Data Integration and Data Warehouse Management -BI Business Intelligence

Centralized Data Management

Data Integration and Data Warehouse Management Module collects data obtained from different systems of your business on a central platform. In this way, the accuracy, consistency and up-to-dateness of the data are ensured. It strengthens your decision-making processes by extracting meaningful information from both structured and unstructured data.

This module accelerates data-driven decisions by making large data sets analyzable. Data is integrated quickly and without errors thanks to automated ETL processes (Extract, Transform, Load). It increases the accuracy of your analyses by using your data in the most efficient way and supports data-driven business strategies.

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Central Data Management Module

The Data Integration and Data Warehouse Management module enables data from different sources to be collected, cleaned and made ready for analysis on a central platform. In this way, decision-making processes are accelerated and become reliable with accurate and consistent data.

Defining Data Source

Data Conversion

Data Warehouse Design

Process Management

Data Security

Data Source Integration Component

It accelerates analysis and reporting processes by combining data from different systems in a central structure. Data from enterprise systems such as ERP, CRM, MES, IoT devices and external APIs are collected on a single platform. In this way, information flow is accelerated, the need for manual entry is reduced and data quality is ensured.

Features

Identifying Data Sources
Connection and Access Authorization
Automation of Data Flow
Data Quality Control

Defining Data Sources
Data from systems such as ERP, CRM, MOM, MES, EAM, LIMS, SPM, QMS, BMS, eLogs, IoT devices and external APIs are integrated into the system. Thus, all operational data is collected in a single center and can be analyzed.

Connection and Access Authorization
Access is controlled by establishing secure connections to data sources. Role-based authorization ensures that only authorized users have access to relevant data. In this way, system security is maintained at a high level.

Automation of Data Flow
Data is automatically transferred to the system at certain times or instantly. This automation eliminates the need for manual intervention, data remains constantly up-to-date and accelerates integration processes.

Data Quality Control
Integrated data is checked by the system for accuracy, consistency and timeliness. Processing of missing or erroneous data is prevented, thus ensuring reliability in data analysis.

Data Transformation and Cleansing Component

It ensures that raw data from different systems are standardized and cleaned for analysis and reporting. It contributes to the right decision-making processes by increasing data quality. Analytical accuracy and scope are expanded with data sets enriched with external sources.

Features

Data Transformation
Data Cleansing
Data Enrichment
Data Validation

Data Transformation
Consistency is achieved by converting data in different formats into standard structures. In this way, suitable and comparable data sets are obtained for analysis processes.

Data Cleaning
Erroneous, missing or duplicate data is detected by the system and corrected or cleaned. This step directly increases data reliability and report accuracy.

Data Enrichment
: Missing information is completed with data from external sources to strengthen the data set, thus creating more meaningful and contextually rich content.

Data Validation
Transformed and cleaned data are subjected to various checks to test its accuracy and validity. In this way, analysis processes are based on secure and consistent data.

Data Warehouse Design and Management

It provides data storage in a centralized and optimized structure for analysis, reporting and decision support processes. It provides high-performance access and long-term data management for large volumes of data. Data warehouse design strengthens data-driven decision-making processes of businesses.

Features

Data Warehouse Modeling
Size and Measurement Tables
Data Indexing and Performance Optimization
Data Retention Policies

Data Warehouse Modeling:
Data architecture suitable for multi-dimensional analysis structure is created using OLAP and OLTP models. Analysis and operational systems are designed separately to provide both performance and flexibility.

Dimension and Measurement Tables
A flexible and detailed analysis infrastructure is created by defining dimensions such as time, product, and customer. This structure allows users to examine data in multi-dimensional terms and create special reports.

Data Indexing and Performance Optimization
Indexing and data partitioning techniques increase query performance on large data sets. This shortens data access time and speeds up reporting.

Data Storage Policies
Active and archive data are separated within the scope of data lifecycle management. Appropriate storage environments are determined according to access frequency; data pollution and system load are prevented.

Process Management Component

It manages ETL (Extract, Transform, Load) processes to analyze and make consistent data from different sources. Thanks to this component, data transfer, transformation and loading into the central data warehouse are carried out in an automatic, reliable and traceable structure.

Features

Data Extraction
Data Transformation
Data Loading (Load)
Process Monitoring and Warning System

Data Extraction:
Data is taken from various sources such as ERP, CRM, IoT, sensor systems and transferred to temporary areas. This step is critical for data integrity and transfer reliability.

Data Transformation:
Raw data is converted to a format suitable for analysis. Missing and erroneous data is corrected, format differences are eliminated and brought into compliance with the standard structure. Thus, data quality is increased.

Data Loading (Load)
The transformed data is loaded into central structures such as a data warehouse or data mart. During this process, consistency checks are performed to ensure that the data can be used smoothly in analysis systems.

Process Monitoring and Warning System
Any disruptions that may occur in the ETL process are automatically detected by the system and warnings are sent to the relevant users. This system ensures that possible errors are quickly corrected and guarantees data continuity.

Data Backup and Restore

Developed to ensure the operational continuity of businesses, this component ensures that all data assets are securely backed up and restored completely when necessary. It keeps downtime to a minimum by ensuring that the system can respond quickly to possible data loss.

Features

Data Backup Planning
Encrypted Backup
Data Recovery
Backup Reporting

Data Backup Planning
Automatic backup plans are defined for all systems and databases at certain intervals. This process, which can be configured according to daily, weekly or instant periods, ensures that the system has an up-to-date backup.

Encrypted Backup:
Backed up data is protected with strong encryption algorithms. This structure, which prevents unauthorized access, maximizes data security against both internal and external threats.

Data Recovery
Backup data can be automatically restored in cases of hardware failures, user errors, or cyberattacks. Advanced recovery options support business continuity while preventing data loss.

Backup Reporting
All backup processes are reported by the system. Thanks to these reports, administrators are informed about the success of the processes and can take proactive measures when necessary.