Name: Smarter, faster, embedded: The future of product intelligence
Uploaded: 2025-06-19T17:30:31.556Z
Duration: 28 min 16 s
Description: Smarter, faster, embedded: The future of product intelligence

Transcript for "Smarter, faster, embedded: The future of product intelligence": Hey. Good morning, good afternoon, or good evening, everyone, wherever you're tuning in from today. Thank you so much for joining the webinar. We'll take a look at the future of product intelligence, how agentic AI is changing the world of analytics and understand Sisense's AI approach. As people are joining in, let's give them a minute or two. So, if any questions come up about today's session, please feel free to reach out to me or reach out to us at sisense dot com or visit us at our Sisense Academy or a community for more information. Before we dive in, I just wanna acknowledge the diverse audience we have today. I'm sure we'll have both newcomers to Sisense, some familiar with it, spanning from roles such as product management, engineering, and business leadership. I'll do my best to keep the session engaging and relevant for everyone. Let's get started. So I'm Pradeep Christian. We're the product marketing team here at Sisense. Throughout my career, I've been fortunate to work with leading technology driven companies such as Airbnb, Linkit, Sisense, Cognizant, getting an extensive experience in enterprise sales, marketing, and product strategy. Outside of my professional life, I have a deep passion for traveling, being active through sports, and maintaining a healthy lifestyle. A little bit more about Sisense now. At Sisense, we are not just building a product. We are defining the future of how businesses leverage data to transform themselves and the world around it. We believe that the future of application development will hinge on the incorporation of analytics, the drive forward product innovation, and delight end users with, tailored in context analytics specific to their needs. And we believe that this is gonna be possible to simplifying the creation of analytics as our mission is to empower every app builder with analytics to accelerate innovation and and turn data into a competitive edge. In this presentation, we'll take a look at agentic analytics, five critical platform capabilities, and also how Sisense positions you to be a market leader in the industry. But before that, let's take let's start off by taking a look at the current state of analytics. Now let's focus on one crucial truth. While AI is ready to transform businesses, many organizations are still held back by slow, outdated analytics processes. First, a staggering 76% of organization still admit to making decisions without the data they need. That means most business leaders are flying blind, relying on gut instinct because their analytics tools cannot just keep up. Second, while nearly half of the organizations can identify multiple valuable use cases, AI use cases, only 9% can deploy a new use case under a week. Now this isn't a lack of ambition. It's a result of clunky integration, expensive scaling, and concerns around data quality. Third, more and more business leaders recognize the importance of invisible ambient analytics. That is insight that are seamlessly woven into workflows so that users get their answers that they need automatically right when and where they need them. All these challenges boil down to one key point. Most businesses want to harness AI, but the but their analytics tools are slowing them down. The technology is ready, but it's time for processes and platforms to catch up. So what's the takeaway? Companies that remove bottlenecks and adopt modern AI powered, analytics will make faster and better decisions, and gain a clear edge over their competitors. Let's dive a little deeper into the gap between what organizations want from AI and what's actually happening on the ground. So today, 92% of organizations say they want AI powered insights. It's a universal demand. Everyone sees the value of unlocking deeper, faster, and more actionable analytics through AI. But 47 are struggling with data standardization. That means that the data is scattered across systems in different formats and often incomplete or inconsistent. What does this really mean in practice? It's like having a supercar in your garage, but not having the right fuel to run it. Companies have access to powerful AI tools. But if the data is not standardized and ready, then they cannot really use it. And then it's a huge opportunity, but but they're at the risk of kind of losing it. What are your biggest data bottlenecks today? So so these are some of the barriers that organizations face every day. Many teams just struggle to connect to all their data sources and retrieve information quickly. If it takes days or weeks to get the data, your insights are always, you know, kinda late to the party. Even when you have the data, is it reliable? Is it secure? Can you meet your internal and external compliance needs? The concerns about quality, privacy, and governance keep data locked up and underused. Excuse me. I have a little bit of a throat situation. So so the last one, even if you've got access and trust, too many analytic tools are just too slow. If you're waiting days for the answers, you miss opportunities and decision making stalls. So when you think about your organization, where do you see the biggest bottlenecks? Diving a little bit more into the future, let's talk about how analytics has evolved through the past decade. We started off with reporting analytics, basic dashboards that that told us what happened, but were static and limited in interactivity. Then we moved on to visual analytics. These visual analytics were interactive dashboards that let users explore, but still required you to know what to look for. Next came augmented analytics, where AI started suggesting trends and patterns, but the humans still had to drive the analysis forward. Now we're entering the era of agentic analytics. Their AI doesn't just assist. It automatically orchestrates workflows, discovers insights, and delivers answers proactively with minimal human input. The shift is about moving from what happened to what is about to happen and what should I do next. Okay. The