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Data Integration Easily Explained for Non-Developers.
What is data integration? Why is it so important – and how does an automated integration process actually work? We break it down in simple terms – no tech experience needed.
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What is data integration? Why is it so important – and how does an automated integration process actually work? We break it down in simple terms – no tech experience needed.
Imagine you work at a company where each department has its own data systems – sales uses a CRM tool, accounting works with separate financial software and the warehouse department manages stock using a WMS. But what happens if you want to answer a simple question, such as: How many customers placed an order in the last three months but have not yet paid their invoice?
Suddenly the situation becomes complicated: Figures don’t match, customers appear several times in different systems and important information is missing. Instead of a quick answer, you end up having to dig around to figure out what the actual data base even is.
The solution? The (automated) integration of data from disparate sources. But how exactly does it work? What methods are out there? And what does this mean for companies that want to optimise their data flows? In this article, we explain the basics of data integration – simply and comprehensibly, even if you have no prior IT knowledge.
Data integration brings together data from different sources into single, central data repository (for example a data lake or data warehouse), converts it into a standardised format, eliminates errors, and makes it available in a central overview.
Data integration helps companies make previously heterogeneous, distributed data sets consistent. The consolidated data can then be used for data analysis, further processing, as well as business process and workflow automation.
A company’s operating systems, software applications, and databases are usually based on different data formats and protocols. This means that data generated in different departments is often not compatible. Data silos form when enterprise data stays isolated and can’t be easily shared or connected with other parts of the business.
This leads to inefficient processes, duplicate, inconsistent, or outdated data, as well as incomplete or incorrect data analyses.
Data integration is a central strategic building block for connecting all of an organisation’s data and making it available in a single user interface. The integration of company data usually brings about positive changes:
There are various methods of integrating data which come with their own advantages and disadvantages. The data integration technique that makes sense for each company also depends on its size and the complexity of its systems.
With manual data integration, users manually collect and combine data from various sources in an Excel spreadsheet, for example.
For small amounts of data, this approach is definitely quicker than setting up an automated data integration system. Plus, there’s no cost for specialised software. However, anyone who’s spent their days building Excel sheets with copy and paste knows just how tedious and error-prone it can be.
The manual integration of data is therefore suitable for one-off tasks, but is inefficient for large, dynamic, or complex systems. As a scalable, long-term solution, it makes more sense to automate using an ETL process (see section “Data Integration Process Step by Step”), middleware or database integration.
The term “middleware” refers to a connection platform that acts as a kind of bridge between different systems and applications and enables them to communicate with each other in a standardised way.
A major advantage here is that the source and target systems do not have to be adapted, but are simply connected to the middleware as a central interface. This ensures real-time data transfers and high scalability. The middleware can also handle data encryption and authentication, which increases security against unauthorised access.
However, middleware solutions can be expensive. When selecting a middleware tool, companies must therefore pay attention to how complex the implementation will be, how intuitive and user-friendly it is to use, and also whether it leads to what’s known as a vendor lock-in – i.e. being tied to a specific provider if they want to access additional features.
In application-based integration, specialised software applications (for example ETL tools) take over the collection, conversion, processing and forwarding of data. The software used is usually customised for specific data integration tasks.
This makes it easier for companies to have direct control over their data pipeline, for example, deciding exactly when to synchronise and forward data. No separate middleware is needed, as the integration logic is built directly in the respective application.
However, specialised software solutions require significant development work, which consumes resources and time. If several different systems have to be integrated, this can complicate integration and even overload the application. In addition, there is often no central data repository like a data store. Middleware-based integration is therefore often the better choice for complex IT environments.
With database integration, data from different sources is merged in a central database, either through ETL processes or by synchronising the respective source databases.
The fact that only a single database is used prevents data inconsistencies and redundant data can be identified quickly. Central databases also offer granular authorisation management and backup mechanisms.
However, databases also have drawbacks, since ETL processes and data migration demand a high level of technical expertise and connecting new data sources often requires extensive adjustments. In addition, a central data warehouse can quickly become a single point of failure. Middleware solutions are therefore usually better suited to real-time application integration or agile data environments.
