The amount of money invested in “Transformation Programs” is staggering. In the past 20 years we have seen Digital and Agile Transformation Programs grow and swell to $1.3 trillion dollars in 2020 alone. According to the HBR, 70% of that spend is wasted. Many companies miss a key component in their Transformation Program. How will a Product-Led Transformation be different?

Matching the experience of your self-service analytics tool to your users' needs has often been difficult when ‘self-service’ is different for everyone. Here's how Guided NLQ tackles this.

With a whole new Guided Natural Language Query (NLQ) capability and several new enhancements across every part of the platform, Yellowfin 9.7 is a must-have upgrade.

Fishies, say hello to the new Yellowfin Brand, and Brand Guidelines. Now, we shouldn’t actually say ‘brand new’ because the logo and some of the elements are a progression from what is currently our brand. But, as you have heard us say many times, our brand is not just the logo, fonts and colors. The Yellowfin brand encompasses the reason we do what we do, and it sits behind why we develop the products we develop. It’s why we code what we code.

Natural language query (NLQ) is fundamentally a feature designed to democratize self-service analytics, and help make it more pervasive throughout the organization.

As part of our series on natural language query (NLQ), this blog details 5 benefits of using Guided NLQ, and how it differs from search-based NLQ to bring users true self-service BI.

It’s long been said the secret to a better answer is to ask a better question. But in the era of self-service analytics and natural language query (NLQ), it’s easier said than done.

Providing the ability to ask questions of data is useful, but only if you can guide analytics users toward the right answers. Read: What is natural language query, anyway?

Providing your analytics users the ability to get answers from their data is useful, but only if the solution can guide them to ask the right questions.

When I started out in my career, it was very much all about the numbers, and thinking, not feeling, when it came to communicating important results found in business data.