In the past decades, massive efforts involving companies, non-profit organizations, governments, and others have been put into supporting the concept of data democratization, promoting initiatives to educate people to confront information with data. Although this represents one of the most critical advances in our free world, access to data without concrete facts to check or the lack of an expert to help on understanding the existing patterns hampers its intrinsic value and lessens its democratization. So the benefits of giving full access to data will only be impactful if we go a step further and support the Data Analytics Democratization, assisting users in transforming findings into insights without the need of domain experts to promote unconstrained access to data interpretation and verification. In this paper, we present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings, automatically generating plausible explanations for observed or selected findings using an external (textual) source of information, avoiding or reducing the need for domain experts. ExPatt applicability is confirmed via different use-cases involving world demographics indicators and Wikipedia as an external source of explanations, showing how it can be used in practice towards the data analytics democratization.

中文翻译：

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可解释的模式：从发现到见解，以支持数据分析民主化

在过去的几十年中，公司，非营利组织，政府和其他机构做出了巨大的努力，以支持数据民主化的概念，推动了旨在教育人们用数据面对信息的举措。尽管这代表着我们自由世界中最关键的进步之一，但是在没有具体事实检查的情况下访问数据或缺少专家来帮助理解现有模式都会阻碍其内在价值并削弱其民主化。因此，只有我们走得更远并支持数据分析民主化，帮助用户将发现转化为见解，而无需领域专家来促进对数据解释和验证的无限制访问，完全访问数据的好处才有意义。在本文中，我们提出了可解释模式（ExPatt），这是一个新框架，可支持外行用户探索和创建数据故事，使用外部（文本）信息源自动为观察或选定的发现生成合理的解释，从而避免或减少了领域专家的需求。ExPatt的适用性通过涉及世界人口统计指标的不同用例得到了证实，而Wikipedia作为解释的外部来源，表明了如何在实践中将其用于数据分析民主化。