pace of change is accelerating rapidly. The interest in agent tech analytics has grown 500% just in the last six months. Why? Because businesses that harness these capabilities are moving faster, being more competitive, and meeting customer needs just before their competitors do. If you are not moving forward to this paradigm, you risk getting left behind. The market has made it clear. The future is agentic, proactive, and powered by AI. Oops. Okay. But even with this surge in interest, there's a human AI delegation bottleneck. At this point in time, the human needs to be in the loop. Only 40% of professionals truly trust AI recommendations, while this number is expected to jump to 71%, of people who are willing to delegate decisions to AI. But why is there such a gap? It's not about the technology. It's about trust, transparency, and accountability. To bridge this gap, organizations must build transparent AI systems, provide effective human oversight, and create clear frameworks for accountability. There's a real measurable impact between those who lead in analytics and those who lag behind. Market leaders achieve 25% lower analytics cost, three times faster time to insight, and a 71% higher user adoption. On the flip side, the laggards deal with rising maintenance costs, growing technical debt, disengaged users, and missed opportunities. The takeaway is clear. Investing in agentic AI powered analytics isn't just about keeping up. It's about pulling ahead. So what is agentic analytics? It's AI driven agents that autonomously manage the workflow from data ingestion to preparation to analysis and insight delivery. The AI takes the wheel with regards to agent activation, orchestrating processes with little human input. Then the agent automates everything with the workflow automation, like, including data prep and analysis and even generating actionable insights. And finally, when we move on to the insight delivery, it delivers proactively right when and where your users need them without any delay in action. So this is not science fiction. It's happening now, and the organizations that implement it are seeing dramatic gains in speed and and decision quality. So let's take a look at, you know, the five nonnegotiable capabilities for agentic analytics. So what do you need to actually enable agentic analytics in your business? Seamless data connectivity. You need to connect to any data source quickly with automatic schema detection and mapping so that there's no bottleneck at the integration stage. Automated data preparation, AI must handle the cleaning, transformation, and enrichment of data without requiring, manual effort. Intelligent workflow orchestration, automation should cover end to end processes, including handling exceptions or unique cases. AI driven automated insights, and system should uncover, discovered patterns, anomalies, and opportunities on its own without waiting for the human in the loop to ask the right question. And finally, your team should be able to ask questions in plain English and instantly get contextual accurate answers. These five pillars are what sets agentic analytics and enable organizations to move at the speed of AI and not just at the speed of, human analysis. Okay. So let's take a look at what we're doing at Sisense and and and kind of what kind of how that associates to, their analytics platform as a service. The more we consider the advancements that have been made in the BI space over the last two decades, the more we recognize the trend emerging among category leaders and identified a set of characteristics that that modern analytics platform as a service shares. This must be a bit of builder tools that appeal to a wider, audience ranging from allowing developers to quickly integrate into their development workflows, to a graphical user interface that allows for visual creation of analytics, with the ability to export the code or directly embedded embedded via iframes into, modern applications. Data tools have become critical for solutions, as it needs to integrate with multiple sources and provide a unified stack for quick developer adoption. Like, builder tools, these need to cater to multiple persona and provide them with multiple data prep experiences from code driven r and Python to a visual drag and drop interface, with an AI driven guide for faster modeling. As AI is revolutioning all these industries, AI permeates across the solution not only providing not only ways of retrieving and visualizing data in context, but also has the ability to create agents for specialized tasks such as data preparation, modeling, and storytelling. And all of this must be delivered using a modern application architecture that is multitenant, cloud native, and ecosystem agnostic, and have a robust set of APIs to deliver a true SaaS experience. Okay. So let me start by framing how Sisense thinks about AI. We just don't see AI as a bolt on. We we went through every step of the analytics journey. With Sisense AI, developers would be able to create synthetic data models for rapid prototyping that accelerates development at scale. Soon, platform would platform would be able to detect auto detect relationships, apply expert level transformation, and create an optimized model. From here, the natural language querying lets any user ask a question and instant and instantly generates the right charts. Also, you can build without boundaries with combos SDK. The need of the look and feel to match the if you need to match look and feel of your product, our Compose SDK generates ready to use code snippets so developers can tailor color, layouts, and entry and then and interactions while still leveraging the smart AI under the hood. And finally, we need users where they are, whether it's inside your application, your portal, or your external websites. We do it through embeddable components and robust API set. So as your product scales, our AI powered, insights travel with it. Oops. Okay. So MeetSizing's intelligence, a unified set of AI driven capabilities built to supercharge how developers, creators, and end users engage with analytics. The goal is simple, make analytics smarter, faster, and infinitely more scalable. Whether it's a creator who's building insights