Intelligent Data Management
Goal:Use data reliably
We bring together data from your existing systems and clarify its business meaning, so your employees and applications work from a shared foundation.
Your data holds information about customers, services and operational relationships. This information is created in several systems and maintained according to the rules of the respective departments. A reliable data foundation makes it accessible for decisions and creates the conditions for using new technologies effectively.
What we stand for
We are convinced that a company should understand its data and take responsibility for it. It holds information about customers, services and operational relationships. A reliable data foundation makes this knowledge accessible for decisions and creates the conditions for using new technologies effectively.
Typical starting point
You want to steer your company's development on a sound basis, automate processes or use AI. The information you need for this is created in different systems and maintained according to the rules of the respective departments. To be used together, this data has to be brought together both in business terms and technically.
Architecture
We start with your business goals, so we open up exactly the sources your project needs.
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We start with your business goals and the specific project, such as a cross-departmental analysis.
Example: an agreed sales report
Outcome
You receive a documented data model (the map of your terms, rules and responsibilities) with regulated maintenance, so you steer the quality and expansion of your data foundation yourself.
Building blocks
Business intelligence, meaning data-based reports and analyses, connects the information from your departments through jointly defined key figures and traceable calculation rules. This lets you assess business development on an agreed basis.
You receive a key figure system with reports, graphical overviews and detailed views. Definitions, data lineage and refresh cycles are documented.
A data warehouse (a central, structured data store) connects the data from your existing systems in an agreed data model. Further analyses and applications build on it.
Source connections, the data model and the processing and quality rules are documented; refresh cycles and operation are aligned with the intended use.
Responsibility for data lies with the people who understand its business context. That is why we define terms, responsibilities, quality criteria and maintenance processes together with your departments.
The datadice Academy supports applying the agreed rules in everyday work.
Forecasting systems support sales, inventory and sales capacity planning. We test every forecast on held-back historical data against an agreed baseline, so the people responsible can judge what it contributes to planning.
Responsibility for the measures derived from it remains with your company.
Comparison
The table compares standard software, custom development and datadice against the same criteria, so you decide on a transparent basis.
| Criterion | Standard software | Custom development | datadice |
|---|---|---|---|
| Starting point | Features of the product | Project specification | Your business goals and a specific project |
| Connecting existing systems | via the vendor's existing interfaces | depending on the contract | Built on your existing systems, connections checked in advance |
| Your own business rules | within the configuration options | freely implementable | Terms and rules defined together with your departments |
| Quality assurance | by the vendor | as agreed in the project | Quality criteria and responsibilities for maintenance |
| Operation and further development | with the vendor's releases | by separate agreement | Expansion according to your business priorities |
| Enabling your team | Product training | as needed | Training in data maintenance through the datadice Academy |
Starting point
Connecting existing systems
Your own business rules
Quality assurance
Operation and further development
Enabling your team
Application
For example a cross-departmental analysis or the introduction of an AI application. This shows early on which information is available and how it can be used.
Quality criteria and checks show where action is needed. Changes to definitions and data sources are documented, so statements remain understandable as your systems evolve.
Business Intelligence
Reporting that every department relies on
Reports and dashboards with documented definitions that show the relevant developments and allow drilling down into the details.
See the exampleData Warehouse
Analysing ERP, sales and accounting data together
A maintained, shared data foundation with a documented data model on which analyses and AI applications can build.
See the exampleData Organisation
Clarifying master data before a system change
Binding definitions, quality criteria and maintenance processes. Data maintenance becomes a manageable task in your company.
See the exampleAn objection we often hear
„Our system partner already takes care of the data.“
Data management brings together data from your existing systems, refines it and clarifies its business meaning. Your employees and applications then work from a shared foundation whose quality you control.
A data warehouse is a central, structured data store for analyses and applications across systems. It connects the information from your existing systems in an agreed data model.
We plan the build on the basis of your existing systems and check their connection options. You know the impact on ongoing operations before implementation begins.
That is the normal case, and the data foundation check starts exactly there. It narrows the review down to the information your project needs and prioritises the preparatory work.
Getting started
For which decision, which process or which application do you need a more reliable data foundation?
In the free initial consultation we discuss your project and the data it needs, and you then receive a short playbook. The strategy workshop with data foundation check then clarifies the starting point and the measures required.
Starting point:Strategy workshop with data foundation check
