Posted on

Artificial Intelligence in Service Desks

“Flawless customer service facilitates opportunity more than anything else; the opportunity to exceed any and all expectations.” ~ Than Merrill, CEO & Founder, FortuneBuilders

Resolving customer issues at the earliest is as critical as delivering a new product or service to customers. While organizations strive to achieve better customer service by optimizing key metrics such as Mean Time To Resolution (MTTR), Defect Removal Efficiency (DRE), etc., Artificial Intelligence comes in handy in catering to our needs to be faster and accurate in providing resolution.

Knowledge Base

Knowledge Base

Posted on

Why Every Organization Needs a Data Analyst

Data-driven decisions make the world go round

There is so much hype around the data scientist role these days that when a company needs a specialist to get some insights from data, their first inclination is to look for a data scientist. But is that really the best option? Let’s see how the roles of data scientists and data analysts differ and why you may want to hire an analyst before any other role.

You may also like: Five Must Read Books to Become a Successful Data Analyst.

Data Scientist or Data Analyst

So, what’s the difference between data scientists and data analysts? The definitions of these roles can vary, but it’s usually believed that a data scientist combines three key disciplines — data analysis, statistics, and Machine Learning. Machine learning involves the process of data analysis to learn and generate analytical models that can perform intelligent action on unseen data, with minimal human intervention. With such expectations, it’s clear that three-in-one is better than one-in-one, and data scientists become more desired by companies.