Define the problem:
Following a high-profile service delivery failure which led to significant reputational damage to an agency, a review to better understand the root cause of the failure was instigated. The review identified that poor data management practices across the agency significantly contributed to the failure. As a result, the agency commenced a broad programme to improve agency wide data management and governance practices.
Identify business drivers:
The primary business driver of this initiative was to increase operational efficiency and reduce the risks associated with lost, duplicated and poor-quality data. To achieve this, it was recognised that mature data governance practices would be crucial in setting standards, assigning roles and responsibilities as well as facilitating the cultural shift required to recognise and value data as an agency asset.
Analyse current state:
The Office of the Information Commissioner(OIC) had recently conducted a self-assessment audit into information management maturity of Queensland government departments, and provided analysis to each department on their individual assessment outcomes. A decision was made to use this as the primary tool to understand the current state of data management and governance practices of the agency. This material was supplemented with reports and statistics generated by the agency’s information management unit over several years, which helped to identify key stakeholders, applications and business issues regarding data and information management practices.
Identify focus areas (i.e. Data management activities):
The primary focus area for this department was to improve data quality across the agency to ensure that all data was fit for its intended purpose/s. Having a known source of truth for use across key systems was also identified as an opportunity for reform. Therefore a decision was made to address reference and master data, in addition to data quality, as part of initial data governance implementation. Some of the activities required to address these two focus areas include:
- Defining what quality (i.e. fit for purpose) means for critical data sets
- Development of data quality policies, guidelines and training
- Implementing metrics to measure improvements in data quality
Reference and master data:
- Validation of data definitions
- Adopting appropriate standards, implementing common data models and integration patterns
- Defining the overall architectural approach
- Publishing reference and master data
Plan your governance response:
Because the data quality issues exist across many business units, a decision was made to take a federated approach to data governance. This resulted in the establishment of one data governance body coordinating activities conducted within individual business units. The benefit of this approach was that implementation of data governance could be staggered across business units according to operational readiness and organisational priority.
The overarching data governance body (drawn from senior executives from each key business unit) was tasked with developing the initial data governance strategy. Ensuring that any outcome sought in this data governance strategy aligned with broader agency objectives, the final strategy outlined several actions that were required to establish data governance practice with the agency. These included defining what policies/standards were required to be adopted, the prioritisation of compliance activities, sequencing of actions to support the achievement of the desired outcomes and ongoing monitoring and oversight of the process.
Due to the size of the organisation and the complexity of the data landscape, a decision was made to incorporate an additional layer of data governance at the divisional level. Each division established a Data Governance Council to co-ordinate and manage local data governance initiatives, in accordance with the strategic direction set by the overarching data governance body. A key responsibility of the Data Governance Councils was the development and approval of business glossaries. Issues which could not be resolved at the local level were to be escalated to the overarching data governance body for consideration and resolution.
In addition, a Data Governance Community of Interest was implemented to promote the importance and understanding of data governance across the organisation and share learnings and opportunities.
Govern your data:
An implementation roadmap illustrating the timeframes for each activity and the relationship between activities was developed to ensure effective co-ordination and staging of activities. The overarching data governance body, while accountable for oversight of the entire program of work, allocated responsibility, authority and control of divisional projects to the Data Governance Councils which provided a true federated data governance operating model.
After a defined period, a reassessment was conducted of the agency’s data management practices against all dimensions of the initial OIC information management maturity model. Any changes to overall data management maturity were identified and reported to senior executives of the agency and used to inform further strategic planning activities for data management and governance within the agency.