近年來，大數據分析（BDA）在商業決策中的應用引起人們的極大關注。然而，幾乎沒有研究討論最基本的大數據問題，即數據收集的適當性，本研究探討如何正確收集數據以提高決策的準確性。
首先，本研究透過文獻回顧找出會影響決策制定的數據收集的品質因素（the quality factors of data collection），其中數據收集品質因素為領域、來源、頻率、長度、量、再生性和折舊度。其次，本研究探索更有層次的問題，即是，在什麼情況下，收集越全面數據收集品質因素，對決策的有用性、有效性有影響；以及，身為調節變數的再生性、貶值度，如何影響資料收集品質因素和決策。
為了解決這些問題，本研究分析五個不尋常的啟示個案，並考慮實務上數據分析和收集在不同部門的差異。最後研究發現數據收集品質因素在製造業和服務業表現截然不同，並且本研究也提出在哪些情境需要收集、分析全面的數據收集品質因素。本研究期望發展成為企業在數據收集和分析方面的衡量標準和指南。

The use of big data analysis (BDA) in business decision-making has attracted significant attention in recent years. However, hardly any research discussing the most basic big data issues which is the appropriateness of the data collection, this study investigate how data can be properly collected to improve the accuracy of decision-making.
First, this study shows that quality factors in data collection affect decision-making, where quality factors are domain, source, frequency, length, quantity, regeneration, and depreciation. Second, this study explores hierarchical questions, indicating the conditions under which the comprehensiveness of the quality factors of data collected impact the effectiveness and efficiency of decision-making, and the contexts under which the data characteristics of the collected data can moderate the relationship between data collection quality and decision-making quality.
To address these questions, this study analyzes five cases of successful companies and considers the gaps between the collection and analysis departments in practice. Finally, it concludes that the quality factors in the data collection show different performance in the manufacturing and service industries and then presents a proposal for appropriate data collection. This study may develop into a measurement standard and guideline for enterprises in data collection and analysis.

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