Building the Foundations
Data quality is built, not born.
Achieving high data quality requires structure, ownership and ongoing management. We support you in creating the framework to ensure lasting data quality.
Improve Data Quality for Reliable Decisions and Business Innovation
Incomplete or inconsistent data leads to lost time, money and trust. We support you in establishing robust data quality frameworks that enable transparency, compliance and innovation.
How We Help You Build Lasting Data Quality
Sustainable data quality depends on structure, ownership and ongoing management. We enable organisations to enhance data quality through strategic guidance, technical expertise and organisational alignment.
Analysis & Transparency
Making Weaknesses Visible
Our Data Quality Analysis uncovers format issues, duplicates, missing standards and organisational gaps, giving you a clear understanding of your current data landscape.
Framework & Roles
Establishing Clear Standards
We design a customised Data Quality Framework, define roles like Data Stewards and Data Owners, and implement processes to ensure lasting data quality.
Technology in Action
Using Modern Tools to Improve Data Quality
We integrate automated solutions for data profiling, validation and correction into your architecture, reducing manual effort and improving efficiency.
Organisation & Culture
Embedding Data Quality in Daily Practice
We ensure that data quality is not seen as an IT project alone. With training, governance frameworks and clear KPIs, it becomes part of the organisation’s daily routines.
Your Advantage
Reliable Data for Better Decisions
Supporting precise analytics, dependable AI models, compliance assurance and confident business outcomes.
Data Quality Analysis: Start with Clarity, Not Assumptions
Our Data Quality Analysis reveals exactly where your data challenges lie, from duplicates and format issues to inconsistencies in business logic and organisational processes. It provides a solid basis for targeted improvements, leading to better data, greater efficiency and more confident decisions.
When Data Quality Becomes the Achilles’ Heel and No One Takes Responsibility
In many organisations, weaknesses in data quality and consistency reveal deeper structural problems. What may seem like a minor technical issue at first quickly turns into a roadblock for processes, analytics and compliance.
Lack of standardisation is one of the most common challenges. Spellings, units, classifications and time formats often differ between departments or locations. For example, an energy provider struggled with billing because meter readings from different regions were formatted inconsistently and sometimes incomplete. The result was manual rework, customer complaints and costly corrections.
Unclear ownership is another core issue. Business units tend to assume that IT is responsible, while IT expects the business to manage the data it uses. This “no one owns it” situation means that errors go undetected, unreported or unresolved. At one insurance company, claims data was maintained manually for years without proper validation or feedback loops. The outcome: inaccurate reports, flawed risk assessments and compliance risks.
Poor data quality becomes especially critical when it affects AI-driven applications. Machine learning models are only as good as the data behind them. Incomplete, inaccurate or biased data leads to false predictions, unreliable recommendations and a loss of trust. One bank, for instance, was unable to deploy a credit scoring model because historical customer data was riddled with gaps and inconsistencies.
The regulatory impact is just as significant. Frameworks such as GDPR, MaRisk, ISO 27001 and GoBD require data to be traceable, consistent and accurate. Poor data quality can therefore create not only operational inefficiencies but also legal exposure.
To use data strategically, organisations must establish clear ownership, create standards and make data quality a shared responsibility.
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FAQs About Data Quality
Many organisations only realise that poor data quality is costing them money when issues start to surface in the form of inaccurate reports, inefficient processes or customer complaints. Our structured Data Quality Analysis identifies both technical and business weaknesses and measures their impact on your organisation.
Both. Data is created within the business, often managed by IT and used by many stakeholders. We help you establish clear role models with Data Owners, Data Stewards and governance structures that promote collaboration and accountability.
Not necessarily, but they do help. We integrate modern tools for data profiling, validation and correction into your existing architecture or recommend suitable solutions based on your needs and maturity level. The important thing is that tools alone do not solve the problem. You also need the right processes and clear responsibilities.
Initial results can often be seen within a few weeks, for example through automated validation rules that detect and correct faulty entries. Lasting impact comes from continuous maintenance, clear standards and strong organisational alignment.
Significantly. AI models are only as good as the data they are built on. Inaccurate, incomplete or biased data leads to false predictions, unreliable recommendations and a loss of trust. High data quality is the foundation of every successful AI initiative.
The costs depend on the scope and your starting point. We work with modular approaches and provide transparent effort estimates. Many of our clients achieve significant impact with small steps, such as cleaning up master data or introducing simple validation rules. The return on investment often becomes visible within just a few months.