Data quality: the basis for your digital success
Your roadmap to data optimization
As the amount of data continues to grow, the ability to identify and use relevant and high-quality information is becoming increasingly important. Only those who manage to identify and use the right data in a targeted manner can make well-founded decisions, optimize business processes and exploit the full potential of automation. But how can valuable insights be gained from this flood of data? At forbeyond, we rely on innovative methods to analyze and sustainably optimize your data landscape and unleash data-driven potential in your company.
What exactly is data quality?
Data quality describes how well data meets the requirements for accuracy, completeness, consistency, timeliness and relevance. It forms the basis for data-driven business models and resource-saving processes. A high-quality database minimizes risks and lays the foundation for competitive advantages.
The "big five" criteria for data quality
Consistency
The product data is consistent, consistent and comparable
Accuracy
The data corresponds to reality and requirements. Tolerances are defined if acceptable
Completeness
All relevant information is available
Topicality
The information is always up-to-date and is updated in regular cycles
Relevance
The data fulfills the user requirements defined by voarb
Why is data quality crucial?
In the digital economy, data is the key to growth and innovation - but only high-quality data creates real added value. Especially in retail and industry, where complex supply chains, omnichannel approaches and customer experience are key, poor data quality can have serious consequences.
High-quality data creates the basis for:
Precise decisions:
Sound analyses are based on reliable and consistent data.
Efficient processes:
Automated workflows and optimized supply chains benefit from high-quality data.
Improved customer approach:
Relevant and complete information enables personalized communication.
Reduction of errors:
Consistent and verified data minimizes operational risks and sources of error.
Scalability:
Systems with a clean database can be easily expanded and adapted to new requirements.
Innovation and growth:
A stable data foundation opens up new business opportunities.
Data quality: the role of AI and automation
Modern technologies such as artificial intelligence are revolutionizing data management and are a key factor in ensuring and improving data quality. Automated systems detect errors and inconsistencies in data, correct them proactively and enable predictive analyses. Content intelligence ensures that potential problems are identified before they arise, thereby increasing the efficiency and reliability of data.
With our expertise in AI-based solutions, we take data quality to a new level through automation
- Automated data checking and cleansing: Intelligent algorithms detect duplicates, inconsistencies and outdated data and cleanse it.
- AI-supported enrichment of product data: Missing information is automatically supplemented and relevant content is added to optimize the database.
- Automation of work processes: Time and resources are saved by automating processes and keeping data consistent and up-to-date.
Double pack: data quality & data governance
Data quality ensures that information is accurate, complete and consistent, while data governance creates the framework conditions for using data securely, compliantly and efficiently. Together, they form a strong foundation for the responsible handling of data and the sustainable optimization of data-driven business processes.
Close integration of both concepts unlocks the actual potential
- Consistency and compliance: Uniform standards promote stable processes and ensure compliance with legal requirements
- Transparency and control: Defined responsibilities create clarity and make the entire data lifecycle traceable
- Continuous improvement: Regular checks and optimizations ensure long-term data integrity and quality
