In DATAVERSITY’s 2023 Trends in Data Management survey, about 64% of participants stated that their companies had Data Governance (DG), the formalization and enforcement of data operations across the company, in the initial stages. Yet, 60.9% listed data silos as the greatest Data Management challenge.

If DG is supposed to break down data silos for better insights while ensuring compliance with legislation, how can it do so effectively? This question leads organizations to consider a new approach: adaptive Data Governance. In this article, we’ll dive into what adaptive Data Governance means, explore why it’s crucial for modern organizations, and provide practical guidance on implementing it successfully.

Traditional vs. Adaptive Data Governance Frameworks

In traditional DG, organizations take a one-size-fits-all approach to governance. This style promotes a reactive approach, chasing compliance with the latest data regulations and responding to crises with heavy-handedness.

With governance priorities and mandates changing frequently, employees get confused about who makes what decisions and when they happen. This lack of balanced and clear methodology leads to bottlenecks, where a simple routine request to add a new customer to a report takes days or weeks for approval. Centralized roles, processes, and technologies contribute to the delay.

Workers try to find more efficient processes as teams get frustrated with Data Governance’s bureaucratic bottlenecks and shifting demands. While productivity improves, data silos grow, with lapses in following governance guidance. Then this traditional program becomes one of the 80% that fail, missing out on business innovations while paying hefty fines.

In contrast, adaptive Data Governance offers a more flexible and innovative approach, based on a solid foundation with a clear methodology. It empowers businesspeople to take an active role, applying governance guidance and escalating issues appropriately, according to the situation. 

With an adaptive approach, an organization handles routine data administrating activity promptly, with minimal data silos, such as adding a new customer to a report. The relevant information about the new customer for that report goes to the right person or team at the right time because of trust, good communication, and processes.

Any technical solution, like a data catalog, supports these conversations and activities. Several key components, discussed in the next section, provide the basis for this successful adaptive approach.

Key Components for an Adaptive DG Framework

Adaptive DG provides businesses options to respond quickly because its framework, a collection of processes, rules, and responsibilities, has a solid foundation. This structure is created from key components. These span two dimensions: support for a data culture and alignment.

Elements supporting a data culture match the governance intent and requirements to business operations and are consistently applied. These components break down data silos and encourage data sharing because DG words agree with action. They include:

• Executive Sponsorship: Executive support and sponsorship are crucial in establishing and grooming Data Governance and championing a flexible approach. Ensuring a good allocation of resources also is key.
• Effective Collaboration: Different stakeholders and business units communicate well and regularly. The culture provides a forum to bring up, listen to, and resolve emerging issues.
• A Data Focus: Businesspeople want to provide business value with the data and are data-literate. So, teams embody trustworthiness, transparency, organization, understanding, and traceability. These aspects take precedence over figuring out how to avoid a fine for non-compliance.
• Continuous Improvement: When necessary, the organization reviews existing DG roles, processes, and technologies. The firm uses objective evidence and testimonies to learn from its successes and failures. Relevant metrics exist to show how much the DG has improved.

Factors that impact alignment are directly stated, typically documented, and understood. Their contents contain relevant information to balance security with flexible decision-making. Data silos are minimized because these resources provide predictable guidance and have agreement across teams as to their applications.

• A Data Strategy: The company has provided clear guidance and agreement regarding how data leads business goals and critical data that takes priority. This direction applies to Data Management aspects across the organization, including Data Governance.
• A Standardized Data Architecture: The organization has described the engineering needed to connect the business strategy and Data Strategy through its technical implementations. Technical resources are in place to respond speedily to Governance needs.
• A Data Governance Policy: This document establishes rules that help safeguard data, defines Data Governance roles and responsibilities, and sets standards for Data Quality and security.
• Risk awareness: Appropriate controls and safeguards mitigate compliance risks. Documented standards guide high-risk and questionable DG requests through appropriate channels for timely handling. 

Together, data culture and standards form the foundation for an adaptive Data Governance framework. With this solid basis, more decision-making can happen at a local level.

Why Consider Adaptive Data Governance?

Building a good foundation for adaptive DG requires maturation, which takes time. However, this approach rewards organizations with more opportunities, fewer risks, and streamlined operations. Find specific examples below.

Reduced IT Operations and Maintenance Costs: As companies continue to use complex architectures, reallocate human resources to different governance roles, and handle a steady increase in regulations, these organizations need to assign and reassign security and access data platforms on the fly.

Through adaptive Data Governance, organizations have clear guidance on data roles and know what to do when questions arise. This leaves teams more freedom to make data sets directly accessible instead of automatically requiring an intermediary like IT to approve or complete the request.

Faster Benefits from New Opportunities: Data Quality (DQ), the confidence in data’s usefulness to meet business needs, empowers organizations to capitalize on opportunities sooner. By practicing adaptive DG, companies develop standards around defining, producing, and using their data, leading to better DQ. 

With more trust in the data, teams generate insights more readily and successfully use data in AI projects. Moreover, teams share data across the organization, reducing silos.

Better Protection from Data Incidents: Adaptive DG provides better protection from data incidents. Businesspeople understand the risks and best practices to prevent compliance issues. 

Plus, organizations comprehend their data and its lineage, where it came from, and where it is going. Having that knowledge on hand gives the company a better chance to notice anomalies and quickly provide information to auditors to demonstrate compliance.

Scalability: DG resources ebb and flow with staff turnover and business expansion and contraction. Likewise, data volumes and complexity grow.

Adaptive Data Governance simplifies this evolution. It applies existing data policy to new data sources, users, and technologies and provides avenues for possible modifications to how data is managed.

Implementation of a Flexible DG Framework 

Adaptive Data Governance has a framework that balances responses to changing business conditions and meets the requirements for privacy and control. Keys to this structure lie in the data culture and alignment, as described in the “Key Components for an Adaptive DG Framework.”

To start, define agile Governance principles that work best with the business culture. Getting this right can prove challenging, because businesspeople may fear losing control of data accessibility, having a diminished data role, or because they find data decision-making challenging. It helps to start with a data maturity model, to understand how well staff values data, find the gaps, and determine the next steps.

From there, establish accountability rights through clear roles and responsibilities. The decision-making processes and resources need to be well-defined, especially what to do around time-sensitive and critical issues, in what general contexts, and how and when to escalate them.

It helps to include a multiple combination of governance styles that can be applied as needed to the governance situation at hand and can respond to change. DATAVERSITY’s DG definition describes the different governance types.

Conclusion

Adaptive Data Governance fosters data sharing and distributes Data Governance decision-making, which reduces bottlenecks. It succeeds from a solid foundation, made up of key components supporting the data culture and standardization of data operations.

Employees act in real time, reducing the time and money needed to handle governance requests. At the same time, this paradigm reduces the risks of bad decisions by guiding businesspeople on how to handle complex tickets that require additional investigation or discussion.

A flexible DG takes time to build and requires iterations. Letting businesspeople loose without the governance structure and support for agility, fails to meet the definition of adaptive Data Governance.

Instead, the framework promotes balance to handle quick changes across situations while having data control and confidence to meet privacy needs. While no DG program is perfect, adaptive DG promises better responses that spur smoother operations, trust, and growth. How will this kind of Governance evolve to meet the demands of the future?

Will we see the emergence of new technologies and tools that enable even greater agility and flexibility? Will the roles and responsibilities of Data Governance professionals shift to keep pace with the changing times? Only time will tell.