Data quality strives for the data to be accurate, complete, and available in a timely fashion. This allows data-driven businesses and organisations to maximise the value of their data, enabling them to make informed decisions more confidently. On the contrary, low-quality data can lead to a variety of problems, such as incorrect decision-making, wasted time and resources, and even lost opportunities to increase revenue or cut costs.
What is the impact of low-quality data?
Low-quality data can be caused by incorrect data entry, duplicates, missing values, outdated information, lack of standardisation during data processing and reporting, and delayed data.
Depending on the industry, the effect of low quality can have a financial impact, such as incorrect information about customer addresses and bank account details. In other cases, it will have a customer experience impact such as when the customer address is incorrect and the products are delivered to a wrong address or when the availability of a product is not up-to-date which will cause false expectations on timely delivery.
To ensure data quality, data-driven organisations can adopt a data quality improvement framework. This involves defining and enforcing data standards, establishing robust data quality processes through automated testing, and monitoring and improving data quality over time by measuring data quality metrics. It is essential to involve all stakeholders in the data quality process, including business leaders, data owners, data users, and IT teams, and quantify the cost of low-quality data.
One way to assess the data quality is to understand the cost of when the data quality degrades. For example, the cost of losing information in 5% of the most important data points in a data source and the effect of financial reporting, customer experience reporting, decision making and product development. Also, another philosophy suggests an assessment of the cost of not using a data source that enhances the accuracy of an existing data source by 5%.
In principle, even if the answers are different for every data-driven business, any improvement strategy justifies the focus on data quality.
What are some of the key metrics of data quality?
Several key metrics are used to measure data quality, including:
Completeness: The proportion of present and free data from missing or null values.
Accuracy: The degree to which data is free from errors, such as typos or incorrect values.
Consistency: The degree to which data is uniform and free from discrepancies, such as differences in formatting or spelling.
Timeliness: The extent to which data is up-to-date and reflects current conditions.
Relevance: The extent to which data is relevant and useful for its intended purpose.
Consistency: The degree to which data is uniform and free from discrepancies, such as differences in formatting or spelling.
Integrity: The degree to which data is protected from unauthorised modification or deletion.
Uniqueness: The degree to which data values are distinct and non-duplicated.
Validity: The extent to which data values conform to established business rules and constraints.
Reliability: The degree to which data can be trusted to be free from errors and inaccuracies.
These metrics are essential in assessing the quality of data and can be used to identify areas for improvement and track the effectiveness of data quality initiatives. By measuring data quality regularly, organisations can ensure that their data is of high quality and supports their business goals.
We believe that high-quality data should be at the core of any business data strategy. Get in touch with us to learn more.