Data complexity and the business demand for data is growing in volume, variety and complexity.
“Businesses want quicker, more data-driven outcomes, but achieving that involves challenges in data management and transformation. While IT provides the systems, it is the business’ responsibility to ensure data is fit for purpose,” says Louis De Gouveia, data competency manager at iOCO data cluster.
He says important elements contributing to data quality include:
- Completeness: ensuring key fields (such as customer name, email) are populated.
- Timeliness: data must be available when needed (for example, for billing).
- Accuracy: correct details such as tax numbers must be in place.
- Accessibility: data should be accessible to the right people at the right time.
“The Impact of poor data quality is immense. One real-world example of how Amazon’s Prime Day pricing error affected its reputation and bottom line,” says De Gouveia. “ Amazon’s pricing error led to a choice between damaging its reputation or absorbing a financial loss. This example demonstrates how poor data quality can severely impact an organisation.”
He confirms, while data quality is everyone’s responsibility, accountability must be clear.
“This can be achieved either through individuals or processes. Sadly, the term everyone is responsible often translates into – no one is responsible. Clear ownership of data quality processes is essential,” he stresses.
De Gouveia says if organisations are to deliver continuous improvement, they must create automated scorecards that track and measure data quality over time. “This visibility is essential for accountability and progress,” he says.
De Gouveia lists the following steps if data quality is to be improved:
- Set up a framework for data improvement – a structured approach to data governance is essential and should incorporate policies, people, and technology.
- Visibility of data quality – creating scorecards and automating data quality metrics is crucial for tracking improvements.
“Implementing a data improvement framework is vital for addressing data quality issues. It involves setting goals, tracking improvements, and continually evolving the process to align with business needs. This is vital for addressing data quality issues. It means setting goals, tracking improvements, and continually evolving the process to align with the business needs.”
De Gouveia says that, if organisations are to build a data strategy that will deliver data driven outcomes they must connect data, ensure it is business-ready; govern it; analyse it, and ultimately extract actionable value from it.
The Importance of Data Governance in Modernisation
De Gouveia cites EOH as a successful example of data modernisation. “EOH’s fragmented data architecture highlighted the need for strong data governance, particularly when moving from multiple ERP systems to a unified system.
“Master data management played a crucial role in standardising processes. Some of the long-term consequences of this successful data modernisation journey for EOH include improved data accuracy, compliance, and operational efficiencies.
“The result is a more agile, data-driven organisation, capable of making informed decisions based on trusted data,” concludes De Gouveia.