How does data quality consulting work at MaibornWolff?

Our data quality consulting service begins with a kickoff workshop, during which we work with your business unit and IT team to identify your most important data domains and areas for improvement. We then work in short cycles and deliver visible results early on, such as an initial profiling report. This allows you to make adjustments early on, rather than waiting months for a finalized plan. The end result is not only a cleansed database but also documented rules, processes, and trained staff, so that you can maintain the achieved standards on your own.

What are the most important aspects of data quality?

Key dimensions include completeness, unambiguity, timeliness, validity, accuracy, and consistency. Depending on the industry and use case, additional criteria may apply, such as traceability or availability. We evaluate your data sets based on these criteria and precisely identify any weaknesses.

How can data protection and data quality be reconciled?

These two topics are not contradictory; they point in the same direction. For example, Article 5 of the GDPR stipulates that personal data must be accurate and up-to-date. This is essentially a legal requirement for good data quality. In addition, data subjects have the right to access, correct, or have their data deleted. This is only possible if your data is well-maintained and can be clearly identified.

What is the difference between data ownership and data stewardship?

The data owner bears the technical and, in most cases, disciplinary responsibility for a data domain, such as customer data or product master data. He or she determines quality standards, access rights, and approvals. The data steward implements these standards in day-to-day operations: he monitors data quality, reports anomalies, and ensures that rules are followed in practice. The two roles complement each other; strategic responsibility lies with the owner, while operational implementation is the steward’s responsibility.

Blue puzzle with a blueprint design, missing pieces, and one mismatched red floral puzzle piece

Data Quality Consulting

Clean data as the foundation for analytics, AI, and your day-to-day operations.

Data Quality: Consulting for Reliable Data

Whether it’s a dashboard, a machine learning model, or annual financial statements: Every analysis inherits the errors in its source data. Duplicates, outdated master data, and inconsistent formats ensure that numbers from different systems don’t match up. The consequences of poor data quality range from incorrect decisions to additional manual and financial overhead, all the way to compliance risks.

With our data quality consulting, we create data that dashboards, AI models, and your day-to-day operations can all rely on. Our motto: Less Technology. Better Business. We don’t just focus on the next tool. We combine processes, clear responsibilities, and targeted technology. This way, data quality becomes an integral part of your processes, rather than a one-time cleanup effort.

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Treating the Cause Rather Than the Symptoms

We not only clean up the current data set, but also identify the actual sources of error. By establishing monitoring processes, key performance indicators, and quality gates, we ensure that the desired quality is maintained over the long term.

Measurable Benefits for Your Business

Better data quality isn't just reflected in metrics related to completeness or consistency. It's evident in everyday operations: less manual rework, faster decision-making, and lower costs resulting from inaccurate data.

Academic Departments and IT in Harmony

Data quality is not purely an IT issue. We serve as a bridge between business units—which know how data is used—and IT—which knows how it is generated. Together, we develop quality standards that both sides support.

Strong Execution Instead of a Set of Slides

We don't just provide recommendations for action—we also implement them upon request. With over 900 experts in IT consulting, software engineering, and data science, we guide you from the initial analysis through to the stable operation of your data quality processes.

Your existing system landscape also stands or falls on the quality of your data. Whether it’s business intelligence, CRM, SCM, or ERP: each of these applications delivers value-adding results only if the underlying data is accurate.

The same applies to new data-driven business models: If you’re building new products, services, or automated decision-making processes on data, you need data sets that are complete, up-to-date, and consistent. This is the only way to make effective use of customer data and reliably offer new digital services.

Data Quality Assessment & Analysis

Before data can be improved, it must be clear where the problems lie and where they come from. We systematically analyze existing data sets for completeness, accuracy, consistency, and timeliness, and identify duplicates, formatting errors, and gaps. In addition, we use data lineage to trace how data flows through systems and processes. This allows us not only to determine that a problem exists, but also where it originates and which downstream reports, analyses, or processes are affected.

Data Cleaning

Inaccurate, duplicate, or outdated data records can undermine any analysis. The goal of data cleansing is to permanently eliminate sources of error and create a database that business units and systems can rely on. Various methods are used to achieve this:

Deduplication (data matching): Identifying and merging duplicate records using matching algorithms.
Validation: Checking data against defined rules, formats, and valid ranges.
Outlier and plausibility checks: Identification of values that are statistically or contextually implausible.
Standardization & Normalization: Harmonizing formats, spellings, and units, as well as breaking down unstructured fields into uniform, analyzable data structures.
Completion & Enrichment: Filling in missing values and adding additional attributes through derivation, plausibility checks, or reliable internal and external sources.

Data Quality Governance

Monitoring & Quality Assurance

Strategy for Data-Driven Projects

Reliable data foundation: Complete, consistent, and up-to-date data that business units, systems, and AI models can all rely on.
Faster decisions: Less time spent checking and reconciling conflicting figures, more time for the actual analysis.
Less manual effort: Automated checks and validation rules eliminate repetitive manual tasks in reporting, sales, and planning.
Clear Responsibilities: Data ownership and data stewardship reside where the subject matter expertise lies, rather than getting lost between departments.
AI and Analytics Readiness: A data foundation that meets the requirements for reliable dashboards, machine learning models, and new data-driven business models.

FAQ: Frequently Asked Questions About Data Quality Consulting

How does data quality consulting work at MaibornWolff?

Our data quality consulting service begins with a kickoff workshop, during which we work with your business unit and IT team to identify your most important data domains and areas for improvement. We then work in short cycles and deliver visible results early on, such as an initial profiling report. This allows you to make adjustments early on, rather than waiting months for a finalized plan. The end result is not only a cleansed database but also documented rules, processes, and trained staff, so that you can maintain the achieved standards on your own.
What are the most important aspects of data quality?

Key dimensions include completeness, unambiguity, timeliness, validity, accuracy, and consistency. Depending on the industry and use case, additional criteria may apply, such as traceability or availability. We evaluate your data sets based on these criteria and precisely identify any weaknesses.
How can data protection and data quality be reconciled?

These two topics are not contradictory; they point in the same direction. For example, Article 5 of the GDPR stipulates that personal data must be accurate and up-to-date. This is essentially a legal requirement for good data quality. In addition, data subjects have the right to access, correct, or have their data deleted. This is only possible if your data is well-maintained and can be clearly identified.
What is the difference between data ownership and data stewardship?

The data owner bears the technical and, in most cases, disciplinary responsibility for a data domain, such as customer data or product master data. He or she determines quality standards, access rights, and approvals. The data steward implements these standards in day-to-day operations: he monitors data quality, reports anomalies, and ensures that rules are followed in practice. The two roles complement each other; strategic responsibility lies with the owner, while operational implementation is the steward’s responsibility.

Data Quality Consulting

Data Quality: Consulting for Reliable Data

Why Choose MaibornWolff for Your Data Quality Consulting?

These companies already rely on our expertise

Data Quality: Consulting as the Foundation for AI and Data-Driven Business Models

Our Data Quality Consulting Services