Business Intelligence (BI) - Data Integration & Modelling

Data integration involves combining data from different sources and providing users with a unified view of the data. Data integration increases as volume and data sharing increases. The industry spends a lot of time and money on research to solve ongoing data integration issues. Our Approach to data integration comprises the practices, tools and techniques for achieving the consistent access and delivery of data across the spectrum of systems, data subject areas and data structure types across various levels and departments. We help organization to meet the data consumption requirements of all applications and business processes.

HashTag Systems Approaches to Data Integration

Replication :: This is the second most commonly used method of performing data integration. The changes to the data source are sent to the peripheral systems and then updated. Companies like this method, because existing personnel often possess the skill-set to execute it.

Change Data Capture (CDC) using slowly changing dimensions concept :: This method is very similar to replication, in that it captures and transmits the changes to the data transaction logs. Change data capture is a way of accessing changes without running a complete ETL process.

Batch process ETL :: As the name suggests this method 'miniaturizes' the ETL batch process. The resultant batches are smaller and more regular. They are stored in real-time partitions that are copied daily to static data marts. While very effective, this method has huge resource requirements.

HashTag Systems consultants provide services for several industry leading data integration tool vendors below:
Data Stage

Our team of experts comes with knowledge of various leading data integration tools and industries. We can review the existing or design new :

Data Discovery methodologies - Analyzing data values and data patterns to identify the relationships that link disparate data elements into logical units of information

Data Governance/Stewardship models – Record the business use for defined data. Identify opportunities to share and re-use data. Identify procedures for disaster recovery and data archiving to ensure effective protection and integrity of data assets.

Data warehouse architecture –Building and implementing data warehouse architecture using Kimball or Inmon methodologies and ETL logic from a wide variety of data sources.

Metadata Management -Supporting Project and program management, Metadata and data governance, Metadata and data quality, Metadata and reference data for master data management and Master data management.

Production Support – We assist production support team as EDW SME during troubleshooting issues. Ensure the stability, integrity and efficiency of data access and data quality across the organization via ongoing database support and maintenance.

For more information about HashTag Systems Data Integration services please contact us

Data Modelling
HashTag Systems consultants bring years of experience to the table. Each of them builds logical physical and conceptual models using the best industry tools and a precise methodology.

Conceptual : Identify the data required at the highest level (Sales, Accounting, Employees, Payroll, etc.). Examine and list the high-level relationships between the most important entities.
Logical : Discover as much detail in the data as possible.
Physical : Determine how the model will be built in the database, including tabular structure and listings.
Constraints : Define Primary Keys (PK) and Foreign Keys (FK)
Relationships : HashTag Systems assures proper validation of the data model.

Our team of experts can help meet your organization data modelling goals by providing following services

Logical data modeling
Ad-hoc modeling
Complex stored procedures
SQL optimization
Data visualization
Data Reporting
Dynamic Dashboards
Statistical modeling
For more information about HashTag Systems Data Integration services please contact us