Many organisations invest heavily in data infrastructure, analytics platforms, and AI initiatives, yet struggle to realise the expected return on investment. While the technology is powerful, the underlying barrier is often an outdated approach to data governance.
Traditional, command-and-control governance models, designed for a different era of centralised IT, frequently operate as a bottleneck. Characterised by rigid policies and IT-centric ownership, this approach can create friction, slow down innovation, and fail to connect with tangible business objectives. The result is a cycle of mistrust in data, poor quality, and difficulty in securing sustained investment for data programmes.
A modern approach is needed: one that reframes governance as a strategic enabler.
The Catalyst Framework is a model designed to achieve this by focusing on three core principles.
A common challenge for traditional governance is its perceived disconnect from business value, often being viewed as a technical or compliance-focused cost centre. This makes it difficult to justify the necessary resources for success.
Value-Driven Governance directly addresses this by requiring every governance activity to be linked to a measurable improvement in a business result.
This methodology aligns governance initiatives with outcomes that leadership prioritises, such as revenue growth, cost savings, and risk reduction. Framing governance in the language of business impact builds a powerful case for investment and positions it as an essential component of strategic success.
A framework is only effective if people use it. Top-down mandates and strict enforcement can be ineffective for changing long-term behaviour and may create resistance within the organisation.
Guided Adoption uses principles from behavioural science to make correct data practices the easiest path to follow. By designing an environment of tools and workflows that gently guide employees towards best practices, the governed path becomes the path of least resistance.
In the Catalyst model, the policy is embedded into the workflow: When a user ingests a new data source, the system can default to requiring a business glossary definition and a designated data owner before the process can be completed. This makes it easier to enter high-quality, well-documented data than to skip these steps.
Friction Reduction: Instead of requiring a user to navigate to a separate platform to document a dataset, a simple, one-click button can be embedded directly within their business intelligence dashboard to "Tag Data Owner."
This systematic approach helps embed desired data habits, such as documenting sources and ensuring quality, into the daily workflows of the organisation.
A data-driven culture depends on individuals feeling a sense of personal accountability for data. This is best achieved by integrating ownership into the organisation's operating model.
Federated Accountability decentralises data ownership by adopting a federated model, a core principle of the Data Mesh paradigm. This approach moves away from the notion that IT is the sole owner of data. Instead, responsibility is assigned to the business domains that create, understand, and are most impacted by the data.
Under this model, domain teams become accountable for delivering high-quality, trusted "data products" to the rest of the organisation. A central governance council sets global standards, but local domain teams are empowered to implement them in a way that fits their specific needs. This hybrid structure provides a balance between decentralised autonomy and centralised order, enabling both agility and scale.
This division of labour is crucial and connects directly to the principle of Value-Driven Governance. The central function focuses on strategic alignment, while the domain focuses on execution and value delivery.
The Central Governance Council is responsible for setting the high-level, enterprise-wide "rules of the road". They define global policies for data security, metadata standards, and regulatory compliance (like GDPR). In the value-driven model, their most important job is to evaluate and approve the business case for major data initiatives. They ensure that any significant investment in data is directly tied to a strategic business outcome, like increasing market share or reducing operational risk.
The Domain Team (led by a Data Product Owner and supported by Data Stewards) is responsible for the day-to-day implementation and value creation. They take the global policies from the council and adapt them to their specific context. They are accountable for the quality, usability, and ultimate business impact of their data products.
Here is what this means in practice:
In the old model, a central governance council would be asked to approve a new sales report. They would get stuck in the technical details, creating a bottleneck. They might also dictate a rigid, universal rule for "customer address format" that is too strict for the marketing team but not strict enough for the logistics team.
In the Catalyst model, the Council would still be involved, but at a strategic level. They would approve the business case for a new "Customer 360 Data Product," because the Sales domain has demonstrated it will help achieve a 10% increase in customer retention. The Council also sets the global policy that all personally identifiable information (PII) must be classified and protected.
The Sales Domain, now empowered, takes over. Their Data Steward defines the specific data quality rules for the customer address fields within their new data product, ensuring it is fit for their purpose. They also implement the council's global PII policy by setting the precise access controls for their sales team, ensuring only the right people can see sensitive customer data. The domain is fully accountable for delivering a data product that achieves the promised 10% retention uplift.
Continuing with a restrictive, control-oriented data governance function can be a competitive liability when data is a primary driver of innovation and efficiency.
The Catalyst Framework offers a structured path forward. By being Value-Driven, it aligns with executive priorities. Through Guided Adoption, it helps build a data-centric culture. With Federated Accountability, it establishes clear ownership throughout the organisation.
Implementing such a framework is a strategic initiative that requires commitment. The goal is to create an environment where every critical decision is informed by data that is trusted, understood, and aligned with the organisation's most important goals.
Effective data governance is critical for leveraging data as a strategic asset. If your organisation is looking to improve its governance practices and drive better business outcomes, we can help.
Contact us today for a chat about how we can help you improve your governance and success with data.