Data Quality
Make the most of your data
Guarantee the quality of the data you use to support your communications and ensure that personalized messages are correct and that deliveries are made without delay.
Reduce costs
with errors
Prevent and significantly reduce costs related to errors in communication with your customers.
Examples of these errors are
Returned letters: production costs, dispatch, handling of returned mail.
Undelivered emails or SMS: opportunity cost of non-delivery.
Content with incorrect variable data: image/reputation cost associated with incorrect data.
Incorrect segmentation: the opportunity cost of not sending the right message to the right customer at the right time.
Validate and complete your data
Data Quality analyzes your databases, allowing you to identify errors and opportunities for improvement in your processes.
This tool also identifies the possibility of cross-referencing external data sources, enriching the data and generating information of great potential:
Validation of standard data using public algorithms (e.g. TIN validation, zip code validation, etc.)
Validation of customer-specific data using private algorithms.
Logical validations (blank fields, sums, duplicates,...).
How does a Data Quality process work?
1. Gathering requirements
Together with the client, we clearly define the objective of the analysis or process in order to identify the requirements that the data must meet.
2. Identification of the data profile
We carefully examine the following aspects of the data: format, patterns, consistency of records, distributions of values and outliers and whether the records are complete.
3. Identification of associated flows
We identify the intended use for the data, in what tools, what cross-references will exist, and what the associated updating processes will be.
4. Implementation
In addition to creating rules and validations in accordance with the information gathered in the previous steps, we ensure:
Integrity
The use of primary and foreign keys plays a crucial role in the case of relational databases. In situations of multiple unrelated systems, we ensure that there are validation conditions associated with each field (check constraint) and the use of mechanisms triggered by specific actions (triggers).
Traceability
Whenever a problem is detected in a record, we ensure that it can be quickly identified and corrected.
Completeness
If necessary, the process can include cross-referencing with external data sources, enriching the data and generating information of great potential.
Integrations that take you even further
When investing in customer communication, we want you to get the most out of each record, which is why we recommend applying Data Quality models to data files before applying them to templates.
The existence of a data quality assessment process is fundamental both to allow cross-referencing of different sources (standardization of expressions) and to ensure that analyses and segmentations are rigorous (identification of invalid or duplicate data).
Even in a simple database, a process like this can find outliers, duplicate records, gaps in information or inconsistencies. They may seem like details, but they can have a big impact on the final result.
Solutions you can build with this block
Multichannel production and shipping
From data conversion to the delivery of communication to customers, whether by post or digital channels.
Document management
Forget your physical mailbox, we organize and forward all your mail digitally.