Data Quality: Understanding Its Value


What is data quality?

Data is often a poorly understood aspect of an organisation, and there are many ways to define the quality of a data landscape. Put simply, an organisation’s data is of a high quality when it is suitable and reliable. Therefore, a data environment’s quality often depends on the context in which the data is being used, but there are several assessment areas which will almost always underpin the quality.

These aspects include:

  • The accuracy of the data: is the data a correct representation of the reality?

  • The validity of the data: does the data conform to the data rules?

  • The timeliness of the data: is the data up-to-date or accurate for the time it should represent?

  • The originality of the data: has the data been duplicated?

  • The consistency of the data: are the data values consistent across different systems?


Why is data quality important?

Data should be a organisational asset which can be relied upon to make important decisions and conclusions regarding various aspects of any business or organisation, including its performance, growth and future direction.

Having a trustworthy source of quality data can present numerous benefits to an organisation, such as:

  • The company can trust their data in order to make well-informed business decisions in order to boost sales, improve their reputation and customer satisfaction

  • Time-wastage on manual process can be reduced through easier implementation of automated processes

  • Reports of varying complexity, such as KPI performance, balance sheets, etc., can be easily generated through automatic processes

  • Quicker adaptation to new systems or business strategies can be implemented in order to stay at the forefront of competition or modern processes

However, there are many negative implications of relying on poor quality data, such as:

  • Making bad business decisions based on “false facts” taken from inaccurate data. The ramification of poor decisions vary depending on organisation type, and can range from poor financial investments, for example when relying on inaccurate target demographic data in a marketing firm, to endangering people’s lives, for example in a hospital pharmaceutical setting.

  • Failure to run or implement automated processes which require accurate, consistent data.  If data becomes inconsistent, it can break processes and lead to costly fixes or even devolution to manual processes.


How can IntoZetta improve my data quality?

IntoZetta has developed highly configurable and easily deployed data quality tools which transforms understanding and measurement of data quality and pinpoints the areas where data quality must improve. The software has been built with the business user in mind, it includes powerful reporting and presentation capabilities, and it brings visibility to data quality regardless of time and location via tablets, smartphones, and laptops.

Using this software, IntoZetta can undertake an initial tactical data quality assessment or design an end to end data quality remediation strategy. Our suite of reporting tools and dashboards will lift the lid on ingrained data quality issues, allowing operational teams to understand, analyse, and report on data quality. Detailed reports can be generated in seconds to show the organisations current data quality holistically, or at a very detailed level for a particular data area. The IntoZetta reporting tools can provide a snapshot of data quality, but they also have the ability to show trends in quality and data cleansing over time, and track overall progress toward a healthy data environment.

View a demonstration of our Data Quality Software by clicking here: Software Demonstration

Dominic Eld