Self-Service Data Analytics and Governance for Managers

Introduction

The breadth and scale of data are growing exponentially, and this growth of data is impacting the shape of organizations. Across industries, many companies have entire departments and functions devoted to processing vast numbers of data points into information, for delivery to internal and external stakeholders. Along with the growth of data, data analytics technology and tooling are advancing at a breakneck rate to process it, to identify and understand relationships and trends, and even to make predictions on future outcomes, before displaying them neatly in low-latency dashboard views for ultimate consumption by managers, executives, clients, counterparties, and regulators.

Data analytics is coming to the fore as an exciting strategic and tactical enabler of higher-order analysis and value creation through insight generation and automation of manual processes. Data analytics includes a number of analytics tools, technologies, and buzzwords readers will have heard thrown about more and more over the last 5 to 10 years: robotic process automation (RPA), machine learning (ML), artificial intelligence (AI), text mining technologies like natural language processing (NLP), optical character recognition (OCR), and intelligent character recognition (ICR), along with neural networks, logistic and linear regression analysis, and many more. At the most basic level, these are disciplines enabling descriptive techniques to understand past events and their drivers and to ...