Kindly fill up the following to try out our sandbox experience. We will get back to you at the earliest.
Unlock the power of data governance: Learn fundamentals and strategies for becoming truly data-driven.
Updated on
Making your company truly data-driven
Disclaimer: This article is not written using Chatgpt :)
I never imagined that my journey as a data engineer would eventually lead me to start a data observability company. But as I look back on my experiences, I see how gaining exposure to data has been a constant thread throughout my career, guiding me to where I am today. Serendipity is not just a fancy word but also a satisfying feeling!
As a data guy, at the beginning of my career, I focused solely on building systems to collect and process data. However, handling sensitive information was always tricky — a fact every person working in data can empathize with. I realized that simply having the technology to collect data, build ETL pipelines, and then use data for modeling & prediction was of no use when data governance principles were not enforced within an organization.
My team and I would spend long exhausting hours troubleshooting issues where the culprit would mostly be “bad data”.
“Data is unreliable, data is missing, data is inaccurate’ was a common dialogue for us.”
Data Life Cycle: Data governance encompasses the entire life cycle of data, including its creation, storage, usage, and disposal.
Data Policies: Internal standards in the form of data policies and guidelines that govern data access, usage, and management within an organization.
Internal Standards: Establishing frameworks and processes to ensure consistency in data management practices across the organization.
External Standards: Compliance with industry best practices and legal requirements set by industry associations and government agencies.
Ensuring that data is ethically and securely in accordance with industry regulations was and is of importance in every organization. Effective data governance ensures that data is consistent and trustworthy and doesn’t get misused. And being a dreamer, I always dreamt of a super simple platform that helps data engineers solve data quality issues at a lightning speed.
This is where my interest in data governance or observability grew multifold. I learned that data governance was more than just data security — it was about ensuring that data was used in a responsible and controlled manner throughout its lifecycle. In fact, I have seen the consequences of poor data governance, such as data breaches leading to business loss.
The overall concept of data governance may sound very basic but it’s often overlooked especially when companies are just starting out. Apart from failing to build a data infrastructure, they often overlook the data security aspect of their business as well.
The importance of data governance has only increased with the rise of new technologies. As a strong advocate for healthy data governance practices, I envision a future where data is used responsibly and ethically, to the benefit of both businesses and their customers.
If we go by the book’s meaning of it, data governance is a set of processes and policies that govern the collection, storage, use, and dissemination of an organization’s data.
Let’s go one by one on what data governance means:
The implementation of effective data governance policies and procedures helps organizations to avoid the costly consequences of poor data management, such as data breaches and loss of customer trust. It can also help to ensure regulatory compliance and promote the responsible and ethical use of data.
Companies have faced massive fines and damage to their reputation due to data breaches and the misuse of customer information.
Here are a few occurrences:
In 2018, a marketing and data aggregation company based in Florida called Exactis made a major mistake that had far-reaching consequences. They left a database with personal information on nearly 340 million people on a server that anyone could access. This information included people’s phone numbers, addresses, email addresses, interests, and details about their families, including the number, age, and gender of their children.
Thankfully, a security expert discovered the breach and brought it to the public’s attention. We are talking about millions of adults’ and businesses’ data being exposed. This example highlights the importance of proper data governance and the serious consequences that can occur when personal information is not properly secured.
By now you must have understood the importance of data governance. It’s undoubtedly relevant to all companies, regardless of size or industry. Every company collects and uses data in some way, and without proper governance, this data is at risk of being misused, lost, or stolen. In addition, new regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) require companies to be more transparent about how they use personal data. So if you are in a company or have a company, there’s no escape from enforcing good data practices. Either the hackers or the Government will be knocking on your door when it gets too late. And I personally wouldn’t wish that on anyone.
Data governance plays a crucial role in various aspects of data management, ensuring accountability, data quality, and discoverability. Let's explore the key use cases where data governance provides significant value:
Data governance empowers organizations to assign accountability and responsibility for data to data stewards. Data stewards act as custodians, overseeing the proper handling, usage, and governance of data. By establishing data stewardship roles and responsibilities, organizations can ensure data is managed effectively throughout its lifecycle.
Data governance is instrumental in maintaining data quality standards. By implementing data governance practices, organizations can establish standardized data quality rules, perform consistent data profiling and cleansing activities, and enforce data validation and verification processes. This promotes the integrity and reliability of data used for decision-making and operational processes.
Data governance plays a significant role in managing data as a valuable enterprise asset. It ensures that data is managed consistently and effectively across the organization, aligning with data management best practices. Through data governance, organizations establish data policies, procedures, and frameworks that facilitate efficient data integration, storage, and retrieval, promoting data efficiency and accuracy.
Effective data governance enables organizations to enhance data discoverability, making it easier to find and access relevant data assets. By establishing metadata management practices, organizations can create comprehensive data catalogs that provide detailed information about data assets, facilitating efficient search and discovery. Improved data discoverability enables teams to make informed decisions, leverage data insights, and enhance collaboration.
Data governance promotes effective metadata management, ensuring the consistent and accurate documentation of metadata across the organization. Metadata, such as data definitions, data lineage, and data classifications, helps users understand the context, quality, and usage of data assets. By implementing metadata management strategies, organizations can enhance data governance practices and enable more efficient data stewardship and data management processes.
