When something goes wrong with a dashboard, report, or KPI, most people start at the end. They ask: was this data checked before publishing? Is the report broken? But these question come too late. By the time data hits the end of the pipeline, many opportunities to catch issues have already passed.  In our previous […]

Most organisations still treat Data Quality as a checklist. Something to sort out after the data lands in a warehouse. But that approach no longer fits. Today, data plays a strategic role as a Data Product. It is built with purpose, owned with intent, and expected to deliver trust.  At Clever Republic, we define Data

Measuring Data Quality of a Data Product Gone are the days when data sat quietly in spreadsheets. Today, it is stepping up as a Data Product, designed with purpose, owned with intent, and built to deliver value. Any BI tool, machine learning model, or even ESG reporting tools can be considered a Data Product. Organisations

The six most used Data Quality dimensions Almost every business leader recognises the value of data: for innovation, growth, customer satisfaction, efficiency, and compliance. But data only creates impact when it is trustworthy. That trust depends on Data Integrity: the degree to which data remains accurate, consistent, and reliable across time and systems. Data Integrity

Ensuring and maintaining Data Quality In the digital age, ensuring Data Quality is essential for businesses to make sound decisions and optimize operations. Inaccurate, incomplete, duplicate, and redundant data are all too common in businesses today. According to Gartner’s Data Quality Market Survey, poor Data Quality costs organisations an average of $15 million annually. At