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Essential Data Governance Strategies for your Data Stack

Jun 15, 2023 by Bal Heroor

According to a report by Forbes, 2.5 quintillion bytes of data are created daily, which continues to grow exponentially. Moreover, with the increasing adoption of cloud technologies, Internet of Things (IoT) devices, and artificial intelligence (AI), data has become the lifeblood of businesses, shaping their strategies and driving innovation. Establishing a robust data governance framework in this landscape has become a top priority to ensure effective data quality, security, and compliance. 

But what even is a data governance strategy? And how should an organization start when implementing one?

What is Data Governance?

Data governance is the practices, processes, and policies governing an organization's availability, integrity, and security. It involves establishing guidelines, roles, and responsibilities to create a transparent and accountable data environment. The end goal is to ensure that all data assets are used transparently and responsibly.

However, modern data architectures, which typically comprise a mix of on-premises and cloud-based systems, have significantly increased the complexity of data management. This has made data governance even more critical for organizations. A well-defined data governance strategy can help businesses navigate this complexity by ensuring data quality, maintaining data lineage, and implementing robust data security measures.

Data quality is a cornerstone of data governance, as poor data quality can have significant consequences for businesses. According to Gartner, poor data quality is a primary reason 40 per cent of all business initiatives fail to achieve their goals. With effective data governance, organizations can implement data quality checks, establish data standards, and set up data validation and cleansing processes. This helps maintain data accuracy, completeness, and timeliness, enabling better decision-making and fewer errors.Data_Stack5

Another essential aspect of data governance is data lineage, which refers to the life cycle of data, including its origin, transformation, and use. Tracking data lineage enables organizations to understand how data flows through their systems, ensuring its reliability and facilitating regulatory compliance. This is particularly important in industries subject to strict regulations, such as finance and health care, in which organizations must demonstrate compliance with data protection laws, GDPR and HIPAA.

Data security is another critical component of data governance. With the increasing prevalence of data breaches and cyberattacks, protecting sensitive data has become a top business priority. The Ponemon Institute's 2020 report revealed that the average total cost of a data breach was nearly $4 million. Organizations can reduce the risk of data breaches and protect their valuable assets by implementing robust data security measures, such as encryption, access controls, and data anonymization.

Implementing a data governance strategy 

One of the key challenges in implementing data governance with modern data architectures is the need for collaboration across multiple departments and teams. To address this challenge, organizations should establish a governance council comprising representatives from various departments to oversee governance initiatives and ensure alignment with business objectives.Data_Stack2

One of the first orders of business for a data governance council should be the creation of a data governance stack. Don’t let the technical phrasing scare you, as a “data stack” is simply a collection of tools, processes, and practices that will support your organization's data governance efforts. Below are seven key components that should be included in every data governance stack.


Data Governance Framework

The foundation of a data governance stack is a well-defined framework that outlines the organization's data governance principles, policies, roles, responsibilities, and processes. This framework guides all data governance activities and ensures consistency in data management across the organization. For best results, ensure that your framework is developed collaboratively through input from all your organization's employees responsible for managing your data.


Data Catalog

A data catalog is a centralized repository that inventory all available data assets within an organization. This catalog includes metadata, such as data definitions, lineage, and usage information, to help users easily discover, understand, and trust the data they need for their tasks.Data_Stack6


Data Quality Tools

These tools help organizations monitor, assess, and improve the quality of their data by identifying and resolving issues such as inaccuracies, inconsistencies, and duplicates. Data quality tools also support data validation, cleansing, enrichment, and profiling processes to maintain high-quality data throughout its lifecycle. When choosing which tools to include in your data stack, consider the purpose of each tool and whether it will help or hinder your data governance efforts. There are almost as many vendors as tools, so it helps to be picky.


Data Lineage and Metadata Management

Data lineage tools track data flow across systems and processes, providing visibility into each data asset's origin, transformations, and dependencies. Metadata management tools help organizations capture, store, and manage metadata, making it easier to understand the context and history of data assets.Data_Stack3


Data Security and Privacy Tools

These tools and technologies, such as encryption, access control, and data masking, protect sensitive data from unauthorized access and potential breaches. They also help organizations comply with data privacy regulations by implementing data protection principles and managing consent, data subject rights, and data processing activities.


Data Integration and Processing Tools

Data integration tools facilitate the movement, transformation, and consolidation of data from various sources into a unified data repository or data lake. Data processing tools like ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) enable organizations to manipulate and prepare data for analysis and reporting.


Data Governance Metrics and Reporting

Establishing key performance indicators (KPIs) and monitoring mechanisms is crucial for measuring the effectiveness of a data governance program. Reporting tools help track and visualize data governance metrics, enabling organizations to assess progress, identify areas for improvement, and demonstrate the value of data governance initiatives to stakeholders.Data_Stack4



In conclusion, data governance is an indispensable element of modern data architectures, enabling organizations to control their data assets and extract maximum value from them. By implementing a comprehensive data governance strategy, businesses can ensure data quality, compliance, and security, ultimately driving better decision-making and unlocking their full potential in today's data-driven world.
Looking to leverage the ideal data governance strategies for your data stack? Learn how Mactores can help you! 
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