Put pragmatic governance in place to capitalise on data assets

16 Oct 2018

Now more than ever before, organisations need timely, accurate data to support everyday operations and business growth. An inability to capitalise on data assets could have far-reaching effects, such as unnecessary costs, operational delays, or exposure to risks.

But not all organisations have the capacity to tackle large data governance initiatives; facing constraints in terms of organisational size, team structures, information system architecture, timelines, or directives.

Michael Willemse is head of technical implementation at Infovest

In these cases, a pragmatic approach to data governance is needed. Organisations need to consider which processes and tools are crucial, how to embark on a culture of continuous improvement, and what needs to be done to build a platform for future governance.

Start with strategy

The first step is to constitute a data governance team with a clearly defined strategy to provide the stimulus and direction for the change required.

This strategy should define the vision, the skills and resources needed to achieve the goals outlined in the vision, the reasons why the change must take place, and an action plan.

Let risk and issues drive priority through the improvement cycle

With the strategy in place, the organisation should draft the data governance policies with high-level statements of intent relating to the functioning and management of data. A useful approach is to first focus on the areas of difficulty or risk that the organisation is experiencing, and then to prioritise and evaluate the courses of action that will address these.

The workflows, data steward behaviours, and flow of data throughout an enterprise will have the most significant impact on data quality. It is vital to understand and document the flow of data, and any processes that act on data. Detailed system architecture diagrams enrich the understanding of workflows.

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With an understanding of the workflows, business domains and processes that act on data, the Data Governance Lead is able to identify the key stewardship roles, including the operational data stewards and data owners.

Only now do we recommend starting to work with the data itself. It is important to understand what the primary sources of Master Data are, and how that data is impacted. Governance controls are essential to ensure the quality of data when enrichment has taken place – through external sources, derived data, or manually modified data.

The Governance Lead or governance working group can now begin to analyse the system and processes to address threats or weaknesses; capitalise on opportunities; or leverage strengths within the existing processes.

Implement governance controls

The following processes and controls should be considered to improve the quality of data and processes:

Data Management Interfaces – Facilities for the management of data need to be understood and reviewed.

Approvals – Clear accountability is vital to the approval process. Visual representations of data through reports or dashboards enable the accountable user to quickly confirm the data quality.

Issue Escalation and Resolution – If a data steward becomes aware of data or process risk, they must be able to reliably log the error for investigation and resolution, or for escalation.

Logging – Not having a record of how or who made the modification to data undermines any efforts to improve the data and related processes.

Decision logic and controls – Workflows by their nature may have conditionality, divergence and convergence, and decision points. The implementation of governance controls will result in additional control points in order to evaluate outcomes and state, or to facilitate manual intervention.

Automated Reporting – Automated reporting can be an exceptionally effective tool to monitor system states and inform the governance team about risk events.

Data Matrices and Data profiles – Data Matrices are invaluable references when analysing data for subject areas, business domains, imports and exports. Data profiles add to this additional detail relating to the context of the data.

Plan, do, refine, repeat

After each iteration or at defined intervals, key lessons are noted and refinements made. This constant feedback loop will allow for quick wins, and ensure that the data governance programme considers organisational changes and remains aligned with stakeholder expectations.