces, validating each piece, making sure it's trusted. It's good bringing the pieces. I think we are.... That is the journey that is underway now, Kailey. We are early in that, I would say we are early in that phase, but that's the direction I'm seeing.
0:15:41.3 Kailey Raymond: I have a thought. It's like off the back of this is if we're like breaking it down into smaller problems and we have all these little agents that are doing this little task, aren't we kind of like using the same workflows that have already existed and just kind of making them incrementally better. Like, what's the step change? Like, do we need to rethink the entire process itself to actually get the efficiency that AI promises? That's the thing where I'm like, how does it really work?
0:16:08.7 Sumeet Arora: Yeah, actually we saw something similar at Teradata where we tried to fit the technology to the process we had instead of changing the process to just rethinking the process itself. I think really that's important. I think as you build these smaller agents, you must never lose sight of first taking a step back and looking at, hey, what is that measurable outcome I'm driving? And you start outcome backwards. So, at Teradata we say if it's data warehousing or data platform, we say start workload backwards. But when it's AI, we say start use case backwards, start ROI backwards. So, you start with what's the use case? What's the big problem? You take a step back, want to make sure that the outcomes are measurable, then you take the next step back and you look at how the system operates today, you rethink the system itself and then you implement the technology. So, you made a great point there, Kailey. And I think we should. In fact, that's so important for us as a leaders to, as a leader to be self aware of as well as we implement these programs.
0:17:16.0 Kailey Raymond: Yeah, I mean, maybe the whole thing needs to be rethought.
0:17:22.3 Sumeet Arora: There are also, you know, that incremental thinking is one thing I also worry about from another angle, Kailey, which is, yes, the whole thing needs to be thought through. We can remove process debt from the system, we can remove complexity debt from the system and maybe do the simpler one. But there is one more thing that bothers me, which is maybe the outcomes need to be different too.
0:17:42.9 Kailey Raymond: Totally.
0:17:44.0 Sumeet Arora: Right. Because now those things are possible that maybe in the past we wanted, but we kind of gave up on them. So, there is that angle also in this whole equation. So yes, this is a time for everyone to take a step back and reimagine things. And that's what I would conclude from just this dialogue between you and I.
0:18:04.5 Kailey Raymond: I agree for sure. And I think that this is a question that I've always wondered, especially product leaders that are building roadmaps with, enterprise customers, which might be balancing reliability a little bit more than speed. But a lot of this conversation has been centered around velocity and innovation and speed. So, how do you think about, like, what's that balance? Feel like you're building a product roadmap? How are you balancing speed, innovation and reliability, stability, the things that frankly your enterprise customers are probably paying you for.
0:18:36.5 Sumeet Arora: Absolutely. Look, they pay us for reliable, stable scale. They pay us for scale performance. I always tell my team that that is feature zero, but I think they're positively correlated, Kailey, the trains that like the bullet trains, run fastest, but if you look at, I mean they have a full concrete bed, they don't run on rock and compacted mud. They actually run on a on concrete beds, special tracks laid out for them. These tracks don't have these cracks. The joints are very far apart. The traction on the top is very special in terms of how much electricity delivers. There is so much that goes into creating velocity. I believe that stability, high quality performance is positively correlated to velocity. So, it is like it is better to build with that foundation, never compromise that and deliver with that. So, I'm also not just a velocity person, but I'm also a little bit of end person. And I feel that these things are important because if I don't take care of the velocity, quality, sorry, the quality, stability, reliability, angles, I'm going to be dragged back backwards anyway. So, no compromise on that. That's feature zero.
0:19:50.8 Sumeet Arora: And then we product manage velocity on top of that. Now I'll say this Kailey, a lot of the times in organizations, as organizations become bigger, time is lost in decision making. Like you could save a lot of time in faster decision, a lot of time. You know, if I could give my engineers, I could make decisions faster in my teams. It's not just sumit making decisions, the entire system is making decisions faster. That will give engineers more time.
