Data governance is a linchpin for data programs; it is the practice of managing and organizing data and processes to enable collaboration and compliant access to data. But what is this term, “adaptive data and analytics governance,” that has gained more traction in recent years? In their report Adaptive Data and Analytics Governance to Achieve Digital Business Success, Gartner analysts Saul Judah and Remi Guzar talk about adaptive data and analytics governance, an approach that helps enterprises remain relevant and respond to rapidly-changing environments within and outside their organizations. Adaptive data and analytics governance recognizes that data governance is about more than just constraint and compliance; adaptive data and analytics governance is about using data governance to drive business value.
What makes adaptive data and analytics governance different?
Adaptive data and analytics governance represents a transformation in the ways organizations use and benefit from data governance. Data professionals who embrace adaptive data and analytics governance understand that data governance impacts the entire business, not just data programs. In order to bring business value, data governance must be flexible to meet the changing needs of the organization and all its team members.
Traditional data governance centers on compliance and regulation. Many organizations often don’t even consider establishing a formal data governance practice until they realize their vulnerabilities to regulations such as GDPR, CCPA, and BCBS 239. But a narrow focus on compliance limits the impact that data governance can make.
Traditional data governance supports a single-style, command-and-control approach to governance. This creates rigid organizational practices that leave companies unable to adapt to changing circumstances, be those market disruptions or new regulations.
Unlike traditional data governance, adaptive data and analytics data governance is flexible and dynamic. Teams that embrace adaptive data and analytics governance understand that business situations change frequently and governance approaches must accommodate those changes. Adaptive data governance allows organizations to balance constraint and liberty while navigating through change. Gartner’s Judah and Remzah illustrate the juxtaposition of traditional and adaptive data and analytics governance in Figure 1 below.
Therefore, adaptive data governance expands the use and applicability of traditional data governance, promoting flexible decision making and business outcomes.
Therefore, adaptive data and analytics governance expands the use and applicability of traditional data governance, promoting flexible decision making and business outcomes.
What are the benefits of adaptive data and analytics governance?
Traditional data governance focuses on protecting the business, but organizations need to leverage data for more than just defensive needs. Adaptive data and analytics governance is beneficial because organizations can tackle offensive use cases while ensuring compliance and minimizing risk. There are three major categories of use cases that adaptive data and analytics governance supports:
- Grow the business (revenue focused)
- Run the business (cost focused)
- Protect the business (risk focused)
Common use cases:
|Grow the business
|Run the business
|Protect the business
With a flexible and action-oriented approach to data governance, data governance is no longer a dirty word to business users; organizations can be nimble even under the face of constraints.
Building a framework for adaptive data and analytics governance
Adaptive data governance empowers efficient decision making. In order to implement adaptive data and analytics governance, organizations need to understand how their data citizens use data. To develop that understanding it can be helpful to consider questions such as:
Who is involved in making business decisions? Are decisions made unilaterally?
Who are the stakeholders that influence the decisions and how are they involved?
What are the key decisions to be made?
What data and analytics assets inform these decisions?
What actions lead to a decision? What key indicators measure performance?
Where can stakeholders get access to data assets?
Where can they collaborate on data assets?
Why are we making these decisions at all? Are they related to specific strategic goals? Can we measure the impact?
Required capabilities for adaptive data and analytics governance
Adaptive data governance helps organizations embrace agility and accelerate digital transformation. Data governance teams looking to enable data stewards and other uses to create value from data should prioritize specific technology capabilities such as:
- Business glossaries to standardize definitions of business terms, rules and regulations
- Reference data management to reconcile data between systems for more accurate analysis and reporting
- Data helpdesk to raise, manage and resolve issues; involve the right stakeholders; and improve trust in data quality
- Data dictionary to document organizational metadata and its use, origin, format and relationship to other data
- Active metadata graph to continuously refresh and improve context around information stored in the data ecosystem
- Privacy by design to manage policies and compliance and incorporate privacy into all data activities
- Embedded enterprise data catalog to enable business users to discover and examine trusted data across the enterprise in minutes
- Intuitive workflow management to streamline processes and facilitate collaboration
- Wide-ranging connectivity to connect to common data sources and systems
Collibra Data Governance
Across the globe, hundreds of organizations use Collibra Data Governance to embrace adaptive data and analytics governance and achieve Data Intelligence. Collibra Data Governance allows organizations to create a shared language around their data to facilitate agile decision making and help them keep pace with change. With Collibra’s secure and scalable platform and automated governance and stewardship tasks, businesses can continue to trust their data while they grow.