Modern Data Governance: A Flexible and Collaborative Approach for Evolving Business Needs
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Modern data governance, unlike its traditional counterpart, must be versatile, agile, and comprehensive to accommodate the complexity of today’s data environments.
In this article, we’ll dive into the characteristics of modern data governance and the challenges that you must overcome.
Table of contents
- What is modern data governance?
- Challenges with traditional data governance
- Characteristics of modern data governance
- Modern data governance: Related reads
What is modern data governance?
Modern data governance is an approach where everyone in the organization, not just data stewards or data governance committees, is responsible for data governance.
It embraces a personalized, community-centered approach toward data enablement with privacy at the core.
The goal of modern data governance is not merely about regulatory compliance, but to enhance the value of data.
This shift represents a significant departure from traditional data governance approaches. Let’s see how by comparing legacy and modern data governance.
|Attribute||Legacy Data Governance||Modern Data Governance|
|Definition||Centralized, hierarchical control over data access||Decentralized, empowering approach promoting widespread data use|
|Accountability||Governance is overseen by select individuals or committees||Shared responsibility, fostering a culture of accountability among all stakeholders|
|Process||Manual processes dominate, including approval granting, data tagging, and classification||Automation is leveraged to reduce errors and scale data governance operations|
|Goal||Prioritizes rigid compliance, often at the expense of innovation||Strives for regulatory compliance while enhancing data’s value and enabling innovation|
|Impact on collaboration||Limited collaboration, typically within specialized data teams||Cross-functional collaboration, encouraging input and insight from all organizational levels|
By emphasizing personalization, community involvement, and automation, modern data governance provides a more flexible, scalable, and effective approach to managing data in today’s complex digital landscapes.
Also read → What is data governance?
Challenges with traditional data governance
Navigating the dynamic landscape of data governance presents a multitude of challenges for businesses, such as:
- A rigid focus on compliance
- Poor data sharing and collaboration
Let’s explore each challenge further.
One of the most pervasive is the misconception of a “one-size-fits-all” approach. However, data is ubiquitous, and its users are diverse. That necessitates a data governance program that can adapt to this complexity.
“A typical ‘one-size-fits-all,’ command-and-control-based IT governance capability has neither the scope nor the agility to meet the needs of digital business.”
Data governance should be flexible and adaptive, tailored to meet the distinct needs of various roles and projects within an organization.
A rigid focus on compliance
Another common misstep is viewing data governance merely as a compliance activity.
While regulatory compliance is a key aspect of data governance, reducing it to a compliance-only exercise diminishes its potential.
The focus on compliance also made data governance an afterthought and the sole responsibility of a select few individuals.
However, data governance can only be successful when it’s woven into the fabric of the data creation process, so each data producer is a steward of that data.
Also, read → The principles of modern data governance
Poor data sharing and collaboration
Data sharing is essential for modern businesses, as it facilitates collaboration and drives innovation. Yet, the necessity to share data introduces its own set of challenges.
Without robust governance controls in place, businesses may face security breaches or noncompliance with regulations. This can lead to severe consequences like loss of reputation and significant fines.
Modern data governance enables data sharing and collaboration while ensuring regulatory compliance by making data democratization a priority.
Manual data governance becomes untenable as the volume of data most companies handle increases exponentially.
This rapid data growth underscores the need for automation in data governance.
So, how does modern data governance overcome these challenges related to adaptability, scalability, compliance, and data democratization?
Let’s look at the characteristics of modern data governance and see how it addresses these challenges.
Characteristics of modern data governance
Modern data governance has several distinctive characteristics that equip it to meet the challenges of our complex data landscape:
- Is built on data democratization: Emphasizes making data accessible to all individuals in an organization.
- Emphasizes clear roles and responsibilities: Though a part of data governance since its inception, the specifics keep evolving over time.
- Uses automation: Always a fundamental cornerstone to any large-scale modern data concern, including data governance.
- Is shared and collaborative: Practically speaking, the adoption and effectiveness of data governance depend heavily on its inclusivity and overcoming siloed workflows.
Let’s explore each characteristic further.
Is built on data democratization
Modern data governance abandons the old top-down approach that typically results in bottlenecks and restricts innovation.
