Data Governance leads us to Data Quality, and not the other way around

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Data governance and data quality have always been separate disciplines. However, it seems that data governance may be a way to achieve data quality.
Data governance and data quality have traditionally been separate disciplines. However, at a recent data governance conference, one of the speakers made a point that really caught the attention of the audience. He talked about data governance as a way to achieve data quality .

An organization may decide to start looking for a data quality tool when its analysis and Business Intelligence projects are affected by poor data quality. Or, due to a regulation or law that would force the company to control the accuracy and integrity of its data much more closely.

Effective Data Governance: The Guide to Minimizing Errors and Achieving Data Governance Goals

However, buying a data quality tool first is like taking medicine before being diagnosed . Companies have thousands and thousands of different data elements. Which ones should we focus on? Which ones should we leave out of scope? Which ones have the biggest impact on the business and should be managed first?

To answer the above questions we have two possible approaches :

Critical data elements – Identify what is critical to the business. This could be a report, a cube, a KPI, or anything else that is key.
Data Value : Estimate the cost of poor data quality or in other gcash database the risk associated with poor quality . Focus first on those areas with the highest risk.
In both cases, once we detect and prioritize the focus areas, data governance creates a collaborative framework for the management and definition of policies, business rules and assets to provide the necessary level of data quality control.

Data owners can define the key processes and systems involved. At the same time, the business can indicate which standards the data must adhere to when moving through the systems. This is where policies, requirements and business rules are created.

Once we know how data flows through the organization and we know what the standards are, it is easy to ask the data quality team to translate these standards into data quality profile rules and make them work on the data in those systems .







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We have now identified exactly where we should focus our data quality efforts.
The next step will be to link metrics coming from one or more data quality tools to critical business assets so that users can see exactly what their status is and where they are lagging.

On a data quality dashboard, a red X will mean that thresholds have not been met for one or more checks.