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Trust in Data: Clarity and Consistency Build Confidence
Trustworthiness in data is crucial, hinging on two key components: understanding and consistent expectation management. Understanding requires clarity on what data represents, enhancing trust in its accuracy. For instance, knowing the specific sources and types of customer orders increases trust in the data's relevance. Consistent expectation involves transparency about how data sets may change over time, helping users adapt their approaches accordingly. Just as clear communication in personal interactions sets expectations and builds trust, data contracts play a vital role in establishing this understanding and expectation alignment between data producers and consumers.