Development of an automated data platform
Why have a central data platform?
Once a certain amount of data has been accumulated, a central data platform becomes the basis for sustainable and scalable processes. It automatically combines data from different sources, checks its quality, and replaces manual processes with reliable automation. At the same time, it ensures security and transparency through clearly defined access controls and enables the seamless historization of past data states. This creates a stable basis for sound analyses and data-driven decisions.
Manual processes are replaced by automated procedures.
The data basis for AI applications is in place.
Storage of past data sets (historization).
A central authorization system for all data. (Data governance)
Data quality issues can be automatically detected and monitored.
Data is automatically consolidated in one system. Reports are generated automatically.
With a centralized data platform
Many manual processes.
AI implementation is hardly possible without an existing data basis.
Past data is overwritten by new data.
Detailed access permissions are hardly possible in merged Excel files.
Cross-system verification of data quality is difficult and time-consuming.
Data must be manually compiled from multiple systems (often Excel), which requires a great deal of effort.
Without a centralized data platform
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The concept of self-service BI describes how business users can independently access data, create analyses, and design reports—without having to wait for the IT department. To make this possible, a central, well-structured data platform, clear governance, and targeted enablement measures are required. We support companies in setting up their data landscape in such a way that employees can generate their own insights securely and efficiently – for faster decisions and greater agility in business.
Data governance refers to the policies, processes, and responsibilities that ensure that company data is managed and used correctly, securely, consistently, and in compliance with regulations.
By centrally managing data sources, metadata, and access rights, companies can implement clear guidelines for data quality, security, and compliance. This reduces redundancies, prevents data silos, and facilitates compliance with legal regulations such as the GDPR.
Business processes, customer information, and product data change over time.
By historizing data, trends can be analyzed, forecasts can be made, and regulatory requirements such as traceability and compliance can be met.