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Dashboards and reports are two common terms used interchangeably in discussions around business intelligence (BI) and data analytics platforms. Both are analysis tools used to present operational information graphically, provide visibility into organizational performance, and enable informed decision-making based on hard-tracked metrics. But what is the actual difference between BI dashboards vs a report?

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 true self-service BI. Many analytics vendors today offer search-based NLQ tools. To explore data and find insights, you must use free text, but you also have to know what, or how to pose a query. The problem here is obvious: There is no guidance on what to ask your data, or how you can use the tool to ask questions and get helpful answers.

Data visualization is one of the most important capabilities of any business intelligence (BI) and analytics solution. It helps people translate complex data into a visual context, like a chart or a graph, identify trends numbers alone can't easily reveal, and discover hidden patterns in your dashboard. Data visualization also provides a wealth of additional benefits for data analysis, such as enabling easier understanding of the correlation between operations and results.

Business intelligence (BI) dashboards, also known as BI dashboards, are a powerful way to help people collect, share and analyze their business data to make informed decisions. Business dashboard software can help users monitor operations, optimize processes, and improve overall business performance by having data readily available, and can also help detect and prevent potential problems before they become issues with its use of real time data.

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. The rising integration of artificial intelligence (AI) and machine learning (ML) in many business intelligence (BI) solutions has enabled new innovative approaches in combining natural language techniques with self-service analytics, resulting in helpful NLQ tools.

Yellowfin 9.15 is a significant version release that introduces a major update to our Yellowfin Guided NLQ feature in the form of AI-enabled Natural Language Query (AI NLQ), as well as a host of general product enhancements, fixes and security updates. In this blog, we will cover what AI NLQ brings to your embedded analytics deployment, as well some of the other highlights arriving in the Yellowfin 9.15 version release. For the full technical list of updates, please visit our release notes page.

The rise of natural language query (NLQ) technology in modern business intelligence (BI) and analytics platforms is empowering many companies to streamline data exploration and analysis, and democratize access to insights for more people - not just data experts. But like any technology, the ongoing challenge is to help stakeholders and customers to see the value in using it.

Today, organizations need to know what data they have, what it means, when it changes, and how it delivers value faster than ever before. Modern businesses have a problem, though – a continuously growing mountain of data, made up of many different operational processes and performance metrics, which users must proactively monitor to ensure business goals are met, and insights are deciphered. Embedded dashboards are one way for your users to keep track of key information while using your application.

Data visualization is an elementary component of human learning and understanding. It's also a significant capability of business intelligence (BI) and analytics solutions today. So, how did data visualization first come to be? The humble origins and gradual evolution of data visualization tools has led to an interesting timeline.

A lack of understanding regarding the role of data in decision-making is a reoccurring challenge for many organizations. However, with the increase in quantity and complexity of data, you need to do everything possible to help your people use data effectively.