Unbiased AI Analytics : The Future of Big Data


Even with leaps of advancement in big data analytics technology and artificial intelligence, companies still rely heavily on people to analyze and interpret the massive of amounts of data they are collecting. Every day, data scientists, analysts, and technology experts invest time, energy, and resources into big data analysis. Traditional means of data analytics use processes such as taxonomies to build the framework for results. These methods can be time consuming and may require multiple iterations to refine the analysis and ensure accuracy. Additionally, they are restricted by their framework and find only what they are built to find, potentially providing biased results. Despite this, many companies still feel that traditional means are the most accurate way to gauge return on investment while supplying a strong level of accountability, regardless of time and resources.  

Machine learning provides more accurate analysis in a fraction of the time. New AI powered platforms, such as the one here at Stratifyd, automatically collect and analyze data from multiple sources such as chat data, customer feedback, and employee surveys. Our platform provides business insights without the need for time consuming taxonomy building or other more traditional methods. Yet, the very nature of unsupervised machine learning creates an accountability problem. While our developers have spent years creating an artificial intelligence they can trust, some companies continue wanting assurances that humans are at the helm.

We use machine learning to augment human experience. Initial data collection and analysis takes place seamlessly, but we still rely on deep domain knowledge from our users in order to refine the results once they are visualized. Using unsupervised machine learning to look at data creates a completely unbiased result based solely on the information available. When AI powered machine learning shows companies business insights they didn’t know existed, it reveals its true value. Unlike a taxonomy, that tends to find only those things that it is specifically looking for, AI reveals those problems that users may not have been looking for at all.


If companies will embrace AI and machine learning, they will find a faster solution for data analytics that provides deep, actionable insights in a fraction of the time afforded by traditional methods. Trust between humans and AI can facilitate a revolution in how data analytics are done.