Supply chain redesign has become an area of critical focus as businesses try to ensure supply chains aren’t overly reliant on any one constituent part.

I know a lot of organizations have really struggled in the current environment, but the last six months have actually been quite exciting for us at Yellowfin. We've achieved a lot and have built a fantastic strategy for the future. We have really focused in on our sales organization, hiring a new VP of Global Sales, Josh Read, and appointing new sales leadership in the regions as well.

Enterprises are reaching strides in Marketing, but are these companies reaping the rewards of unified data analysis?

Evaluating a new, unknown technology is a complicated task. Although you can articulate the goals you’re trying to achieve, you’re probably faced with multiple solutions that approach the problem in different ways and highlight varying features. To cut through the clutter, you need to figure out what questions to ask in order to evaluate which technology has the optimal capabilities to get the job done in your unique setting.

In the first part of the blog series, we discussed how correlation analysis can be leveraged to reduce time to detection (TTD) and time to remediation (TTR) by guiding mitigation efforts early. Further, correlation analysis helps to reduce alert fatigue by filtering out irrelevant anomalies and grouping multiple anomalies stemming from a single incident into one alert. In this part, we throw light on the applicability of correlation analysis in the realm of eCommerce, specifically, promotions.

A North American telecom company struggled for years trying to react quickly enough to new categories and new levels of spam texts and calls. They also did not have a good way to know when and why they would need additional capacity on their own, or any other telecom company’s networks.

For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets.

Random forest is one of the most widely used machine learning algorithms in real production settings.