Data Quality
Reliable decisions start with reliable data: measure, control and improve the quality of the data systematically.
Technologies and tools
We apply cloud-native tools and open source frameworks to detect, monitor, and maintain the quality of your critical data.
What is putting your data at risk?
With the wrong approach:
Hidden errors in critical data that impact reports and decisions without anyone noticing
Constant manual corrections that consume time of the team
Analytics projects that They fail because the data is incomplete or inconsistent
Quality as a reactive correction rather than a preventive practice
The 70% of the analytics projects They fail due to problems with the data quality
With the right approach:
Errors detected at the source, before they spread systems or reports
Automatic controls on the flows, without relying on manual reviews
Clear quality metrics: completeness, uniqueness, consistency and validity
Continuous monitoring for detecting deviations before of how they impact the operation
Data ready for analysis and decision-making sustained quality over time
We turn your data into a reliable and governed asset
DIAGNOSIS AND OBJECTIVE MEASUREMENT
We measure the real quality of your data with clear metrics, prioritizing the domains with the greatest impact on the business.
AUTOMATIC PREVENTIVE CONTROLS
We implement automatic rules in data flows to detect and correct errors at an early stage.
CONTINUOUS QUALITY MONITORING
GOVERNMENT AND SUSTAINABILITY
REDUCTION OF OPERATIONAL RISK
We prevent quality issues from causing errors in target systems, reports, or critical processes.
Organizations on average lose 15% of their annual revenue due to decisions based on low-quality data. Data quality is not a project: it is an operational practice that protects the business.
WHAT WE DO
(in the order that really matters)
1. Diagnosis and measurement of data quality
- Evaluation of critical data
We measure quality using objective metrics: completeness, uniqueness, consistency, and validity, prioritizing the domains with the greatest impact on the business. - Identification of hidden risks
We have detected errors, inconsistencies, and missing data that affect reports, operational processes, or regulatory compliance. - Priority based on impact
We deliver a quality risk map with specific recommendations to start the improvement where it matters most.
15%
Organizations on average lose 15% of their annual revenue due to decisions based on low-quality data.
McKinsey – Data Quality Management
Success story: A global transactional solutions company was undertaking a critical migration and needed to validate millions of records before loading them into the new system.
Results: Early identification of risks that could affect operational continuity and a more reliable database from day one of go-live.
2. Implementation of quality rules and controls
➤ We define and implement automatic quality rules aligned with processes and business rules, preventing errors from spreading across systems.
- Automated rules aligned with the business
We design quality controls based on real processes using Azure Data Factory Data Flows, AWS Deequ, or Google Cloud Dataform. - Early detection and correction
The checks are executed automatically in the flows, identifying and correcting errors before they reach the target systems. - Integration with existing pipelines
We adapt the quality controls to your current architecture without the need to redesign from scratch.
Success story: During a data migration for a global company, we implemented automatic quality rules at every stage of the load process.
Results: Improved over 90% in the quality of critical data before its migration to the new system and zero operational continuity errors.
50%
Organizations with quality preventive controls reduce operational data errors and executive reports by up to 50%.
McKinsey – Data Quality Management
3. Continuous monitoring and sustainability of quality
➤ We enable continuous monitoring of the data quality to detect deviations, trends, and risks before they impact the operation.
- Real-time quality dashboard
We enable continuous visibility of quality metrics by domain, with automatic alerts when deviations or negative trends are detected. - Sustainable quality culture
We convert data quality into an operational practice with clear roles, processes, and metrics, not into reactive correction. - Integration with data governance
We connect quality monitoring with governance rules to ensure traceability, compliance, and continuous improvement.
60%
More than 60% of the organizations lack continuous monitoring of data quality, which leads to silent errors in reports and critical processes.
Gartner – Data Governance Programs
Success story: After a critical migration, we enabled continuous monitoring to maintain the quality of the data over time within the new system.
Results: Quality levels exceeding 90% sustained continuously, Greater confidence in reports and a reduction in operational rework.
HOW DO WE DO IT?
- Evaluation of critical data
We measure the actual state of the data using objective metrics and prioritize risks based on their impact on the business. - Definition of rules and controls
We design quality standards aligned with real processes and decisions, not just technical criteria. - Integration of controls in data flows
The controls are executed automatically within the processes, preventing errors from spreading. - Continuous monitoring and sustainable governance
We enable continuous visibility through roles, processes, and metrics that maintain quality over time.
We do this so that the Data quality is a sustainable operational advantage, Not a constant source of errors and corrections.
RESULTS
that speak for themselves
Organizations that implement quality preventive controls reduce operational data errors by up to 50% and improve the confidence in their critical data by more than 90%. McKinsey, Gartner
%
Quality achieved in critical data during migration
%
reduction of errors in operational data
%
Organizations without continuous quality monitoring
%
lost revenue due to low-quality data
Why us?
At Grupo Scanda, we convert data quality into a sustainable operational practice. We do not make ad-hoc corrections: we implement automatic controls, continuous monitoring and robust governance that protect the decisions of the business.
Proven experience:
Success stories in diagnostic and data quality improvement in critical migrations, integrations, and analytical processes across multiple industries.
Cloud-native tools:
Azure Purview, AWS Deequ, Google Cloud Dataform, Snowflake, and Python routines for validation, transformation, and anomaly detection.
Preventive approach:
We detect and correct errors at an early stage of the process, not after they have already impacted the operation or the reports.
Integration with government:
We connect quality with roles, lineage, and compliance so that the results are sustained over time, not just in isolated projects.
Ready to boost your business?
We have the solution for youContact by telephone
(55) 1251-2700
Location Mexico City
Miguel Laurent #804, Col. Letrán Valle, Alc. Benito Juárez
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