0:20:18.3 Kailey Raymond: How do you quantify that and solve for that?
0:20:22.5 Sumeet Arora: Yeah, if I can quantify and measure that very well, that's when AI and engineering would succeed. AI and engineering productivity, I'm talking about engineering productivity is not measured by how many lines of code were accepted from a copilot or from code AI assist or how many code reviews were done by AI or how many unit tests were written. It is actually measured by the idea to customer value latency. How much were you able to. And it's not an easy topic to measure, but that is a very important metric there. And I think if my systems are product managed for velocity, my entire life cycle is, then I would have looked at every layer, every step in the process. The initial decision making, the customer discovery, the validation, the metrics, the inner loop of development, the outer loop of development, the production loop, the support, everything would have been accelerated. And so every, you so kind of you measure all these components, like divide a big problem to smaller ones and then you get the output. So, you have the input metrics and you have this outcome metric. So, that's kind of how you do it.
0:21:35.3 Kailey Raymond: Not an easy thing to do.
0:21:39.3 Sumeet Arora: Not easy.
0:21:40.0 Kailey Raymond: Yeah, but makes it.... You're right. It makes a huge, huge difference. It's like if you can give everybody in the company enough context to make their own decisions and you know you're going to accelerate way, way faster. It's almost an impossible task to do though. How do you accomplish that?
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0:22:49.1 Kailey Raymond: Tangible example from you. Maybe something that like, didn't go so well. So, we're talking a lot about velocity as kind of like a center of some of the ways that you think about building. So, I wonder if what direction you're going to head in here, but what's a product or a strategy decision that you led that didn't go exactly as you had planned, but maybe it taught you something really valuable.
0:23:13.7 Sumeet Arora: I have lots of examples of that. I think the biggest failures have been one where we didn't keep the economics in mind. Like when you embraced complexity and built a lot of capabilities and features into a product, added a lot of complexity, but it got beat in the market by good enough, but hugely much better economic solutions. And I think that those have been the costliest ones, like real costly ones. Like we're talking hundreds of millions of dollars worth of losses at that scale. And that I have seen quite a few examples of that. At least two or three I can recall right now. So, I think those are the big ones. The other one, which may be a little different because economics is obvious. Right? I mean, you came out with this product and it's like the Rolls Royce, but then people want to buy maybe a Tesla. Yeah, you know, there you go, you lost, you're out. Right? But the other one that actually I learned recently in the last five, six years is how important it is not just to build a great product, have the right economics, but also think about distribution, innovation.
0:24:30.5 Sumeet Arora: Like how do you reduce friction and create leverage in the distribution of your product? Like really making it easy for your partners and your customers and your other builders to use your stuff. And that is a more relatively newer learning for me. In the last six years, I would say I've seen that's where things have failed. And the last one was more fixable. But I have seen that if your pricing model is not done well, it can be a huge issue, but it gets a more fixable one. If you are receptive to market feedback, you can fix it sooner. The second one, the distribution one, is fixable but takes a lot more effort and work. The first one, if you got it wrong, that's a costly mistake.
0:25:13.5 Kailey Raymond: Yeah, because the first one is the product itself. Everything else is like the spokes of the wheel, which are a little bit faster, maybe as well to be able to redesign. But really what you're talking about is almost like need to have versus nice to have in some cases, or at least in the eyes of your customer. You're building all these amazing features and they might be a little bit niche for the vast majority of your customers. So, how do you think about like, you know, you're building products that are designed to solve for real business outcomes, not just hype. I feel like we can get lost in this hype cycle sometimes.
