An ordinary RNN is useful because the model takes in information about your output at a previous time step to predict your current and future outputs. Similarly, a Bidirectional RNN depends upon both outputs at previous and FUTURE outputs. This is useful in applications where your predictions in your model not only depend on the past, but the future. This would include things like handwriting recognition, speech recognition, and bioinformatics.