By leveraging data stewardship, ensuring data quality, managing data effectively, enabling data discoverability, and implementing proper metadata management, organizations can harness the full potential of data governance and achieve data-driven success.
Data governance and data management are two interconnected concepts that play crucial roles in managing and leveraging an organization's data assets. While data governance focuses on establishing policies and standards for data use, data management encompasses the broader scope of managing data throughout its lifecycle, ensuring its quality, availability, and usability.
Data governance is the framework that defines how data is controlled, protected, and consumed within an organization. It involves establishing data governance councils or committees, defining data governance principles, and implementing processes to ensure adherence to data policies and standards. The goal of data governance is to enable effective decision-making, mitigate risks, and ensure data compliance with regulatory requirements.
Data management, on the other hand, encompasses the operational aspects of handling data. It involves activities such as data capture, storage, integration, analysis, and archival. Data management focuses on implementing the necessary technologies, processes, and practices to ensure data quality, accuracy, and reliability. It also includes data stewardship, which assigns accountability and responsibility for data to specific individuals or teams.
Effective data governance relies on robust data management practices to ensure the integrity and usability of data. Here are some key areas where data governance and data management intersect:
To better understand the relationship between data governance and data management, refer to the table below:
Data governance encounters various challenges due to the complex nature of data management and evolving data landscapes. Several factors impede the effectiveness of data governance initiatives, requiring organizations to address these obstacles strategically.
Data silos pose a significant challenge to data governance efforts. In many organizations, data is scattered across different systems, applications, and departments, resulting in fragmented data landscapes. These silos hinder data integration and the ability to gain a holistic view of the organization's data assets. Data integration becomes essential to unify diverse data sources and ensure accurate and comprehensive data analysis.
Data governance faces unique data engineering problems, such as data quality issues, data transformation complexities, and data lineage tracking. Data engineers play a crucial role in implementing data governance practices and overcoming these challenges. They must ensure the availability, accessibility, and reliability of data through efficient data engineering processes.
As the use of data continues to evolve, organizations face the challenge of adapting their data governance frameworks to address emerging data use cases. New business requirements, technological advancements, and changing regulations introduce unprecedented data management scenarios that demand agile and adaptable governance strategies. Organizations must continuously evaluate and update their data governance practices to accommodate these evolving use cases.
In order to overcome these challenges, organizations must establish standardized tool usage to streamline data governance processes. Additionally, acknowledging the human side of data governance is crucial. This involves fostering a culture of data stewardship, where individuals take ownership and are accountable for data governance. Finally, implementing best practicesrecommended by industry experts can help organizations optimize their data governance strategies and achieve success.
Addressing these challenges enables organizations to establish efficient and effective data governance practices, ensuring data integrity, compliance, and maximizing the value of their data assets.
Implementing effective data governance requires following best practices that enhance the overall management and control of data. These practices include:
Implementing these best practices will enable organizations to establish a strong foundation for data governance, enhance data management practices, and achieve data-driven decision-making. By leveraging existing metadata, starting small, using change management techniques, following implementation steps, and creating a data governance framework, organizations can ensure the success and effectiveness of their data governance programs.
A data governance framework consists of various components that collectively form the foundation of a successful governance program. These components encompass policies, rules, processes, organizational structures, and technologies aimed at ensuring effective data management and control.
The key components of a data governance framework are:
Implementing a data governance framework with well-defined policies, rules, processes, organizational structures, technologies, and a mission statement creates a solid foundation for effective data governance practices. By leveraging the right components, organizations can ensure data consistency, integrity, security, and compliance throughout their data lifecycle.
Effective data governance goes beyond data observability and cataloging tools. It also encompasses master data management, data asset ownership, and decision-making processes. Master data management ensures that the organization's core data, such as customer information or product data, is accurate, consistent, and reliable across systems. Data asset ownership involves assigning responsibility for data quality and ensuring that data is treated as a valuable asset. Additionally, data governance should include processes for making data-driven decisions, ensuring that stakeholders have access to reliable and trustworthy data to inform their choices. By incorporating these elements into your data governance strategy, you can ensure the integrity, reliability, and value of your data assets.
In the era of data-driven decision-making, effective data governance is crucial to ensure data accuracy, security, and ethical use. By implementing policies and procedures, organizations can protect against data breaches and maintain customer trust while complying with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) [1][2]. In countries like Australia, the United States, and South Korea, data governance is essential for businesses to prevent costly consequences and protect sensitive information [3][4]. Data observability tools, such as Monte Carlo, Databand, and Acceldata, can help improve data management efficiency and reliability. Start thinking about data governance today to safeguard your company and customers. Decube is among the few platform to manage data observability, catalog and governance.
[1] https://gdpr.eu/[2] https://oag.ca.gov/privacy/ccpa[3] https://www.oaic.gov.au/privacy/australian-privacy-principles/[4] https://www.ftc.gov/tips-advice/business-center/privacy-and-security
First step to AI Readiness
Table of Contents
Sneak peek from the data world.
_For%20light%20backgrounds.svg)
.svg){kind=small}
.webp)