0:25:53.6 Sumeet Arora: That's important. I always ask a question like, see, all of us have a tendency to jump into solutioning quickly, but how much time do we spend on why we are building something? Who are we building it for? What problem are we solving? Is the problem that we are solving worth solving? Does it help a lot of people? Does it help a very few set of people? Why are we the best to solve that problem? How will we remain the best to solve that problem? Why is our solution going to be the best in the world? Can we validate it with seven customers or prospects? The principles that a startup would apply, a seed funded startup would apply that building that company, that startup. That principle must not be lost in midsize or bigger sized companies. That's why in Teradata, the first day, day one, my message to the team was we're going to operate like a startup. Which doesn't mean just velocity. It means the principles of a startup. Finding the market pull, establishing product market fit for anything you build, gaining the right to exist because you built the best thing. Otherwise startups don't have a right to exist, only the big companies would exist. So, those are the philosophies that apply to any size of a company.
0:27:18.6 Kailey Raymond: Obsessing about the problem. Yeah.
0:27:20.0 Sumeet Arora: The problem. Spending a lot of time on the problem than the solution.
0:27:25.6 Kailey Raymond: Of course. I mean, it's like the advice that makes perfect sense. But you lose, especially as your organization maybe gets more complex and have more products that you're thinking about prioritizing shipping different features or launches at different times. It's not an easy thing to roadmap.
0:27:43.8 Sumeet Arora: It's not an easy thing. But look, I don't view, for example, at Teradata I have a large number of engineers. I don't view this as one monolithic team. I view it as a formation of startups. Each startup having six to 10 engineers. That's it. And each, I call them pods. Each pod needs to share how they are establishing excellence in whatever they are building. Why are they building, who are they building it for, why is it going to be the best? So, that is part of the template for each pod.
0:28:16.6 Kailey Raymond: I love it. I grew up in startups. I was totally the biggest company I've ever worked for by a large percentage. Like the largest company before this was probably 100 people. So, I'm very, very familiar with being in the trenches and being intimately familiar with the customer base and obsessed with the problems. I'm wondering if you can walk us through a recent product or feature rollout that you're really proud of that really did move the needles for your customers.
0:28:47.5 Sumeet Arora: Sometimes it's not a feature, it's the performance. You know, imagine somebody's analysis gets done 7x faster than before and what that means to the way that company is operating, what does it mean to them? Like, what's the business result that you drive if something took X, but Now it takes 1/7 of that, it changes their, like it changes their business, right? And I noticed that our engineers did this amazing work on performance and I found that it was generally applicable that time savings was material to different industries. I saw it in financials, I saw it in healthcare. And a lot of times it's not this, AI this or AI that actually it's raw performance. Like brilliant engineers coming together and figuring out a way to make things really fast. I think you asked me a question on embedded analytics. It was quite a revelation that making analysis available at the point of decision, inside of apps, processes and where people are spending their time was a lot more effective than asking people to come to your system. Like, if they have to come to Teradata or any other system to get their insights.
0:30:08.6 Sumeet Arora: That is a lot more friction than insights just showing up where people are. And I know that's very obvious, very intuitive, but I think it's important that from a business perspective, from a customer delight perspective, every which way, this push versus pull was a huge thing for me and I see that even in data platforms this push versus pull, so critical.
0:30:31.7 Kailey Raymond: It is and it isn't obvious. I mean I think that a lot of folks might measure success by like the number of folks in their product in this page and you know, whatever. So, it is a little bit of a different way to think about it. But it kind of comes back to a lot of the thesis around this which is like time to value speed, velocity, reducing friction. Those are the center points and that's what we need to really be focusing on. I'm wondering if you have one aha moment customer insight, data signal, whatever that you might remember that changed the way you thought about your product roadmap or even a go to market strategy.