Common data integration models are usually based on ETL processes. ETL stands for “Extract, Transform, Load” and means that the data is first read (extracted) from one or more data sources, then processed (transformed), and finally loaded into a central data warehouse, for example. In general, the data integration process runs as follows:
More efficiency. Better decision-making. More competitiveness. The automated integration of data from different systems has many advantages for companies:
Automated data integration reduces errors, inconsistencies, and data redundancies, i.e. data replication.
If, for example, customer master data is available in both the ERP system and the CRM system, possibly even with different addresses, integration can eliminate this redundancy and correct the data set.
A central view of all data from different data sources makes real-time data analysis much easier. It allows companies to respond much more flexibly to changing conditions.
Retailers, for example, are able to identify stock and delivery bottlenecks in good time and reorder accordingly or optimise supplier management.
Data integration helps to automate the flow of data between companies (B2B) and public authorities (B2G). This greatly reduces manual data entry and the risk of errors, while also saving companies time that can be used more effectively for their core business activities.
Typical examples include the automatic comparison of invoices between accounting software and the ordering system or the direct transfer of tax data records via the Internet.
Consistent customer data helps companies create personalised offers and respond to customer inquiries more quickly and individually. The consistency of data across different channels also improves the user experience.
For example, customers benefit from an ordering process in which they can directly see which goods are in short supply or from a reminder email when goods are available again. The automated connection of payment systems also simplifies the purchase for the customer.
A shared database helps break down information silos, facilitating cross-departmental collaboration – as everyone involved can access the same up-to-date data.
In practice, a 360-degree customer view helps marketing, sales, customer service and other departments plan and run campaigns in a more targeted way.
Companies require a large amount of data to drive their digital transformation. This also means that large volumes of data need to be available at low cost and can be filtered for analysis. Seamless data integration makes it easier for companies to deal with growing data volumes.
The introduction of middleware for data integration, for example, can help a logistics company use integrated IoT data to better monitor its fleet. Or to optimise route planning in order to meet sustainability targets.
Centralised data management makes it much easier to comply with data protection regulations such as the GDPR or HIPAA, as data quality is high and data is always up to date. A standardised system also makes it possible to introduce better security measures such as clear access rights and encryption, and to centrally monitor system access.
Structured, error-free and timely information gives even smaller businesses a significant competitive advantage. Well-prepared data provides the ideal foundation for both purchasing and sales. In addition, companies can react much faster to market changes and recognise trends at an early stage.
In many cases, the use of standards, for example from the EDI (electronic data interchange) environment is even a prerequisite for being able to cooperate with other companies and meet their integration requirements.
Providers offer a wealth of solutions, tools and services, in the cloud or on-premises. And this variety is good! But which data integration tools make sense for a company’s specific problem? This is where organisations sometimes lack the support they need to precisely define their requirements and select the most suitable software.
Data integration without prior analysis can lead to companies failing to achieve the desired goals – and to the chosen integration solution falling short of its actual potential.
To avoid frustration during implementation at both management and employee level, companies should draw up a precise plan in which the status quo is assessed, and financial and human resources as well as integration requirements are precisely defined.
Data integration tools are an ongoing cost, and of course they also have to be integrated into your own system environment first. Accordingly, you should choose a provider that offers flexible licence models, compact employee training and short implementation times.
To choose the right data integration software, you should also first answer a few questions about the type of application, the planned area of use and the range of its functions:
A good tool for data integration should also be able to seamlessly connect legacy systems. This helps to avoid costs for retrofitting or completely replacing legacy hardware and software. In this context, further questions need to be clarified:
Lobster’s Data Platform is a powerful software for merging data that allows you to master all the challenges of data integration with ease.
With numerous ready-made connectors and templates for programming interfaces of all kinds, our platform acts as an iPaaS middleware between different types of data and systems. Data can be retrieved, transformed, and made available from any source in various formats – without any programming effort on your part!
Plus, you can simply map business processes and workflows using drag & drop – the associated data flow, including data ingestion, analysis, and further processing, is automated.
And the best bit? No matter how many external partners you want to connect with to simplify data integration and exchange – we not only offer standardised Data Products (e.g. for e-invoicing), but also the Data Network, a huge data ecosystem that connects companies from a wide range of industries along the supply chain.