Filtering is an underappreciated feature of business intelligence and analytics. Yet filters are critical to data analysis. Filters will probably be the primary method, of all the possible interaction types, that end users utilize. Welcome to part 3 of Yellowfin 9 Fresh Features. If you missed part 2, check out the enhancements to Yellowfin's automated data discovery - Signals. Yellowfin has a rich filter functionality that isn't available in some of the other leading analytics platforms.

Over the course of my career in Financial Services, I have struggled with how few options I really had when it came to delivering the right information, to the right people, at the right time. It sounds sort of ridiculous considering how much time, money and effort the firms, for which I worked, spent on data warehouses, reporting systems, business intelligence tools and advanced analytics.

A really interesting development I’ve seen in the data and analytic space lately is the rise of the data catalog. You may know these by another name such as a semantic or metadata layer, but they’re all fundamentally the same thing. Data catalogs aren’t new, they’ve been around for a long time. While some vendors like Yellowfin and Cognos have always had them, others like Tableau and Qlik are now just getting to them.

For public universities, metrics such as retention rate and graduation rate are important indicators for standing out in the competitive landscape. These success metrics are paramount to bringing in more students, making them successful, and continuing to grow a strong alumni network.

Here’s part 2 of ‘Fresh Features’ of Yellowfin 9. And this week, we’re looking at Signals. Signals is Yellowfin’s unique and powerful automated data discovery product. It will automatically scan your dimensional data and find any significant changes in your data. Then, it automatically sends you an alert, complete with analysis and correlations to help you take swift action to nip issues in the bud or build on successes.

In the first and second blog posts we explained the importance of DSAR as well as how the customer experience can be impacted if the process is not well managed. In this last part, we will go through a few tips that could help you to be DSAR champions!

This week, R users from around the world convene in San Francisco for rstudio::conf 2020. With a packed agenda of new package announcements and case studies highlighting successful applications of R across different industries, it’s evident that R and the ecosystem of tools around it make up a vital part of the data science and machine learning landscape.

One of the really interesting topics in business intelligence is the desire for organizations to take action from data. While most use dashboards as a source of information, it certainly doesn't prompt or drive them to act. This means you can’t close the loop on your insights and the decisions that they drive.