We use first-party cookies to improve our services and compile statistical information. If you continue browsing and carry out any affirmative action, we will consider that you consent to their use. You may set or reject the use of cookies or find out more about our cookies policy by clicking HERE
Connect with market pulse and communities
Detect and understand influencer communities that matter to your brand and product.
Track trending topics from Twitter, Instagram, Google, News etc., and use NLP algorithms to analyze their content.
Understand positive and negative market sentiment based on specific narratives.
Discover Graphext endless value and see first hand how it can change your business.
Attempting to discover the most influential features of a loan application when considering risk, our team built a model using the features of a loan application to predict whether an applicant would have a good or bad risk rating.
Andy and María meet with Jake to talk about a dataset he's building about himself. From skating to people he sees to whether he flosses or not - Jake's data offers a unique and deeply personal insight into his life. But what makes the difference between good and bad days?
Our team analyzed 7043 current and former customers of a telecoms provider in order to better understand what types of people are most likely to cancel their contracts.
To showcase how a company could reduce employee turnover, our team clustered a dataset containing information about IBM employees to discover the reasons why employees left their jobs.
Want to know more about our smart data discovery tool?
Ask us for a demo!
