The ultimate objective of alternative data is to provide an informational advantage in the competitive search for trading signals that produce alpha, namely positive, uncorrelated investment returns. In practice, the signals extracted from alternative datasets can be used on a standalone basis or combined with other signals as part of a quantitative strategy. Independent usage is viable if the Sharpe ratio generated by a strategy based on a single dataset is sufficiently high, but is rare in practice (see Chapter 4, Alpha Factor Research for details on signal measurement and evaluation).

Quant firms are building libraries of alpha factors that may be weak signals individually but can produce attractive returns in combination. As highlighted in Chapter 1, Machine Learning for Trading, investment factors should be based on a fundamental and economic rationale, otherwise, they are more likely the result of overfitting to historical data than to persist and generate alpha on new data.

Signal decay due to competition is a serious concern, and as the alternative data ecosystem evolves, it is unlikely that many datasets will retain meaningful Sharpe ratio signals. Effective strategies to extend the half-life of the signal content of an alternative dataset include exclusivity agreements or a focus on datasets that pose processing challenges to raise the barriers to entry.