---
title: Data Quality and Governance Documentation
description: A comprehensive guide to data quality frameworks, validation rules, anomaly detection, data profiling, lineage tracking, and SLAs.
keywords: [data quality, data validation, anomaly detection, data profiling, data lineage, SLAs, data governance]
category: data engineering
tags: [data quality, data validation, data anomaly detection, data profiling, data lineage, SLAs, data governance, data engineering]
---

## Data Quality Frameworks

Data quality frameworks provide a structured approach to managing the quality of data within an organization. These frameworks typically include the following key components:

1. **Data Quality Dimensions**: The specific aspects of data quality that need to be measured and monitored, such as accuracy, completeness, consistency, timeliness, and validity.
2. **Data Quality Metrics**: The quantitative measures used to assess the quality of data against each dimension, such as the percentage of missing values, the number of duplicate records, or the compliance with business rules.
3. **Data Quality Thresholds**: The acceptable levels of data quality for each metric, which can be used to trigger alerts or remediation actions when data falls below a certain threshold.
4. **Data Quality Processes**: The procedures and workflows for monitoring, reporting, and improving data quality, including data profiling, data cleansing, and data governance.
5. **Data Quality Roles and Responsibilities**: The assignment of roles and responsibilities for data quality management, such as data stewards, data quality analysts, and data quality managers.

Example data quality framework configuration:

```yaml
data_quality_dimensions:
  - name: accuracy
    description: The degree to which data correctly represents the true or intended value.
    metrics:
      - name: percent_valid_values
        description: The percentage of values that pass validation rules.
        target: 95%
      - name: percent_correct_formatting
        description: The percentage of values that conform to the expected format.
        target: 98%
  - name: completeness 
    description: The degree to which all required data is present.
    metrics:
      - name: percent_missing_values
        description: The percentage of required values that are missing.
        target: 5%
      - name: percent_null_values
        description: The percentage of values that are null or empty.
        target: 2%
data_quality_processes:
  - name: data_profiling
    description: Analyze data to identify quality issues and generate metrics.
    schedule: weekly
  - name: data_cleansing
    description: Apply data transformation and enrichment rules to improve quality.
    schedule: monthly
  - name: data_quality_reporting
    description: Generate and distribute data quality reports to stakeholders.
    schedule: monthly
data_quality_roles:
  - name: data_steward
    description: Responsible for defining and enforcing data quality standards.
  - name: data_quality_analyst
    description: Analyzes data quality metrics and coordinates improvement efforts.
  - name: data_quality_manager
    description: Oversees the data quality program and reports on performance.
```

## Data Validation Rules

Data validation rules are a set of checks and constraints that ensure data meets specific criteria and business requirements. These rules can be applied at various stages of the data pipeline, including data ingestion, transformation, and storage. Common examples of data validation rules include:

- **Syntactic Validation**: Checks the structure and format of data, such as ensuring that a date field is in the correct date format or that a numerical field only contains numeric characters.
- **Semantic Validation**: Checks the meaning and relationships of data, such as ensuring that a customer's age is within a reasonable range or that a product's price is greater than zero.
- **Referential Integrity**: Checks that data references other related data correctly, such as ensuring that a customer's order references a valid product or that a foreign key matches a primary key in another table.
- **Business Rules**: Checks that data adheres to specific business policies and requirements, such as ensuring that a customer's zip code matches their state or that a transaction amount does not exceed a daily limit.

Example data validation rules:

```sql
-- Syntactic validation
CREATE RULE customer_email_format
AS CHECK (email LIKE '%@%.%');

-- Semantic validation  
CREATE RULE customer_age_range
AS CHECK (age BETWEEN 18 AND 120);

-- Referential integrity
CREATE RULE order_product_reference
AS CHECK (product_id IN (SELECT id FROM products));

-- Business rules
CREATE RULE daily_transaction_limit
AS CHECK (transaction_amount <= 10000);
```

These validation rules can be implemented as database constraints, application-level checks, or a combination of both, depending on the specific requirements and architecture of the data system.

## Anomaly Detection

Anomaly detection is the process of identifying data points or patterns that deviate significantly from the expected or normal behavior of a dataset. This can be useful for identifying data quality issues, detecting security threats, or monitoring the performance of a system. Some common techniques for anomaly detection include:

1. **Rule-based Anomaly Detection**: Defining a set of rules or thresholds that define normal behavior, and then flagging any data points that fall outside of those boundaries as anomalies.

Example rule-based anomaly detection:

```python
import pandas as pd

# Load data
df = pd.read_csv('sales_data.csv')

# Define rules for normal behavior
rules = {
    'revenue': lambda x: x > 0 and x < 1000000,
    'units_sold': lambda x: x > 0 and x < 10000,
    'avg_order_value': lambda x: x > 0 and x < 1000
}

# Detect anomalies
anomalies = df.apply(lambda row: any(not rule(row[col]) for col, rule in rules.items()), axis=1)
```

2. **Statistical Anomaly Detection**: Using statistical models to define the expected range of behavior, and then identifying data points that fall outside of that range as anomalies.

