Master Data Management

Quality and compliance assurance in master data maintenance

Data has become the heart and soul of businesses operating in today’s economy. Experts estimate that consumers around the globe create 328.77 million terabytes of data each day, giving businesses the opportunity to leverage that data to understand their customers and optimise their performance efficiency. However, not every bit of this data is useful for everyone, and businesses have to focus on working with quality data.

Leveraging data quality tools can help your business develop a robust data quality framework. Let’s explore some of the tools, techniques, and strategies you can use to ensure your business relies on high-quality data.

What is data quality, and why is it important?

Before moving on to data quality management, it is important to understand what data quality actually is. There are many ways to define data quality, but at the crux of it, you have quality data if it is suitable and complete to address your use case. Some key data quality metrics and standards universal to most use cases include:

  • Accuracy
  • Completeness
  • Consistency
  • Validity
  • Timeliness
  • Uniqueness

Once you have clear data quality metrics and standards in mind, they can guide your data governance and data quality frameworks, serving as benchmarks for any new data you collect. Data quality tools and techniques like data cleansing, data validation, data format, data relationships, and data governance, coupled with regular data audits, can help you access high-quality data that will offer benefits such as:

  • Informed data-driven decision-making.
  • More effective strategic position.
  • Optimised operational costs.
  • Enhanced operational efficiency and productivity.
  • Improved customer experience and satisfaction.

With these, you can build a sustainable competitive edge, leveraging data to be agile and adapt to the evolving market trends and customer expectations.

Ensuring data quality and compliance in master data maintenance

In addition to maintaining data quality for actionable insights, an effective data quality framework is also necessary for regulatory compliance, ensuring ethical data use, data security and privacy. Here are some strategic steps you can take to ensure data quality and compliance in master data maintenance:

Establish a robust data governance framework

A robust data governance framework is the foundation of effective data quality management as it guides how an organisation collects, stores, manages, and uses its data. Some of the key elements of a data governance framework include:

  • Communication structure
  • Data requirements
  • Data quality standards
  • Data access protocols
  • Metadata guidelines
  • Security policies

This framework depends on the master data management architecture you choose and can progress with evolving regulations, technological advancements, and changing organisational values.

Define data governance roles and implementation steps

Although several employees will share the responsibility for data governance and data quality, three key data governance roles include data owners, data stewards, and a steering committee.
Once you define these roles and responsibilities, the next step is defining implementation steps. Some of the common activities when implementing a data governance framework include:

  • Reviewing the current data governance policies and identifying the most important data assets
  • Establishing new data governance policies for the data lifecycle
  • Promoting a culture of collaboration and transparency surrounding data use
  • Avoiding data silos
  • Creating clear audit trails

Establish quality assurance metrics and benchmarks

Not every data quality metric is worth monitoring for every use case. Determine what data quality standards are necessary for the use case at hand and only focus on those quality assurance metrics. Establish benchmarks that can help you profile the data, identify potential quality issues, and improve quality control measures.

Ensure compliance with regulatory requirements

Regulatory compliance has to be an underlying consideration at every stage of data governance framework development and implementation. Understand the data security and privacy regulations relevant to your business, such as GDPR, HIPAA, EU AI Act, CCPA, and many more, making sure you are compliant with applicable legislations across the data lifecycle. Some of the standard regulatory requirements may include:

  • Anonymising data before analysis
  • Implementing security measures to protect personal and sensitive information.
  • Being transparent about data collection and use

Constant and clear communication with your team, keeping the data quality framework straightforward, and using a peer review system to promote data quality are some best practices for ensuring the successful implementation of the data governance framework.

How can Infosys BPM help?

Data quality management has to be the top priority for businesses today to ensure data-driven decision-making and streamlined operations. Infosys BPM offers a robust suite of master data maintenance services to help you ensure regulatory compliance as you improve operational efficiency with digital interventions. With solutions that support data resolution and quality assurance, benchmarking with best practices, operational and data analytics dashboards, and process standardisation, you can stay on top of data governance and data quality and be competitive in the data economy.

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