Instead, it advocates a democratized and decentralized approach where every stakeholder has a role in governing the data. So, all users across the organization can access and interpret data without requiring specialized technical skills.
This is a transformation in the way organizations handle their data and fosters a data-driven culture, enhancing operational efficiency and promoting innovation.
Emphasizes clear roles and responsibilities
A hallmark of modern data governance is the clear delineation of roles and responsibilities within an organization.
Roles in modern data governance are not merely titles—they are integral to shaping the data culture of an organization. They represent a shift from a centralized model of data governance to a shared, collaborative model where everyone plays an active role.
Moreover, assigning roles isn’t about restricting access. It’s about ensuring each stakeholder knows their part in preserving and enhancing data quality, integrity, security, and privacy.
Take Brainly as an example. This global peer-to-peer learning platform for students understood the imperative of having distinct roles and responsibilities within their data governance framework. This clarity empowered every stakeholder to contribute effectively to the data governance efforts. They enhanced the value and trustworthiness of their data while simultaneously ensuring regulatory compliance.
Automation plays a crucial role in various aspects of data governance. By harnessing automation, organizations can streamline operations, reduce manual errors, and improve the speed and accuracy of data-related tasks.
Let’s see the role of automation in common aspects of data governance, such as:
- Role-Based Access Control (RBAC)
- Data lineage
- Policy propagation and enforcement
- Audit trail
Let’s explore each aspect further.
Role-Based Access Control (RBAC)
Access to data should be controlled using Role-Based Access Control (RBAC) down to the columnar level.
You can set up rule-based automation to ensure RBAC.
For example, you can automate the access request process by setting up a self-service platform where users can request access rights or changes to their existing permissions. These requests get routed through an approval workflow that’s designed based on predefined rules and policies.
This enables precise control and monitoring of data access, significantly reducing the risk of breaches and misuse.
Automation is also instrumental in tracking data lineage, which allows organizations to trace changes to data and keep track of its origin, transformations, and movements across systems.
You can automate various steps in lineage mapping, such as extracting metadata, capturing transformation logic, and identifying data sources and destinations.
Policy propagation and enforcement
Automation aids in the propagation and enforcement of governance policies, ensuring consistency across the organization.
For instance, you can set up automated scripts that leverage metadata to identify similar data assets and apply the relevant policies automatically.
This eliminates the need for manual monitoring and enforcement, thus enhancing efficiency.
When all actions are logged automatically, a comprehensive audit trail is the result. This aids in ISO compliance and accountability.
For instance, you can identify the critical events and activities that should be tracked in an audit trail. This can include data access requests, modifications, system changes, user authentication, and more. You can set up an automation that automatically tracks these changes at set intervals.
Is shared and collaborative
Shared and collaborative practices are pivotal in modern data governance. Gartner recognizes these as key pillars of a successful governance program, reinforcing the notion that data governance thrives on inclusivity.
It’s about people and interactions.
Shared responsibility for data stewardship fosters a culture where every individual within the organization contributes to maintaining the integrity and value of data. This inclusivity ensures consistency, accountability, and transparency in data management.
Collaboration, furthermore, paves the way for more accurate decision-making. By involving diverse perspectives, organizations can understand and interpret data more effectively, capturing nuances that may be missed in a siloed approach.
Moreover, a shared and collaborative approach helps eliminate data silos, a common challenge in today’s data-rich environments.
Navigating the intricate world of modern data governance is a daunting task, but an immensely rewarding one. Effective governance fuels trustworthy data democratization, drives open access to high-quality, governed data, and encourages responsible usage.
Modern data governance: Related reads
- What is Data Governance? Its Importance, Principles & How to Get Started?
- Data Governance in Manufacturing: Steps, Challenges, and Practical Examples
- Data Governance in Retail: Best Practices, Challenges, and Viable Solutions
- Data Governance in Insurance: Why is it Important and How it Drives Positive Business Outcomes
- Key Objectives of Data Governance: How Should You Think About Them?
- Data Governance Framework — Examples, Templates, Standards, Best Practices & How to Create One?
- Data Governance and Compliance: Act of Checks & Balances
- How to implement data governance? Steps, Prerequisites, Essential Factors & Business Case
- How to Improve Data Governance? Steps, Tips & Template
- 7 Steps to Simplify Data Governance for Your Entire Organization
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