Process Optimization and Monitoring Along Big Data Value Chain

Abstract

The article deals with Big Data (BD), which is big not only by its volume, but also by velocity or variety - as the combined effect of specific characteristics, create different “portraits” of BD in various domains challenging value extraction from data. Big Data value chain means that the enterprises have to raise skills for dealing with data in all stages of its life cycle: starting from recognizing need to register and store data items, moving forward to their appropriate representation and visualization, processing data with the help of best-fit algorithms, applying methods in order to get insights, finding valuable decisions in uncertain situations, and elaborating tools for control of effectiveness of BD value chain processes. We will follow all the entirety of the processes used for BD monitoring along its value chain and optimize them for extracting highest possible value. The goal of paper is to describe innovative solutions in domain driven process optimization and monitoring along BD value chain. We analyse the specific characteristics of BD by suggested BD portrait concept, its impact for BD analysis along entire value chain, and transferring research results to other research domains. The presented case-study highlights the problems of practical big data value chain implementation.