First, a word of praise. PUMA manages data quality with its own tool: PUMA takes an innovative and pragmatic approach to data governance, relying on agility, automation and the integration of modern tools to manage business data securely and efficiently.
High address and data quality cannot be achieved with standard tools. I can understand that: PUMA’s self-developed workflow and data management tool ARGOS and collaboration with a service provider enable the company to ensure data quality and compliance worldwide.
The focus here is on a balanced global-local governance strategy that allows for flexible adjustments, as well as on a step-by-step, long-term quality improvement process.
Because address and data quality is NOT a cost factor,
but a value-adding factor – and a confidence-building measure.
The 5 most important aspects of data quality at PUMA with its own tool:
- Pragmatic approach – the marathon:
- PUMA relies on a step-by-step data cleansing approach and improves data quality with each interaction, rather than aiming for immediate perfection.
- Automation and efficiency:
- ARGOS automates data validation and standardization processes, reduces manual intervention and avoids duplicates.
- Integration of services and service providers:
- PUMA uses a service provider for real-time data validation, fraud detection and ensuring high data quality, tailored to the company’s specific requirements.
- Global and local governance:
- Using a “layer cake” architecture, PUMA achieves a balance between global standards and regional adaptability to take local characteristics into account.
- Especially for global companies, the “controlled mix of central and local measures and quality strategies” is so important.
- Long-term data quality:
- PUMA regards data governance as a continuous process in which automation and intelligent tools contribute to a step-by-step and sustainable improvement in data integrity.
You can find more information about address and data quality here
Tip: What may be missing (we don’t (yet) know because nobody likes to talk about it)
The anchoring of all measures and goals in the control and management system.
Is PUMA different from most companies? Maybe!
Data quality at PUMA with its own tool: PUMA takes a pragmatic and agile approach to data governance, which is characterized by the following special features:
- In-house development of tools:
- With ARGOS, PUMA has developed its own workflow and data management tool that is specifically tailored to the company’s needs.
- This tool can be used to efficiently automate data processes and reduce manual intervention.
- Every target group and every business model is different. That’s why Standard Gift is committed to quality.
- Integration of external services:
- PUMA integrates external data quality services into its systems by working with CDQ.
- These include functions such as tax ID checking, bank data validation, and duplicate checking, which help ensure data integrity.
- Even if you do a lot of in-house development, there are tools that make your life easier.
- Balance between global and local governance:
- PUMA relies on a “layer cake” architecture that makes it possible to define global data standards while also making regional adjustments.
- This ensures flexibility and efficiency in different markets.
- Whether it’s languages or different character sets, whether postcodes are added or removed, there are so many international specifics that do not allow for a standard approach.
- Step-by-step improvement of data quality:
- Instead of aiming for complete data cleansing right from the start, PUMA takes an iterative approach.
- With each interaction, data quality improves, leading to continuous and sustainable optimization.
- It doesn’t make sense to achieve 100% from the outset. Aiming for 100% doesn’t make sense either. That’s why this step-by-step approach is the right way forward.
Conclusion on: Data quality at PUMA with its own tool
With these strategies, PUMA sets itself apart from other companies that rely more heavily on standardized solutions or are less flexible in their data management.
Standard is usually average, and thus poison for high demands.
Text source: CDQ; image source: www.Puma.com
