The Big Data Governance Rebrand: Common Misconceptions Set Straight

By Judith Kirkwood-Law, Practice Lead

Data governance has undergone a shift, becoming a higher priority for most asset managers. With client expectations for AI and data-driven insights growing and regulatory requirements intensifying, many firms are recognising the importance of understanding and governing their data. The traditional approach and perception of data governance is no longer sufficient. This “re-brand” aims to dispel widespread misunderstandings and re-establish data governance strategy and implementation as a strategic tool for optimising data for business success. In this article we’ll address the most common misconceptions we encounter and the truth behind them.

Misconception: Data Governance Lives Only in the Data Function

Truth: While data governance might live in the data function, for it to be effective, everyone needs to become responsible and take ownership. Silos can easily form when, for example, data architecture sits with data teams, business architecture is in business units and enterprise architecture in IT.

When data governance isn’t active and embedded across departments, key problems arise. These include low business context, misaligned priorities, inadequate risk management, poor policy adoption, enforcement challenges, resource constraints, incomplete data understanding, and limited accountability.

Avoiding these issues begins with defining ownership and shifting ownership attitudes: agree on owners, define responsibilities and crucially, provide support to those owners. The second part to consider is the model that will work most effectively for your organisation. A federated or de-centralised approach can support more accountability, whereas central control over centrally used critical data elements can ensure coordination. For successful implementation, start with high-impact use cases, and create clear role definitions.

Misconception: Data Owners Must Know Everything About Data

Truth: In many organisations, there’s often a reluctance to stepping into the role of Data Owner. There’s two parts to this. Firstly, support needs to be in place for owners so they can succeed without being overwhelmed by technical complexity. Secondly, owners do not need to know absolutely everything about the data – their role is to make business decisions about data usage and quality standards, not to understand every technical detail of how it’s stored, processed, or implemented.

What data owners actually do is provide business context for technical implementations, ensure compliance with regulatory requirements, and resolve business-related data conflicts. The technical implementation details can and should be handled by technical specialists. Where we have seen the most success in this area is where collaboration is strong between data owners and technical teams – it’s all about the relationships.

Misconception: Data is a One-Off Project

Truth: As much as some stakeholders may like data to be a single task where once it’s done, it’s done – data is never done. We set the expectation with organisations that it’s about the long game and a continual commitment to achieve good data governance and a fit for purpose data platform. No data programme is ever truly complete. The goal is delivering value iteratively but continually optimising underneath to meet the evolving needs of the business.

This project-based thinking fundamentally misunderstands the nature of data governance and management, even more so with businesses constantly developing and regulatory environments changing. Market conditions change, regulations evolve, and business requirements shift – all requiring ongoing governance adaptation. Success comes from building data capabilities that can adapt to changing requirements while maintaining momentum through regular wins and visible improvements.

Misconception: Outsourcing Data Doesn’t mean Outsourcing Governance

Truth: Operational and technology data functions can be a valuable part of an outsourcing decision, however responsibility for oversight and governance of data should remain with the firm. In our experience, the most effective outsourcing decisions come from collaborative evaluation involving procurement specialists who understand cost and contract implications, business leaders who can assess strategic fit and operational impact, data professionals who can evaluate governance implications, and risk managers who can assess compliance requirements. Data governance can provide crucial input while recognising that other factors – cost, expertise, strategic alignment, and operational efficiency – are equally important.

Once an outsourced arrangement is agreed, data governance should be front and centre defining the data roles and responsibilities for the insourced vs outsourced capabilities and ensuring that outsourced vendors or service providers are adhering to agreed policies.

Misconception: Small Governance Gaps Don’t Matter

Truth: Good data governance serves as the foundation for trustworthy decision making. Without it, organisations face multiple, critical vulnerabilities including data quality and reliability issues, regulatory compliance and risk management challenges, cost control and efficiency problems, business value limitations, and organisational alignment failures.

These risks are particularly acute in the financial industry, where data quality can directly impact investment performance, regulatory compliance, and client trust. Poor data governance leads to inconsistent, unreliable information that undermines investment decisions and client reporting. It creates compliance vulnerabilities and increases operational risk across the organisation. Without proper governance, firms often experience data duplication, inefficient processes, and increased operational costs. We’ve observed many cases where governance is put in place “after the fact” as a remedial action rather than proactively. This often undermines trust not only in that specific data but in whole systems and processes, as users tend to conflate data issues with the platforms where they are observed.

Perhaps most importantly, poor governance limits the organisation’s ability to leverage data for competitive advantage and client value creation, while creating confusion about data responsibilities and decision-making authority.

Putting Good Governance into Practice

For firms looking to improve their data governance, success requires moving beyond these common misconceptions toward a more business-focused, holistic approach. Start by assessing your current governance maturity honestly and identifying the misconceptions that may be limiting your effectiveness. Then develop a roadmap that addresses both immediate needs and long-term strategic objectives.

Remember that effective data governance doesn’t exist to produce perfect data or complete control – it creates the right framework for making good decisions about data in the context of your specific business requirements and constraints.

At Liqueo, we work with asset managers to design and implement data governance frameworks that stick, embedded, scalable, and tailored to how your organisation really operates. Whether you need to turn around a failing approach or build a new one from scratch, we bring deep industry experience, proven methodologies, and a practical mindset that gets results. Let’s make your data governance work harder for your business – get in touch.