Data Governance —
Ensuring Data Excellence

Data foundation starts with
people & processes not technology

In the evolving digital landscape, organizations often encounter difficulties in effectively managing and leveraging their data, which is crucial for scaling Artificial Intelligence (AI) use cases. The challenges typically stem from undefined data ownership, inconsistent data quality, and lack of standardized data processes and policies. Moreover, the absence of a data-centric culture and literacy can hinder the realization of data's full potential. Additionally, ensuring compliance with the ever-tightening data privacy regulations can pose significant risks. To navigate these complexities and fully harness the power of AI, organizations require a structured and strategic approach.

At HYGHT, we draw from our team's hands-on experiences, including former Chief Data Officers and Heads of Data, to address data management complexities. Rather than implementing a rigid concept, we adapt data governance to current initiatives and regulatory requirements, tailoring it to each organization's unique structure, business processes, and decision-making dynamics. This bespoke approach, aligned with clear value propositions, enables effective scaling of AI use cases, empowering organizations to maximize their data potential.

"Data is useful. High-quality, well-understood, auditable data is priceless."

Ted Friedman
Gartner

Data governance is about management of data,
tightly interwoven with business processes

Defining the data governance journey

Define Data Governance Strategy

Establish a clear data governance strategy with defined sponsorship, considering whether a top-down or bottom-up approach is most appropriate
Decide on the overall data governance model, whether it be centralised, decentralised, or federated, to best suit the organization’s needs
Ensure the strategy aligns with the organization’s broader business goals and regulatory compliance requirements

Identify strategic priorities

Identify key strategic priorities for data governance, including areas such as data quality, data privacy, and regulatory compliance
Priorities should align with the defined data governance strategy and the organization’s broader business objectives.
Prioritization ensures resources and efforts are focused on the most critical areas first

Analyze as-is business processes

Conduct a thorough analysis of the current business processes, including decision-making dynamics across different lines of business
Understand how data is currently managed and used within these processes
This analysis provides a baseline for planning the data governance transformation and identifying process improvements

Deploying, evolving, and enhancing data governance operations

Tailor data governance framework

Based on the strategy and analysis, tailor a data governance framework that includes clearly defined roles and responsibilities and an effective interaction model
The framework should align with the organization’s unique structure, business processes, and decision-making dynamics
This tailored approach ensures that the data governance framework is both theoretically sound and practically applicable

Drive change management activities and operationalize

Tailor training around individual value propositions to ensure it is meaningful and relevant, and drive adoption of the new data governance practices
Define clear standards and policies, and establish councils and working groups to oversee the implementation and ongoing management of the data governance framework
Define and measure metrics to track the success of the data governance program and drive continuous improvement

Understanding organizational uniqueness
underpins successful data governance

What many companies do

Implement generic data governance frameworks without considering the unique dynamics of their organization
Focus on theoretical concepts of data governance rather than practical, business process-linked approaches
Overlook the importance of change management activities in driving adoption of the data governance framework
Fail to clearly define roles and responsibilities within the data governance framework, leading to confusion and lack of ownership
Neglect continuous improvement and regular review of the data governance framework, leading to outdated practices and inefficiencies

What we do differently

Define a clear data governance strategy that takes into account the unique dynamics of each organization, including decision-making processes and business structure
Focus on practical, business process-linked approaches to managing data effectively across the lifecycle
Drive change management activities to ensure meaningful adoption of the data governance framework, tailoring training around individual value propositions
Clearly define roles and responsibilities within the data governance framework, fostering accountability and ownership
Emphasize continuous improvement, regularly reviewing and adjusting the data governance framework based on performance metrics and evolving business needs