How to Stay Ahead of the Regulatory Curve with Robust Data Governance?

Data fuels modern businesses. But as the volume of data increases, so do the risks associated with it. Data privacy and protection are a top concern, with laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) taking center stage.

In fact, according to data from the United Nations, over 70% of the world’s countries now have data privacy legislation, with another 9% in the process of reviewing draft legislation.

Complying with regulations relating to data privacy and protection is a way companies can demonstrate their responsible use of data. But still, users are not entirely convinced. Only 25% of users believe companies are responsible with their personal data, and just 15% believe companies use personal data to improve their lives.

The concerns go beyond data privacy and protection. Many industries ranging from banking and insurance to telecommunications and healthcare are facing a host of regulations related to the responsible use of data.

Financial services firms scrambled to comply with regulations such as BCBS 239 and Solvency II reporting when they emerged, and today, new standards and regulations ranging from the AI Act to ESG disclosure requirements are set to cause more disruption when it comes to regulatory reporting and compliance.

While it is clear that regulatory reporting and compliance have become a routine part of day-to-day operations in modern business, it is also apparent that many organizations still struggle to keep pace.

In many cases, it is not a lack of understanding that causes organizations to struggle. Instead, it is the lack of a data governance culture and the tools to support it.

What is a Data Governance Culture?

A data governance culture elevates the importance of - and commitment to - responsible data management. It ensures that the business is managing data effectively and using it to drive business success.

Companies with data governance cultures establish a clear data governance framework that outlines the policies, procedures, and guidelines for data management, including data quality, security, privacy, and compliance.

They also define the roles and responsibilities of different stakeholders, from the Chief Data Officer and data stewards to business data users. They engage these stakeholders in ongoing training and education programs as well as encourage effective communication and collaboration between them.

When companies establish a data governance culture, they also set the tone for how the organization will collect, store, and manage data. They set data security and privacy protocols that enable them to protect sensitive data and ensure only the right people have access to it. And they share these protocols and practices with everyone across the business.

Key Imperatives for a Data Governance Culture

While establishing a data governance culture is priority number one, there are a number of other, related imperatives that organizations must embrace to efficiently and effectively comply with regulatory reporting and compliance:

1. Data Lineage

2. Data Traceability

3. Data Quality

1. Data Lineage

Data lineage is the process that documents where your data came from, where it is stored, and if or how it has changed over time. It can also provide insight into the end destination for data, such as business intelligence (BI) or a self-service analytics tool.

Data lineage falls into two categories:

• Business data lineage follows the data that the business uses. This type of lineage tends to be simpler and focuses primarily on ensuring the data is reliable and comes from a trustworthy source. It does not need to contain every data step or revision.

• Technical data lineage goes deeper into how the data is used and transformed over time. Technical lineage reveals data flow through various applications and services to update and structure a data architecture.

Because data lineage provides a clear and precise visual output of all your data, it provides critical support for regulatory reporting and compliance. Using data lineage, organizations can clearly explain where data comes from, show where it is stored, and demonstrate how it has changed over time.

Without data lineage, it would be difficult for organizations to track how data flowing through their various systems transforms along the way. And it would be equally difficult to demonstrate to regulators that their data flows in ways that comply with regulations and compliance standards.

2. Data Traceability

Data traceability evaluates data and ensures its proper usage by monitoring access, values, and changes throughout its lifecycle.

Data traceability focuses on the appropriate and ethical use of data and how it adheres to its expected process flows. Effective traceability typically involves approval of access, timestamps, and electronic signatures.

For organizations that handle sensitive information such as financial institutions, healthcare providers, and government agencies, data traceability is crucial. By tracking who accessed specific data, when it was accessed, and what changes were made, data traceability enables these organizations to maintain data security, safeguard sensitive information, and ensure compliance with industry-specific regulations.

3. Data Quality

Data quality assesses the state of an organization’s data based on factors including completeness, accuracy, consistency, and reliability. It also evaluates whether or not it’s up-to-date.

Many organizations struggle with data quality. In fact, according to Gartner, poor data quality costs organizations around US$12.9 million annually. The ripple effects of poor data quality are far-reaching, ranging from bad decisions to missed revenue to inefficient business processes.

Even worse, poor data quality can compromise an organization’s ability to remain compliant with privacy and industry-specific regulations, opening them up to business risk or hefty fines.

Enabling a Data Governance Culture With a Data Catalog

Establishing a data governance culture requires the right combination of people, process, and technology. Defining the right roles and responsibilities (people) and developing the right data governance framework (process) are steps in the right direction. But without the right tools (technology), it becomes difficult at best for a data governance culture to succeed.

A data catalog is a critical tool for organizations looking to establish a data governance culture. It gives business users, many of whom are not data experts, clarity on data definitions, synonyms, and essential business attributes so they can understand and use their data more effectively.

Data catalogs show who owns the data, allowing for greater collaboration across the business. They provide a self-service way for everyone in the organization to find the data they need and turn what used to be tribal knowledge into useful and accessible information that they can use to make better business decisions.

When organizations use a data catalog, they reduce the risks associated with regulatory reporting and compliance. Using capabilities such as data lineage, users can understand the impact of the changes they are making.

The business can also show who can access the data, where it comes from, and how it is used throughout the organization, all of which are critical when demonstrating compliance with the regulators.

Bouygues Telecom: Establishing a Data Governance Culture With a Data Knowledge Catalog

Bouygues Telecom is experiencing exponential growth in data volume, with data flowing from both their network installations and their commercial applications. To keep pace with its ever-growing data volume, the company identified the need to establish a data quality assurance plan to manage this huge influx of information.

Like all telecommunications providers, their plan also needed to address the strict regulatory and compliance guidelines that are becoming increasingly less forgiving.

These challenges prompted the central data governance team at Bouygues Telecom to focus on increasing data literacy and strengthening data governance within the company.

Their goal was to ensure that every user understood not just the importance of using data responsibly to make better decisions and move the business forward, but also reinforce the need for data governance to comply with the high stakes facing telecom organizations today.

The team at Bouygues Telecom knew they needed to put in place a personalized data governance plan – and that they needed a data governance tool that would engage and support both data stewards and business experts, serving as a companion to support them throughout their data journey.

The team selected DataGalaxy’s Data Knowledge Catalog because it would enable them to increase data literacy and promote a data governance culture across the organization by delivering the key capabilities they needed, including the ability to:

• Discover and access data

• Track lineage

• Collaborate and interact with communities of data stewards

With DataGalaxy, Bouygues Telecom is working towards achieving its short-term goal of reaching a high level of academic quality, while building towards the long-term goal of achieving operational quality as the company matures.

With DataGalaxy’s Data Knowledge Catalog, Bouygues Telecom has begun to establish a data perimeter that allowed them to master, understand, and control their data, enabling them to improve quality, gain trust, and mitigate risks associated with regulatory reporting and compliance.

To learn more about how a data governance culture supported by a data catalog can reduce the risks associated with regulatory reporting and compliance, visit www.datagalaxy.com. Here, you can also read the full story of Bouygues Telecom’s journey to establish a data governance culture through the use of DataGalaxy’s Data Knowledge Catalog.

About the author:

Kash Mehdi is the VP of Growth at DataGalaxy (the industry’s first data knowledge catalog). He spent the last decade scaling go-to-market functions at hyper-growth SaaS companies. He was Collibra’s 5th employee from its startup phase to achieving a multi-billion dollar valuation and with Informatica when it first entered the data governance space to its 2021 IPO achievement.