We help you to have high-quality data by the user technology since the data value is zero if it is not suitable for the purpose that the company is using the data for and the company is seeking to reach, moreover, the data quality is measured by the ability to use it for planning and decision-making purposes. For instance: what is the purpose of having data about the clients’ study and degrees if the company’s products are consumable goods and meant for everyone not for a specific level of education.
In data quality management the goal is to exploit a balanced set of remedies in order to prevent future data quality issues and to cleanse (or ultimately purge) data that does not meet the data quality Key Performance Indicators (KPIs) needed to achieve the business objectives of today and tomorrow.
The data quality KPIs will typically be measured on the core business data assets within the data quality dimensions as data uniqueness, data completeness, data consistency, data conformity, data precision, data relevance, data timeliness, data accuracy, data validity, and data integrity.
The data quality KPIs must relate to the KPIs used to measure business performance in general.
Most organizations produce and consume vast quantities of data. Many decisions, ranging from the mundane to the strategic, are based on data from several sources. As such, high data quality is essential for the efficient function of the organization.
Even when the importance of data quality is recognized, issues often go unnoticed or remain uncorrected for prolonged periods of time. This often stems from the fact that humans are good at dealing with low-quality data (a postman can usually deliver to a poorly written address).
Automated systems, on the other hand, often cope badly with poor quality data and this often leads to increased manual labor or sub-optimal decisions that have negative financial consequences.
