An Accessibility Guide for Using Colors in Data Visualization
The current state of wide data is that it is not as widely used for Artificial Intelligence as it is for analytics. While analytics needs a treasure trove of historical data, AI merely needs a variety of big data.
And big data needs AI, too. It’s the most efficient and effective way for organisations to optimise their processes and identify their audiences.
But how can we use machine learning practices and AI to tackle critical business challenges?
In this 3 part EM360 Podcast series with Findability Sciences we have previously discussed 'What Big Data Discussions Ignore' In Episode 1. In this second episode, we are joined once again by the Founder and CEO Anand Mahurkar to talk about:
- The relationship between wide data, learning and machine learning
- Critical business challenges when it comes to AI mimicking a more human process
- Why we should be using wide data for AI
Meet the panel
Monte Carlo: Establishing Trust through Data Observability
Portworx: Driving Data Services and Making Developers’ Lives Easier
Informatica: Predictive Data Intelligence and Leveling Up Your Business
Chronosphere: The State of Cloud-Native Observability
Keyfactor: Why You Need to Care About Machine Identity
Intermedia: Cloud Migration is the Key to Enterprise Communications
Radware: Bot Mitigation is Key for Application Protection
Informatica: How Shifts in the Cloud are Changing the Game
Sifflet: Observability and the Future of Data Engineering
Safe Software: Low-Code/No-Code in Data Engineering