Pratik Ravani
With nearly 15 years of industry experience, Pratik works as a delivery head for global analytics projects at a Bangalore-based MNC. Involved in various innovative projects and concepts, he applies a range of Machine Learning and Deep Learning algorithms to create and deliver strategic insights. As part of his wide range of assignments, he pieces together new technology trends and shifting business demands to bring about cutting-edge applications. For years he has been blending his analytical prowess and people skills to tap into the unexplored and less-explored business dimensions and convert them into value creators. Passionate about sharing his continual learnings, he is also a corporate trainer and a speaker at events. Pratik holds an MBA in Finance with Information Technology and a bachelor’s degree in Industrial engineering.
At the outset, let’s get down to the basics, especially for those who don’t know what this titular term knowledge graph means.
Imagine that you have applied for a visa for an important business initiative.
With the increasing adoption and rise of electronic health records (EHRs), especially because of the HITECH act of the US, there has been an unprecedented surge in volumes of clinical information.
Every data-servicing organization has now made peace with the fact that being able to manage its data well along with enabling its clients to manage their data well is a sure-fire path to gain a competitive advantage. So how do many data-talking organizations differ from those few data-driven ones? Why can’t they make a decisive cut?
The term in the title is a recent entry to the list of something-as-a-service family (SaaS, PaaS, IaaS). It is a web-based analytics service being offered by a specialist vendor who does it well, offers the economical, scalable, and flexible custom options to let the data-leaning organizations try in either a full-blown manner or a phased one depending on their need.
For a typical organization, following are statements that must have been a part of almost every employee’s career journey:
Confronting frauds and fraudsters is unarguably one of the most important and urgent strategic challenges every organization faces today. Fraud is no longer an occasional outlier arising from the incidental mistake from a compliance standpoint. It has taken a centre stage as it had begun to strike from multiple channels and routes thanks to technological advancements enabling ease of access.
When technologies are shaking hands with one another, why can’t organizations from various verticals, especially when the question of survival looms over their heads? In these exciting times of automation outburst many old players are feeling the generous heat from the young disruptors. Seasoned insurers who used to bet highly on their in-house experts and primitive processes have begun to take notice; and notice seriously. They are now willing to partner with these potential collaborators who successfully bridge the gap amongst technologies, insurance processes, and Gen-Z demands.
Artificial Intelligence is easily applicable in anything that has a standardized system and less or no discrepancy or deviation. First, as an organized industry, banking runs on a set of regulatory guidelines and deals with numbers, it was only about time that it would board the AI bus. Secondly, there is this deviation angle.
Artificial Intelligence is today’s buzzword that has been buzzing around more frequently with higher and ever-increasing intensity every passing day. Reason is as clear as a crystal: Its power and possibilities it can create.
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