Data Quality

Data Quality

Data Quality

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.

Data quality management

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.

Why Is Data Quality Important?

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.

Data Quality Best Practices

1

Fixing Data at Source : There are many situations where data propagate to multiple different systems from a single source application. Failure to enforce data quality in the source application results in low-quality data propagating to those other systems and having a multiplying effect.

2

Data Validation on Input : Any process that submits data should include validation. Many downstream issues can be prevented by implementing basic validation checks such as data validation, verifying values against lists and checking for reasonable values of all required fields

3

Data Quality Monitoring : Monitoring often consists of data validation rules that are applied to each record as it is transformed into its destination format.

4

Reporting : Reporting is a key part of maintaining data quality. Well executed reporting ensures that stakeholders get all the status information very quickly and are able to react in a short time frame.