Data/System Integration
Data integration is also known as system or product integration. When a new product or system is bought or developed, it must be integrated with the existing systems so that they stay synchronized. This requires work at the data level, such as data migration, data consistency, data synchronization, data quality and data replication. When an existing system is changed or upgraded to meet new business requirements, those changes must be applied across all related systems, which calls for the same data-level work. Together, this work is called data integration.
Data Migration
InfoTelica moves data between databases, platforms and applications with a planned, tested and reversible approach, so that business operations continue with minimal downtime. A typical migration includes
- Analysing source and target systems and data volumes.
- Mapping source data structures to the target data model.
- Profiling and cleaning data before it is moved.
- Building and testing extraction, transformation and loading scripts.
- Running trial migrations and reconciling the results.
- Planning the cut-over and a rollback strategy.
- Validating data completeness and integrity after migration.
- Supporting users and systems after go-live.
Data Cleaning
Data cleaning, also called data cleansing, deals with detecting and removing errors and inconsistencies from data in order to improve the quality of data. Data quality problems are present in single data collections, such as files and databases, due to misspellings during data entry, missing information or other invalid data. When multiple data sources need to be integrated, e.g., in data warehouses, distributed database systems or global web-based information systems, the need for data cleaning increases significantly. This is because the sources often contain redundant data in different representations. In order to provide access to accurate and consistent data, consolidation of different data representations and elimination of duplicate information become necessary. In general, data cleaning involves several phases.
Data analysis
In order to detect which kinds of errors and inconsistencies are to be removed, a detailed data analysis is required.
Definition of transformation workflow and mapping rules
Depending on the number of data sources, their degree of heterogeneity and the dirtiness of the data, a large number of data transformation and cleaning steps may have to be executed. Sometimes, a schema translation is used to map sources to a common data model. Early data cleaning steps can correct single-source instance problems and prepare the data for integration. Later steps deal with schema/data integration and cleaning multi-source instance problems, e.g., duplicates. For data warehousing, the control and data flow for these transformation and cleaning steps should be specified within a workflow that defines the ETL process.
Verification
The correctness and effectiveness of the transformation definitions should be tested and evaluated, e.g., on a sample or copy of the source data, to improve the definitions if necessary. Multiple iterations of the analysis, design and verification steps may be needed, e.g., since some errors only become apparent after applying some transformations.
Transformation
Execution of the transformation steps by running the workflow for loading and refreshing related databases.
Backflow of cleaned data
After (single-source) errors are removed, the cleaned data should also replace the dirty data in the original sources in order to give applications the improved data and to avoid redoing the cleaning work for future data extractions.
What we deliver
Data Security and Masking
We design and implement robust data-security and masking solutions to protect sensitive information across production and non-production environments. Our approach combines role-based access control, encryption and dynamic data masking to ensure that only authorized users can view identifiable data while developers, testers and external partners work with safely de-identified records. This helps organizations comply with regulatory frameworks such as GDPR and KVKK while minimizing operational risk and data-breach exposure.
Data Governance
Our data governance services establish the policies, standards and stewardship model required to manage data as a strategic corporate asset. We work with business and IT stakeholders to define ownership, data quality rules, metadata standards and approval workflows, aligning with leading frameworks such as DAMA-DMBOK. The result is a clear, sustainable governance structure that increases trust in data, improves regulatory compliance and accelerates analytics and reporting initiatives.
Enterprise Data Modeling
We provide end-to-end enterprise data modeling services that translate business processes into consistent conceptual, logical and physical data models. Using industry-proven modeling techniques and notations, we harmonize disparate sources into a unified structure that supports data warehouses, operational systems and advanced analytics. This common data language reduces integration complexity, improves data quality and creates a scalable foundation for future digital initiatives.
Data Testing and Synthetic Data
Our data testing and synthetic data services ensure that critical data platforms, ETL processes and reports behave reliably under real-world conditions without compromising confidentiality. We design automated test suites to validate data completeness, consistency and performance, and we generate statistically representative synthetic datasets that mirror production patterns while removing identifiable information. This enables safe development, regression testing and benchmarking, improving system quality while preserving privacy and regulatory compliance.
Data Auditing
We provide comprehensive data auditing services to assess how data is collected, stored, accessed and shared across the organization. Our audits review compliance with internal policies and external regulations, verify the effectiveness of security and governance controls, and trace data flows from source systems to analytical platforms. Using platform-specific capabilities such as Oracle Database Vault and Unified Auditing, SQL Server Audit, and database activity monitoring solutions, we analyse access logs, privilege usage and configuration changes to identify gaps and potential risks. The outcome is a set of actionable recommendations that enhances transparency, strengthens accountability and supports ongoing regulatory compliance.
Not sure where to start?
Tell us about your systems and goals. We will suggest the right mix of services and a first step.