Enterprises of all stripes have started to invest heavily in AI to help them understand their customers more deeply and to gain the advantages of superior customer experience (CX). Yet as leaders strive to form a more complete picture of customer preferences and behaviours, they continue to rely on ageing survey-based measurement systems that for decades have formed the backbone of CX efforts.
In the age of VUCA world & Industry 4.0 revolution, AI continues to dominate the business landscape. Whilst the naysayers have advocated the doomsday for humanity by predicting the advent of singularity, AI for good & all have several applications to usher a new change in how we make decisions at enterprise and personal fronts.
Featured Article: Author: Vinodh Ramachandran, Neiman Marcus Group In my previous article, I had spoken about some challenges due to a siloed working model within data & analytics teams in large retailers and how setting up a Data & Analytics CoE within the GCC can be a good solution. Lets look at a few ways […]
Featured Article Author: Sidhartha Shishoo, SG Analytics Businesses are gaining the ability to gather data at an unprecedented pace. The Internet of Things (IoT), unstructured data from media, social media, and other digital activities like product purchases and consumer reviews are driving this. Companies now have mountains of data at their disposal, but the knowledge […]
Featured Article: Author: Tarana Chauhan, Procurement Analyst, AB InBev Dependency on Commodities and Associated Risks: Companies with Agricultural commodities as their core raw material face several risks in supply security. Agricultural commodities not only suffer from the risks associated with market dynamics like all other commodities but are also impacted by environmental factors making them […]
Featured Article: Author: Abhishek Sengupta, Walmart Global Tech India Introduction: A necessary but often tedious task data scientists must perform as part of any project is quality assurance (QA) of their modules so as to prevent any unforeseen incidents during deployment. While Quality Assurance and Excellence is quite prevalent in Data Engineering, QE in Data […]