Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency.
Asian Development Bank (March 2021) Digitalization has gained more prominence amid COVID-19 and has highlighted the value of big data for public sector management. The brief explains the potential benefits of big data for public services such as health, social protection, and education and how this can contribute to the post-pandemic recovery. It also assesses the key enablers and policy actions needed to realize big data benefits in the region.
Terms like “business intelligence” and “data analytics” mean different things in different contexts and the only way to hack through the Forest of Jargon is with a Machete of Specificity. Whether you're building an analytics tool, shopping for a business intelligence application, or just looking to get a better handle on IT terms, it's useful to be familiar with the analytics spectrum. In this article, we’re going to focus on disambiguating the term data analytics by breaking it down into types and aligning those with business objectives.