Data integration

Integrating data from different sources can present a number of challenges:

• Data inconsistencies: Data inconsistencies arise when data from different sources has different formats and structures, i.e. different names, file types or encodings mean that data can no longer actually be managed and therefore has to be cleansed manually. This in turn requires a great deal of effort, for which most companies do not have the capacity.

• Complexity: The more complex the data, the more difficult it is to analyze it. However, data and function integration is also quite challenging when data is highly complex: In this case, you often have to overcome other challenges in addition to the aforementioned data inconsistency, such as a large number of data sources, high data volumes, different quality standards or demanding security and compliance requirements.

• Real-time processing: Real-time processing requires data to be processed and integrated as soon as it is captured, which requires high computing power and low latency. This means that the systems must not only work quickly and efficiently, but should also be able to process large data streams from different sources in parallel and without delay. However, this significantly increases the technical complexity and resource requirements.

• Security requirements: Wherever large amounts of data are processed, aspects such as data protection and compliance are of crucial importance, as they ensure that personal and business-critical data is adequately protected. In recent years, numerous new laws have been passed to help prevent misuse and data leaks. Security failures in data integration can not only have legal consequences, but can also lead to significant reputational damage. The Yahoo data protection scandal from 2016 serves as a cautionary tale.

If the data integrity in your company is at a high level, this opens up new opportunities in terms of analysis, process optimization and customer service personalization. You will benefit from consistent data integration in the following application areas in particular

• Business Intelligence (BI): Data integration sets the course for well-founded analyses and reports by bringing together information from various sources in a central system. This gives your company a holistic overview, enables you to recognize patterns, gain deeper insights and make well-founded decisions based on this reliable data - instead of having to rely on isolated or incomplete information, which often leads to incorrect analyses and wrong decisions.

• Customer Relationship Management (CRM): With the help of centralized customer data, companies are able to obtain a comprehensive picture of their customers by bringing together information from different sources. Through data integration, this data is brought into a uniform form and provided consistently - creating the basis for personalized offers and improved customer service. This allows you to target your communication and offers more specifically to individual customer needs, which has a positive effect on customer satisfaction and loyalty.

• Data migration: Smooth data migration is crucial when changing systems, as incorrect or incomplete transfers can lead to data loss, system failures or serious delays in business operations. Careful data integration ensures that all relevant information is correctly transferred to the new system and that existing data structures and formats are taken into account.

• Machine learning: The integration of structured and unstructured data supports machine learning and analytical processes by making large, comprehensive amounts of data accessible. This enables more accurate predictions and informed decisions, as companies have access to a wider range of information - and are able to extract patterns and trends from big data that would otherwise have remained hidden.
  

• ETL (Extract, Transform, Load): ETL is a structured process that enables companies to collect data from different, often heterogeneous sources, transform this data into a standardized format and then load it into a target system such as a data warehouse (DWH). This process involves several steps: First, the relevant data is extracted from sources such as databases, APIs or CSV files. Then they are cleansed, normalized or aggregated through transformations to eliminate inconsistencies and improve quality. Finally, they are stored in a central repository where they are available for analysis and reporting.

• ELT (Extract, Load, Transform): ELT represents a modern approach to data integration in which the data is first loaded into the target system (often a powerful cloud data warehouse or data lake) in its unaltered raw format. This approach allows companies to use the enormous storage capacity and processing speed of modern databases and data lakes to transform the data directly there. This enables more flexible and faster data analysis. It also reduces the need for predefined transformation processes, as the data can be used for different analyses and applications at any time - without having to extract or move it again.
  

• EAI (Enterprise Application Integration): EAI is designed to link different enterprise applications and systems together so that data and information can be exchanged between these applications in real time. This not only enables increased efficiency and consistency in business processes, but also reduces the likelihood of errors that can occur when data is exchanged manually. This in turn ensures a unified view of company data - which is crucial for informed decision-making and improved collaboration between departments.

• Data virtualization: Data virtualization enables companies to access data from different sources and present it in a uniform format without having to physically move or copy the data. This is achieved by using "virtual data models" that enable dynamic querying of data in real time and thus support faster decision-making. This significantly reduces the effort required for data management and integration and at the same time creates a consistent view of the information.

• CDC (Change Data Capture): Change Data Capture is a method that makes it possible to track and record every change to the data sources - such as additions, updates or deletions - in real time. By reacting immediately to these changes, CDC can ensure continuous data integration by ensuring that all systems are always supplied with the most up-to-date information. This increases data consistency and the efficiency of data processing.
  

• API-based integration: API-based integration uses standardized interfaces to facilitate the exchange of data between different applications and systems, enabling seamless data exchange. This method promotes flexibility and scalability, as developers can quickly implement new functions and easily adapt existing systems - without the need for in-depth changes to the infrastructure.