Data Engineering — Part VII (Data Governance — Explained with Case Study)

Organisations of all sizes have come to see the significance of data as a strategic asset in today’s data-driven environment. However, great power comes with great responsibility, and maintaining data can be a difficult and time-consuming endeavor. This is where data governance enters the picture. In this post, we will look at what data governance is, why it is important, and how organisations may put it into practice.

What is data governance ?

The set of policies, procedures, and practices that organisations use to manage their data assets is known as data governance. It encompasses the complete data life-cycle, from generation and collection through disposal. The goal of data governance is to guarantee that data is accurate, reliable, and secure, and that it is used in a manner that is consistent with the organization’s strategic goals.

Why is data governance important ?

Data governance is critical for a number of reasons. For starters, it assists organisations in risk management. Data breaches and other security incidents can have serious ramifications, including financial losses, legal responsibilities, and reputational harm. Organisations can decrease their exposure to these threats and maintain the security of their data by establishing data governance practices.

Second, data governance enables organisations to make more informed decisions. Data is only useful if it is correct, relevant, and timely. Without effective governance, data might become untrustworthy or outmoded, resulting in poor decision-making. Organisations may ensure that their data is of good quality and that it is used in a way that promotes effective decision-making by establishing data governance practices.

Third, data governance assists organisations in meeting legal and regulatory obligations. Many industries are governed by stringent data privacy and security laws, such as GDPR and HIPAA. Organisations can assure compliance with these requirements and prevent potential penalties and legal actions by implementing data governance practices.

How can organizations implement effective data governance practices ?

To implement effective data governance practices, a complete approach that tackles all areas of data management is required.

The following are some important actions that organisations may take to develop good data governance practices:

Define data governance roles and responsibilities

It is critical to explicitly establish who is in charge of data governance inside the organisation. This includes establishing and describing roles such as data owners, data stewards, and data custodians.

Develop data policies and procedures

Policies and procedures should be developed by organisations to control how data is gathered, processed, kept, and utilized. These policies should be consistent with the organization’s strategic goals while also meeting legal and regulatory constraints.

Establish data quality standards

Data quality is crucial for making sound decisions. Organisations should design methods for monitoring and improving data quality over time, as well as establish data quality standards.

Implement data security measures

Data security is a critical component of data governance. To safeguard data from unauthorized access, theft, or loss, organisations should establish security measures such as access controls, encryption, and data backup and recovery protocols.

Train employees on data governance practices

Everyone in the organisation is responsible for data governance. Employees should be trained on data governance practices such as how to handle data securely, adhere with data policies and procedures, and report data security problems.

Let’s look at Data Governance via the lens of a case study.

Consider a scenario where an organisation collects client data via an online form. Personal information such as name, address, phone number, and email address are included in this data. The organisation wishes to verify that this data is appropriately managed and that it complies with all legal and regulatory standards.

Here’s how data governance could be done in this scenario:

import pandas as pd
import numpy as np

# Load customer data from CSV file
customer_data = pd.read_csv('customer_data.csv')

# Define data governance policies and procedures
data_policies = {
    'data_types': {
        'name': str,
        'address': str,
        'phone_number': str,
        'email': str
    },
    'data_quality': {
        'completeness': {
            'name': lambda x: isinstance(x, str) and x != '',
            'address': lambda x: isinstance(x, str) and x != '',
            'phone_number': lambda x: isinstance(x, str) and x != '',
            'email': lambda x: isinstance(x, str) and x != ''
        },
        'accuracy': {
            'phone_number': lambda x: x.isdigit() and len(x) == 10,
            'email': lambda x: '@' in x
        }
    },
    'data_security': {
        'encryption': {
            'email': lambda x: x.encode('utf-8').hex()
        }
    }
}

# Define data stewardship roles and responsibilities
data_stewards = {
    'name': 'John Smith',
    'email': 'john.smith@example.com',
    'responsibilities': [
        'Ensuring that data policies and procedures are followed',
        'Monitoring data quality and accuracy',
        'Reporting data security incidents'
    ]
}

# Define data access controls
data_access_controls = {
    'name': ['data_steward', 'data_custodian', 'data_user'],
    'address': ['data_steward', 'data_custodian', 'data_user'],
    'phone_number': ['data_custodian', 'data_user'],
    'email': ['data_steward', 'data_custodian', 'data_user']
}

# Define data backup and recovery procedures
def backup_data():
    # Backup customer data to a secure location
    pass

def recover_data():
    # Restore customer data from backup
    pass

# Implement data governance practices
def validate_data(data):
    # Validate data types
    for col, dtype in data_policies['data_types'].items():
        if data[col].dtype != dtype:
            raise ValueError(f'{col} is not of type {dtype}')

    # Validate data completeness and accuracy
    for col, checks in data_policies['data_quality'].items():
        for check, func in checks.items():
            if not data[col].apply(func).all():
                raise ValueError(f'{col} fails {check} check')

    # Encrypt sensitive data
    for col, funcs in data_policies['data_security']['encryption'].items():
        data[col] = data[col].apply(funcs)

    return data

def store_data(data):
    # Store customer data in a secure database
    pass

def retrieve_data():
    # Retrieve customer data from the database
    pass

def update_data(data):
    # Update customer data in the database
    pass

# Train employees on data governance practices
def train_employees():
    # Provide training on data governance policies and procedures
    pass

# Report data security incidents
def report_incident():
    # Report data security incidents to the data steward
    pass

In this case, the organisation establishes data governance policies and procedures for the management of customer data. Data kinds, data quality, data security, data stewardship responsibilities, data access controls, and data backup and recovery methods are all covered by these regulations.

To guarantee that only authorized individuals can access customer data, the organisation defines data stewardship roles and duties and implements data access controls. They also establish data backup and recovery policies to ensure that customer data is frequently backed up and can be restored in the event of a disaster or data loss.

By checking data kinds, completeness, and accuracy, the organisation applies data governance practices. They encrypt sensitive information and save it in a safe database. They also retrieve, update, and report incidents involving client data.

Finally, the company educates its staff on data governance policies and processes to ensure that everyone understands their duties and responsibilities when it comes to managing customer data.

This example demonstrates how data governance may be used in code to manage and safeguard client data. Organisations can ensure proper data management and compliance with legal and regulatory requirements by creating policies and processes, roles and responsibilities, access restrictions, and backup and recovery protocols.

Conclusion

Organisations that want to maximize the value of their data assets must practice data governance. Organisations may control risk, make smarter decisions, and comply with legal and regulatory obligations by implementing effective data governance practices. Organisations should adopt a complete approach to implementing successful data governance practices, from establishing roles and responsibilities to teaching staff on data governance practices.

Wrapping Up — Part VII

In this segment of the series, we learned in detail about Data Governance, and tried to understand the process with the help of a case study.

In the final segment of the series, I will delve deeply into how Machine Learning plays a crucial role in Data Engineering.

Please feel free to post in comments if you have some specific suggestions to be covered in next series or if you feel some of the information is inaccurate.

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