‘The matrix is omnipresent. It surrounds us. You can’t see it, but you can feel it,’ explains Morpheus in the film “The Matrix”. If we replace ‘matrix’ with ‘poor data quality’, science fiction quickly becomes everyday life for companies. Incorrect, outdated or duplicate data creeps into systems unnoticed and acts like an invisible enemy in the background.
Just as Neo has to gain control of the virtual world in ‘The Matrix’, companies are faced with the huge challenge of mastering their data quality and maintaining the upper hand when countless systems are connected and data is transferred back and forth.
One of the most common challenges, for example, is the transfer and synchronisation of master data. This involves centralised information on personnel, customers, suppliers or products, which is often maintained in different systems.
The problem: Manual interventions, copy-paste errors or outdated data lead to poor data quality and a lot of effort to clean up databases. Employees often lose a lot of time due to follow-up checks, telephone enquiries and manual corrections.
That doesn’t have to be the case. With Cloudomation Engine, data is automatically extract, transformed and loaded into the desired system. Step by step, you can sustainably improve existing, poor data and ensure optimum data quality.
We will show you exactly how this works in this article.
Cloudomation Engine at a glance
Cloudomation Engine is a platform for software automation and integration. With Engine, you can connect third-party systems and define fully automated processes, such as for ETL. Data from different sources – such as relational databases, industry or ERP systems – is read, validated and consolidated. The platform can be seamlessly integrated into existing systems and ensures that data is available in the correct format and can be processed further.
Example: Automation of a data integration process
Imagine a company that deals in used cars. Vehicles are recorded in different branches and must be regularly synchronised across different systems. Until now, all steps have been carried out manually.
With Cloudomation Engine, the process looks like this:
- Automatic data exchange: Data is transferred from the industry software to a central database (triggered manually or automatically via a predefined schedule)
- Validation: Engine recognises missing values or conflicts between data records. For example:
- A BMW i3 has no specified range.
- Two data records of an Audi A3 differ in the year of manufacture.
- User integration: Employees are informed by e-mail and receive a link to edit the incorrect data.
- The link leads directly to the industry software or to a simple Google spreadsheet.
- Alternatively, you can also use Cloudomation Engine to provide user-friendly forms.
- Data correction: Employees correct the values – for example the range or the year of manufacture – and confirm the processing.
- Synchronisation: The corrected data is automatically transferred to all relevant systems. Employees receive an e-mail notification confirming that the process has been successfully completed.
This step-by-step approach continuously improves data quality. Instead of large, one-off clean-up operations, small quantities of data records can be processed on a daily basis. The goal is to get to a fully automated process.
Summary
With Cloudomation Engine, you can automate your ETL processes efficiently and improve the overall data quality. Thanks to the flexible involvement of users and seamless integration into existing systems, you save time, reduce sources of error and create a reliable database.
If you would like to find out more about how the Cloudomation Engine can optimise your ETL process, visit our ETL page.
