by Yuwaraj Shinde | Data, Data Science
Numbers and statistics are the ultimate truth with the condition that they are presented neutrally. Numbers can be very simple to understand yet there are numerous methods available to manipulate their representation which can fool anyone if not everyone. Ronald Coase...
by Srinivas Raghavan | Data, Data Science
Background Modern Data Engineering involves creating and maintaining software and systems for accessing, processing, enriching, cleaning data and orchestrating data analysis for business purposes. Data engineers build tools, infrastructure, frameworks, and services....
by Srinivas Raghavan | Data, Data Science
Background The availability of petabytes scale of structured and unstructured data has lent itself for large-scale harnessing thanks to Data Science and AI. Advances in parallel and distributed computing, and the algorithms to process data of all types including text,...
by Srinivas Raghavan | Data, Data-Lake, Data-Warehouse
Background Organisations of all types – be it for-profit, not-for-profit, or Governmental – have all been shaken by the pandemic for over a year now. The need to be in constant touch with the end-users or customers, as well as the key service delivery agents or...
by Srinivas Raghavan | Bi-Analytics, Data
Background The success of a BI and Analytics system or platform is characterised by how well it has been architected and built, by considering a variety of factors. Some of these are listed below: Data access: Does it account for volume, variety, velocity, veracity...
by Srinivas Raghavan | Bi-Analytics, Data
Background Business intelligence (BI) is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. It was coined first by Gartner group in the 1990’s. It is a content-free expression, so it means different...
