As data proliferates along with artificial intelligence models, the need to spot data bias, collaborate, prioritize use cases and tell stories become more important. The problem? Traditional visualization approaches can't keep up.
Speaking on DisrupTV Episode 326, Michael Amori, CEO of Virtualitics, utlined how 3D visualizations and AI can better navigate massive datasets and make recommendations. Virtualitics aims to explore data and visualize massive, interrelated datasets. Its platform integrates across descriptive, diagnostic, predictive and prescriptive analytics platforms as well as cloud companies storing large datasets.
What intelligent data exploration does? Amori said data is growing at an exponential pace and new approaches are needed to find insights that are unbiased. "We are focused on the data exploration process, which to a lot of people sounds like it's an already solved problem but it actually isn't," said Amori. "In a typical data analytics process, you start out preprocessing the data and then explore the data set to figure out what you want to do with it. Then you come up with predictive models using AI. The problem with the traditional way is that humans approach the data set already with a bias and preconceived hypothesis and what they want to see."
Amori said AI can explore the data and tell the humans what the hypotheses in the data are that ae worth pursuing. "Humans can decide what path to take as opposed to starting with a path in mind that leaves gems in the data," said Amori.
Generated visualizations and explanations. "AI can handle huge amounts of data and we can take advantage of natural language to really explain what's going on with the data," said Amori. "Data visualizations and explanations can really open up for the user what's going on in the data."
Time to intelligent data exploration. Amori said it typically takes one or two months to configure Virtualitics to an enterprise's data sets and training. "Once they are ready, they can load up a new data set and see all the possibilities and areas of interest," said Amori. Virtualitics turns regular data in columns and rows and turns it into a tabular data set that can be turned into a network graph with explanations generated by AI.
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Storytelling and collaboration with data. 3D analytics and visualizations from different end points of data help with storytelling, said Amori. One example would be a company that wants to understand market segmentation and data describing customers. A visualization could explore socioeconomic metrics as well as buying patterns. "There are hundreds of metrics for each of these customers and now you have AI look for patterns in the data," said Amori. "It looks like a network representation of communities of customers across quantitative, categorical and unstructured features. AI can come up with recommendations based on 8 different things going on in your data."
