Yellowfin have done a lot of work with software vendors over the years, helping them to embed analytics into their applications and we've learned a lot through that process. One of the things we have learnt is that there are some recurring fundamental issues with how product managers bring analytics into their product, an approach which can prevent them from realizing the full potential of their embedded analytics.

These bundled analytics tools help organizations facilitate and increase the adoption of self-service BI practices among regular business users in a specific operational domain, such as finance, marketing and sales. It does so by improving the availability and measurement of important, relevant historical data for your end users’ decision-making.

One particular feature requested by our customers is 3D body mapping, and whether exporting 3D models and utilizing its visualization and filtering can be applied easily. This technical walkthrough shows you how to leverage Yellowfin to integrate 3D models within Yellowfin and then use them to create a fully interactive display in your dashboard.

If you're looking to embed an analytics solution into your software product in 2021, it’s important that you don’t just think about the short-term. Take a long-term perspective and think about these three key criteria to find the best fit solution for your business.

One of the things we've done a lot of work on at Yellowfin is automated business monitoring (ABM), specifically with our product Yellowfin Signals. It can truly transform organizations and help them to deliver insights faster, ones they can react on. ABM has been in the market for about five years but we haven't seen it take off just yet. One reason is that automated business monitoring challenges the status quo of the data analyst.

Natural language generation (NLG) is best described as a sub-type of artificial intelligence (AI) that generates linguistically rich descriptions of insights, both written and spoken, in plain English. It does this by automatically scanning and finding the most interesting and important concepts in structured data that resides in our databases or apps, and translating it into a consumable, text-based narrative that is easier for the average business user to access and understand.

Once, dashboards and reports were limited to being embedded into our apps as standalone modules - essentially, as separately accessed product features. This limitation sometimes meant analytics is forgotten by users, and more often than not, underutilized in its potential. Today, contextual analytics makes it possible to embed analytics directly into the core workflow of your software.