What is Data Integration (DI)? - Meaning ETL Tools

Data integration - a comprehensive understanding

Data integration is the process of combining data from different sources to provide a unified view of data. This integration of data is fundamental to business analysis and decision-making, as it enables data to be extracted, transformed, and cleaned in real time. By using ETL tools and data integration tools, companies can ensure high data quality and minimize inconsistencies.

The application of data from different sources in a data warehouse or data lake enables the strategic use of large amounts of data (big data). In addition, companies can benefit from real-time access to data, which improves agility and the ability to make data-driven decisions.

Data integration ensures that data remains consistent and enables optimized data migration. Combining data from multiple sources ensures that the required data is available. Companies that master the challenges of data integration can significantly improve their operational processes and reap the benefits of data integration.

Data and information are among the most important factors for a company. Employees access, analyze, and process this data over and over again in many different work steps. The problem is that this data is usually stored in different systems, which makes accessing it very time-consuming. This is where data integration comes into play. Its goal is to integrate data from different systems into one tool so that employees can access all information from a single data source. Read this article to find out how this works and how it benefits companies.

Definition: what is data integration?

Companies use many different systems and tools at their respective locations to manage their areas of responsibility. A CRM or service management software in customer service, a campaign overview tool in marketing, customer applications, etc. – what they all have in common is that they contain a lot of important data. Although this information is well stored in the applications, what if, for example, a sales employee needs all the available information about a customer? Including the promotional emails received, previous calls, conversation content, and most recent purchases? The sales employee has to log into many different systems to do this, which leads to increased workload and is often not feasible due to workload.

Here data integration comes in. It provides all data and information from the various sources in a single view. A process is carried out that includes the recording, cleaning, assignment and transformation of the data to match the target system.
The advantages of data integration are diverse:

• The collaboration and standardization of different systems is improved
• The company's employees save a lot of time through the uniform presentation of the data.
• The susceptibility of the data records to errors is minimized.
• More useful data can be accessed at the same time.

The company’s goal is to increase the clarity so that decisions can be made more easily, in order to ultimately remain competitive.

“Reliable information is absolutely necessary for the success of an enterprise.” Christopher Columbus

The integration of the data brings with it some challenges that companies have to cope with. It starts with the need to be clear about what kind of data is being integrated and what is going to happen to them. Do they need to be available for special analyzes, are they for reference purposes, and how often do they need to be updated? In addition, a lot of data is stored in systems that are out of date. You are missing information such as time or date information. The situation is similar with data from external systems, which often lack details that are available in internal data.

In order to be able to respond to all requirements, companies can choose from various data integration options:

• Manual data integration: With manual data integration, all data from the various sources is compiled by hand. Due to the high effort involved, this process is only useful for small companies with very little data.
• Middleware-based data integration: With this form of data integration, the middleware tool acts as a kind of middleman, collects the data and transfers it to the master data pool. This is mainly used when the actual data integration tool has problems with certain data sets, for example because they come from outdated systems.
• Application-based integration: Different software is used here to localize and integrate data. To do this, they convert the data into a form that is compatible with the target system.
• Uniform Access Integration: In Uniform Access Integration, the main component is a front end that displays all data from the various sources in a uniform manner, while the actual storage location remains the original source. The data are therefore only displayed in the same way, while they remain essentially different.
• Common Storage Integration: Common Storage Integration is the continuation of Uniform Access Integration. Copies of all data records are made and integrated in the target system. There they are then displayed in a uniform view.

When it comes to data integration, you have to pay attention to a number of things and make many decisions. To make the process as uncomplicated as possible, a data integration tool is recommended. There are a large number of different uses here. How do you find the right data integration tool from this mass?

Data lakes in the context of data integration

Data lakes offer a flexible way to store and analyze data from different sources. Unlike traditional data warehouses, they allow unstructured and structured data to be stored in its original form. This gives companies access to a unified view of their data and enables them to analyze it in real time. The challenge is to ensure data quality and resolve inconsistencies. With the right data integration tools, companies can consolidate the data they need from different systems and make strategic decisions. Integrating data into data lakes enables agile use of big data and facilitates data migration and transformation, allowing valuable insights to be gained.

ETL stands for "extraction, transformation and loading" and is an important process in data integration. Here are the individual steps of the ETL process:

Importance and procedure of data extraction

Data extraction is the first step of the ETL process. It involves extracting data from various sources, such as databases, CSV files or APIs. Data extraction can be done in real time or at regular intervals, depending on the needs of the organisation.

Transformation of data for integration

The transformation phase involves cleaning and transforming the extracted data to prepare it for integration. This includes cleaning up erroneous or missing data, transforming data into the desired format and applying business rules or logic to the data. Transformation plays a crucial role in harmonising data from different sources and ensuring a consistent data format.

Loading the data into a data warehouse

The final step of the ETL process is to load the transformed data into a data warehouse. A data warehouse is a special storage location for integrated data that allows companies to use the data for analysis and reporting. Loading the data into a data warehouse enables easy and efficient data availability for users.

Data integration tool: EcholoNs Data Workflow System

The main difficulty of data integration is that these data have to be filtered, sorted, broken up or put together, regardless of the original system from which they originate. Not every tool can do that. In addition, it should be possible to manually determine the intervals at which information and data are to be imported.

The data workflow system (DWS) from EcholoN offers exactly that. With the graphical user interface, you can intuitively master data integration. The entire data communication takes place without programming, which makes the exchange extremely convenient. The aim of the software is to avoid duplicate data maintenance and thus to create free resources. All data is always up-to-date and correct - from a single interface.

In addition to the above, the Data Workflow System offers several other advantages over other tools available on the market. Feel free to contact us; together we will find the right solution for you!