Data quality management services

Data profiling and cleansing are essential to determine whether your data is suitable for analysis. For instance, data profiling defines a percentage of zero, blank, and null data values to identify missing or unknown data. Using such data for analysis can lead to inaccurate or insufficient insights and affect decision-making.

To ensure efficient data profiling and data cleansing, our team works with a modular technology stack that includes Apache NiFi, Talend Data Fabric, dbt (Data Build Tool), and custom Python-based pipelines. These tools allow us to build scalable, transparent data quality management solutions tailored to your cloud, hybrid, or on-premises infrastructure. Key characteristics of our approach to data profiling and cleansing tool selection:

• Easy to extend, audit, and integrate into your current workflows
• Compatible with enterprise systems in finance, healthcare, logistics, and manufacturing

Our data quality management specialists are fully equipped to work with data in any format or volume. We can build a custom DQM strategy that processes structured and unstructured data, using tools including Apache Tika, AWS Glue, and Apache NiFi. Whether you’re dealing with structured tables from ERPs, CRM database quality issues, or unstructured logs, documents, and emails, we help you bring everything into a standardized, usable format. Our team:

• Combines operational and analytical data from existing data silos
• Extracts, normalises, and enriches the organization’s data assets
• Improves data accessibility across departments and systems

We build data quality solutions with privacy, security, and traceability in mind. Thus, our data quality management and assessment services include applying encryption, anonymization, role-based access controls, and audit logging where needed. We also work closely with your legal or compliance teams to make sure our approach aligns with both your internal policies and external regulations like GDPR, HIPAA, or PCI DSS.

High data quality plays a critical role in compliance and risk objectives because:

• Accurate and complete records reduce the risk of reporting errors and violations
• Consistent data structures support reliable data access control and retention policies
• Clear data lineage makes it easier to prove compliance during audits
• Validated and standardized data ensures sensitive information is correctly classified and handled

Timelines can vary based on the complexity of your data environment and your specific goals, but most projects fall within the 6–12 week range. We start with an Assessment Phase (around 4 weeks) and offer data quality consulting services to evaluate the current data state, uncover bottlenecks, and define a clear roadmap on how to efficiently manage your data quality.

From there, we move into a focused PoC Development phase (typically 6 weeks) to build a targeted proof of concept that addresses your highest-impact areas, whether that’s regulatory compliance, data unification, or real-time monitoring.

Our experts adapt to the different needs of your organization, with full transparency, quick wins early on, and clear checkpoints so you always know where things stand.

Real-time data quality checks are a part of our end-to-end data quality management services. Such checks are great for catching data quality issues as data flows into your system and are suitable for time-sensitive operations. For example, healthcare enterprises can ensure that patient monitoring data or EHR entries are complete and accurate before making treatment decisions. And for logistics companies, real-time data validation of sensor and GPS data supports accurate order tracking and delivery status updates.

Batch processing works well for large data volumes or scheduled validations, such as reconciling billing data at the end of the day in manufacturing or supply chain systems, and auditing historical customer data or applying quality scoring in banking and insurance.

We specialize in both types of data quality checks to give you comprehensive coverage across all your data flows, so nothing slips through the cracks.

Once the core solution is in place, we stay involved to make sure it keeps running smoothly. This includes:

• Technical support for ongoing maintenance and issue resolution
• Proactive system monitoring to catch and address anomalies before they impact operations
• Role-specific training for your internal teams—data engineers, analysts, and business users—so they can confidently manage and optimize data quality processes

Our objective is to help you build internal capability and reduce long-term dependency. We equip your team with the knowledge, data quality tools, and best practices to own your data quality management framework, make informed decisions, and respond quickly to change.