Solution to Manage the Provenance & Governance of Big Data Analytics

“Big Data and data science are riddled with data governance and provenance issues, making analytics that are computed downstream suspect of quality even if the ML or Graph analytics in and of themselves are competent and solving the core analysis problem, at least, in a lab environment.”  What is the way industry at-large is finding to fix this problem, are there new technologies or techniques or best practices that address the problem? Introduction Though modern day and excellent mathematical and technological interventions to solve several Big Data analytics challenges, the output from Machine Learning or Graph Analytics is still subject to scrutiny. The only pervasive reason for this disparity is the controlled environments within which these solutions often produce results. The process of integrating the solution in to a larger data pool, often enamored by multitudes of data sources and data formats, is anxiously awaited to succeed. Organizations would much rather such high