Machine learning and artificial intelligence make big data analytics possible by automating the fundamental task of identifying patterns in large amounts of data. However, for the most part, human engagement and interactions have been necessary both at the beginning of the process, to prepare data for analysis, and on the back end to apply analytic insights to real-world business conditions. The next phase of big data, “augmented analytics,” is changing that equation and automating more of the analytics process than ever before. The implications for businesses are immense.
These tools aren’t pie-in-the-sky technologies. Rather, they’re here right now, and they’re about to become ubiquitous.
According to research firm Gartner, within the next two years augmented analytics, “will be a dominant driver of new purchases of business intelligence, analytics and data science and machine learning platforms and of embedded analytics.”
Some of the benefits of augmented analytics include:
- Data Preparation Automation – collecting, cleaning and labeling data slow down the analytics cycle in the real world and require significant resources, including scarce data scientists, to complete. New augmented analytics tools apply machine learning and AI techniques to automate these tasks with minimal human interaction.
- Natural Language Capabilities - Augmented analytics tools now have the ability to generate relevant insights when presented with queries in natural language such as: “Why are sales declining for this category of products while all others are increasing?” “Who are our most valuable customers?” “What is causing delays in our supply chain?”
- Outside the Box Insights: Humans have preconceived notions that can limit new insights. When analytics tools aren’t limited to what “everyone knows,” it can often find answers that nobody would have expected.
- Conversational Problem-Solving: Rather than a question-and-answer session, augmented analytics can automate complex data science processes to empower more users to achieve analysis outcomes that were previously restricted to professional data scientists.
The smartest augmented analytics tools today – and the state of the art in the near future – reinvent the big data analytics process by making business-critical insights accessible to virtually everyone.
Wonder what that would look like in your organization? Contact us today.