or a product leader looking to differentiate by integrating AI, Sisense intelligence offers two key advantages. You can create faster with the assistant by using natural language to model data, build dashboards, and integrate it within your applications. And finally, for your, you know, deliver smarter end user experiences, your you can empower your users with built in AI functionalities that we provide out of the box, giving them faster access to meaningful insights. Okay. So our vision is to make the analytics intuitive, fast, and seamlessly integrated into everyday experiences of end users, developers, and nontechnical creators. Analytics should feel natural and accessible wherever decision making happens for each each person accessing, a platform, whether they're building or consuming analytics. This vision sets the foundation for everything that kind of follows. Okay. So earlier this year, we launched our assistant feature where anyone working with Fusion, our flagship product, could set up natural language assistant to interface within their dashboards and create visualizations. We are excited to announce an expansion of this feature, which is a full end to end tool for analytics creation that is now in private preview. Think of it as having a data expert at your beck and call anytime you need it. It delivers an AI first workflow that streamlines analytics from data prep all the way to insight generation and even embedding. The assistant provides a conversational NLQ interface where users can ask questions and explore data in in in real time. So does it let me see if we have time to do a short demo real quickly. Let me see if I missed another yes. I did. In today's saturated environment, not everyone is a data expert. Right? So narrative bridges that gap, and it makes data more accessible and understandable by providing plain English explanations of what's happening in charts and graphs. This basically boosts data literacy across the board where users can quickly identify what's important without needing advanced analytical skills. Okay. So Sisense Intelligence is delivering real impact today. Our customers face all the real challenges we talked about. And, for BioForum, we transformed, how they reported clinical data to their customers and pharmaceutical companies. Okay. So entering the agent agentic error. So because Sisense is an API first platform, AI IDEs, like, cursor, pairs naturally with our Compose SDK. The agent can con easily consume our well documented modular APIs, making it simple to generate, understand, and extend embedded analytic workflows. This truly creates a composable environment where where where AI accelerates everything from onboarding to visualization. So teams can focus on building, and not managing infrastructure. With versus agent mode, AI is no longer just a helpful assistant. It becomes a full on colleague level collaborator. The agent understands your code base and executes tasks that otherwise takes a significant time and attention. For the for Sisense developers, this translates more into a faster iteration, better onboarding, and more time spent innovating. Okay. So we're almost at the end of our session today, but, wanted it more wanted more to be more wanted it more to be, interactive. So let's step on to, a couple of demos where we can showcase what's going on and how the product functions. Just give me a minute. Okay. So what you're seeing here is the second assistant. We we had a brief, you know, outline to it. Let's take a look look at the functionality now and how it can, you know, possibly help you. Right from right from, right from data modeling to insight generation to embedding. This is a one stop shop, with regards to, generative AI assisting you in your creative journey. So let's let's just start off with a suggested query. Let's ask the assistant to create a trial data model and generate some data within the data model. Okay. Let's do health care. So if you notice, it's generating the, data model and including relevant dimension tables. For instance, like a patient or a doctor and other fields that would, that would help. The schema has been created. It's asking me whether I want the physical data sources to be created, which is essential for visualization. Let's ask it to go ahead. So this is extremely useful for rapid prototyping. And you can see it's it's filling out data on its own. So let's think for a for instance that you are a nontechnical creator, does not know how to proceed with analytics and needs some assistance with regard to visualization. The model first needs to be loaded into the system, though. So let's first create the data model, already exist. So let's let's load it. Yeah. Let's load it into the system. It's been loading in the system, and we can use it for analytics and visualizations. Let let me ask the AI to guide us guide guide me. Help me with insights. Let me show why. So it's generating recommended queries? And it's gonna build corresponding visualizations. Create a dashboard with And as easy as that, it has created a dashboard with the visualizations. If you wanna know more about what it is built, it has built, a bar chart with a total cost by hospital, average duration of by by doctor. It's filtered up, you know, it's filtered up top down. The total cost by year and the number of unique treatments by treatment name, and also average cost by treatment type. So let's say I wanna exit out of this AI interface and get into a dashboard. And it's as simple as that, and now we're in the dashboard. Now we've created the dashboard, and we wanna, embed it within within your application. All we need to do is, click on these three dots, get the embed code. So either you can copy the code over here or the iframes. Let's see if the we can do anything. We'll be able to export it. And, basically, do whatever you, you know, you kind of, need to ensure that your data is embedded within your applications. So, I hope this session was really valuable for you today. Again, if if there are, any questions, and please feel free to connect feel free to connect with me on LinkedIn. Reach out to us at Sisense dot com, or look into our academy for for for more information and material. Thank you so much. You have a wonderful day.