Data Quality
Get the most out of your data
Ensure the quality of the data you use to support your communications and ensure that personalized messages are correct and that deliveries are made smoothly.
Reduce costs
with errors
Prevent and significantly reduce costs related to errors in communication with your customers.
Examples of these errors are:
Returned letters: costs of production, shipping, handling of returned mail.
Undelivered emails or SMS: opportunity cost of non-delivery.
Content with wrong variable data: image/reputation cost associated with incorrect data.
Incorrect Segments: Opportunity cost of not sending the right message to the ideal customer at a given time.
Validate and complete the data you have
Data Quality analyzes your databases, allowing you to identify errors and opportunities for improvement in your processes.
This tool also makes it possible to identify the possibility of crossing external data sources, enriching the data and generating information of great potential:
Validation of standard data from public algorithms (eg validation of VATs, validation of postal code, etc.)
Validation of customer-specific data from private algorithms.
Logical validations (blank fields, sums, duplicates,…).
How does a Data Quality process work?
1. Collection of requirements
Together with the client, we clearly define the purpose of the analysis or process, in order to identify the requirements that must be met by the data.
2. Identification of the data profile
We carefully examine the following aspects of the data: format, patterns, record consistency, value distributions and outliers, and whether records are complete.
3. Identification of associated flows
We identify the intended use of the data, in which tools, which crossings will exist, and which are the associated update processes.
4. Implementation
In addition to creating rules and validations in accordance with the information collected in the previous steps, we ensure:
The integrity
The use of primary and foreign keys plays a crucial role in relational databases. In situations of multiple unrelated systems, we ensure the existence of validation conditions associated with each field (check constraint) and the use of mechanisms triggered by specific actions (triggers).
The traceability
Whenever a problem is detected in a log, we guarantee that it is possible to quickly identify its origin and correct it.
The completeness
If necessary, the process may include cross-referencing with external data sources, enriching the data and generating information of great potential.
Integrations that take you even further
By investing in customer communication, we want you to get the most out of each record and that’s why we advise you to apply Data Quality models to data files before applying them to templates.
The existence of a data quality assessment process is essential both to allow cross-referencing of different sources (standardization of expressions) and to ensure that analyzes and segmentations are rigorous (identification of invalids or duplicates).
Even in a simple database, a process of this type makes it possible to find outliers, duplicate records, information gaps or inconsistencies. They may seem like details, but they can have a big impact on the final result.
