Cristian Randieri is Professor at eCampus University. Kwaai EMEA Director, Intellisystem Technologies Founder, C3i official member.
Data quality in the modern economy, where data-driving action is critical to business success, can no longer be perceived as mere tech detail. Business leaders increasingly use data to make strategic choices, optimize efficiency and create better experiences.
Yet, data value equals data quality; bad data quality can result in bad decisions, inefficient operations and loss of competitive edge. Having up-to-date data that is accurate allows businesses the ability to make quality strategic and operational decisions. This is why measuring data quality should be the crux of how businesses succeed with their data.
Why Data Quality Matters
When we speak about data today, it is customary to refer to it as the “new oil,” but just like crude oil, its value becomes concrete only when refined. Raw or unclean data is not optimal in business since misleading data leads to wrong decisions. The outcomes of decisions made with incorrect data can be substantial, such as incorrect inventory levels, wrong marketing campaigns due to poor data quality, various costly errors, and other factors.
Therefore, high-quality data is needed to allow businesses to trust their insights, make accurate predictions and personalize customer interactions. According to Gartner, poor data quality is estimated to cost about $12.9 million annually. A Deloitte survey found that 67% of executive respondents are uncomfortable using data from advanced analytics systems, even in companies with data-driven solid cultures, where 37% of executives still express discomfort. A 2022 SAS survey report found that 42% of data scientists said that “data science results were not used by business decision makers,” for analytical projects.
Finally, a Cognizant and ESI ThoughtLab survey emphasized the growing demand for AI-driven data spurred by the rapid digital transformation during the coronavirus pandemic. It revealed that two-thirds of executives see AI as a crucial element for the future of their businesses.
Defining Data Quality
Data quality comprises several dimensions that serve as the foundation for effective data governance: accuracy, completeness, consistency, timeliness and relevance. However, measuring data quality can be complex and context-dependent; what constitutes “good” data varies between industries and individual businesses.
For example, a financial institution prioritizes accuracy since even minor data discrepancies can have significant economic impacts. For instance, an e-commerce organization might serve time-critical needs such as real-time inventory management and customer satisfaction. Organizations must build data quality frameworks that complement the business operations and objectives to accomplish this.
Critical Metrics For Assessing Data Quality
Organizations must regularly assess their data using specific metrics to manage and improve data quality, including:
Accuracy
Healthy data reflects the real-world values it represents. For example, in a customer database, accurate data means that names, addresses and contact information are correct and up to date. High accuracy ensures that business decisions are based on reliable information, reducing the risk of errors in analysis and execution. More accurate data can lead to better customer segmentation and effective marketing strategies.
Completeness
All necessary data should be present. Complete data allows for comprehensive analyses, leading to better decision-making. Missing data often results in biased insights. For example, if critical demographic information is missing from customer profiles, marketing campaigns may reduce effectiveness, failing to reach the intended audience.
Consistency
Ensure that data remains uniform across different systems and datasets. For instance, a customer’s information should be identical across a company’s CRM and billing systems. Inconsistent data creates confusion, hampers operational efficiency and erodes trust in the insights generated from the data. It can also disrupt processes like inventory forecasting or financial reconciliation.
Timeliness
Measure whether data is up- o date and available when it’s needed. Real-time data are crucial in sectors like logistics or finance. Outdated data can lead to decisions based on obsolete information, causing significant issues such as stockouts or financial losses.
Relevance
Evaluate whether data is suitable for its intended purpose. Even accurate, complete and timely data may only be helpful if it aligns with current business objectives. Irrelevant data can be dangerous because they can add noise to analyses, reducing the effectiveness of AI models and other decision-making tools. Focusing on relevant data ensures businesses concentrate their resources on the information that matters most.
Building A Data Quality Strategy
Maintaining high data quality goes beyond tracking these metrics; it requires a strategic, company-wide approach. This approach should integrate data-driven decision-making into the organizational culture, ensuring that every employee properly comprehends the importance of data quality and their role in maintaining it. To get started:
• Establish clear data governance. Business leaders should define who is responsible for data quality, how to measure it and the processes to maintain it; providing a robust data governance framework ensures accountability and establishes a structure for continual data quality improvement.
• Consider implementing data quality tools. Modern data quality tools can automatically detect anomalies, standardize formats, provide real-time quality checks, reduce manual efforts and ensure more consistent, accurate data across the organization.
• Regularly audit and cleanse data. Data quality requires periodic audits and data cleansing to help sustain high standards over time. This ensures data remains accurate, complete and consistent as new information is collected and processed.
• Educate and train staff. All company employees, from data scientists to front-line staff, should understand the importance of data quality and best practices for maintaining it. This knowledge ensures data integrity throughout its life cycle.
High-quality data can help organizations remain competitive today. Leaders need to make better-informed decisions, optimize operations and stay ahead of the competition by monitoring and improving key data quality metrics. The focus on data quality today could pave the way for business success tomorrow.
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