For Analytics teams, working with SD is becoming increasingly common. It involves receiving data and managing the analytics activities from that point onwards without having to worry about validity, accuracy and origins of the data received. This article has been written to provide a brief overview of the SD validation issues as well as strategies for addressing those issues. Read More

Interpretive Analytics focuses not only (and not so much) on number crunching (quantitative analysis of the data available) but also on qualitative interpretations of the data sets. Furthermore, interpretivism is sometimes expanded beyond the data sets and is applied to the contextual settings (such as the assumption in the case discussed above where the predecessing economic decline is to be. Read More

Apache Hadoop is a collection of utilities for open-source applications. To solve problems involving large quantities of data, they encourage using a network of several computers. It provides distributed processing and distributed computing with a software platform. Read More

People analytics is raising the agenda for companies worldwide. Technology that provides companies with many more methods of collecting data from people is projected to accelerate by using this data to enhance insight into the workforce, increase business efficiency, and enhance employee experience. Read More

Data blending is a recently developed approach used mainly by those working in big data analytics. It's a method of integrating data from different sources into a single structure. Blending offers a relatively fast and straightforward way to access several disparate data sources and identify correlations between them without the trouble and costs of conventional data integration. Read More

Following the COVID-19 pandemic, more people are connecting with eCommerce than ever before in the history of the internet. This is both good and bad news. Here are some emerging trends in eCommerce. Read More

Getting your workforce data-ready will soon be essential for all businesses, regardless of job roles. It may not be compulsory to work with data at the moment; it will be soon. Read More

The demand for people with data skills is increasing dramatically as organizations begin to make more use of their data. As a result, salaries have increased, and more people are looking to develop a career in this sector. The question is what skills do I need to get started? Read More

Deepfakes are so named as the technology used to create fake, but extremely believable video and media representations, is based on deep learning, a branch of machine learning that applies neural net simulation to massive data sets. Read More

Understanding Analytics tools and their usage. Read More