The quality of organisations data and associated information and knowledge is now being considered as important as the quality and accuracy of their financial data.

We are fast moving to a scenario where the Profit and Loss and the Balance Sheet view of an organisation will sit alongside the assessment of the value and quality of data assets and the activity that is occurring to create and use them.

It is clear the well-worn audit processes for financial information will be applied to the management and quality of data. Organisations will be reporting the status of their data quality and value with the same focus and competitive positioning to their stakeholders. Audit processes for organisations will increasingly require a sign off on Data Governance and data quality and value by an independent process measuring against acceptable levels that will evolve around compliance and value assessment polices.

Financial and data outcomes are of course connected. Organisations with a clear and invested focus on Data Governance and the ongoing improvement of data quality will see this reflected in a range of business areas:

• Accurate and personalised customer data will increase customer engagement satisfaction and drive retention, acquisition and share of wallet.
• Operational efficiency – spending less time on data and information assembly and more time on analysis and insight. Strong Data Governance will also identify opportunities for process efficiency where access to the right data and information when required will remove unnecessary activities.
• Compliance management – managing exceptions to a well understood set of data governance requirements rather than managing each situation and regulation on a case by case-by-case basis in a reactive manner.

Strategically, Data Governance needs to make its way to the board table. The impact on brand value associated with good or poor data governance is growing in comparison and in value.

Organisations that present their Data Governance position and direction including the value of good data quality will be rewarded with investor or stakeholder confidence about how they are managing risk and growth within their organisation.

What are the key components of a strategic and leading data governance approach?

With an understanding of the strategic importance of Data Governance and data quality there is increased focus within organisations to build capability in this area. The key components of this capability are as follows:

• A clear view of the outcomes required and how to measure their achievement – the data Governance scorecard and supporting governance model.
• Operating and organisational model capability and accountability – ensuring that there are processes and roles in place with the right accountabilities to achieve the outcomes the organisation is seeking.
• Involvement of experienced domain experts in the Data Governance and data quality areas – key business and technical capability.
• Identification of critical data elements which include consideration of Industry reference models and external compliance requirements – identifying the data that is critical generally and by industry vertical.
• An integrated platform to manage the quality of critical and other data elements within the organisation – identification, management and remediation where required.
• Establishing a highly automated approach to reporting and outlier management – dealing with exceptions rather than individual case investigations and actions.

How do you create a top quadrant Data Governance environment?

There are several key steps to develop the appropriate level of Data Governance within an organisation.

1. Establish the current state of the organisations Data Governance maturity – Organisations will be at different Data Governance maturity levels. Some will not know what level they are at and it will be important to benchmark their current position against the appropriate best practice for their organisation. The maturity assessment will identify the status of the key components (outlined prior) against this benchmark.
2. Establish the target state Data Governance scorecard and expected outcomes of the next 2-3 horizons (most likely over a 3-5 year period) across the core components. The priority areas for action will be identified in the first horizon. Future targets and outcomes will align with maturity model level aspirations and reflect the strategic intent and expected value of a leading Data Governance position.
3. Establish a transition plan which includes ‘working on’ and ‘working in’ the Data Governance model and operations over the planning horizons. The transition plan will identify the key activities by key components and outline the capability, capacity, timeframes, investment requirements, dependencies and risks to be managed.

If you would like to find out more about how to architect your Data Governance and realise outcomes for your business, then contact us at info@charterx.com.au