def find_correlated_data(data_df, correlation_threshold):
    """Identifies column values with strong correlation to other column values.

    Parameters
    -----------
    data_df : pd.DataFrame)
        Dataframe containing data to be analysed.
    correlation_threshold : float
        Thershold value above which correlation is assumed.

   Returns
   --------
    tuple
        Tuple of two-column data sets, with correlation.
    """

    # Remove time column.
    modified_df = data_df.drop("cycle", axis=1)

    # Compute pairwise correlation of columns.
    data_corr = modified_df.corr()

    # Analyse data correlations; Keep values above threshold.
    high_correlation = []

    for column_no, column in enumerate(data_corr):
        # Create slice to ignore symmetry duplicates.
        col_corr = data_corr[column].iloc[column_no + 1 :]

        # Use a bool mask to identify highly-correlated data.
        mask_pairs = col_corr.apply(lambda x: abs(x)) > correlation_threshold

        index_pairs = col_corr[mask_pairs].index

        # Create list of highly-correlated data sets.
        for index, correlation in zip(index_pairs, col_corr[mask_pairs].values):
            high_correlation.append((column, index, correlation))

    return high_correlation