Enterprise Data Governance & Quality (CDMP)
Collection
Interactive data profiling agents and benchmark suites aligned with DAMA-DMBOK2 quality dimensions. • 2 items • Updated
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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 10 new columns ({'country', 'customer_id', 'last_name', 'registration_date', 'phone_number', 'email', 'account_status', 'lifetime_spend_eur', 'age', 'first_name'}) and 6 missing columns ({'patient_id', 'gdpr_consent_signed', 'diastolic_bp', 'triage_priority', 'systolic_bp', 'gender'}).
This happened while the csv dataset builder was generating data using
hf://datasets/iialonso/dama-dmbok-data-quality-benchmark/data/customer_360_benchmark.csv (at revision f45a6391b1498f3b77aff26cedee52dc4edcd532), ['hf://datasets/iialonso/dama-dmbok-data-quality-benchmark@f45a6391b1498f3b77aff26cedee52dc4edcd532/data/clinical_records_benchmark.csv', 'hf://datasets/iialonso/dama-dmbok-data-quality-benchmark@f45a6391b1498f3b77aff26cedee52dc4edcd532/data/customer_360_benchmark.csv', 'hf://datasets/iialonso/dama-dmbok-data-quality-benchmark@f45a6391b1498f3b77aff26cedee52dc4edcd532/data/financial_ledger_benchmark.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
customer_id: string
first_name: string
last_name: string
email: string
phone_number: string
country: string
age: int64
account_status: string
registration_date: string
lifetime_spend_eur: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1488
to
{'patient_id': Value('string'), 'gender': Value('string'), 'systolic_bp': Value('float64'), 'diastolic_bp': Value('int64'), 'gdpr_consent_signed': Value('bool'), 'triage_priority': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 10 new columns ({'country', 'customer_id', 'last_name', 'registration_date', 'phone_number', 'email', 'account_status', 'lifetime_spend_eur', 'age', 'first_name'}) and 6 missing columns ({'patient_id', 'gdpr_consent_signed', 'diastolic_bp', 'triage_priority', 'systolic_bp', 'gender'}).
This happened while the csv dataset builder was generating data using
hf://datasets/iialonso/dama-dmbok-data-quality-benchmark/data/customer_360_benchmark.csv (at revision f45a6391b1498f3b77aff26cedee52dc4edcd532), ['hf://datasets/iialonso/dama-dmbok-data-quality-benchmark@f45a6391b1498f3b77aff26cedee52dc4edcd532/data/clinical_records_benchmark.csv', 'hf://datasets/iialonso/dama-dmbok-data-quality-benchmark@f45a6391b1498f3b77aff26cedee52dc4edcd532/data/customer_360_benchmark.csv', 'hf://datasets/iialonso/dama-dmbok-data-quality-benchmark@f45a6391b1498f3b77aff26cedee52dc4edcd532/data/financial_ledger_benchmark.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
patient_id string | gender string | systolic_bp float64 | diastolic_bp int64 | gdpr_consent_signed bool | triage_priority string |
|---|---|---|---|---|---|
PT-01 | M | 120 | 80 | true | P1 |
PT-02 | F | 135 | 85 | true | P2 |
PT-03 | F | 110 | 70 | false | P3 |
PT-04 | M | 145 | 90 | true | P2 |
PT-05 | NB | 95 | 60 | true | P4 |
PT-06 | M | 125 | 82 | null | P1 |
PT-07 | Female | 290 | 195 | true | P1 |
PT-08 | M | 130 | 84 | true | P3 |
PT-09 | F | 115 | 75 | false | P3 |
PT-10 | Male | 140 | 88 | true | P2 |
PT-11 | F | 118 | 78 | true | P4 |
PT-12 | M | null | 80 | null | P2 |
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YAML Metadata Warning:The task_ids "tabular-data-quality" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
🏛️ DAMA-DMBOK Data Quality Benchmark Dataset
Curator: Ioseba Ongay Arana · Certified Data Management Professional (CDMP®)
Standard: DAMA-DMBOK (Data Management Body of Knowledge, 2nd Edition)
License: MIT
📌 Dataset Summary
This benchmark provides curated enterprise datasets and a machine-readable data quality rule taxonomy designed to benchmark automated data governance agents and data quality auditing engines.
The dataset includes synthetic scenarios across three core business domains:
- Customer 360 Master Data (
customer_360_benchmark.csv): Master Data Management (MDM) records with contact syntax flaws, categorical casing conflicts, and duplicate identifiers. - Financial Transactions Ledger (
financial_ledger_benchmark.csv): Banking ledger with orphaned foreign keys, negative amounts, and statistical outliers. - Clinical Healthcare Registry (
clinical_records_benchmark.csv): Electronic health records with consent flags, boundary check violations, and missing clinical attributes. - Data Quality Rule Taxonomy (
rules_taxonomy.json): Formal classification of audit rules across the 6 DAMA-DMBOK quality dimensions: Completeness, Uniqueness, Validity, Consistency, Integrity, and Precision.
📐 The 6 DAMA-DMBOK Dimensions
| Dimension | Description | Benchmark Validation |
|---|---|---|
| Completeness | Degree to which required data is recorded | Null cells, missing key columns |
| Uniqueness | Records represent single real-world entities | Primary key collisions, duplicate tuples |
| Validity | Conformance to syntax, schema, and domain rules | RFC 5322 emails, E.164 phones, ISO dates |
| Consistency | Uniformity across representations | Categorical casing, ISO country code standardization |
| Integrity | Schema constraints & relational links | Referential integrity, primary key completeness |
| Precision | Statistical distribution & boundary bounds | IQR extreme outlier detection |
💻 Usage with Python / Datasets
import pandas as pd
# Load Customer 360 benchmark
df_cust = pd.read_csv("data/customer_360_benchmark.csv")
print("Records:", len(df_cust))
# Load Rules Taxonomy
import json
with open("data/rules_taxonomy.json") as f:
taxonomy = json.load(f)
print("Framework:", taxonomy["framework"])
🔗 Related Resources
- Interactive Hugging Face Space: Data Quality & Governance Audit Agent
- Author GitHub Profile: ioseba
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