Challenges and opportunities in visual interpretation of Big Data
This is an incremental discussion of problems in data interpretation for analysts and decision-makers, without proposing new solutions.
The paper addresses the challenge of interpreting large datasets for decision-making, highlighting the reliance on expert analysts and existing tools that focus on exploratory analysis rather than sense-making.
We live in a world where data generation is omnipresent. Innovations in computer hardware in the last few decades coupled with increasingly reliable connectivity among them have fueled this phenomenon. We are constantly creating and consuming data across digital devices of varying form factors. Leveraging huge quantities of data involves making interpretations from it. However, interpreting data is still a difficult task. We need data analysts to help make decisions. These experts apply their domain knowledge, understanding of the problem space and numerical analysis to draw inferences from the data in order to support decision making. Existing tools and techniques for interference serve users making decisions with hard constraints. Consumer systems are often built to support exploratory data analysis in mind rather than sense making.