Data Quality Checklist – Unidy Documentation

When integrating a new service, it is advisable to migrate current users to UNIDY to alleviate the burden of creating new accounts and minimize the risk of losing their existing data and track records within the integrated service. Further, the migration presents an opportunity to convert these users to other services and explore cross-selling possibilities. The following explains the reasons for poor data quality and why good quality and preparation of data are important. Further, you will find a checklist that will help you to confirm your data quality.

1. Why Ensure High Data Quality in Migration Process?

Ensuring good data hygiene is crucial for any organization, as it can lead to increased security, productivity, compliance, and efficiency. By using only clean, correct, and relevant data in your systems and business processes, you can avoid potential errors, inaccuracies, and security breaches.

One of the key benefits of good data quality is that it provides a comprehensive and accurate overview of your organization's data. This enables you to make informed decisions and take action based on reliable information. Additionally, when your data is well maintained, you can easily share datasets with others, enabling collaboration and efficient workflows.

For instance, when integrating services, having well-organized and accurate data can significantly streamline the process. This makes it easier for users to migrate and saves time as well as resources.

2. Factors for Low Data Quality:

There are several reasons why data quality can be poor in many cases. It is important to address these factors in order to effectively utilize the data.

Data duplication: Also known as data redundancy, occurs when records are repeated within a database. This means that certain information, such as an email address, is used for multiple accounts, or customers may be registered with multiple email addresses. In the first case, the repeated email addresses may be overwritten, leading to a reduction in data quality.

Incompleteness of data: Occurs when the user data is lacking mandatory information such as an email address or information that can not be provided by the user him/-herself and hinders the creation or identification of users. Regarding the lack of mandatory personal data that can be provided by the user, UNIDY is offering required fields, that a user has to enter before being able to log into a service.

Data inconsistency: Occur due to errors in data entry or processing, lack of standardization or data integration across systems, or issues with data quality control. Data inconsistency can negatively impact data synchronization between services as well as the basic functions of the integrated service.

3. Data Quality checklist

By following the data hygiene checklist, you can ensure that your dataset is well-prepared for a smooth migration to Unidy.

#	Quality features	Description	Check?
1	Consistency	Is the data in the file consistent and does not include any errors or special characters, where they are not required? Often umlauts are affected by these.
3	Completeness	Does the import file contain all required data fields?
4	Relevance	Does the data set only contain data relevant for the service and or other connected services?
5.1	Formats	Countries are designated according to ISO 3166-1 alpha 2 standards? ISO 3166-1 alpha 2?
5.2	Formats	Dates are set as YYYY-MM-DD?
5.3	Formats	Times are set in UTC?
5.4	Formats	Salutation is set as ? ◦ Mister = mr ◦ Miss = mrs ◦ Divers or others = mx
5.5	Formats	Address is separated in the respective fields? ◦ Street & house number ◦ Zip code ◦ City ◦ Country
5.6	Formats	Phone numbers are complete and start with a “0” for German numbers or the respective country code?
5.7	File formats	File is in one of the following formats ◦ .CSV
6	Doublicates	The data set does not include any duplicates, especially regarding the email address or other identifiers.
7	Timeliness	The data is as up-to-date as the purpose requires?