Data as a goldmine
Many organisations struggle with utilising their data. They want to extract more value from various data sources, such as Dynamics environments and other systems, but often lack the capacity to arrange this internally. The goal? Unambiguous use of data within the organisation and reliable, complete and traceable data for everyone. How do you tackle this in practice?
Involve the users
Our vision is simple: bring data as close as possible to the end user. This means everyone in the organisation, regardless of their role or prior experience, should be able to work with data. Users can consume data via dashboards and reports, while advanced users can independently analyse data. Support from experienced data analysts - internal or external - is crucial here.
In our vision, the following matters play a role:
- Availability of source data
- Development of a uniform data model
- Data security
- Discoverability of data (metadata management)
- Data quality and data validity
- Use of the data platform
- Sharing data with third parties
- Use of data from public sources
- Future-readiness
1. Availability of source data
Valuable data starts with correctly registering and unlocking data from various systems. Modern Dynamics implementations such as Business Central and Customer Experience are well accessible via standard connections (APIs). However, the old adage "rubbish in, rubbish out" still applies. It is crucial that everyone in the organisation understands the importance of correct, timely and complete data entry.
2. Uniform data model
A good data model translates technical data into usable information. This model must be tailored to the organisation, robust against errors, and flexible enough to enable new analyses. We do not believe in a one-size-fits-all approach.
3. Discoverability, usage & metadata management
Data must naturally be easily discoverable and definitions must be unambiguous. A well-configured data catalogue with smart software for recording the origin and usage of data (lineage) is essential here. Automation of metadata management reduces the burden of manual work and increases efficiency.
4. Data quality and data validity
Data quality checks are essential to prevent errors and maintain trust. Rules for data quality must be stored in the data catalogue, and deviations must be flagged automatically. Public data sources can help with data validation.
5. Data security
Data security goes beyond firewalls and two-factor authentication. It concerns compliance with legislation (such as GDPR), technical measures to prevent breaches, and a solid data access policy. Data sovereignty and avoiding vendor lock-in are also important aspects.
6. Use of data
In a data-driven organisation, all employees should be able to independently find answers in the data. Modern techniques and open standards make this possible, without IT departments having to implement and support all technologies.
7. Sharing data
Sharing data, both within and between organisations, is crucial in a data-driven world. Data contracts and federated data warehousing are emerging trends that make data exchange more efficient.
8. Use of data from public sources
Public data, such as that from national statistics offices, can enrich internal data and help obtain context and correct interpretation.
9. Future-readiness
A data platform must be flexible and future-proof. We believe in using open-source, thoroughly tested technologies and container infrastructure to easily replace or upgrade components. Generative AI will have a major impact on data analysis, but developing data models remains a valuable investment.
Conclusion Want more value from your data? Then ensure proper registration and unlocking of data, a tailored data model, clear metadata, strict data quality checks, and robust data security. Only then can you deploy data effectively and truly make your organisation data-driven. A brief summary that encompasses much. That is what we are available for, of course…