If even the training error is higher than the desired performance level, adding more data alone will not allow the dev-error curve to reach that desired level. Training error can only stay the same or get worse as more training data is added, and dev error is usually higher than training error.

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A standard learning-curve pattern for high avoidable bias occurs when, at the largest training-set size, training error remains far above the desired performance level. If the gap between training error and dev error is small, this indicates small variance despite the large avoidable bias.

Learning Curve Pattern for High Avoidable Bias

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

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