Automated Analytics: A Game Changer for Self Service

There’s no denying that self-service analytics and data tools have been a game-changer for today’s modern enterprise. Business analysts have unparalleled tools at their disposal to analyze data and answer today’s most important business questions easier and faster than ever before. But now there’s something new on the horizon, and it’s shaping up to be a game-hanger for the concept of “self service” itself.

I’m talking about Automated Analytics, a term that marries the best of today’s self-service analytics with the power of modern Artificial Intelligence (AI) and Machine Learning (ML) technologies.

Today’s AI tools are incredibly powerful, able to scan immense datasets to identify patterns, correlations and trends that would take mere humans months to see. But, today’s AI tools are still hard to use if you’re not a data scientist. Fortunately, bringing advanced technologies to everyday business users is something Qrvey is already very familiar with.

In a typical machine learning analysis, it’s up to the analyst to determine which questions need to be asked of the data and which data needs to be analyzed in order to deduce the answers. This requires extensive knowledge of both the questions being asked as well as the intricacies of datasets being analyzed, as well as the science of teaching the machines what to look for. Once all of the correct procedures and algorithms have been determined, the machines take over and make quick work of the analysis.

There are, however, other ways to implement machine learning. In a recent AI competition, several data analysis teams were tasked with determining the biggest predictor of future income for recent college graduates based on the data from past college graduates. The teams were provided with multiple datasets to analyze and were put to work. All of the teams set out in the scenario outlined above – all that is, except for one.

A group innovative data scientists from a young startup took a different approach. They choose instead to turn their AI loose on the raw data itself, without first preparing questions or assumptions. This, of course, initially produced a ton of inconsequential patterns, trends and correlations that signified absolutely nothing. Fairly quickly however, their AI also suggested that parental income was highly correlated with the income of their children, more so than college, major, student debt load or a host of other metrics. The parental income metric was one the other teams hadn’t even thought to consider. It was also the winning answer.

This second type of AI approach has been most intriguing to Qrvey. That’s because it bypasses the data preparation and data science steps in much the same way as our self-service data platform has removed data preparation from our analysis and automation tools. For example, we automatically profile the data and find correlated fields to guide your analysis. We also apply machine learning profiles on certain data types, including text, images and video, to uncover even more insights. And we provide integrated tools business users can easily run as part of their daily workflows to help drive AI-based business decisions.

By removing human biases from the initial stages of AI and allowing the data to “speak for itself,” there are countless opportunities for new types of analysis that can be performed. Humans, of course, will still be required to verify whether the AI’s findings are relevant or not, but they don’t have to be a part of the cumbersome setup of the initial analysis.

Qrvey is currently integrating state-of-the-art AI and ML technologies to our platform so that data can be intelligently analyzed around the clock and used to feed insights to an entire organization using intelligent information broadcasting, rather than just scheduled reports to a handful of data scientists, analysts or executives. Soon, working with your company’s data will be as easy as building a music playlist on your favorite streaming service. With just a few clicks, you can tell Qrvey what’s important, and what’s not, and soon you might uncover those elusive key metrics that your entire business depends on.