2025 is poised to be another year of significant advancements in business intelligence (BI) and analytics. Building on the momentum of 2024, which saw a surge in self-service BI adoption, our attention turns to newer, sophisticated artificial intelligence (AI) solutions. As the data landscape evolves, it’s important to keep agile and adapt to emerging technologies to stay competitive and maximize the value of your analytics investments.

Data analytics and business intelligence (BI) are key for driving informed decisions, and today's best solutions make use of artificial intelligence (AI) to help users along the way. AI chatbots, powered by natural language and machine learning (ML), are one such intelligent tool that can quickly analyze large datasets, helping your business users find helpful answers to queries, get recommendations, and even uncover valuable insights.

The way we interact with computers and digital systems has come a long way. Gone are the days of cryptic commands and rigid syntax; today, we can speak naturally to our devices, ask questions in complete sentences, and even have (somewhat) intelligent conversations with chatbots. This transformation is largely thanks to a fascinating field of artificial intelligence (AI) called natural language processing (NLP).

From artificial intelligence (AI) to machine learning (ML) to conversational chatbots, the tools we use to interact with and consume information are rapidly changing thanks to powerful new technologies that make understanding our data more accessible than ever. One particularly influential field is natural language technology (NLT) and its branches.

Being able to create and share insightful data-led stories to support your dashboards and reports is a critical capability in today’s modern workplace. You want your data to be accessible for more people, and ensure everyone gets value from your investments. Data storytelling tools bring valuable context to the ‘why’ behind the results, while inspiring your audience to care about and act on insights.

From ancient Greek myths of talking statues to the modern-day Alexa and Siri, the concept of machines capable of understanding and responding to human language has captivated us for centuries. In recent years, this concept has evolved into AI chatbots, highly sophisticated tools that can read our queries and perform tasks ranging from customer service to automated alerts.

In today’s fast-paced digital environment, delivering value quickly is critical to staying competitive and gaining buy-in from both stakeholders and customers. One of the key enablers of this rapid value delivery is embedded analytics, a business solution that allows you to integrate interactive data visualizations and business intelligence (BI) features directly into your existing applications.

Welcome to the latest entry in Yellowfin Japan’s ‘How to?’ blog series! This series of blogs aims to provide your team with another hands-on example of adding data visualization to your Yellowfin dashboards, using our array of chart and graph types. In the previous blog, we created a Combination Chart by aggregating on the basis of Year and Month.

Choosing the right business intelligence (BI) solution is pivotal for independent software vendors (ISVs) and enterprise organizations aiming for data-driven decision-making. But with so many preferences and use cases to account for, certain considerations can, initially, get overlooked. Centralized business intelligence (BI) is one such topic that requires careful preparation to achieve a nuanced understanding.

Have you ever struggled to make the value of your data clear to your audience? Numbers and charts are great, but on their own, they often lack the power to inspire your audience to care and act. People also find it hard to understand the results from metrics alone. Conveying the importance of your data, and translating what it means (and why your audience should care) thus requires more context. That's where Stories comes in.