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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?

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.

“There is a shortage of data scientists, as the demand for analytics rises.”

The COVID-19 global pandemic has changed the ways we work, including how we use data, forever. Amidst the crisis, the analytics industry is experiencing its own paradigm shift.

While the AI and automation capabilities of augmented analytics dominate today’s business intelligence (BI) conversations, data storytelling is fast becoming a hot topic - and together, they’re set to re-shape the future of BI.

For a long time, many in the analytics and business intelligence (BI) industry, including most of our competitors, have loosely labelled data visualization in the form of charts and dashboards as ‘data storytelling’.

Simone Clancy, Director of People Strategy at Yellowfin, and Jessica Maree, Program Director at VICT4W, had the opportunity to interview each other about leadership and mentoring, and share some valuable lessons they've learned.