The rapid increase in the importance of data means that it is processed and stored in a wide variety of places. Even if companies are aware of the value of this data, its existence is no guarantee of usability. Many business objectives require centralized access to uniform sources. Data integration takes on this task, brings together what belongs together and thus forms the basis for comprehensive analyses.
What is data integration?
Data integration describes the process of combining data from different systems and storage media to enable a uniform view. It includes steps such as cleansing, formatting and standardizing the data and is therefore an essential part of data management. A key aspect is the consideration of certain quality features that ensure optimal usability and interpretation.
By collecting the relevant data in a central repository, they form a "Single Source of Truth (SSOT)" - a valuable basis for all kinds of data-driven projects in large companies. Data integration is largely implemented with the help of automated applications, but it can also involve manual work steps.
Why is data integration important?
In the course of the digital transformation, the number of data-based processes in companies has risen sharply. As a result, data is being generated and used in every conceivable operational area. Data integration overcomes the heterogeneity of these data sources and thus also the segmentation of valuable information. This has significant advantages:
Data availability - Data from a specific department can also be relevant for other departments. For example, sales figures from the sales department are also extremely important for financial planning. Data integration helps to ensure that relevant information is available quickly and centrally to anyone who needs access. This also prevents errors that arise when departments and partners work with different versions of a data set.
Efficiency increase - Both the manual collection of data and the resulting errors cost companies an enormous amount of time. An increase in efficiency therefore results from the central availability of data alone. The effect increases further with the degree of automation of the applications used. Their improved interaction with each other is also one of the benefits of data integration.
Data quality - Data integration influences not only the storage location, but also the accuracy, completeness, consistency and therefore the Integrity of data. On the one hand, these quality features can be improved in a targeted manner, while on the other, a Cleanup to bad or incorrect data parts.
Improved decision making - - An overarching goal of data integration is the general usability of information for forward-looking analyses, forecasts and ultimately decisions - for example with the help of big data and artificial intelligence. The more comprehensive and versatile the database created, the more well-founded the decisions will be.
Types of data sources
In order to bring data together in the sense of data integration, it is necessary to consider the various sources. Their number is constantly increasing, but some categories are proving to be particularly consistent and deserve a special focus:
Databases - As a collection of structured data, they are designed for the targeted further processing of the information they contain and are therefore a particularly valuable source for data integration. Relational databases are almost without exception based on Structured Query Language (SQL).
File systems - Formats such as Excel, CSV, JSON and XML are among the most common information carriers and can be stored in archives of any kind.
Cloud applications - Data output by applications in the cloud can be queried directly as part of data integration without having to be stored temporarily.
IoT devices - The "Internet of Things"(IoT) connects physical objects, machines and sensors and enables the digital exchange of information. This can also be the subject of data integration, which is of great importance in the manufacturing industry, for example.
Programming interfaces (APIs) - APIs facilitate communication between different applications and can be specifically triggered for a data query - in the case of REST APIs via the web. It is a question of whether you see the API or the associated application as a data source.
Unstructured data - This refers to information that does not follow a tabular or other structure and is available in e-mails, text documents or presentations, for example.
Challenges
The overview of the multitude of possible data sources alone illustrates the overriding difficulty of reducing everything to a common denominator. Specifically, the following points can be a hindrance:
Incompatibilities - The relevant applications can be based on completely different interfaces, protocols and data formats, which makes standardized access during data integration difficult. Particularly challenging are outdated legacy systems, where the deviations from current approaches are often greater.
Data quality and consistency - Heterogeneity and deviating quality can only be compensated to a certain extent and are time-consuming. In addition to duplicates and missing values, deviations in the data form - such as numerical or textual - can also hinder the process.
Data silos - If the differences have not yet been bridged at all, or if the data storage is completely isolated, this is referred to as data silos. These must first be identified in order to implement new processes for data integration.
Other difficulties include, for example:
- Real-time analyses
- Further development and updating
- Dependence on different providers
- Growing data volume
Methods and strategies
There are various approaches to bringing data together in a standardized way. However, especially in large organizations where the infrastructure has grown over decades, full data integration is like a utopia. It is crucial to define the specific goals of an individual data strategy in order to select suitable methods. The following are particularly common:
ETL - This standard method, which gained popularity as early as the 1970s with the advance of the first databases, comprises three steps, namely...
- The EExtracting data from different sources.
- The Transform into a standardized format.
- The immediate Laden to the target memory, for example a data warehouse.
ELT - As a modern variation of ETL, two steps are reversed in ELT: After extraction, the data is transferred directly to the target memory - in many cases Data warehouses - loaded. The data is then processed and transformed into a standardized format. A particular advantage is that the computing power of the storage system can be used for this.
Data virtualization - This method makes it possible for companies to access data without actually having to move it. Only a virtual view of the data based on a front-end application is used. This enables real-time integration, which has become an important need to support big data and cloud projects.
Other methods and techniques are
Areas of application
Although this article has already highlighted some of the general benefits of integrating data, there are certain areas of application that offer further opportunities with positive effects:
- IoT (Internet of Things) - The integration of data from sensors and devices in real time makes it possible to optimize capacity utilization and make calculations about material consumption, wear and tear or the need for maintenance.
- Big Data - Data integration is of unparalleled importance for analyzing the large volumes of data that are frequently generated in modern companies, as the number of different data sources is even higher.
- AI (Artificial Intelligence) - The integration of large and diverse data sets is the basis for the training of AI models and the automation of complex tasks.
Integrate data with Konfuzio
As flexible AI software, Konfuzio supports data integration with comprehensive API and cloud functionalities. The platform not only enables the seamless exchange and processing of data between different systems, but also uses AI technologies to efficiently integrate unstructured data from documents and emails and make it usable. In this way, Konfuzio helps companies to exploit the full potential of their data and develop innovative solutions.
If you are planning to integrate and process data from different sources, Contact us directly. Our experts will be happy to advise you.
Conclusion
Data integration plays a central role in today's highly data-driven business world. It enables companies to combine data from different sources and thus create a uniform and high-quality basis for analyses and decision-making processes. However, as the number of possible data sources grows, so do the challenges: Deviations in the quality and format of the data as well as incompatibilities between the applications used can complicate the process.
In order to deal with this adequately, it is advisable for companies to develop a data strategy, for which the support of an external software partner is also advisable. The handling of unstructured data in particular should be an important component. Modern technologies such as artificial intelligence are now indispensable and unlock the full value of information.
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