Assessing Data Governance Maturity is a Best Practice
Imagine trying to build a master data management or a data analytics capability without knowing who the decision-maker is for the data in scope. You are going to face challenges and delays if you don’t know who in the business and technology teams appropriately owns data definitions, requirements, and risks. What do you do if you have a data quality issue or have a question about the purpose and use of a data element?
That’s why Data Governance (DG) is key, and a maturity assessment will help you build a roadmap to get your organisation on the right data governance framework and operating model in place.
Why Bother with Assessing Your Organisations Data Governance Maturity?
Data governance is a foundational capability because it ensures that an organisation’s has consistent definitions, assigned accountabilities and responsibilities ensure data is fit for purpose, enabling informed decision-making, regulatory compliance, operational efficiency and the strategic use of data assets to drive innovation and business value.
Data maturity/capability assessmsnts are typically conducted when working on data strategies and related roadmaps. It will have some valuable:
Provides a shared understanding of your current state and governance challenges across business and IT teams.
Identifies where to prioritise efforts, time, and resources to address gaps and where the most significant business impact and value lie.
How to Complete the Assessment
Step 1: Establish Goals - Define why the assessment is being undertaken. Whether it’s to enhance high-quality data to harness the power of AI, improve data quality, ensure compliance, or support business and digital transformation.
Step 2: Select an appropriate Framework - Many frameworks are available e.g., DAMA-DMBOK, CMMI. Ensure alignment with your organisational goals and objectives.
Step 3: Gather Data - Interview key stakeholders across departments. Review current processes, policies, and tools (I talk briefly about tools later). Audit existing data governance documentation.
Step 4: Evaluate - Use the chosen framework’s criteria to assess maturity levels across domains (e.g., roles, processes, tools). Score the organisation on a scale (e.g., Initial, Managed, Defined, Measured, Optimised).
Step 5: Analyse Results - Identify strengths, gaps, and opportunities. Prioritise areas of focus based on business value and risk.
Useful Artefacts for Building the Target Operating Model
The main outputs from the assessment are:
Maturity Assessment Report: A summary of findings, including maturity levels, gaps, and strengths.
Gap Document: Details discrepancies between current and desired maturity levels.
These will help you shape your roadmap to the desired Data Governance Operating Model.
It’s worth considering how you approach defining your operating model. Build upon existing workflows and governance forums. Leveraging and enhance the structures, roles, and processes already in place is more likely to be successful. By focusing on practical, incremental changes rather than wholesale transformations, this approach helps minimise resistance which typically there will be allot of.
The Role of Tools in Data Governance
I would be remise to not mention tools vital role in implementing and scaling governance initiatives. These include:
Data Glossary: The window for the business to align on shared definitions and standards.
Data Catalogue: A technical view of where the data resides, along with technical definitions and lineage.
Data Quality Tools: Measure, monitor, and enhance the accuracy and reliability of critical data.
Using these tools will also help with tracking and showing demonstrable value to stakeholders by tracking key metrics such as:
Data Quality Improvements: Track metrics such as data accuracy, completeness, and consistency over time.
Resolution Times: Measure the time taken to resolve data issues, demonstrating the efficiency of governance processes.
Compliance Rates: Monitor adherence to regulatory or organisational data policies.
While these tools alone don’t solve governance challenges, they provide the infrastructure to make governance efforts scalable and measurable.
I will share my experience with tooling adoption in another blog.
Summary
Completing a maturity assessment is a helpful tool to better understanding areas of improvement to better manage and understand your data. It helps you shape a data goverance model where:
The business own their data requirements and risk. That is, clear on responsibilities and accountabilities on the data they need and use to deliver business outcomes.
IT is clear on responsibilities to implement and maintain the technical infrastructure, tools, and processes required to ensure data is securely managed, accessible, and aligned with governance policies and organisational objectives.
How We Can Help
At pinnerhouse, we are a practitioner-led consultancy specialising in data and AI. With years of hands-on experience leading and delivering complex business change and digital transformation programmes, we bridge strategy and execution to help organisations unlock the full potential of their data and AI investments.
We help businesses harness data and technology to enhance products and services, streamline operations, unlock insights, and discover opportunities for innovation and growth.
Are you ready to take the next step on your data governance journey?
Let’s explore how we can help. Book a consultation today.
Learn more about our services on the What We Do page.
