Data Guards: Challenges and Solutions for Fostering Trust in Data

TL;DR

This paper explores the challenges of establishing trust in data artifacts and proposes a set of data guards—methods and tools—to improve data validation and verification in data ecosystems.

Contribution

It provides insights from interviews on trust issues and introduces novel data guards to enhance trustworthiness of data artifacts.

Findings

Lack of standards hinders data trust

Data validation is a recurring need

Proposed data guards aim to address trust challenges

Abstract

From dirty data to intentional deception, there are many threats to the validity of data-driven decisions. Making use of data, especially new or unfamiliar data, therefore requires a degree of trust or verification. How is this trust established? In this paper, we present the results of a series of interviews with both producers and consumers of data artifacts (outputs of data ecosystems like spreadsheets, charts, and dashboards) aimed at understanding strategies and obstacles to building trust in data. We find a recurring need, but lack of existing standards, for data validation and verification, especially among data consumers. We therefore propose a set of data guards: methods and tools for fostering trust in data artifacts.