Intelligent Data Exploration

Intelligent Exploration led us to our new favorite True Crime podcasts. But what if we were on the business side of things, not the listener? How could Intelligent Exploration help us find a competitive advantage?

Taking a "random" sample of your data for analysis that will drive your strategy is inherently risky. Why? Let's talk about it.

Virtualitics is excited to announce our Natural Language Processing application that enables you to parse documents, description files, and more.

Our last true crime analysis? That was just "elementary" my dear listener. This time, let's talk about how Virtualitics Intelligent Exploration uses Natural Language Processing and Smart Mapping to find the right suspects. 

Customer churn impacts businesses of all sizes in every industry. To tackle churn, you need to be able to pinpoint who is leaving… and why.

We love a good true crime story, and we’re always looking for something new. Could AI-led exploration help us sift through the thousands of podcasts out there and help us discover a hidden gem?

Big Data can cause big problems for companies. How do you make sure your data leads you to success, not expensive confusion?

We love a good true crime story, and we’re always looking for something new. Could AI-led exploration help us sift through the thousands of podcasts out there and help us discover a hidden gem?

How does Virtualitics build innovative AI solutions? By building innovative people. 

Join us to learn about why it's hard to find data scientists, how you can keep up with market demand, and how you can empower your company to help use your data scientists to their full potential.

If you want to build a successful AI program, you need to embrace these things: Data exploration using AI, true 3D data visualizations, network graphs and network analysis, commitment to finding the most effective, simple solution, and responsible AI. Resources referenced in this article: - https://virtualitics.com/resources/what-is-intelligent-exploration/ - https://virtualitics.com/resources/a-b...

Armed with your carefully plotted use cases, the result of thorough exploration and consultation with the business, you, your team, and your business leadership will be able to select the right AI use case to move forward with.

Whether your goal is to create business-critical AI models or leverage your data to provide better analytics and shape business strategy, you need to proactively guard against bias.

Hypothesis-driven exploration is leading you astray--it's time for Intelligent Exploration to lead the way

How do Data Scientists learn more about the nuances in their dataset before embarking on an AI project? We asked–they answered. Here are the results.

Just how “predictive” is your predictive maintenance? Are you only looking at the time to failure, or are you considering the supply chain, time to repair, and technician availability, too? Our platform shows how each element impacts your overall mission readiness—and how we can help your teams optimize their efforts. Check out our latest blog by Jerami Reyna, Virtualitics Solution Leader and reti...

AI is on the brink of becoming mainstream, but many organizations aren’t even attempting to use AI yet. Where does your team fall on that spectrum? No matter where you are on the AI maturity curve—just getting started or preparing to dive deeper—our experience has shown that there are nine steps to creating a successful AI strategy.

This episode is the narration of the Virtualitics blog post Patenting Innovation at Virtualitics by Dr Ciro Donalek, CTO and co-founder of Virtualitics.

This episode is the narration of the Virtualitics blog post Get started with network graphs—what they are and why they matter. To see the visuals referenced in the podcast, go to https://blog.virtualitics.com/

When we think of AI we tend to think of predictive models and supervised learning. But AI can be applied to exploration and understanding or data right from the start, leading to better business value. Co-head of AI, Aakash Indurkhya sits down with author and data scientist, Tobias Zwingmann to talk about how hypothesis-driven exploration isn’t getting the job done and is leaving valuable insight...

The future is no-code or low-code, but as more and more companies implement AI solutions and enable non-technical users,  how do you maintain accuracy and responsible use? The answer: explainable AI paired with multi-dimensional visualizations. These combined provide a more complete picture of the story playing out in a predictive system and are a must for ensuring responsible AI applications.  Jo...