Data Quality

According to report by KPMG “2016 Global CEO Outlook,” 84% of CEOs are concerned about the quality of the data they’re basing their decisions on. The role data plays in enabling future technologies such as artificial intelligence and the Internet of Things is critical — but one that will be undermined if businesses do not make data quality a priority.

A study from Gartner indicates that the average financial impact of poor quality data costs organisations $9.7 million per year. In 2016, research found that poor data cost the US economy $3.1 trillion.

The consequences of poor data can be experienced in day-to-day life. For example, delayed delivery of a letter/parcel, mostly blamed on postal service could be incomplete data in the address database. The double delivery of automatically generated mail indicate duplicate records in database.

Data is driving the fate of business world; Good data is a valuable corporate asset and bad data is financial burden. After being hit by hidden cost of bad business data, many organisations now have rolled their sleeves up for data quality.

Data Quality is defined as Its Fitness for use. Data that meets stakeholder’s expectations — in a Not-So-Fancy way.

Data quality refers to methodical approach, processes and policies by which an organisation manages the validity, accuracy, timeliness, uniqueness and consistency of its data in the system and the data flows.

It is not just about zero defects, quality is also conformance to valid requirements and standards set by the business. It is a shared responsibility between data exchange partners (Providers and Recipients).

Data Quality Dimension is a frequently used term in data world to describe a data feature which can be measured or assessed against defined standards for quality of data.

The Seven dimensions of data quality are:

Top contributors for poor quality data -

There is no universal agreement on key data quality dimensions.
These dimensions serve as overall Data-Quality-Health indicator, helping to measure and communicate the quality of data.

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