Intelligent Data Exploration

Data analytics teams are often overtasked and understaffed. To keep delivering results, they are oftentimes forced to take shortcuts or significantly reduce the scope of their analysis. This is especially true when it comes to dealing with large, complex datasets. Data sampling can provide relief for analysts, but it also introduces new challenges.

Organizations are striving to make data-driven decisions that will keep them ahead of the competition and position themselves for strong, healthy growth. They have vast amounts of data at their disposal that potentially hold the answers; they’re missing enough data science skill to extract those answers. The key to closing that gap is in the untapped potential of the data analysts. With the right tools and techniques, upskilled analysts can supercharge an organization’s entire data strategy.

According to a survey by PwC, "85% of business leaders agree that companies need to upskill their workforce significantly to meet the challenges of the digital age." The rate of change and a high degree of competition means that to get an advantage you need to stay ahead of your competition. Pivot faster and pivot sooner, because success is a matter of degrees. 

I spoke at the Gartner Data and Analytics Summit, along with Virtualitics Head of Product Bryan Katis, about how Intelligent Exploration is the superpower analysts need to help them surface the answers that are hiding in your data and provide the insight that will drive successful strategies. 

We surveyed data and data science leaders early this year and asked them how their teams could be more effective. They agreed that there’s not enough data science talent and that the lack of resources is holding back their exploration and overall AI strategy. They acknowledged that the data they’re putting so much effort and money into capturing, storing, and organizing isn’t being used effectively. 

We were just at the 2023 Gartner Data and Analytics Summit, which is always a valuable opportunity to hear directly from data scientists and analysts... and they sure had questions. The biggest one was "What do all these vendors mean when they say they have 'AI' and what does this mean for me?"

Gathering a sample from your dataset that properly represents the nuances in the data is challenging for anyone, even seasoned data scientists. Since it’s likely that the data you need to explore is massive, sampling can’t be avoided. But taking shortcuts–first and last rows, time-bound, and even the classic random sample algorithm–can introduce bias into your exploration. With our latest release, we’ve introduced a new Smart Sampling application that streamlines data sampling, while still allowing you to tailor the approach to suit your needs.

We're talking to longtime data science leader, Justin Smith, about how the business can work better with the data science teams.

Referenced in this podcast is an AI algorithm to triage patients and known biases in healthcare that impact patient pain treatment.

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.