0:31:21.9 Sumeet Arora: Personally I love to be in front of customers, Kailey, and I love to go and talk about all the work my team is doing right? Hey, here is the beautiful world we are building and there is a natural tendency to talk about our stuff. But how much time was I spending in listening to my customers, just open listening. And to me my biggest aha moments came from that starting every conversation with hey, instead of me doing a roadmap pitch to you, let's hear about what is top of mind for you. Like if you had a magic wand and all my organization was reporting to you, what would you build? What would you do differently? And a lot of my product improvements came from those conversations. It's even more effective than presenting your stuff and asking for feedback. So, going this, giving the floor and giving it first to your customer, to the prospect, opening it up. Like for example recently I've been surprisingly no surprise but we've been on this for years. But it's still the same things like hey, I want the power of data and insights to reach every employee in the company. I hear that as the number one thing even today when I ask the open ended question.
0:32:52.5 Sumeet Arora: So, and that kind of drives me on the product strategy. Now we are going to make it possible to work with Teradata as if you're working with another human. It's going to be that simple an interface. You're basically going to become an outcome centric interface. It's not my creation, it's basically what I have learned from these types of conversations. So, maybe the way I would answer that question in summary is it's the way you do it than the exact insight. It's the method to get to that insight, which is, I know it's very simple, very basic, but it's like a lot of listening.
0:33:26.1 Kailey Raymond: Yeah. I mean, oftentimes you're not surprised, you're not going to be surprised by this. But oftentimes the thesis statement of these conversations become, yeah, you have to listen to your customers. You know, it's like, of course, and yet it's hard to find the time to prioritize that. And you're right. A lot of folks will build the thing and have reactions to it versus opening the door to allow for folks to go in whatever direction that they want. What you were saying was really interesting too. It immediately brought me back to your idea around proactive versus reactive. If every person in every organization needs data at their fingertips, not everybody's going to open an app. Not everybody's going to like go to the little analytics panel. But like if you proactively served up analytics to folks in Slack or email or whatever, it's in their face. And then everybody becomes a little bit more context, a little bit more data driven. So, I into it, we're going to leave folks with some inspirational ideas. So I'm going to flip it over to some kind of like lightning round questions to round us out, Okay?
0:34:37.2 Sumeet Arora: Sure. Let's do it.
0:34:39.3 Kailey Raymond: All right. What's a analytics or data trend that you're watching closely right now?
0:34:46.0 Sumeet Arora: I would say one where analytics and data is invisible, but the power is everywhere.
0:34:52.4 Kailey Raymond: I like it. What's a company or product you admire for how they build with data or analytics?
0:34:59.0 Sumeet Arora: Well, besides Teradata, I would say that I do believe that when Apple puts things out there, they may not be the first to market, but they definitely are best to market. And I feel it in also how they leverage data and analytics to make things really easy for the end user. It's invisible to you.
0:35:17.7 Kailey Raymond: Yeah.
0:35:18.5 Sumeet Arora: There is a lot of data and analytics driving that experience.
0:35:21.4 Kailey Raymond: What's one thing every product leader building for enterprises should stop doing? And what's one thing they should start doing?
0:35:28.1 Sumeet Arora: Start building minimal lovable products. Lovable products. And stop some features. Like cancel some features. That's what I would do.
0:35:36.9 Kailey Raymond: Beautiful. I love that advice. Last question to round us out. What's your advice for builders that are trying to turn data insight into action in their organizations?
0:35:49.1 Sumeet Arora: I think focusing on making sure that that is trusted is the most important thing. How are you able to explain that? How are you able to prove that those actions were built on the right insights and the insights were calculated correctly or derived correctly is super, super critical. And I think focusing on trust is the number one thing in that.
0:36:18.0 Kailey Raymond: Don't make decisions with bad data. Amazing. Thank you so much, Sumeet. There's so many nuggets in here for folks to latch onto. I really appreciate your time and you sharing your ideas with us.
0:36:30.6 Sumeet Arora: I really enjoyed it. Kailey, you asked great questions, very insightful ones, and I think I had to think a lot while answering them. So, it was great, great to spend time with you.
0:36:40.5 Kailey Raymond: Great. That's the goal. I'm glad we got you thinking today. Thanks again.
0:36:44.6 Sumeet Arora: Thank you so much.