{'fraction_non_alphanumeric': 0.00945179584120983, 'fraction_numerical': 0.0, 'mean_word_length': 4.760869565217392, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Statistical Techniques to Facilitate the Population of a Data Warehouse

One of the difficulties in constructing a data warehouse is that each legacy system has a unique picture of the business, the customers, and the products. None of these systems necessarily agree with one another and it is almost impossible to get a view of what is occurring within the entire enterprise. Another difficulty is that some of the data in the legacy systems is either incorrect or missing and these errors will propagate to the data warehouse.