At a glance
- Broad fields of application: from business intelligence through AI to cloud migration.
- Enables time savings, cost efficiency, higher data quality, and scalability.
- Foundation for data warehouse, ETL, predictive analytics, and reporting.
- Bringing together and unifying data from various internal and external sources.
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Data integration definition
Data integration refers to the process in which data from various sources is brought together and prepared in a unified form in order to enable a consistent and comprehensive data foundation. This is essential for many further activities of data management and business intelligence. Successful data integration thus ensures that all relevant information is correctly linked together and accessible, in order to achieve maximum efficiency and value creation from the data. Through this process, data is therefore prepared in such a way that it is usable for analysis purposes, reporting, and operational applications. The bringing together takes place via various methods and tools that ensure that all data, whether structured or unstructured, from internal as well as external sources, is integrated and cleansed for further processing.
Particularly against the backdrop of growing data volumes through Big Data, IoT, and SaaS, data integration is becoming increasingly important, as it supports companies in gaining insights from data more quickly and easily.
„With DeltaMaster, we bring together data from our ERP and several upstream systems into a consistent foundation. As a result, our KPIs are finally reliable and comparable across all areas.“Head of Controlling
Industrial company
Why is data integration important? – Benefits of integrated data
Data integration is more than just a technical aid – it is a strategic element for long-term corporate success, because the integration of data from different sources offers companies several central advantages:
- More efficient data management: Data integration eliminates the tedious manual preparation of data. Teams can access current and consistent information more quickly.
- Improved decision-making: Integrated data enables a comprehensive view of customers, processes, and markets. This allows strategic decisions to be made more precisely and more quickly.
- Time and resource savings: Automated data flows and standardized processes reduce the time required for analyses and at the same time lower operational costs.
- Higher data quality: Cleansing and validation in the data integration process ensure that data is as complete, consistent, and error-free as possible.
- Improved collaboration: Unified data promotes cross-departmental communication and creates a common foundation for all areas of the company.
- More security: Centralized data enables simpler management of access rights and stronger encryption, which minimizes the risk of data leaks.
- Customer satisfaction and partner relationships: A better understanding of customer and partner needs leads to personalized solutions and thereby strengthens business relationships.
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How does data integration work?
Data integration is a central process that helps companies transfer data from various sources into a unified system. To achieve this, a clearly defined strategy is required, which can be based on different approaches and tools.
The data integration process
The process begins with the identification of relevant data sources, such as databases, cloud services or APIs. This is followed by data extraction, in which modern tools are used to retrieve information efficiently from the sources. The extracted data often has to be transformed in order to unify different formats and structures. This data transformation is crucial to ensure the consistency of the datasets. In parallel, data cleansing ensures that incorrect or duplicate entries are removed and the quality of the data is improved. After processing, the data is loaded into a central system such as a data warehouse – a step referred to as loading the data. To keep the information always up to date, data synchronization follows, which enables regular updates or even real-time synchronization. Finally, measures for data security and governance ensure that the data is protected and regulatory requirements are met.
Data integration tools
- ETL tools for extracting, transforming and loading data into centralized systems
- Data catalogs for finding and inventorying data silos
- Data cleansing tools for optimizing data quality through automatic error detection
- Data connectors for migrating and transforming data between systems
- Streaming tools for the real-time transmission of data
Data integration approaches
- Common storage integration copies data into a unified repository.
- Data virtualization creates a virtual view of the data sources instead of physically moving data.
- Manual integration collects data manually from various sources, prepares it and merges it.
- Middleware-based integration normalizes data between incompatible systems.
- Uniform access integration presents data in a unified way without physically merging it.
Through this combination of clear process steps, powerful tools and flexible approaches, data integration enables a seamless connection of data silos and lays the foundation for data-driven decisions in companies.
Data integration in practice: application in various areas
Data integration forms the basis for innovative technologies and offers a variety of use cases, such as:
- Optimization of machine learning and artificial intelligence: The integration of high-quality data from different sources makes it possible to train precise models.
- Predictive analytics: By integrating and analyzing historical data, companies can predict trends, for example for the preventive maintenance of equipment, and thus reduce costs, for instance.
- Efficient cloud migration: With data integration strategies such as middleware tools, legacy systems can be gradually moved to the cloud without affecting business operations.
- Big data and data lakes: Integrated systems can store huge volumes of data in data lakes and make them usable for analyses or AI models.
- Business intelligence (BI): The consolidation of data sources in data warehouses enables powerful BI software such as DeltaMaster. This makes it possible to create reports and make strategic decisions based on unified information.
These use cases show how versatile data integration can be when used to help companies foster data-driven growth and innovation.
What does a company need for data integration?
Successful data integration does not begin with the tool, but with the right architecture and clear conditions. The following points are decisive for this.
Data integration and Bissantz
At Bissantz, data integration is a central component of the business intelligence platform DeltaMaster – and at the same time an essential part of the consulting services.
Because successful data integration does not begin with the tool, but with the right architecture. Bissantz supports companies in the conception and implementation of their data integration strategy: Which source systems are relevant? How must data be transformed so that key figures are consistent and reliable? How can ERP, CRM, cloud services, and legacy systems be cleanly brought together?
Technically, DeltaMaster relies on AI-supported data integration processes, with which data from the most diverse source systems is consolidated, cleansed, and structured. Standardized key-figure logic and automated visualization ensure that integrated data is presented consistently and understandably – for planning, analysis, and reporting. In this way, technical data integration becomes a central foundation for fact-based decisions throughout the entire company.
| Function | Benefit |
|---|---|
| AI-supported data integration | consolidates, cleanses and structures data from a wide range of source systems |
| Standardized KPI logic | ensures a consistent and reliable presentation of integrated data |
| Automated visualization | presents integrated data in an understandable way for planning, analysis and reporting |
| Consulting on integration architecture | supports the design and implementation of the data integration strategy |
„The consulting from Bissantz helped us connect our source systems cleanly and standardize data transformation. Reporting and planning now access the same consolidated data foundation.“IT Manager
Retail company
Frequently asked questions
What is data integration, explained simply?
Data integration means bringing together data from various systems, unifying it, and making it usable. Instead of searching separately in each system, everyone works with a shared, consistent data foundation. Like a translation service between systems that speak different languages.
What is the difference between ETL and data integration?
ETL (Extract, Transform, Load) is a specific method of data integration – the technical process of extracting, transforming, and loading data. Data integration is the overarching term that includes ETL as one of several possible methods.
What are typical challenges in data integration?
The most common challenges include: differing data formats and structures in source systems, insufficient data quality, missing metadata, real-time requirements with large data volumes, as well as compliance with data protection and compliance requirements.
How are data integration and business intelligence connected?
Data integration is the prerequisite for functioning business intelligence: BI systems can only deliver reliable analyses if the underlying data is completely, consistently, and correctly integrated. Without solid data integration, faulty reports and incorrect decision foundations arise.
How does Bissantz support data integration in practice?
Bissantz supports companies from the conception of the integration architecture through to technical implementation. DeltaMaster automates ETL processes, consolidates data from ERP, CRM, and other source systems, and provides it as a consistent data foundation via a central data warehouse – for reliable analyses, planning, and reporting.
Summary
Data integration is the technical and strategic foundation of modern corporate management: it transfers data from heterogeneous sources into a consistent, usable data foundation – and thereby creates the prerequisite for powerful business intelligence, well-founded analyses, and data-driven decisions. With DeltaMaster and its consulting expertise, Bissantz supports companies from the integration architecture through to decision-oriented analysis – end-to-end, automated, and proven in practice.