Managers often believe that collecting more data will continually improve the
accuracy of their machine learning models. However, we argue in this paper that
when data lose relevance over time, it may be optimal to collect a limited
amount of recent data instead of keeping around an infinite supply of older
(less relevant) data. In addition, we argue that increasing the stock of data
by including older datasets may, in fact, damage the model's accuracy.
Expectedly, the model's accuracy improves by increasing the flow of data
(defined as data collection rate); however, it requires other tradeoffs in
terms of refreshing or retraining machine learning models more frequently.
Using these results, we investigate how the business value created by machine
learning models scales with data and when the stock of data establishes a
sustainable competitive advantage. We argue that data's time-dependency weakens
the barrier to entry that the stock of data creates. As a result, a competing
firm equipped with a limited (yet sufficient) amount of recent data can develop
more accurate models. This result, coupled with the fact that older datasets
may deteriorate models' accuracy, suggests that created business value doesn't
scale with the stock of available data unless the firm offloads less relevant
data from its data repository. Consequently, a firm's growth policy should
incorporate a balance between the stock of historical data and the flow of new
data.
We complement our theoretical results with an experiment. In the experiment,
we empirically measure the loss in the accuracy of a next word prediction model
trained on datasets from various time periods. Our empirical measurements
confirm the economic significance of the value decline over time. For example,
100MB of text data, after seven years, becomes as valuable as 50MB of current
data for the next word prediction task.

本文研究了数据的时效性对机器学习模型准确性和商业价值的影响，发现随着时间的推移，数据的价值会递减，与此同时包括更多的过时数据可能会降低模型的准确性，并提出了在历史数据与新数据之间寻求平衡的增长策略。