Both Data analytics and Process analytics involve working with and understanding data generated by processes, extracting insights from data, and using that intelligence to boost your company performance. So, the questions most people ask is "What are the fundamental differences between these two functions, and what problem each is trying to solve?
Process analytics focus is on business implications of the data and the improvement actions resulting from this data. The term process analytics implies the individual use or a large combination of skills and techniques from tools of Lean Manufacturing, Lean Services, Agile concepts, Risk Management, Six Sigma, Management Systems standards that allows the business to measure and improve the effectiveness, in terms of quality and productivity, of core processes first and general processes then.
Data analytics involves the recollection, analysis and control of full datasets or representative data samples to extract hidden patterns, trends and information, with the end objective of interpreting and drawing conclusions, or at the very least formulating hypotheses to support the right business decisions. Data analysis attempts to answer questions such as, “What is the influence of geography or seasonal factors on customer preferences?” or “What is the likelihood a customer will defect to a competitor?”. The practice of data analytics encompasses many diverse techniques and approaches and is also frequently referred to as data science, data mining, data modeling, or big data analytics
