Sisense

🧮 Time to nerd out on some numbers...Because statistical overconfidence is dangerous and easy. Imagine you have a small online business. This month 200 users signed up on your website, and 10 of them bought your $800 service. Great! You’ve made $8k of income. How much should you expect to make this year? The straightforward answer is $8k * 12 = $96k. But how confident should you be? Will your conversion rate always be so close to 5%? You could pad the estimate ±20% for safety, guessing at $77k to $115k. This is a question of binomial probability. Using our favorite binomial confidence interval calculator, the 95% confidence interval for your conversion rate is about 2.5% to 9%. With a confidence interval that wide, you should expect to make somewhere between $48k and $172k. Yikes! You could end up with half of your simple guess, and that’s if your business doesn’t change. These confidence intervals are very informative, but turning to a calculator for every metric is tedious. If you’ve got hundreds of metrics across dozens of dashboards, it’s downright unsustainable. Fortunately, the math for calculating confidence interval is simple to implement: n = number of users x = number of conversions p = probability of conversion = (x / n) se = standard error of p = sqrt((p * (1 - p)) / n) confidence interval = p ± (1.96 * se) Flip through the images below to learn how to implement the formula in SQL. 👀 Got questions? We'd love to talk about this! #SQL #DataDriven #BusinessStrategy

AI is changing the game when it comes to democratizing analytics. How? It makes data insights and analytics significantly more accessible to a wider range of users. We'll be honest, this change is LONG overdue. Analytics and business intelligence promised to put the power of data into the hands of people in all types of roles so that everyone could make smarter, data-driven decisions. But, as of January 2024, 80-90% of knowledge workers lack the technical skills, data literacy, or access to use today’s supposedly easier analytics tooling effectively. Here's three specific ways AI is helping companies become more data-driven: https://bit.ly/3Sn9Flm #DataAnalytics #AI #DataDriven

This week, the Sisense team held its AI Tinkerers meetup at our office in New York. Everyone had a great time, with ten large pizzas fueling attendees watching six different demos from the participants showcasing the latest GenAI capabilities. Later, our CRO, Brian Weinberger, and the team hosted a dinner for product leaders at Del Frisco’s Double Eagle Steak House. They discussed the ever-changing market and how leaders are driving predictable growth. #GenAI #SaaS

There are many challenges facing a product manager looking to integrate embedded analytics into a product roadmap, ranging from resource constraints to getting buy-in from the C-suite. Let's break down how to overcome common hurdles. ⬇️ ‣ Analytics roadmap pitfall #1: In-house resource constraints negatively impacting time-to-market. Few development teams have the resources to build satisfactory modern analytics from scratch. However, a range of sophisticated analytics and visualization tools are now available. Integrating a well-established platform into your product is a much faster route to market! ‣ Analytics roadmap pitfall #2: Not attracting or retaining analytics end-users. Yes, misaligned analytics features lead to poor user engagement and hinder adoption. But a composable and API-first embedded analytics platform empowers you to integrate relevant metrics, descriptive and prescriptive analytics directly within the user interface. ‣ Analytics roadmap pitfall #3: Not scaling successfully. Sluggish performance (multi-second response times) can drive users away. Managing increasing data volumes and complex queries can be challenging. The ideal embedded analytics platform alleviates the burden on your operations and engineering teams by automatically applying the latest optimization techniques to every end-user query, which saves time and money and supports your scalability as your deployment expands. ‣ Analytics roadmap pitfall #4: Not developing a solid business case. As a product manager, building a compelling business case for analytics is crucial as it forms a significant part of your analytics roadmap. You can effectively secure internal support by aligning your analytics solution with your organization's strategic initiatives and emphasizing its ability to address high-value challenges. Develop a business case that addresses the challenges of each stakeholder, and create a quantitative analysis that shows how embedded analytics can help. #ProductManagement #DataDriven #analytics

Have you heard the news? "Sisense debuts GenAI-powered analytics narratives and natural language querying." Check it out from KM World 👇

Ready to learn how Generative AI can seamlessly integrate with your analytics processes to streamline operations and unlock new ways to monetize data? This on-demand webinar is for you! In it, our experts share real-world applications and tips on integrating GenAI technology to elevate your business processes and revenue streams, bolstered by real-world examples. Tune in here: https://bit.ly/3zTXnKD #GenAI #DataAnalytics #DataDriven

We're in a new era of conversational analytics, one in which the intersection of analytics and GenAI will profoundly impact your product roadmap. Whether you're embarking on your analytics journey or looking to evolve the analytics you're already providing, it's time to consider how you will add GenAI data analytics to your apps. When it comes to embracing this new era of analytics and being successful, there's a fundamental path that product and engineering teams can follow. We call it The Five Cs of Conversational Analytics: 1️⃣ Conversational and contextual. Give your users the power to quickly identify relevant insights. This starts with natural language analytical starter prompts based on their data model, like "What is my revenue by country?" Then, let the questions flow, enabling contextual follow-on prompts later in the conversation, such as asking for narrative guidance. 2️⃣ Composable. If conversational analytics looks and feels different from the rest of your app, that creates a roadblock to adoption, which is where composable analytics SDKs come in. A composable SDK enables your product team to build the exact conversational experience and workflow for users, making natural language more intuitive. 3️⃣ Capable, responsible, and trusted. Trust is paramount regardless of how and where we engage with an LLM. A foundational way to ensure trust for conversational analytics is to build it on a semantic layer or data model. Another important consideration is what controls are in place over data, prompts, and outputs. 4️⃣ Cloud and LLM agnostic. When you embed conversational analytics, it's important that it doesn't lock you into any one LLM—which may not be the best LLM to use for your use case in the future. Remember, your compliance needs may dictate that your organization shift to using its own private LLM. 5️⃣ Cost. With any discussion around LLMs, it's critical to consider the cost. Ultimately, it's about flexibility because managing cost requires the flexibility to plug and play different LLMs from public to in-house models. #LLM #GenAI #analytics

"Sisense Analytics Chatbot and the accompanying narrative storytelling capabilities are now in beta testing. Public preview is expected within the next few months, with general availability by the end of the year, according to Ayala Michelson, Sisense's chief product and technology officer." Read the full article from TechTarget News here.

Explore real-world examples of AI integration in analytics, empowering teams to outperform the competition. Read the whitepaper 👇

Thanks for the feature, StartupHub.ai! We're thrilled to be at the forefront of API-first embedded analytics. Read more about the beta launch of our GenAI-powered analytics chatbot and enhanced narration capabilities!