Yellowfin is an embedded analytics solution with several features that can help improve the data preparation, reporting and analysis process for your end-users, and many configurations and customizations for your developers to achieve a seamless integration. With so many development scenarios, use cases and end-user requests unique to your business, however, we understand it can initially be a bit daunting to start your project.

Earlier this year, Yellowfin celebrated its 20th anniversary. While we have been around for a while, some things remained constant - our focus on customer and innovation. Yellowfin was borne out of the frustrations felt by our founders, and millions of other information workers, in accessing the data they need to do their jobs effectively, a frustration just as relevant today as it was 20 years ago.

After 20 years of dashboards, today’s line-of-business users expect more value from their analytics, and it’s up to your business to keep updated - or risk getting left behind. There was a time when product teams could purchase basic dashboards and data visualizations, and that was more than enough to satisfy the average user’s business intelligence (BI) and analytics needs. Today, however? Not so much.

An effective dashboard requires careful design to present data in the best way, and to help more people (users, customers) find insights without feeling overwhelmed. Yellowfin BI comes with a wide variety of chart types as part of its extensive data visualization tools, and while it is tempting to use a lot of eye-catching charts to make a dashboard that looks great, it is important to select the right chart for the right situation.

Yellowfin 9.12 is an extensive release that incorporates a large number of bug fixes, additional data connectors, and customer requested enhancements. The biggest changes in this release focus on Export and Broadcast capabilities. Yellowfin already boasts exceptional reporting capabilities, and 9.12 adds additional features and performance improvements in this area. Yellowfin also has the ability to connect to just about any source of data.

Whether you’re an independent software vendor (ISV) or enterprise-sized company, you want the analytics software you invested in to enhance your users’ decision-making, open up greater access to key data, and improve operational performance for the long-term. However, continuously achieving these business outcomes requires a modern solution. Many organizations still rely on older business intelligence (BI) tools for reporting due to long-term licensing.

The value proposition of embedded analytics in 2024 has never been clearer: real-time insights that help your user base make smarter decisions, while enhancing their engagement with both data analytics as a tool, and your product or business systems. But what about the costs?

The retail industry is constantly evolving, and so is the way that businesses use BI tools. Retail business intelligence (BI) dashboards are now more important than ever, as they allow businesses to monitor their operations in a more comprehensive way. In this article, we explore the benefits of using BI dashboards in the retail industry, and discuss some of the ways in which they are transforming how it operates.

From business-wide objectives to long-term targets, every organization has specific metrics they need to track. One way to communicate those insights is a KPI dashboard. Key performance indicator (KPI) dashboards are a great way for executives to improve their management of strategic goals, and keep on top of changes, issues and trends in performance at a high-level, with many useful applications when used correctly, alongside other modern analytics tools.

Imagine transforming raw business data into actionable insights with minimal effort. This is the value proposition of automated analytics, a form of data analytics fast becoming more accessible among modern business intelligence (BI) and analytics software solution vendors. Independent software vendors (ISVs) and enterprise organizations at the cusp of investing in analytics struggle with manual data processes that are time-consuming and prone to errors.