How AI Drives Innovation in Next Generation Cloud Business Intelligence?November 28, 2018
Today, we have access to a huge amount of technology and other systems through the internet – Artificial Intelligent systems are one of those. AI is becoming a larger part of our lives with each passing day, and the chances are that AI systems would already have affected us in some way or the other.
AI, in essence, is a predictive technology. The main function of every AI system is to essentially make a prediction based on the amount of data and information that it analyses. Since it can sift through any large amount of data, it is thus a type of technology that improves our lives in a huge manner. Similarly, the role of business intelligence and business analytics has changed too – it is now something that deals with increasing amounts of predictive analysis rather than historical analysis, and is available to users as an interactive, easy-to-use tool.
Thought Spot is one of the pioneers in the segment of Business Intelligence – the California based company can be credited for creating a Google-like search engine which can analyse large amounts of data quickly and completely so as to provide the user with some great insights into the data. Thought Spot’s Ad-hoc version of data analytics provides various amazing services, like extremely transparent calculations into how each insight was derived, accompanying of natural language narratives with the rendered charts and a guided, curated search experience which generates suggestions for the users based on the role, the data model and the search history of the person. Thought Spot and its data analytics model is truly something to watch out for, in the future.
Companies like Thought Spot and other data-driven Business Intelligence organisations are considered to be the forerunners of the next, and perhaps the largest wave in Business Intelligence called the anticipatory intelligence. They aim to leverage the usage of AI in a number of scenarios, like anticipatory devices, conversations and contexts. In this first one, the aim is to automate something that a large number of users are trying to do in a small time period so that it happens quicker and better. In the second and third, natural language processing systems are used so as to predict what the users are going to say, and thus promote rapid communication.