Data structuring and insights
Data structuring involves organising and standardising your data to make it usable and easy to integrate into digital solutions. Well-structured data provides valuable insights that support better decision-making and help improve work processes.
Data structuring creates valuable insights
Creating value through data structuring requires more than just technical expertise. With years of experience across industries, we help organisations move from fragmented data to a coherent data strategy. We take the time to understand your business as a whole – not only in terms of data management, but also how data can support strategic decisions, value creation and compliance.
We work closely with you to ensure that your data structure supports both operational needs and broader business goals, whether it involves ESG reporting, energy data or complex product information.
Examples
• Structuring ESG data for accurate reporting and compliance.
• Automated handling of energy data for analysis and optimisation.
• Standardization of product data to ensure consistency across systems.
• Integration of customer and transaction data into BI and ERP platforms.
Data structuring is often an important step towards, for example, efficiency improvements with machine learning or a digital calculator.
How we help you through the process
Data structuring is a systematic process aimed at creating a framework where data is reliable and ready to support your strategic decisions and digital solutions.
More about data structuring
Advantages
- Provides insights and better decision-making
- Can reduce manual processes through automation
- Possibility for integration between different systems
Quality-assured data basis
It is important to get a complete overview of the data base. That is why we collect relevant data from both internal systems and external sources. We analyse the datasets to identify inconsistencies, missing data and structural challenges. A validation is carried out to ensure that the data is accurate, up-to-date and suitable for further use.
Organising data
Data must be organised into logical structures with consistent formats, units of measurement and terminology to ensure it can be used effectively across systems and platforms. During this process, we remove duplicate, irrelevant or outdated data. At the same time, we optimise data storage and access methods to ensure high performance.
Data integration and automation
Once all relevant data is collected, organised and quality assured, we integrate it with existing systems. Automating workflows ensures efficient data management and reduces the risk of manual errors. At the same time, we establish processes for monitoring and updating data to keep it accurate and relevant over time.
