Big Data & Analytics
Today we see the astonishing rise in data volume, velocity and variety; computational power; connectivity; the emergence of advanced analytics and business intelligence capabilities. The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyse large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things.
Traditional data warehouses based on relational database may not cut it in dealing with semi-structured and unstructured data. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually – for example, real-time data on the performance of mobile applications or humidity measurement of the environment.
Big data can be analysed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Mainstream BI software and data visualization tools can also play a role in the analysis process.