Example statistical anomaly detection:

```python
import numpy as np
from sklearn.ensemble import IsolationForest

# Load data
df = pd.read_csv('sales_data.csv')

# Train anomaly detection model
model = IsolationForest()
model.fit(df)

# Detect anomalies
anomalies = model.predict(df)
```

3. **Machine Learning-based Anomaly Detection**: Training a machine learning model to learn the patterns and relationships in the data, and then using that model to identify outliers or anomalies.

Example machine learning-based anomaly detection:

```python
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Load data
df = pd.read_csv('sales_data.csv')

# Train autoencoder model
model = Sequential()
model.add(Dense(32, activation='relu', input_dim=df.shape[1]))
model.add(Dense(df.shape[1], activation='linear'))
model.compile(optimizer='adam', loss='mse')
model.fit(df, df)

# Detect anomalies
reconstructed = model.predict(df)
anomalies = np.where(np.mean((df - reconstructed)**2, axis=1) > 0.1)[0]
```

Anomaly detection can be a powerful tool for identifying data quality issues, but it is important to carefully define the appropriate rules, thresholds, or models for the specific context and data being analyzed.

## Data Profiling

Data profiling is the process of analyzing the structure, content, and quality of data to understand its characteristics and identify any potential issues or anomalies. This information can be used to improve data quality, inform data modeling and schema design, and support data governance initiatives. Some common data profiling techniques include:

1. **Column Profiling**: Analyzing the individual columns in a dataset, including data types, value distributions, null counts, and unique value counts.

Example column profiling:

```sql
SELECT 
  column_name,
  data_type,
  count(*) as total_rows,
  count(distinct(column_name)) as unique_values,
  count(case when column_name is null then 1 end) as null_count
FROM 
  my_table
GROUP BY 
  column_name, data_type;
```

2. **Cross-column Profiling**: Analyzing the relationships and dependencies between different columns in a dataset, such as identifying foreign key relationships or functional dependencies.

Example cross-column profiling:

```sql
SELECT
  a.column_name AS column_a,
  b.column_name AS column_b,
  count(*) AS total_rows,
  count(distinct(a.column_name)) / count(distinct(b.column_name)) AS cardinality_ratio
FROM
  my_table a
JOIN
  my_table b
  ON a.foreign_key = b.primary_key
GROUP BY
  a.column_name, b.column_name
ORDER BY
  cardinality_ratio DESC;
```

3. **Metadata Profiling**: Analyzing the overall metadata of a dataset, such as data sources, data owners, data lineage, and data transformation history.

Example metadata profiling:

```sql
SELECT
  table_name,
  data_source,
  data_owner,
  last_updated_date,
  data_lineage
FROM
  data_catalog
WHERE
  table_name = 'my_table';
```

Data profiling can be a valuable tool for understanding the quality and characteristics of data, and can help inform data governance and data quality improvement initiatives.

## Data Lineage Tracking

Data lineage tracking is the process of capturing and maintaining information about the origin, transformation, and movement of data as it flows through an organization's data ecosystem. This information can be used to understand the provenance of data, identify the root causes of data quality issues, and ensure compliance with data regulations and policies. Some common components of data lineage tracking include:

1. **Source and Target Systems**: Identifying the systems and applications that are the sources and targets of data as it moves through the data pipeline.
2. **Transformation and Processing Steps**: Capturing the specific data transformation and processing steps that are applied to the data, such as data cleansing, enrichment, or aggregation.
3. **Data Lineage Graphs**: Visualizing the end-to-end flow of data through the organization's data ecosystem, showing the relationships and dependencies between different data sources, transformation steps, and target systems.
4. **Metadata Management**: Maintaining a centralized repository of metadata about the data, including data definitions, business rules, and data quality metrics.

Example data lineage tracking using Apache Atlas:

```
# Define data assets and their relationships
create_entity(
  type='hdfs_path',
  name='/raw/sales_data.csv',
  qualified_name='/raw/sales_data.csv@clust
er1'
)

create_entity(
  type='hive_table',
  name='sales_data',
  qualified_name='sales_data@cluster1'
)

create_edge(
  type='derivedFrom',
  src='/raw/sales_data.csv@cluster1',
  dst='sales_data@cluster1'
)

# Define data transformation steps
create_entity(
  type='spark_job',
  name='sales_data_etl',
  qualified_name='sales_data_etl@cluster1'
)

create_edge(
  type='uses',
  src='sales_data_etl@cluster1',
  dst='/raw/sales_data.csv@cluster1'
)

create_edge(
  type='produces',
  src='sales_data_etl@cluster1',
  dst='sales_data@cluster1'
)
```

By capturing and maintaining this data lineage information, organizations can improve data governance, troubleshoot data quality issues, and ensure compliance with data regulations and policies.

## Service Level Agreements (SLAs)

Service Level Agreements (SLAs) are a set of metrics and targets that define the expected level of service and performance for a data system or service. SLAs can be used to measure and monitor the quality, availability, and reliability of data and data-related services, and can help ensure that data consumers receive the level of service they require. Some common SLA metrics include:

1. **Data Availability**: The percentage of time that data is accessible and available to users, measured as uptime or downtime.
2. **Data Latency**: The time it takes for data to be processed and made available to users, measured in seconds or minutes.
3. **Data Accuracy**: The percentage of data that is free from errors or anomalies, measured as the percentage of valid or correct data.
4. **Data Completeness**: The percentage of required data that is present and available, measured as the